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The paper throws light on the review of detection of fault and isolation for aerospace systems. Developing detection framework for small satellites is critical task due to limited availability of on board sensors and computational budget. In aerospace operations there are many subsystems and or components which can fail anytime. Once the fault has occurred, it can cause uncoverable losses and pollution of the environment and so forth. It is important to detect, isolate and find the remaining usefulness of that defective component and enable a suitable conclusion making before such faults make a great damage. Hence the fault detection is very important aspect for improving the economy as well as the safety of the system. The following research is intended to introduce an overview for fault diagnosis and prognosis techniques.

In this study, the authors propose a fault detection algorithm used in an on-line magnetic monitoring system in the hydropower plant. The proposed algorithm is based on two magnetic measuring methods: measurement of (inner) flux within generator in air gap and measurement of stator leakage (outer) flux outside of the generator. The system has to ensure, in situ and real time, magnetic monitoring and fault detection of hydrogenerator. The monitoring system is also useful for modern maintenance approach such as a condition based maintenance without generators’ work interruption and better planned and preventive maintenance in the power plant. The proposed system successfully detects the presence of magnetic unbalance of hydrogenerator, occurring as a consequence of shorted turns in the windings of the rotor poles or air gap asymmetry. The presented measurement results are achieved in real exploitation conditions.

DC-DC converters are widely used in a large number of power conversion applications. As in many other systems, they are designed to automatically prevent dangerous failures or control them when they arise, this is called functional safety. Therefore, random hardware failures such as sensor faults have to be detected and handled properly. This proper handling means achieving or maintaining a safe state according to ISO 26262. However, to achieve or maintain a safe state, a fault has to be detected first. Sensor faults within DC-DC converters are generally detected with hardware-redundant sensors, despite all their drawbacks. Within this article, this redundancy is addressed using observer-based techniques utilizing Extended Kalman Filters (EKFs). Moreover, the paper proposes a fault detection and isolation scheme to guarantee functional safety. For this, a cross-EKF structure is implemented to work in cross-parallel to the real sensors and to replace the sensors in case of a fault. This ensures the continuity of the service in case of sensor faults. This idea is based on the concept of the virtual sensor which replaces the sensor in case of fault. Moreover, the concept of the virtual sensor is broader. In fact, if a system is observable, the observer offers a better performance than the sensor. In this context, this paper gives a contribution in this area. The effectiveness of this approach is tested with measurements on a buck converter prototype.

Asynchronous motors are frequently used in many industrial applications, especially pumps and fans. Placement, bearing and coupling faults are common faults in these types of engines. Misalignment error is a common type of error that is seen very often among these errors. This error may cause efficiency decrease in a short run and vibration may cause short circuit and wear in moving parts in the stator windings in a long run. Early diagnosis of such faults is important in terms of machine health and productivity. In this study, loose connection and angular imbalance of the asynchronous machine were investigated. In the experimental works, a 1 Phase 0.75 KW power asynchronous motor, Y-0036-024A Electromagnetic Brake and SKF Microlog vibration meter were used during the measurements. The Frequency components of motor caused by the settlement errors were investigated under the different loads. A loose assembly error and angular imbalance were investigated from the misalignment errors. The engine was run idle and without any positioning errors and measurements were taken from different points with the accelerometer and the frequency spectrum examined. Measurements are repeated when the misalignment errors are occurred on purpose and the FFT frequency components were compared under the load of 12.50Nm using magnetic brake. The results show that the FFT frequency components are examined and the placement error can be determined with high success and accuracy. It has been found that harmonic components are formed in the frequency spectrum at 25Hz Coefficients. After the settlement error is generated it is seen that, undesired frequency components that are unloaded are lowered under load when the frequency spectra is examined. In this study, theoretical and experimental comparisons of settlement errors are made. Although many errors in this subject are examined in the same publication in general, only the results of the settlement errors are examined specifically as a contribution to the literature. The results and graphs are presented comparatively to the reader's knowledge.

In this paper, a novel photovoltaic (PV) hot-spotting fault detection algorithm is presented. The algorithm is implemented using the analysis of 2580 polycrystalline silicon PV modules distributed across the U.K. The evaluation of the hot-spots is analyzed based on the cumulative density function (CDF) modeling technique, whereas the percentage of power loss (PPL) and PV degradation rate are used to categorize the hot-spots into eight different categories. Next, the implemented CDF models are used to predict possible PV hot-spots affecting the PV modules. The developed algorithm is evaluated using three different PV modules affected by three different hot-spots. Remarkably, the proposed CDF models precisely categorize the PV hot-spots with a high rate of accuracy of almost above 80%.

Fault detection and diagnosis (FDD) algorithms for building systems and equipment represent one of the most active areas of research and commercial product development in the buildings industry. However, far more effort has gone into developing these algorithms than into assessing their performance. As a result, considerable uncertainties remain regarding the accuracy and effectiveness of both research-grade FDD algorithms and commercial products—a state of affairs that has hindered the broad adoption of FDD tools. This article presents a general, systematic framework for evaluating the performance of FDD algorithms. The article focuses on understanding the possible answers to two key questions: in the context of FDD algorithm evaluation, what defines a fault and what defines an evaluation input sample? The answers to these questions, together with appropriate performance metrics, may be used to fully specify evaluation procedures for FDD algorithms.

Fault detection and diagnosis &#x0028 FDD &#x0029 facilitates reliable operation of systems. Various approaches have been proposed for FDD like Analytical redundancy &#x0028 AR &#x0029, Principal component analysis &#x0028 PCA &#x0029, Discrete event system &#x0028 DES &#x0029 model etc., in the literature. Performance of FDD schemes greatly depends on accuracy of the sensors which measure the system parameters. Due to various reasons like faults, communication errors etc., sensors may occasionally miss or report erroneous values of some system parameters to FDD engine, resulting in measurement inconsistency of these parameters. Schemes like AR, PCA etc., have mechanisms to handle measurement inconsistency, however, they are computationally heavy. DES based FDD techniques are widely used because of computational simplicity, but they cannot handle measurement inconsistency efficiently. Existing DES based schemes do not use Measurement inconsistent &#x0028 MI &#x0029 parameters for FDD. These parameters are not permanently unmeasurable or erroneous, so ignoring them may lead to weak diagnosis. To address this issue, we propose a Measurement inconsistent discrete event system &#x0028 MIDES &#x0029 framework, which uses MI parameters for FDD at the instances they are measured by the sensors. Otherwise, when they are unmeasurable or erroneously reported, the MIDES invokes an estimator diagnoser that predicts the state&#x0028 s &#x0029 the system is expected to be in, using the subsequent parameters measured by the other sensors. The efficacy of the proposed method is illustrated using a pump-valve system. In addition, an MIDES based intrusion detection system has been developed for detection of rogue dynamic host configuration protocol &#x0028 DHCP&#x0029 server attack by mapping the attack to a fault in the DES framework.

A large part of energy usage in buildings occurs during the operational phase, emphasising the need for efficient and improved facility management, operation and control. Model Predictive Control (MPC) or Fault Detection and Diagnosis (FDD) are among the strategies that allow minimising energy use and costs during operation. However, the need for fast and accurate dynamic models (e.g. grey box model), which are time-consuming and challenging to implement, precludes their systematic integration in the built environment. A typical grey-box modelling approach consists of manually implementing several grey-box model structures with an increasing level of complexity before performing a forward selection procedure to identify the optimal configuration. The link between the different grey-box models and the monitored data is also established manually. Such an approach can be both time-consuming and error-prone and involves a significant cost that hampers the broad adoption of strategies such as MPC and FDD. This study proposes a tool-chain that uses BIM to automatically generate several grey-box structures with added complexities stemming from the specific geometry and design of the building. More specifically, an existing rule-based IFC to Modelica interface is extended to automatically create several Modelica-based grey box models that gradually take into account the building’s specific information and characteristics. Additional rules are also proposed to automate the connection between the models and the building monitoring system. As a forward selection approach, a multi-objective optimisation using the NSGA-2 algorithm is adopted. The application of the tool-chain on two case studies shows that the integration of BIM to automate the implementation of grey box models, not only reduces the human involvement in the modelling process but can also produce more accurate models. Besides, this study shows that the use of multi-objective optimisation with datasets from two different seasons results in models that are valid for all seasons. ispartof: Energy And Buildings vol:188 pages:209-225 status: published

For the purpose of refined fault detection in DC grids, linear models of components, cabling and possible faults can be applied in order to model the first milliseconds after a possible sudden event. Using a semi-analytical model, a factorial analysis of signals at voltage and current sensors is set up – specific signal patterns representing either fault events or changes between normal operation modes are estimated. With these results, refined fault detection methods avoiding false tripping can be implemented and parametrized.

A real-time fault diagnosis method utilizing an adaptive genetic algorithm to optimize a back propagation (BP) neural network is intended to achieve real-time fault detection of a liquid rocket engine (LRE). In this paper, the authors employ an adaptive genetic algorithm to optimize a BP neural network, produce real-time predictions regarding sensor data, compare the projected value to the actual data collected, and determine whether the engine is malfunctioning using a threshold judgment mechanism. The proposed fault detection method is simulated and verified using data from a certain type of liquid hydrogen and liquid oxygen rocket engine. The experiment results show that this method can effectively diagnose this liquid hydrogen and liquid oxygen rocket engine in real-time. The proposed method has higher system sensitivity and robustness compared with the results obtained from a single BP neural network model and a BP neural network model optimized by a traditional genetic algorithm (GA), and the method has engineering application value.

While modern control methods were becoming widespread, in addition to demanded repeatability and accuracy specifications, reliability and detection and isolation of probable faults have become an obligation for automatic control systems. In the early 70’s, first studies were appeared on this subject. While the first studies on fault detection and isolation (FDI) were implemented for supervisory of chemical processes, following studies were extended to systems like air and spacecrafts, automobiles, nuclear reactors, turbines and HVACs with high reliability mandatories after especially aircraft accidents with high mortality. In 1991, with extending and increasing studies, IFAC SAFEPROCESS comittee was founded and in 1993, this comittee issued some definitions about fault types, fault detection and isolation, fault diagnosis and fault tolerant control (FTC) (Isermann & Balle, 1997). Robots are accepted as an assistant subsystem or an individual part of a complex system in most applications. In addition to applications like serial product lines in which they can work harder, faster and with higher accuracy than humans, they are assigned to missions like waste treatment in nuclear reactors, data and sample collection, maintenance in space and underwater tasks which can be very risky for humans. As a consequence, a fault in one product line may cause a pause in all connected lines even in flexible automation systems or a developing and undetected fault may cause abortion of a whole space or underwater mission with big money costs, it may even cause harm to humans. With the increase of these events in real-life applications and with 90’s, studies on robot reliability and fault detection and diagnosis in robotics have become common. In addition to these studies, NASA and US Army issued some standards on robots and on the reliability and fault possibilities of robotparts (Cavallaro & Walker, 1994). This study is focused on model-based FDI schemes, how they can be applied to robot manipulators, how soft computing techniques can be used in these schemes and three different FDI schemes are proposed. Soft computing techniques which can overcome the difficulties of schemes using analytical methods for nonlinear systems are used as modelling, fault isolator and fault function approximator tools in the proposed schemes. In the following section, a literature overview on FDI for nonlinear systems and robot 7

In this research, a Customized SCADA is built to provide automatic fault isolation for low distribution system. The contribution of this research includes developing a complete fault isolation algorithm based on an open loop distribution system. Service Substation Panel, Customer Service Substation Panel and Customer Panel have been built to validate the proposed methodology. In an open loop distribution system, two feeders are used to provide electricity power supply to the loads. Any section of the feeder can be isolated without interruption. The algorithm is written to check the fault point starting from one of the section feeders or OLUC algorithm and repeated with another section feeder or OLDC algorithm. At the beginning, this algorithm needs to clarify with which point is the fault point by supplying the power supply to each load after the fault is detected by the ELCB. When the fault point is being activated, the ELCB detects the fault and trip mechanism is operated. The algorithm will find the false point and reset the ELCB to restore the power supply to the loads. This time, only the un-faulted point will be restored. In Customer Service Substation panel, two contactors are used to activate one load. Although in Service Substation panel only uses one feeder, the same algorithms (OLUC and OLDC) are applied to control the switching operation of MCCBs by using customized solenoid. MCCBs have trip mechanism that is able to detect faults. MCCBs are different from the loads used in Customer Service Substation because they don't need to be accompanied by two switching devices to control their operations. The HMI is capable to communicate with the I/O devices. An HMI for SCADA is developed in this research by using an embedded Ethernet controller as the converter to communicate with the I/ O devices. By integrating the ELCB and MK2200 into the SCADA system, the SCADA system is capable to respond to the faults by resetting both devices in order for the algorithm to check the fault point. Based on the experimental results, the system correctly locates the fault point, isolates the fault point and reenergizes the un-faulted loads. However, during the fault isolation operation, the system has to detect the fault point by simply switching on the fault load. After the system acknowledges the fault point, the appropriate switching functions are executed. The developed system has a potential in reducing the outage time while comparing to the manual operation by the technicians and engineers. From the analysis done, the outage times for both panels to locate fault and restore electricity power supply to healthy loads are 50.7 seconds. By assuming that it takes 1 hour for the technician to restore the electricity power supply, thus there is a 98.59% improvement in the outage time operation. This system will help the utility company to save money if the outage times are reduced. As described in Table 8, the failure percentage for the system in detecting the fault and isolating the fault point is none. This means that the system is reliable.

This chapter presented a model-based FDI for wheelchair sensors and actuators. Hard faults of the internal sensors and the wheel motors were detected based on the IMM estimator. Fault isolation of the wheel resolvers and the motors was achieved based on the information of the fault-free gyro. A soft fault of the internal sensors was diagnosed based on the velocity estimate of the wheelchair from laser scan matching, using the fault-free LRS. The LRS fault was detected based on errors related to scan matching. Abrupt faults of the LRS can be detected by our algorithm. However, incipient faults (a slow degradation of LRS performance), which can occur in the real world, allows scan matching, but estimates the wheelchair velocity inaccurately, and causes incorrect LRS fault detection. Our research effort is directed toward FDI for sensors and actuators in various fault patterns.

With the widespread availability of high-speed, high-capacity microprocessors and microcomputers with high-speed communication ability, and sophisticated energy analytics software, the technology to support deployment of automated diagnostics is now available, and the opportunity to apply automated fault detection and diagnostics to every system and piece of equipment in a facility, as well as for whole buildings, is imminent. The purpose of this book is to share information with a broad audience on the state of automated fault detection and diagnostics for buildings applications, the benefits of those applications, emerging diagnostic technology, examples of field deployments, the relationship to codes and standards, automated diagnostic tools presently available, guidance on how to use automated diagnostics, and related issues.

In this paper a new method for gear pitting fault detection is presented. The presented method is developed based on a deep sparse autoencoder. The method integrates dictionary learning in sparse coding into a stacked autoencoder network. Sparse coding with dictionary learning is viewed as an adaptive feature extraction method for machinery fault diagnosis. An autoencoder is an unsupervised machine learning technique. A stacked autoencoder network with multiple hidden layers is considered to be a deep learn ing network. The presented method uses a stacked autoencoder network to perform the dictionary learning in sparse coding and extract features from raw vibration data automatically. These features are then used to perform gear pitting fault detection. The presented method is validated with vibration data collected from gear tests with pitting faults in a gearbox test rig and compared with an existing deep learning-based approach.

Collecting data from mechanical systems in abnormal conditions is expensive and time consuming. Consequently, fault detection approaches based on classical supervised learning working with both normal and abnormal data are not applicable in some condition-based maintenance tasks. To address this problem, this paper proposes Fusing Convolutional Generative Adversarial Encoders (fCGAE) method to create fault detection models from only normal data. Firstly, to obtain an adequate deep feature space, encoder models based on 1D convolutional neural networks are created. Then, these encoders are optimized in an unsupervised way through Bidirectional Generative Adversarial Networks. Finally, the multi-channel features collected from the system are merged with One-Class Support Vector Machine. fCGAE is applied to fault detection in 3D printers, where experimental results in two fault detection cases show excellent generalization capabilities and better performance compared to peer methods. (C) 2020 Elsevier Ltd. All rights reserved. GIDTEC Research Group of Universidad Politecnica Salesiana; National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [51775112, 71801046]; National Key RD Program [2016YFE0132200]; MoST Science and Technology Partnership Program [KY201802006]; Chongqing Natural Science FoundationNatural Science Foundation of Chongqing [cstc2019jcyjzdxmX0013]; CTBU Project [KFJJ2018107, KFJJ2018075] info:eu-repo/semantics/publishedVersion

In order to reduce wind energy costs, prognostics and health management (PHM) of wind turbine is needed to reduce operations and maintenance cost of wind turbines. The major cost on wind turbine repairs is due to gearbox failure. Therefore, developing effective gearbox fault detection tools is important in the PHM of wind turbine. PHM system allows less costly maintenance because it can inform operators of needed repairs before a fault causes collateral damage happens to the gearbox. In this paper, a new acoustic emission (AE) sensor based gear fault detection approach is presented. This approach combines a heterodyne based frequency reduction technique with time synchronous average (TSA) and spectral kurtosis (SK) toprocess AE sensor signals and extract features as condition indictors for gear fault detection. Heterodyne techniques commonly used in communication are used to preprocess the AE signals before sampling. By heterodyning, the AE signal frequency is down shifted from MHz to below 50 kHz. This reduced AE signal sampling rate is comparable to that of vibration signals. The presented approach is validated using seeded gear tooth crack fault tests on a notational split torque gearbox. The approach presented in this paper is physics based and the validation results have showed that it could effectively detect the gear faults.

This paper presents a trajectory tracking fault-tolerant control strategy inside an autonomous emergency landing architecture to control a quadrotor in case of partial rotor fault. The proposed architecture, which is composed of required hardware and subsystems, aims to ensure a fully autonomous safe landing of the impaired quadrotor to a suitable landing site. The controller strategy, which is tried to be coincident with the proposed emergency landing architecture and the Pixhawk autopilot contains a cascade three-loop structure of adaptive sliding mode and a modified PID algorithm along with a fault detection algorithm. The adaptive sliding mode and the PID algorithms are applied to the fast dynamics of angular velocity rates and the position control of the quadrotor, respectively. A lightweight fault detection algorithm is developed to detect and identify the partial faults of engine using the controller outputs and the filtered angular rates. The simulation results demonstrate that the proposed fault-tolerant controller can control the multi-rotor in partial engine faults with satisfactory tracking performance. The results also demonstrate the effect of fault detection time delay on the overall control performance.

One of the most significant research trends in the last decades of the aeronautic industry is the effort to move towards the design and the production of “more electric aircraft”. Within this framework, the application of the electrical technology to flight control systems has seen a progressive, although slow, increase: starting with the introduction of fly-by-wire and proceeding with the partial replacement of the traditional hydraulic/electro-hydraulic actuators with purely electro-mechanical ones. This evolution allowed to obtain more flexible solutions, reduced installation issues and enhanced aircraft control capability. Electro-Mechanical Actuators (EMAs) are however far from being a mature technology and still suffer from several safety issues, which can be partially limited by increasing the complexity of their design and hence their production costs. The development of a robust Prognostics and Health Management (PHM) system could provide a way to prevent the occurrence of a critical failure without resorting to complex device design. This paper deals with the first part of the study of a comprehensive PHM system for EMAs employed as primary flight control actuators; the peculiarities of the application are presented and discussed, while a novel approach, based on short pre-flight/post-flight health monitoring tests, is proposed. Turn-to-turn short in the electric motor windings is identified as the most common electrical degradation and a particle filtering framework for anomaly detection and prognosis featuring a self-tuning non-linear model is proposed. Features, anomaly detection and a prognostic algorithm are hence evaluated through state-of-the art performance metrics and their results discussed.

This paper analyzed the magnetostrictive characteristics of iron core and stress mechanism of the silicon steel of HVDC converter transformer. Vibration test of iron core were carried out in the field and in the factory. And excitation voltage frequency and amplitude dependence of vibration acceleration signals of iron core were studied as well. This paper might provide certain support for fault detection for iron core based on vibration analysis technology.

Future health monitoring concepts in different fields of engineering require reliable fault detection to avoid unscheduled machine downtime. Diagnosis of electrical induction machines for industrial applications is widely discussed in literature. In aviation industry, this topic is still only rarely discussed. A common approach to health monitoring for electrical induction machines is to use Motor Current Signature Analysis (MCSA) based on a Fast Fourier Transform (FFT). Research results on this topic are available for comparatively large motors, where the power supply is typically based on 50Hz alternating current, which is the general power supply frequency for industrial applications. In this paper, transferability to airborne applications, where the power supply is 400Hz, is assessed. Three phase asynchronous motors are used to analyse detectability of different motor faults. The possibility to transfer fault detection results from 50Hz to 400Hz induction machines is the main question answered in this research work. 400Hz power supply frequency requires adjusted motor design, causing increased motor speed compared to 50Hz supply frequency. The motor used for experiments in this work is a 800W motor with 200V phase to phase power supply, powering an avionic fan. The fault cases to be examined are a bearing fault, a rotor unbalance, a stator winding fault, a broken rotor bar and a static air gap eccentricity. These are the most common faults in electrical induction machines which can cause machine downtime. The focus of the research work is the feasibility of the application of MCSA for small scale, high speed motor design, using the Fourier spectra of the current signal. Detectability is given for all but the bearing fault, although rotor unbalance can only be detected in case of severe damage level. Results obtained in the experiments are interpreted withrespect to the motor design. Physical interpretation are given in case the results differ from those found in literature for 50Hz electrical machines.

This paper deals with the design of an integrated scheme of actuator fault diagnosis and accommodation. The fault detection, isolation and estimation are given by the diagnosis task based on an Unknown Input Observer UIO. The UIO allows a robust diagnosis throughout decoupling disturbances from faults and provides estimation for the faults amplitude. This latter is used in order to recompute, on line, a feedback control gain that guaranteeing the fault accommodation and the compensation of its effect. The control gain is determinate based on the Pseudo-Inverse Method PIM

Once injected into the orbit, the satellites operate in space all the time and they are exposed under very harsh space environment such as extreme temperature variation in vacuum condition, radiation etc. As a satellite requires significant amount of investment, it is desired to understand its lifespan and performance degradation before a replacement satellite is launched. The degradation process of an electronic component is strongly related to the thermal, radiation and pressure conditions in the low earth orbit. This research focuses on the degradation and fault monitoring of the electronics components in the DC-DC converters of satellite power system. The main contributions of this research are listed below.  Developed a low sampling rate online power converter circuit parameter estimation method. The estimation accuracy is compatible with the conventional high sampling rate methods as indicated in both simulation and experimental results.  Derived the interleaved boost converter’s state space model to include the most important parasitic effects.  Developed a grouped immigration algorithm to accelerate the solution convergence rate in highly inter-relative multi-variable problems using biogeography based optimisation (BBO). The degradation in the converters are detected through the online parameter estimation. The previously reported fault detection methods for the DC-DC converter require a sampling frequency to be at least 25 times of the switching frequency. In this thesis, the new parameter estimation methods are developed based on the averaged converter circuit model. This allows the measurements to be taken only once in every few switching cycles. Therefore, this method can be implemented using low cost and low power processer. This is particular important for miniaturized satellites which have limited power budget due to the limited area of the solar array available for the satellite. The low sampling rate method has been verified through simulation and experimental study for a buck converter. The average estimation error for the circuit components are less than 5.17% in the simulation and 7.5% for the experimental results. The parameter estimation method can be extended to other converter topologies. To provide redundancy in the power system, the multi-phase interleaved converter has been developed for various applications. Such feature is useful for satellite to eliminate single point failure. To develop an online parameter monitoring system, a general state space based interleaved DC-DC converter averaged model is formulated in this thesis. Moreover, the number of measurements and unknowns in this model are scalable . As additional unknowns are added into the optimization problem for multiphase interleaved converter, the convergence rate of the conventional BBO approach dropped. To accelerate the convergence rate of BBO with moderate calculation complexity, the grouped immigration method is proposed in this research. Results have demonstrated that the convergence rate improved significantly such that the estimation error deviation for the circuit component is less than 2% and the averaged error is less than 7% when the function evaluation is restricted to 200,000. Thus it outperformed the conventional BBO method, in which the estimation error is greater than 100% due to premature termination under the same function evaluation limit. Doctor of Philosophy (EEE)

Building worldwide contributes to 40\% of the global energy consumption, and most of that is due to Heating, Ventilation, and Air-conditioning (HVAC) systems. A large part of this energy is wasted because of poor maintenance, inevitable degradation, and improperly controlled equipment. Therefore, it is of practical relevance and significance to study Fault Detection and Diagnosis (FDD) techniques for smart buildings aiming at saving energy and offering more comfortable dwelling environment. Researchers have been tackling building FDD task with a wide variety of techniques, such as analytical model-based, signal-based and data-driven methods. Recently the data-driven method has shown its advantage in dealing with complex systems with random penetrations. Most of the existing works tend to formulate the data-driven FDD as a pure fault types identification task. Problems such as severity levels identification, inter-dependence information incorporation, and essential features selection have long been ignored. This dissertation addresses the aforementioned problems, and the details are summarized as follows. First of all, the building FDD task is directly formulated as a multiple classification problem. A Discriminant Analysis-based Fault Classification (DAFC) method is driven to conduct the detection and diagnosis. Linear Discriminant Analysis (LDA) is firstly adopted to project the high dimensional data into a lower dimensional space so as to achieve optimal class separation and maximum original information maintenance. Derived from the K-means Clustering, DAFC classification applies two criteria to make a decision. The testing data set is classified to a certain cluster if: 1), it is the closest to that cluster by Manhattan distance; 2), Manhattan distances between the testing data set and that cluster are within a certain range. By feeding the training and testing data to DAFC, fault type is diagnosed at the first stage, and the corresponding severity level is identified at the second stage. The proposed two-stage data-driven FDD strategy is validated by the experimental data collected by the ASHRAE Research Project 1043 (RP-1043). Results show that it can detect and diagnose chiller faults and the corresponding severity levels effectively. Although the two-stage FDD strategy generates satisfactory results, it only works well when the number of included classes is small. Formulating the FDD task as a pure multiple classification problem is not effective enough when the number of included classes becomes large. Thus, a Tree-structured Fault Dependence Kernel (TFDK) method is proposed to identify fault type as well as fault severity level in a unified large margin learning framework. TFDK adopts structured labeling to incorporate the inter-class dependence information and deals with the streaming data with a corresponding on-line learning algorithm. As an improvement of traditional classification methods, it encodes the dependence information in its feature mapping and takes regularized misclassification cost as the learning objective. Similarly, following the ASHRAE Research Project 1043 (RP- 1043), TFDK is applied to solve the FDD for a 90-ton centrifugal water-cooled chiller. Experimental results show that compared to conventional classification methods, TFDK can significantly improve the FDD performance and recognize the fault severity levels with high accuracy. Lastly, previous works have justified that buildings and their operation can greatly benefit from rich and relevant data sets. More specifically, data has been analyzed to detect and diagnose system and component failures that undermine energy efficiency. Among the vast amount of measured information, some features are more correlated with the failures than others. However, there has been little research to date focusing on determining the types of data that can optimally support FDD. Thus, a novel optimal feature selection method, the Information Greedy Feature Filter (IGFF) method, is proposed to select essential features. On the one hand, the selection results would serve as a reference for configuring sensors in the data collection stage, particularly when the measurement resource is limited. On the other hand, with the most informative features selected by IGFF, the performance of building FDD could be improved and theoretically justified. A case study on Air Handling Unit (AHU) FDD is conducted based on the ASHRAE Research Project 1312 (RP-1312). Numerical results show that compared with several baselines, the FDD performances of conventional classification methods are greatly enhanced by IGFF. In summary, this dissertation studies the data-driven techniques and proposes several effective strategies to solve the FDD problem for building chillers and AHUs. Compared with previous works, the proposed DAFC can identify the fault severity level at a second stage after fault types have been diagnosed. This dissertation also focuses on recognizing both fault types and the corresponding severity levels in a unified learning framework. Hence, the inter-class fault dependence information is included with tree-structured labeling by the proposed TFDK algorithm. Besides, by selecting essential subsets of variables that are more correlated to faults with the proposed IGFF algorithm, not only the FDD accuracy is improved, but also the FDD application becomes more convenient and practical. Doctor of Philosophy (EEE)

Over the past five years, two of the Slocum underwater gliders operated by the UK National Oceanography Centre have lost a wing mid-mission without the pilot being aware of the problem until the point of vehicle retrieval. In this study, the steady-state data collected by gliders during the two deployments has been analysed to develop a fault detection system. From the data analysis, it is clear that the loss of the wing was a sudden event for both gliders. The main changes to the system dynamics associated with the event are an increase in the positive buoyancy of the glider and the occurrence of a roll angle on the side of the lost wing, with significant difference between dives and climbs. Hence, a simple effective system for the detection of the wing loss has been designed using the roll angle. Since sensors are known to fail and the roll sensor is non-critical to the operation of the glider, a back-up diagnostics system has been developed based on the dynamic model of the vehicle, capturing the change in buoyancy. Both systems are able to correctly detect the loss of the wing and notify pilots, who can re-plan missions to safely recover the vehicle.

To detect substation faults for timely repair, this paper proposes a fault detection method that is based on the time series model and the statistical process control method to analyze the regulation and characteristics of the behavior in the switching process. As the first time, this paper proposes a fault detection model using SARIMA, statistical process control (SPC) methods, and 3σ criterion to analyze the characteristics in substation’s switching process. The employed approaches are both very common tools in the statistics field, however, via effectively combining them with industrial process fault diagnosis, these common statistical tolls play excellent role to achieve rich technical contributions. Finally, for different fault samples, the proposed method improves the rate of detection by at least 9% (and up to 15%) than other methods.

Gears are one of the most important power transmission elements in every area of the industry. Because of its importance, the gear design must be carefully performed. Unfortunately, due to the changing of the boundary conditions, gears are exposed to failures such as cracks, pitting, tooth missing etc. during the operation. Thus the gear diagnostic and monitoring become a very critical phenomenon for the gearboxes. A dynamic transmission error (DTE) based numerical fault detection model is proposed. Firstly, numerical finite element model is created to calculate single tooth stiffness with different crack levels. Furthermore, the model is used for the asymmetric gear profile which has a great importance nowadays for different areas. After that, the time-varying mesh stiffness is calculated by using single tooth stiffness with different crack levels for both symmetric and asymmetric types of the gears. To understand the effects of the gear cracks along the tooth thickness on dynamic transmission error of the gear system and to detect the gear crack faults for symmetric and asymmetric gear profiles, a four-degree of freedom dynamic model is created. The results show that with the increment of the crack level, the mesh stiffness of the gears is decreased.

As data analytic techniques evolve and the accessibility of process measurements improves, data-driven process monitoring has enjoyed a quick development in both theoretical and application perspectives recently. Although abundant process measurements will facilitate data-driven process monitoring and lead to better monitoring indices, it becomes difficult to identify the underlying variables that are responsible for a fault directly with the monitoring indices as the scope of measured variables is getting broader. To restrain the scope and identify the source of fault, contribution plots are commonly used in fault diagnosis in order to quantify the influence of process variables in presence of fault. Nevertheless, as sophisticated monitoring techniques become more and more complicated, deriving corresponding contribution plots is challenging. The concept of deviation contribution plots is proposed to address this issue. By extending the original definition of contribution for linear processes, the deviation contribution is defined to quantify the contribution of deviations in originally measured variables to the deviation of monitoring indices. The ability of proposed deviation contribution plots to identify influential variables in monitoring algorithms based on nonlinear feature extractions is verified by both numerical simulation and the Tennessee Eastman Process benchmark case study.

Şebeke Kalkışlı Daimi Mıknatıslı Senkron (ŞKDMSM) yüksek verim, yüksek güç faktörü ve self starting üstün özelliklerinden dolayı bant sistemleri, fan sistemleri gibi endüstriyel ortamlardaki birçok uygulamada kullanılmaktadır. Bu motorların arızalarının erken tespiti, üretim kayıplarının yanısıra yüksek bakım ve onarım masraflarını da ortadan kaldıracaktır. Bu çalışmada ŞKDMSM’nin rulman arızalarının tespiti için SCADA tabanlı gerçek zamanlı durum izleme ve arıza tespit yöntemi önerilmiştir. Bu amaçla öncelikle motor akım ve gerilim verilerinin izlenmesi amacıyla SCADA tabanlı durum izleme otomasyonu gerçekleştirilmiştir. Sağlam bir ŞKDMSM’den farklı devir ve yük koşulları altında izlenen akım sinyallerinin üstel ağırlıklı hareketli ortalama (ÜAHO) tabanlı bir istatistiksel proses kontrol yöntemi ile analiz edilerek motorun normal çalışma limitleri belirlenmiştir. Daha sonra arıza durumundaki bir ŞKDMSM’nin akım sinyallerine ait ÜAHO verileri kullanılarak bu limitlerin aşımlarına göre arıza tespiti yapılmıştır. Elde edilen sonuçlar tasarlanan SCADA otomasyonunun güvenli veri toplama ve kaydetme özelliğine sahip olduğunu ve önerilen arıza tespit yönteminin ise ŞKDMSM’nin rulman arızalarının tespiti için başarılı bir araç olduğunu göstermiştir., Line start permanent magnet synchronous motor (LSPMSM) is used in many applications in industrial environments such as belt systems and fan systems due to its high efficiency, high power factor and self-starting features. Early detection of LSPMSM failures will eliminate production losses and high maintenance and repair costs. In this study, SCADA-based online condition monitoring and fault detection method is proposed for detecting bearing failures of LSPMSM. For this purpose, SCADA based condition monitoring automation was carried out primarily to monitor motor current and voltage data. The normal operating limits of the motor were determined by analyzing the current signals monitored under different speed and load conditions from a healthy LSPMSM with an exponential weighted moving average (EWMA) based statistical process control method. Then, fault detection was made according to the exceeding of these limits by using EWMA data of the current signals of a the LSPMSM in case of faulthy. The obtained results showed that the designed SCADA automation has the ability to collect and save data safely, and the proposed fault detection method is a successful tool for the detection of bearing failures of LSPMSM.

A fault detection and diagnostics (FDD) tool is a type of energy management and information system that continuously identifies the presence of faults and efficiency improvement opportunities through a one-way interface to the building automation system and the application of automated analytics. Building operators on the leading edge of technology adoption use FDD tools to enable median whole-building portfolio savings of 8%. Although FDD tools can inform operators of operational faults, currently an action is always required to correct the faults to generate energy savings. A subset of faults, however, such as biased sensors, can be addressed automatically, eliminating the need for staff intervention. Automating this fault “correction” can significantly increase the savings generated by FDD tools and reduce the reliance on human intervention. Doing so is expected to advance the usability and technical and economic performance of FDD technologies. This paper presents the development of nine innovative fault auto-correction algorithms for Heating, Ventilation, and Air Conditioning pi(HVAC) systems. When the auto-correction routine is triggered, it overwrites control setpoints or other variables to implement the intended changes. It also discusses the implementation of the auto-correction algorithms in commercial FDD software products, the integration of these strategies with building automation systems and their preliminary testing.

This paper presents a prognostics and health management (PHM) 2 scheme for a multiple redundancy aileron actuator (MRAA), 3 which includes fault detection, fault diagnosis, and performance assessment. The scheme utilizes the system input, system output, force motor current (FMC), 4 and aerodynamic loads for fault detection, diagnosis, and performance assessment. Fault detection is implemented using a two-step radial basis function (RBF) neural network. The first RBF neural network is employed as an observer and generates the residual error, and the second RBF neural network synchronously generates the adaptive threshold. Fault diagnosis is carried out using a system observer and an FMC observer. First, a force motor observer is used to estimate the FMC. Then, the FMC ratio of each channel can be calculated using the estimated FMC and actual FMC. Finally, a fault diagnosis is achieved by comparing the FMC ratios for the channels. For performance assessment, the system observer is adopted to generate the residual error. Then, time-domain features of the residual error are extracted. Finally, the features are input into a pre-trained self-organizing map neural network to realize the performance assessment. The effectiveness of these approaches is demonstrated using several tests at the end of this paper.

Vibration from incipient faults of rolling element bearings (REBs) is usually too weak to be observed in a conventional spectrum analysis. The envelope analysis or high-frequency resonance technique is an effective tool for the incipient fault detection of REBs. The newly developed envelope order tracking, an improved version of the envelope analysis, can be well performed even in a varying-speed condition. However, the envelope order tracking can be invalid for multi-impulsive sources. To address this issue, a scheme for the weak feature extraction of faulty REBs has been proposed in this paper by combining the envelope order tracking and the constrained independent component analysis (cICA). In the proposed scheme, the envelope order tracking approach is utilized to obtain the envelopes of sensor observed mixtures at different positions. Then, the envelopes are turned from the time domain into the angle domain by the constant-angle increments resampling scheme in the computed order tracking (COT). Subsequently, the cICA method is employed to extract the interesting envelope independent components (ICs) by a reference signal, which is constructed according to the prior-known feature frequency of the bearing. As a result, the faults related features can be clearly exposed in the spectra of the obtained interesting envelope ICs. Simulations and experimental results support the proposed method positively.

© 2018 Elsevier B.V. The Air Handling Unit (AHU) is one of the most energy consuming devices in building systems. Fault Detection and Diagnosis (FDD) methods integrated into AHUs can help to ensure that they comply with the intended design, and their efficiency is maintained throughout the entire operational stage of the building. Nonetheless, the implementation and deployment of FDDs at the operational stage require an extensive effort. Especially, FDD approaches that rely on first principle models (model-based FDD) need to be manually implemented, and the information necessary for this process is scattered between several exchange formats and files, thus making it time-consuming, error-prone and subject to modellers’ poor judgment. This study aims at facilitating and partially automating the implementation and deployment of model-based FDD. An automated tool-chain that combines a BIM (Building Information Model)-to-BEPS (Building Energy Performance Simulation) tool with a model-based FDD approach is developed. The contribution of this paper lies in the extension of an existing BIM to Modelica BEPS method with an automated calibration approach and a novel model-based FDD. These three elements are integrated in a framework (implemented using Python) to reduce experts’ involvement in FDD implementation and deployment. The developed model-based FDD combines a parity relation procedure for fault detection and profile identification for fault diagnosis. The latter uses the robust multi-objective optimisation algorithm NSGA-2. An error is detected when the difference between prediction and measured data over a specific time window is superior to a predefined threshold. The origin of the error is subsequently identified by estimating the profile of the different controllable components’ control signal. The developed tool-chain was applied to an actual AHU as well as on several numerical scenarios to identify typical AHU faults such as faulty dampers, valves and sensors. This study shows that the developed model-based FDD approach can identify some of the most common faults in AHUs, but more importantly that BIM can facilitate the deployment of model-based FDD in building systems. ispartof: Automation in Construction vol:96 pages:508-526 status: published

The complex and large power systems of modern time with distributed generation are more prone to the faults. Conventionally, the zone based primary and backup protection relies mainly on the information of positive sequence impedance sensed by the relay to protect these lines. The phenomena such as power swings, load encroachment, etc., can also result into low impedance and can trigger the cascade tripping of relays. In this paper, we proposed a robust fault detection and discrimination (RFDD) technique for transmission lines, which utilizes a robust method of phasor estimation to compute accurate fault impedance along with a feature value extracted from the samples of voltage and current signals. The effectiveness of the proposed RFDD technique has been tested and validated on IEEE 14-bus system, six generator 23-bus system and reduced NEREB 29-bus Indian power system with distributed generation using Siemens PSS/Sincal software. The results from simulation explore that RFDD technique can enhance the capability of distance protection relays for the future electric grids.

Early detection and isolation of faults in wind power generators increases the availability of wind turbines and reduces their down times and maintenance costs. Wind turbines constitute a complex system that includes sub-systems from different physical domains such as electrical, mechanical, and hydraulic systems. They constitute hybrid dynamical systems comprising discrete parts due to presence of power electronic switches and continuous parts such as induction machines, pitch actuator and mechanical drive-train. In this paper, a multi-physics graphical model-based fault detection and isolation (FDI) method is developed for doubly fed induction generator-based wind turbines. The model of the wind turbine is developed using hybrid bond-graph theory that captures causal, temporal, and structural properties of the system. Causality inversion method is then employed to derive analytical redundancy relations (ARRs) based on the developed model. The FDI is performed based on the changes in the values of ARRs. A systematic approach based on Chi-square criterion is developed to determine the values of thresholds, based on which the change in ARRs due to occurrence of faults are detected. The capability of the proposed method in accurate and fast detection and identification of mechanical, electrical, and hydraulic faults is demonstrated by the simulation results.

Fault location algorithms require the input of voltage and current phasors when estimating the distance to a fault. In the case of momentary faults, phasor calculations are often complicated by the presence of an exponentially decaying dc offset. Fourier filters and cosine filters, popularly used by most phasor estimation algorithms in relays, are successful in filtering out most, but not all, of the exponentially decaying dc offset. This dc offset affects the accuracy of voltage and current phasors and may result in a significant error in location estimates. Therefore, this paper presents a dc offset removal algorithm to improve the fault location estimates of momentary faults. The algorithm uses the rms-wavelet method for fault detection and estimates voltage and current phasors using non-linear least squares methods. The proposed method uses variable window size in calculating phasors and estimates a single, but more accurate fault location than multiple locations estimated by the Fourier and cosine filters. The method is validated using simulated and actual field data. The location estimates are shown to be accurate within 5.6% in all cases.

This paper develops a structural diagnosis approach for fault detection and isolation in hybrid systems. Hybrid systems are characterized by continuous behaviors that are interspersed with discrete mode changes in the system, making the analysis of behaviors quite complex. In this paper, we address the mode detection problem in hybrid systems as the first step in diagnoser design. The proposed method uses analytic redundancy methods to detect the operating mode of the system even in the presence of system faults. We define hybrid minimal structurally overdetermined (HMSO) sets for hybrid systems. For residual generation, we develop the HMSO selection problem, formulated as a binary integer linear programming optimization problem to minimize the number of selected HMSOs and reduce online computational costs of the diagnosis algorithm. The proposed structural approach does not require preenumeration of all possible modes in the diagnoser design step. Therefore, our approach is feasible for hybrid systems with a large number of switching elements, implying that the system can have a large number of operating modes. The case study demonstrates the effectiveness of our approach. We discuss the results of our case study, and present directions for future work. Note to Practitioners —Developing feasible approaches for online monitoring, fault detection, and fault isolation of complex hybrid and embedded systems, such as automobiles, aircraft, power plants, and manufacturing processes, is essential in securing their safe, reliable, and efficient operation. Frequent changes in the operational modes of these systems because of operator actions, such as changing gears in an automobile, or environmental changes, such as driving on a wet or icy road make the fault detection and isolation task in these systems challenging. It is important to detect and isolate faults in all the operating modes, and at the same time, not mistake a mode change as a fault in the system. In this paper, we propose an approach that exploits the equation structure of hybrid systems behavior to combine mode detection and diagnosis in nonlinear hybrid systems. The proposed algorithm is scalable and efficient. We demonstrate its effectiveness using a case study of a reverse osmosis subsystem in an advances life support system for long duration manned space missions. Important challenges that can affect the success of our approach include the need for sufficiently detailed hybrid models that capture nominal and faulty behavior, and a sufficient number of sensors to make simultaneous mode detection and fault isolation possible.

A new differential current-based fast fault detection and location scheme for multiple Photovoltaic-based dc microgrid is proposed in this paper. A multiterminal dc (MTDC) distribution network is an effective solution for present grid scenario, where local distribution is incorporated primarily by power electronics based dc loads. PV systems with auxiliary power sources and local loads are used for MTDC connection, especially when ac utility grid is integrated with it by voltage source converters. Pole to pole and pole to ground faults are basically considered as dc distribution network hazards. As PV is connected through dc cable, high resistive dc arc fault is also studied in present literature. The proposed PV system is considered with arc-fault circuit interrupters as backup protection and is used to detect arcing series fault. Fast acting dc switching is considered for proposed differential current-based unit protection. A discrete frame differential current solution is considered to classify the fault type by modified cumulative sum average approach. By calculating unknown dc cable resistance accurately by non-iterative Moore-Penrose pseudo inverse technique, the fault distance is calculated. TMS320C6713 DSP based test-bench is used for verification of the scheme.

A traditional power transmission system (TPTS) is composed of many electrical substations (ESs) and transmission lines. When the latter meet faults, the output of the ESs may be interrupted. In this paper, a methodology is proposed to construct controlled systems with battery energy storage systems to avoid the output interruption during the fault detection and restoration for some important ESs. In a TPTS, an important ES can be controlled by a supervisor that can be thought of as a control agent. Moreover, the ES can be preconnected with other ESs. If a fault occurs in the input lines of the ES, the fault can be detected by its supervisor and restored by its preconnected ESs. Furthermore, the ES can contain a battery energy storage system to store electric power for its temporary output during the fault detection and restoration. Therefore, the output interruption of the ES can be successfully avoided. This work formally models the structures of TPTSs and their control systems by using Petri nets and then verifies the correctness of fault detection and restoration. Finally, a case study is presented.

Air brake systems are crucial systems for safe and stable operation of electric multiple units (EMUs). The brake cylinder system, which includes brake cylinders, corresponding pressure sensors, and connection pipes, plays a vital role in the EMU air brake system. This is because brake cylinder pressures directly affect the brake operation. Currently, brake cylinder pressures are monitored by univariate control charts, i.e., the brake cylinder system will be considered as faulty if a certain pressure goes beyond its allowed range. Besides, serious sensor hardware faults such as open circuit or short circuit can also be detected by system self-inspection circuits. However, three kinds of faults, including brake cylinder component fault, soft sensor fault, as well as gas leakage fault, cannot be well handled by current monitoring methods if these faults are not very serious. In this paper, a fault detection index called intervariable variance (IVV) is first presented to perform fault detection for these faults. Fault detectability analysis is provided, and the IVV statistic is also compared with the univariate control chart approach. Then, a fault isolation strategy is proposed to distinguish different kinds of faults and determine the location of the occurred fault. Finally, the effectiveness of the proposed fault detection and isolation method is demonstrated via extensive experimental studies that are carried out on the EMU brake test bench of Qingdao Sifang Rolling Stock Research Institute Co., Ltd., China.

A healthy photovoltaic (PV) array has a specific impedance between node pairs, and any ground fault changes the impedance values. Reflectometry is a well-known technique in electromagnetics, and it could be exploited to detect fault and aging-related impedance variations in a PV system. A fault detection algorithm using the spread spectrum time-domain reflectometry (SSTDR) method has been introduced in this paper. SSTDR has been successfully used for detecting and locating aircraft wiring faults. However, the wide variation in impedance throughout the entire PV system, which is caused by the use of different materials and interconnections makes PV fault detection more challenging while using reflectometry. Unlike other conventional ground-fault detection techniques specifically developed for PV arrays, SSTDR does not depend on fault-current magnitudes. Therefore, SSTDR can be used even in the absence of the solar irradiation, which makes it a very powerful fault-detection tool. The proposed PV ground-fault detection technique has been tested in a real-world PV system, and it can confidently detect PV ground faults for different configurations of PV arrays (single and double strings) and fault resistances (0.5, 5, and 10- $\Omega$ ). Moreover, it has been experimentally verified that our proposed algorithm works at low irradiance and can detect specific ground faults that may remain undetected using the conventional ground-fault detection and interrupter (GFDI) fuses.

With the Industry 4.0 era coming, modern chemical plants will be gradually transformed into smart factories, which sets higher requirements for fault detection and diagnosis (FDD) to enhance operation safety intelligence. In a typical chemical process, there are hundreds of process variables. Feature selection is a key to the efficiency and effectiveness of FDD. Even though artificial immune system has advantages in adaptation and independency on a large number of fault samples, antibody library construction used to be based on experience. It is not only time consuming, but also lack of scientific foundation in fault feature selection, which may deteriorate the FDD performance of the AIS. In this paper, a fault antibody feature selection optimization (FAFSO) algorithm is proposed based on genetic algorithm to optimize the fault antibody features and the antibody libraries' thresholds simultaneously. The performance of the proposed FAFSO algorithms is illustrated through the Tennessee Eastman benchmark problem.

High-impedance fault detection (HIFD) is crucial in an effectively grounded distribution system because of the potential threat of fire and electric shock. HIFD has been extensively researched for more than 30 years, and a harmonic component in zero-sequence current is typically used in detection algorithms since waveform distortion is often produced by arc flash. For a stable HIF, it would be invalid or has lower sensitivity because of the limited harmonic content in zero-sequence current waveform. To overcome this problem, in this paper, a novel detection algorithm is proposed for detecting waveform distortion in the time domain. First, for the analysis of arc flash, the solid dielectric electrical breakdown theory is verified to be more suitable than theories based on the heat accumulation theory, and a nonlinear arc model that can be used for analyzing arc flash is then proposed. Furthermore, a novel HIFD algorithm based on the identification of nonlinear voltage–current characteristic profiles (VCCPs) is proposed, with the focus on the quenching and restrike dynamic process of an arc flash. The high performance of the algorithm is verified by performing various simulations and examining field data. An HIFD prototype using the proposed algorithm was developed, and it showed excellent performance in real-time digital simulator tests. The VCCP based disturbance detection approach could also be popularized in the applications of relay protection, harmonic source location and early fault alarming in smart grid.

In this paper, a new strategy for service restoration of de-energized loads is proposed using advanced technologies of microgrids (MGs) that accommodate distributed energy resources (DERs). In particular, backup distributed generators (DGs), initially used for critical loads within a building, have recently been operated under normal grid conditions while exporting excess power to other loads on the same or different feeders. In addition, smart switches (SSWs) have been installed in a distribution grid to reduce the frequency and duration of power outages. Using flexible communication links, the sensors of the SSWs exchange control and measurement signals with grid management systems, namely, an advanced distribution management system, an MG energy management system, and a DER management system. This paper focuses on developing a methodological framework to determine the operating modes of synchronous-machine DGs and the on–off operation of SSWs, so as to change dynamically the boundaries of MGs that are formed in a distribution grid for fault isolation and load restoration. Simulation case studies demonstrate that the proposed strategy is effective in mitigating the influence of a network fault and restoring de-energized loads while successfully maintaining frequency and voltage levels in MGs.

Batch processing is used extensively in the production of high value products, and there are strong economic incentives for developing methodologies for ensuring the successful completion of batches via process monitoring and fault detection. Building on our previous work using time-explicit Kiviat diagrams for continuous processes, this paper introduces data visualization, data-driven process monitoring and fault detection for batch systems. Handling batch data, including unfolding and alignment are addressed as well. The proposed methodology is demonstrated on data obtained from a benchmark bioreactor simulator and a semiconductor etching process.

In this paper, a dual estimation methodology is developed for both time-varying parameters and states of a nonlinear stochastic system based on the particle filtering scheme. Our developed methodology is based on a concurrent implementation of state and parameter estimation filters as opposed to using a single filter to simultaneously estimate the augmented states and parameters. The convergence and stability properties of our proposed dual estimation strategy are shown formally to be guaranteed under certain conditions. The advantage of our developed dual estimation method is justified by handling simultaneously and efficiently both the state and time-varying parameters of a nonlinear system. This is accomplished in the context of a health monitoring scheme that employs a unified approach to fault detection (FD), isolation, and identification in a single framework. The performance capabilities of our proposed FD methodology is demonstrated and evaluated by its application to a gas turbine engine through providing state and parameter estimation objectives under simultaneous and concurrent component fault scenarios. Extensive simulation results are provided to substantiate and justify the superiority of our proposed FD methodology when compared with another well-known alternative diagnostic technique that is available in the literature. ? 1993-2012 IEEE. Qatar National Research Fund, Qatar Foundation Scopus

With the emergence of smart factories, large volumes of process data are collected and stored at high sampling rates for improved energy efficiency, process monitoring and sustainability. The data collected in the course of enterprise-wide operations consists of information from broadly deployed sensors and other control equipment. Interpreting such large volumes of data with limited workforce is becoming an increasingly common challenge. Principal component analysis (PCA) is a widely accepted procedure for summarizing data while minimizing information loss. It does so by finding new variables, the principal components (PCs) that are linear combinations of the original variables in the dataset. However, interpreting PCs obtained from many variables from a large dataset is often challenging, especially in the context of fault detection and diagnosis studies. Sparse principal component analysis (SPCA) is a relatively recent technique proposed for producing PCs with sparse loadings via variance-sparsity trade-off. Using SPCA, some of the loadings on PCs can be restricted to zero. In this paper, we introduce a method to select the number of non-zero loadings in each PC while using SPCA. The proposed approach considerably improves the interpretability of PCs while minimizing the loss of total variance explained. Furthermore, we compare the performance of PCA- and SPCA-based techniques for fault detection and fault diagnosis. The key features of the methodology are assessed through a synthetic example and a comparative study of the benchmark Tennessee Eastman process.

With high production efficiency and robustness, automatic component pick-and-place technology is widely used in modern electronic industries. As one of the core components of this technology, the visual measurement system requires a suitable positioning and fault detection algorithm with high speed, accuracy, robustness, and generalization abilities, especially for positioning and inspecting ball grid array (BGA) components. This paper examines the online positioning and defect inspection problem of BGA components for component placement machines. Incorporating coarse and fine positioning, an accurate, efficient, and robust universal positioning algorithm is proposed. Two types of key points are introduced to characterize the alignment of solder balls in BGA components. A distance transform-based circle detection method is first applied, and then a distance-based edge filter and a circle fitting method are employed to obtain the coarse and fine locations of solder balls, respectively. The approximate location of the component is estimated using a geometrical method and the fine location is calculated by solving a least-squares problem. A pair of overlapping ratios is introduced to conduct fault detection and inspect the alignment accuracy. The effectiveness of the proposed method is verified by applying it to several real component positioning and defect inspection experiments.