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This paper proposes a new geometric fault detection and isolation (FDI) strategy for uncertain neutral time-delay systems (UNTDS). Firstly, the concept of unobservability subspace is extended to the considered system. Subsequently, utilizing the geometric properties of factor space and canonical projection, the fault is divided into different unobservability subspaces. Therefore, an algorithm for constructing the subspace is developed for fault isolation. Finally, a set of observers is designed for the subsystems, and generates a set of structured residuals which is sensitive only to a specific fault. Additionally, the H ∞ technique is utilized to suppress the disturbances and error signals due to time-varying delays on the residual. The simulation examples verify the effectiveness of the proposed approach. • A new geometric FDI strategy can decouple all the residuals from the faults. • The H ∞ technique is used to suppress error signals due to time-varying delays. • Geometric approach is used to uncertain neutral time-delay system for the first time. • The occurrence and disappearance of multiple faults can be detected simultaneously. [ABSTRACT FROM AUTHOR]

This paper proposes a data-driven actuator fault detection and isolation approach for the general class of nonlinear systems. The proposed method uses a deep neural network architecture to obtain an invariant set of basis functions for the Koopman operator to form a linear Koopman predictor for a nonlinear system. Then, the obtained linear model is used for fault detection and isolation purposes without relying on prior knowledge about the underlying dynamics. Moreover, a recursive method is proposed for fault detection and isolation that is entirely data-driven with the key feature of global validity for the system's whole operating region due to the Koopman operator's global characteristic. Finally, the approach's efficacy is demonstrated using two simulations on a coupled nonlinear system and a two-link manipulator benchmark. • A novel data-driven DMD-based Koopman FDI (K-FDI) method is presented for detecting and isolating actuator faults. • A new Koopman-based state-preserving DNN architecture is proposed for actuated systems. • The nonlinear FDI problem is formulated as linear FDI using Koopman operator. • The presented Koopman FDI method is extended to be computed incrementally for real-time situation. [ABSTRACT FROM AUTHOR]

Classification is an essential task for many applications, including text classification, image classification, data classification, and so on. The present study investigates the accuracy of different machine learning classification algorithms with three different data smoothing techniques for gas turbine fault detection and isolation task. The gas turbine performance model was developed by considering variable inlet guide vane and bleed air. Fouling and erosion were injected into all six main components of the gas turbine engine. Faulty and non-faulty data were generated from the developed performance model. Based on sensitivity analysis, 12 measurement parameters and 11,824 data points were selected for the development of a fault detection and isolation model. The faulty and non-faulty data were balanced, smoothed, corrected and normalized. Finally, the classification accuracy of the machine learning techniques was analyzed. The result shows that K-Nearest Neighbours, Neural Network and Decision Tree classifiers exhibited high classification accuracy, about 99% with all three data smoothing techniques. It is also observed that the computation time of Support Vector Machine is higher whereas K-Nearest Neighbours shows the lowest. Finally, the research proves that K-Nearest Neighbours is the best classification technique for gas turbine engine fault detection and isolation application. [ABSTRACT FROM AUTHOR]

Two significant motivations for continuing development in early process detection problems are technological plant safety and dependability. To avoid economic losses in oil exploration, faults in Field Digitizing Units (FDUs) instruments must be detected. The creation of algorithms that can detect process issues before they reach their threshold is a big task, and trend checks associated with a particular measured value are typical. The efficiency of the Deep Neural Network (DNN) technique employing Matlab and a lowcomputational power device, such as the Raspberry Pi 4, for drift fault detection in FDUs is evaluated in this article. The DNN classifier is among the deep learning algorithms being studied. The FDUs instruments provided the data for this experiment. In training and testing data, the six features (Distortion, Noise, Common-Mode Rejection (CMRR), Gain Error, Phase Error, and Crosstalk) were extracted from free fault and faulty FDUs. The trained model has been offline tested, with the model being used to detect drift faults using FDU performance. Accuracy, specification, precision, recall, and F-measure were used to determine the efficiency of the classifier, with 99.7% accuracy in the DNN with Matlab and 98% accuracy in the DNN with Python. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

We study fault detection and isolation of multi-agent systems existing fault signals. Based upon the designing idea of unknown input observer, we proposed an innovative fault detection and isolation strategy. When the existence conditions are satisfied, the observer can be constructed using only relative information. A threshold logic and the corresponding algorithm are presented to detect and isolate the agent suffering from fault. Furthermore, a distributed implementation of our method is provided which helps to reduce the observer dimensions and simplify the design procedure. Practical simulations are given to demonstrate the validity of the theoretic results. • Fault detection and isolation is achieved for multi-agent systems with directed topology. • Unknown input observers are constructed using relative information. • A distributed implementation is presented to decrease computing loads. [ABSTRACT FROM AUTHOR]

• A sparse Bayesian model is proposed for process monitoring. • More informative latent variables are extracted by the spike-and-slab component. • EM algorithm is presented for the maximum likelihood estimation of the model parameters. • More persuasive regularization provides better effect of fault detection and isolation. [Display omitted] This article develops a Bayesian latent variable model for inferential fault detection and isolation using a spike-and-slab regularization technique. Different from conventional probabilistic models like probabilistic principal component analysis (PPCA), a mixture prior with spike-and-slab component is introduced to indicate whether the latent variables are sensitive to the abnormal state. The relevant information of latent variables is selected by the probability of assigning the effect to the slab component, while the influence of the non-informative ones is eliminated by the spike component. Furthermore, a Bayesian inference scheme based on the expectation-maximization framework is developed for the parameter learning procedure of the model. The fault detection and diagnosis method based on the inferential model is subsequently developed. The performance of the proposed method is illustrated by the benchmark Tennessee Eastman process and an application to an industrial methanol distillation process. [ABSTRACT FROM AUTHOR]

We study a fault detection problem in the context of Markovian Jump Linear Systems (MJLS). The major contribution of this paper is the design of a mode-dependent mixed H ∞ / H − Fault Detection Filter (FDF) under the MJLS formulation, which is obtained by solving a Linear and Bilinear Matrices Inequalities (LMI / BMI) optimization problem. As a secondary contribution, we present a simulation of the theoretical results using a Control Moment Gyroscope unit, which is extensively used as an actuator for attitude control of spacecraft and satellites. The attitude control of satellites is a class of problem that is within the framework of MJLS, since it allows us to model the loss of communication between the various components of the network system. Therefore, designing Fault Detection Filters under this framework provides reliable solutions. From the simulations results, it is possible to observe that the proposed mixed H ∞ / H − fault detection solution outperformed the approach using only the H ∞ norm, showing that it could be a valuable alternative for the fault detection problem. [ABSTRACT FROM AUTHOR]

This paper aims to design, develop and compare fault detection and isolation (FDI) schemes including inversion-based fault reconstruction and optimal state observers for a class of nonlinear system subject to concurrent faults and unknown disturbances that represents the nonlinear dynamic model of a gas turbine engine. Towards this end, for each fault, utilizing an existing coordinate transformation, the original system is transformed into a new form in which both observers are applied for fault diagnosis. The coordinate transformation comes from observability concept in differential geometry. The inversion-based observer is highly beneficial for straightforward detection and directly isolating the faults, and the state observer is optimal with respect to the magnitude of observer gain and convergence time. The mentioned approaches are implemented for FDI of a gas turbine model subject to compressor efficiency, compressor mass flow capacity, and actuator faults in addition to an unknown disturbance. The simulation results illustrate that the proposed FDI schemes are a promising tool for the gas turbine diagnostics. [ABSTRACT FROM AUTHOR]

The present work proposes a fast-convergent fault detection and isolation (FDI) scheme for linear systems affected by model uncertainties, such as unknown inputs or unbounded nonlinearities. The finite-time convergence is attained by transforming the I/O signals through Volterra operators with suitably designed kernel functions. A novel feature of the proposed approach is the exploitation of a system decomposition that allows removing the effect of intractable uncertainties while recasting the system dynamics in a form applicable for Volterra operators to achieve non-asymptotic estimation. Remarkably, the proposed approach can reconstruct the state variables of the system in an arbitrarily short time and the fault can be diagnosed efficiently by imposing detection and isolation thresholds on transformed signals. The detectability and isolability of the fault are also characterized. The proposed FDI scheme is applied in simulation to a web process system to diagnose the presence of actuator faults. Simulation results confirm the effectiveness of the proposed scheme in two scenarios with nonlinear uncertainties. Furthermore, comparisons are made between the proposed method and a Sliding Mode Control (SMC) method in terms of estimation performance and computational complexity. [ABSTRACT FROM AUTHOR]

This paper proposes a fault detection and isolation (FDI) method for a class of uncertain systems with actuator faults. First, based on the multiple model schemes, a bank of parallel sliding mode switching observers are designed to match the fault-free, stuck and loss of effectiveness fault models. A switching strategy is introduced to maintain a sliding motion even in the presence of actuator faults. Stability analysis of the sliding motions and the reachability conditions are given in various fault models. Without the assumption that all states are available, the residual signals are constructed by using the measurable output estimation errors. Then, the fault detection scheme is designed by comparing the residual evaluation function with the threshold. The fault isolation scheme is derived by looking for the minimum residual evaluation function, which is generated by the observer that matches the current system model well. Finally, an L-1011 aircraft model is presented to illustrate the effectiveness of the FDI method obtained in this paper. [ABSTRACT FROM AUTHOR]

Abstract The Principal Component Analysis (PCA) is one of the most known and used linear statistical methods for process monitoring. However, the PCA algorithm is not designed to handle the uncertainty of the sensor measurements that is represented by an interval type data. Including uncertainty of the sensors measurements in the analysis requires extending the PCA methodology to the Symbolic Data Analysis (SDA). The SDA refers to a paradigm where statistical units are described by interval-valued variables. In this regard, Symbolic Principal Component Analysis (SPCA), particularly Midpoints-Radii PCA (MRPCA) technique, is investigated for modeling and diagnosis of uncertain data. The aim of the present paper is to propose an extended version of the linear SPCA technique, based on midpoints and radii, to the nonlinear case of kernel PCA method (MR-KPCA). The basic idea is to construct a robust KPCA model from midpoints and radii of the nonlinear uncertain process data. Then, the robust KPCA model is used for diagnosis (FDI) purpose. In fact, the FDI decisions are improved by taking in to account the uncertainties on the nonlinear data. The MR-KPCA algorithm is applied for sensor fault detection and isolation of an automatic weather station. The results of applying this algorithm show its feasibility and advantageous performances. Highlights • Nonlinear modeling technique designed to handle uncertain nonlinear systems. • Robust KPCA modeling methodology can handle the nonlinear interval type data. • Improved FDI decisions by considering the uncertainties in the nonlinear data. • Nonlinear fault isolation method to identify the roots of the uncertain faults. [ABSTRACT FROM AUTHOR]

The early fault detection and isolation in industrial systems is a critical factor in preventing equipment damage. In the proposed method, instead of using the time signals of sensors, the 2D image obtained by placing these signals next to each other in a matrix has been used; and then a novel fault detection and isolation procedure has been carried out based on image processing techniques. Different features including texture, wavelet transform, mean and standard deviation of the image accompanied with MLP and RBF neural networks based classifiers have been used for this purpose. Obtained results indicate the notable efficacy and success of the proposed method in detecting and isolating faults of the Tennessee Eastman benchmark process and its superiority over previous techniques. [ABSTRACT FROM AUTHOR]

This paper aims to document the application of a new generation of artificial immune systems (AIS) in fault detection and isolation problems. These kind of algorithms are able to explore normal and anomalous behavior evidences, however, they may often require a more explicit prior knowledge provided by experts, usually difficult to obtain in some practical cases. Thus, many immune inspired approaches applied to fault detection and isolation (FDI) in the literature are based on negative selection algorithms. Considering these points, this work presents a review on three AIS approaches. Once reviewed and contextualized, the evaluated techniques are properly adjusted considering their main parameters and ways of processing data, and then, applied to a case study of fault detection and isolation in order to provide a performance analysis of these techniques, according to their applicability to these problems. [ABSTRACT FROM AUTHOR]

In this research we investigate the on-board implementation in real-time MATLAB simulation environment of a Particle Filter (PF) estimator applied to estimate the state-of-charge (SOC) of a generic nickel - metal hydride (Ni-MH) battery. It is integrated into the battery management system (BMS) structure in order to drive a hybrid electric vehicle (HEV). The Particle Filter (PF) estimator can be used as a possible alternative to Kalman filters (KF) techniques, such as the most popular Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) nonlinear Gaussian estimators. Similar as the concept, the particle filtering methods becomes today the most popular class of numerical on-line algorithms for optimal estimation of nonlinear and non Gaussian state-space dynamic models. The novelty of this approach is that the applicability of Particle Filter estimator is extended from routinely computer vision, econometrics, tracking problems, robotics and navigation fields to BMSs in automotive industry. Furthermore, it can be tailored to estimate the battery SOC of different chemistries, and also in fault detection and isolation (FDI) applications to detect, isolate and estimate the severity of possible BMS faults. The preliminary results obtained in this research are encouraging and reveal the effectiveness of the real-time implementation of the proposed PF estimator in the Battery Management Systems for driving the Hybrid Electric Vehicles (HEVs). [ABSTRACT FROM AUTHOR]

This study presents a data-based methodology for fault detection and isolation in dynamic systems based on fuzzy/Bayesian approach for change point detection associated with a hybrid immune/neural formulation for pattern classification applied to the Tennessee Eastman benchmark process. The fault is detected when a change occurs in the signals from the sensors and classified into one of the classes by the immune/neural formulation. The change point detection system is based on fuzzy set theory associated with the Metropolis–Hastings algorithm and the classification system, the main contribution of this paper is based on a representation which combines the ClonALG algorithm with the Kohonen neural network. [ABSTRACT FROM AUTHOR]

This article presents the design of a sensor Fault Detection and Isolation (FDI) system for a condensation process based on a nonlinear model. The condenser is modeled by dynamic and thermodynamic equations. For this work, the dynamic equations are described by three pairs of differential equations which represent the energy balance between the fluids. The thermodynamic equations consist in algebraic heat transfer equations and empirical equations, that allow for the estimation of heat transfer coefficients. The FDI system consists of a bank of two nonlinear high-gain observers, in order to detect, estimate and to isolate the fault in any of both outlet temperature sensors. The main contributions of this work were the experimental validation of the condenser nonlinear model and the FDI system. [ABSTRACT FROM AUTHOR]

Copyright of Journal Européen des Systèmes Automatisés is the property of International Information & Engineering Technology Association (IIETA) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Monitoring of the faults is an important task in mechatronics. It involves the detection and isolation of faults which are performed by using the residuals. These residuals represent numerical values that define certain intervals called thresholds. In fact, the fault is detected if the residuals exceed the thresholds. In addition, each considered fault must activate a unique set of residuals to be isolated. However, in the presence of uncertainties, false decisions can occur due to the low sensitivity of certain residuals towards faults. In this paper, an efficient approach to make decision on fault isolation in the presence of uncertainties is proposed. Based on the bond graph tool, the approach is developed in order to generate systematically the relations between residuals and faults. The generated relations allow the estimation of the minimum detectable and isolable fault values. The latter is used to calculate the thresholds of isolation for each residual. [ABSTRACT FROM AUTHOR]

• A fault-tolerant control strategy is applied to an innovative heat-exchanger/reactor. • The active fault-tolerant control method is based on adaptive observers and backstepping technique. • Both dynamic faults and sensor faults are considered for the heat-exchanger/reactor. • A backstepping control law is designed for the hear-exchanger/reactor to maintain the output temperature. • Adaptive observers are designed to detect and diagnose the fault of the proposed system. In this paper, an active fault tolerant control system is proposed and applied to a new intensified heat exchanger/reactor system. This method consists of an adaptive observer-based fault detection, isolation, and identification scheme and a control law redesign method based on the backstepping approach. The objective of the application of the fault tolerant control system is to ensure the safety and productivity of this intensified heat exchanger/reactor even in the presence of a fault. Both parameter and sensor faults are considered. The effectiveness of the fault tolerant control method based on adaptive observers is validated by simulations on the heat exchanger/reactor system. [ABSTRACT FROM AUTHOR]

Despite perceived simplicity of solar thermal collectors, failures occur during operation. Therefore fault detection and isolation tools for these systems should be investigated. One of the critical parts for the development of fault detection and isolation is model selection. Within the paper, a specific algorithm for the selection of fault detection and an isolation model is elaborated and presented. The developed algorithm was applied for a solar and pellet combisystem. Through application of the proposed algorithm, a model based approach with recurrent structure of artificial neural networks is chosen for the development of a fault detection and isolation model. [ABSTRACT FROM AUTHOR]

Copyright of Transactions of Beijing Institute of Technology is the property of Beijing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Detecting and locating leaks in water distribution systems is of great interest. For the localization of leaks we make use of pressure sensors alongside a calibrated hydraulic EPANET model of the investigated system. Leakage localization is solved with a Differential Evolution algorithm. For sensor placement we use a non-binarized leak sensitivity matrix with a projection-based leak isolation approach. Additionally, the effect of uncertain hydraulic model parameters on the measurement quantities is investigated by Monte Carlo simulations and was incorporated in the sensor placement algorithm. Uncertainty analysis, sensor placement and leakage location was tested on two hydraulic systems. [ABSTRACT FROM AUTHOR]

The fault detection and isolation of generators used in wind turbines gathering interest as to maximize the reliability and avail of distributed energy systems with recent unmatched growth in construction of offshore wind farms. In particular it is interested in performing fault detection and isolation (FDI) of incipient faults affecting the measurements of the three-phase signals (currents) in a controlled DFIG and PMSG. Although different authors have dealt with FDI for sensors in induction machines and in DFIGs, most of them rely on the machine model with constant parameters. However, the parameter uncertainties due to changes in the operating conditions will produce degradation in the performance of such FDI systems. The robust techniques to detect faults are exist but there is a need of extra sensor. This paper proposed a systematic methodology for the design of sensor FDI systems with the following characteristics: i) capable of detecting and isolating incipient additive (bias) faults, ii) robust against changes in the references/disturbances affecting the controlled DFIG and PMSG as well as modeling/parametric uncertainties, iii) residual generation system based on a multi-observer strategy to enhance the isolation process, The designed sensor FDI systems have been validated using measured voltages, as well as simulated data from a controlled DFIG. First the state space models of DFIG and PMSM explained followed by kalman filter introduction and current sensor fault detection using a bank of kalman filter named dedicated Observer Scheme and generalized Observer scheme to detect simultaneous and multiple faults was theorized and simulated using MATLAB simulation tool .The simulation results were summarized with and without Sensor fault. [ABSTRACT FROM AUTHOR]

Recent industry requires efficient fault discovering and isolation solutions in process equipment service. This problem is a real-world problem of typically ill-defined systems, hard to model, with large-scale solution spaces. Design of precise models is impractical, too expensive, or often non-existent. Support service of equipment requires generating models that can analyze the equipment data, interpreting the past behavior and predicting the future one. These problems pose a challenge to traditional modeling techniques and represent a great opportunity for the application of Al-based methodologies, which enable us to deal with imprecise, uncertain data and incomplete domain knowledge typically encountered in real-world applications. In this paper the state of the art, theoretical background of conventional and Al-based techniques in support of service tasks and illustration of some applications to process equipment service on bio-ethanol production process are shortly described. [ABSTRACT FROM AUTHOR]

Abstract: Complexity of industrial plants and their stringent environmental and safety regulations have necessitated early detection and isolation of process faults. All the existing fault isolation methods can be categorized into two general groups: model-based and data-based. Transfer entropy is a data-based method for measuring propagation direction of disturbance and finding its root cause. In this paper, a new transfer entropy-based method is proposed to isolate different process faults. The novelty of this paper lies in using the transfer entropy idea to generate distinct patterns of information flow among process variables, recognize their correlations in the context of the transferred information in any abnormal condition, and finally isolate different process faults. The experimental results clearly demonstrate the superiority of the proposed method to the conventional methods. [Copyright &y& Elsevier]

In this paper, real time dynamic information of complicated electrical engineering provided by phasor measurement units to analyze and locate the fault is utilized. Based on fuzzy cluster analysis theory and the principle of minimum expected cost of misclassification, we have explored a comprehensive detection and isolation system of fault in complicated electrical engineering. It has been proved by a large number of simulation experiments that the comprehensive fault detection and isolation system can accurately and reliably detect and isolate faults and provide guarantee for the safety and stable operation of electrical power and energy system. [ABSTRACT FROM AUTHOR]

This paper proposes an actuator fault diagnosis approach based on unknown input observers for the attitude control system of satellite. More specifically, by considering an actuator fault as the unknown input vector, an unknown input observer is designed so that the residual is sensitive to the other faults while insensitive to the appointed one. In this work, the design of unknown input observer is formulated as a set of linear matrix inequality (LMI), which can be solved by the LMI toolbox conveniently. Finally, the proposed approach is applied to the satellite attitude control system. Simulation examples illustrate the presented method is able to detect and isolate the actuator fault effectively. [ABSTRACT FROM AUTHOR]

Abstract: The malfunction of sensors, actuators, and erroneous actions of human operators can have some disastrous consequences in high risk systems especially if these systems have multiple faults that can lead to undesirable shutdowns and consequently mass reduction. A reduced interpreted Petri net (IPN) diagnoser has been devised only for safe Petri net models with an output function that associates an output vector to each net marking. The main drawback of this approach is that the Petri net model of the system to be monitored should be diagnosable i.e. all faults can be detected that limits its application on a set of diagnosable models. For non diagnosable Petri net model, the conventional diagnoser incidence matrix has columns with null or similar values that fail to detect a single fault. The conventional diagnoser also cannot detect multiple faults even for diagnosable models. This paper introduces a new local diagnoser to overcome such problems. It decomposes the central IPN-diagnoser into a set of local diagnosers that are linked with multi sessions of the process to be monitored. This decomposition should guarantee that the developed local diagnosers have incidence matrices that their columns are different from each other. For null values contained in the incidence matrix of a local diagnoser, this paper proposes a set of rules based on the synchronic composition idea to overcome this problem. This proposed scheme allows multiple faults detection and isolation in quick and accurate manner for all Petri net models. Industrial processes are employed for testing the soundness of the proposed scheme. [Copyright &y& Elsevier]

Abstract: The main objective of this paper is to develop a dynamic neural network-based fault detection and isolation (FDI) scheme for pulsed plasma thrusters (PPTs) that are employed in the attitude control subsystem (ACS) of satellites tasked to perform formation flying (FF) missions. A hierarchical methodology is proposed that consists of three fault detection and isolation (FDI) approaches, namely (i) a “low-level” FDI scheme, (ii) a “high-level” FDI scheme, and (iii) an “integrated” FDI scheme. Based on the data from the electrical circuit of the PPTs, the proposed “low-level” FDI scheme can detect and isolate faults in the PPT actuators with a good level of accuracy, however the precision level is poor and below expectations with the misclassification rates as expressed by False Healthy and False Faulty parameters being too high. The proposed “high-level” FDI scheme utilizes data from the relative attitudes of the FF mission. This scheme has good detection capabilities, however its isolation capabilities are not adequate. Finally, the proposed “integrated” FDI scheme takes advantage of the strengths of each of the above two schemes while reducing their individual weaknesses. The results demonstrate a high level of accuracy (99.79%) and precision (99.94%) with a misclassification rates that are quite negligible (less than 1%). Furthermore, the proposed “integrated” FDI scheme provides additional and interesting information related to the effects of faults in the thrust production levels that would not have been available from simply using the low or the high level schemes alone. [Copyright &y& Elsevier]

Abstract: With growing technology, fault detection and isolation (FDI) have become one of the interesting and important research areas in modern control and signal processing. Accomplishment of specific missions like waste treatment in nuclear reactors or data collection in space and underwater missions make reliability more important for robotics and this demand forces researchers to adapt available FDI studies on nonlinear systems to robot manipulators, mobile robots and mobile manipulators. In this study, two model-based FDI schemes for robot manipulators using soft computing techniques, as an integrator of Neural Network (NN) and Fuzzy Logic (FL), are proposed. Both schemes use M-ANFIS for robot modelling. The first scheme isolates faults by passing residual signals through a neural network. The second scheme isolates faults by modelling faulty robot models for defined faults and combining these models as a generalized observers scheme (GOS) structure. Performances of these schemes are tested on a simulated two-link planar manipulator and simulation results and a comparison according to some important FDI specifications are presented. [Copyright &y& Elsevier]

The fault detection and isolation (FDI) of centrifugal chiller system is essential for improving energy efficiency and reducing energy consumption. Considering the non-Gaussianity of chiller measurement, this paper proposes an effective non-Gaussian chiller FDI method based on independent component analysis (ICA) and k-nearest neighbor (KNN) classifier. First of all, an enhanced fault detection method is developed by combining exponentially weighted moving average and ICA (EWICA). The exponentially weighted moving average is employed to develop a dynamic threshold scheme, which reduces the false alarm rate (FAR) and improves the fault detection rate (FDR) of chiller faults. Then, based on the KNN with cosine similarity metric, a novel fault isolation method using the direction of the residual vector is presented to isolate the chiller faults. To further improve the isolation performance, the number of neighbors (k) is optimized by 10-fold cross validation. Finally, the proposed FDI method is validated by using a data set from the ASHRAE Research Project 1043 (RP-1043). The FDI performance of the proposed method is comprehensively analyzed and compared with six state-of-the-art methods. Both of detection and isolation results suggest that the EWICA-based method delivers superior performance for chiller FDI. [ABSTRACT FROM AUTHOR]

Degradation monitoring is of paramount importance to safety and reliability of aircraft operations and also for timely maintenance of its critical components. This two-part paper formulates and validates a novel methodology of degradation monitoring of aircraft gas turbine engines with emphasis on detection and isolation of incipient faults. In a complex system with multiple interconnected components (e.g. an aircraft engine), fault isolation becomes a crucial task because of possible input–output and feedback interactions among the individual components. This paper, which is the first of two parts, presents the underlying concepts of fault detection and isolation (FDI) in complex dynamical systems. The FDI algorithms are formulated in the setting of symbolic dynamic filtering (SDF) that has been recently reported in literature. The underlying concept of SDF is built upon the principles of symbolic dynamics, statistical pattern recognition, and information theory. In addition to abrupt large faults, the SDF-based algorithms are capable of detecting slowly evolving anomalies (i.e. deviations from the nominal behaviour) based on analysis of time series data of critical process variables of different engine components. The second part, which is a companion paper, validates the concept, laid out in the first part, on the simulation test bed of a generic two-spool turbofan aircraft engine model for detection and isolation of incipient faults. [ABSTRACT FROM AUTHOR]

The first part of this two-part paper, which is a companion paper, has developed a novel concept of fault detection and isolation (FDI) in aircraft gas turbine engines. The FDI algorithms are built upon the statistical pattern recognition method of symbolic dynamic filtering (SDF) that is especially suited for real-time detection and isolation of slowly evolving anomalies in engine components, in addition to abrupt faults. The FDI methodology is based on the analysis of time series data of available sensors and/or analytically derived variables in the gas path dynamics. The current paper, which is the second of two parts, validates the algorithms of FDI, formulated in the first part, on a simulation test bed. The test bed is built upon an integrated model of a generic two-spool turbofan aircraft gas turbine engine including the engine control system. [ABSTRACT FROM AUTHOR]

A critical component of an advanced gas-cooled reactor station is the graphite core. As a station ages, the graphite bricks that comprise the core can distort and may eventually crack. Since the core cannot be replaced, the core integrity ultimately determines the station life. Monitoring these distortions is usually restricted to the routine outages, which occur every few years, as this is the only time that the reactor core can be accessed by external sensing equipment. This paper presents a monitoring module based on model-based techniques using measurements obtained during the refuelling process. A fault detection and isolation filter based on unknown input observer techniques is developed. The role of this filter is to estimate the friction force produced by the interaction between the wall of the fuel channel and the fuel assembly supporting brushes. This allows an estimate to be made of the shape of the graphite bricks that comprise the core and, therefore, to monitor any distortion on them. [ABSTRACT FROM AUTHOR]

In model-based quantitative multiple fault detection and isolation (FDI), fault disambiguation is based on parameter estimation. In this paper, the fault hypothesis is generated by evaluating a set of analytical redundancy relations (ARRs) and parameter values corresponding to the unstructured part of the fault subspace are estimated by minimizing a function of the ARRs. Process and measurement uncertainties are handled by using a passive approach for robust FDI. Bond graph modelling is used to describe process models and to derive the ARRs. The bond graph model of the process is differentially causalled and it is then converted into a diagnostic bond graph form. The diagnostic bond graph is further converted into its corresponding sensitivity bond graph form, which gives the residual sensitivity to parametric changes. The developed algorithm provides quicker fault isolation because only a few parameters are estimated and it does not need several model simulations, thereby making it suitable for real-time process supervision. [ABSTRACT FROM AUTHOR]

This paper deals with the design of a residual generator for fault detection and isolation in the dynamic closed-loop systems based on the balance of energy which "enters" and "leaves" plants. The main contribution of this paper consists in developing a suitable fault detection and isolation technique to detect faults in single-input single-output closed-loop system based on major signals without the requirement of an accurate static or dynamic model. Indeed, in the absence of conventional input-output models, the proposed method involves the on-line energy balance evaluation to detect a sensor fault. The application .to the monitoring of a galvanizing line in steel industry shows the effectiveness of the suggested approach when a sensor fault occurs. [ABSTRACT FROM AUTHOR]

A design approach to sensor fault detection and isolation (FDI) of helical coil steam generator (HCSG) systems of the International Reactor Innovation Secure (IRIS) reactor is presented. In the design phase, a physical model is first developed to provide a realistic simulation and generate data characterizing the system dynamics. A subspace identification technique is then used to extract a low-order linear state-space model from the data. Finally, a robust dynamic parity space approach is utilized to design residual generators for FDI. This design approach is able to achieve fault isolation following a predetermined logic without the need to use data during fault conditions, which is an unrealistic assumption of many FDI approaches studied for nuclear power plants. The results of the HCSG application show that the approach is robust to not only measurement and process noises but also operation condition changes and has the capability of correct FDI during reactor power transients and during the propagation of sensor faults in a control loop. [ABSTRACT FROM AUTHOR]

This paper presents a novel fault detection and isolation scheme for non-linear dynamic systems. This scheme utilizes a fuzzy observer to generate the diagnostic residual signal for fault detection and isolation. This fuzzy observer, based upon the idea of Takagi–Sugeno fuzzy models, comprises a number of locally linear observers and the final state estimate is a fuzzy fusion of all local observer outputs. To ensure good estimation performance, the eigenvalues of the fuzzy observer are assigned in a pre-defined region in the complex plane. The stability as well as eigenvalue constraint conditions for the fuzzy observer design are presented and solved in the linear matrix inequality framework in this paper. Finally, the paper demonstrates the application of fuzzy observers in detecting and isolating intermittent faults in the induction motor of a railway traction system using a real-time test-rig implementation. [ABSTRACT FROM AUTHOR]

In this paper, a real-time approach to detect the faults affecting the mean and the covariance matrix of the Kalman filter innovation sequence is presented. The ratio of two quadratic forms for which the matrices are theoretical and selected covariance matrices, as monitoring statistics, is used. The arguments of the optimal quadratic form that maximize the above statistics are determined to detect the faults in sensors rapidly. The longitudinal dynamics of an aircraft control system, as an example, is considered, and detection of the faults in pitch gyroscope affecting the mean and the covariance matrix is examined. [ABSTRACT FROM AUTHOR]

The paper proposes a new, robust cluster-based classification technique for Novelty Identification in sensor networks that possess a high degree of correlation among data streams. During normal operation, a uniform cluster across objects (sensors) is generated that indicates the absence of novelties. Conversely, in presence of novelty, the associated sensor is clustered distinctly from the remaining sensors, thereby isolating the data stream which exhibits the novelty. It is shown how small perturbations (stemming from noise, for instance) can affect the performance of traditional clustering methods, and that the proposed variant exhibits a robustness to such influences. Moreover, the proposed method is compared with a recently reported technique, and shown that it performs 365% faster computationally. To provide an application case study, the technique is used to identify emerging fault modes in a sensor network on a sub-15MW industrial gas turbine in presence of other abrupt, but normal changes that visually might otherwise be interpreted as malfunctions. [ABSTRACT FROM AUTHOR]