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The continuous growth of wind power technology makes condition monitoring of wind turbine components crucially important for their operational efficiency. The main shaft bearings in wind turbines have been identified as one of the most critical components in the system, especially with the ongoing increase in rotor size and weight. This increase made the 4-point suspension drivetrain more preferable. In this study, we present a novel approach for condition monitoring of the main shaft bearings in a 2 Megawatt wind turbine with 4-point suspension drivetrain using primarily acoustic emission (AE). The focus was on the analysis of time and frequency domains of the AE signal, where the dominant frequency of each AE hit was identified and plotted back in the time domain to create the so-called dominant frequency map in specific time intervals for each bearing. A comparison between the two dominant frequency maps of the two bearings gives valuable insights into the condition of the two bearings. The distinctive nature of the dominant frequency bands in the dominant frequency maps presented promising potential for this method. The presented method is straightforward and can be automated and then integrated into a planned predictive maintenance programme for this wind turbine. [ABSTRACT FROM AUTHOR]

Industry 4.0 has become a reality by fusing the Industrial Internet of Things (IIoT) and Artificial Intelligence (AI), providing huge opportunities in the way manufacturing companies operate. However, the adoption of this paradigm shift, particularly in the field of smart factories and production, is still in its infancy, suffering from various issues, such as the lack of high-quality data, data with high-class imbalance, or poor diversity leading to inaccurate AI models. However, data is severely fragmented across different silos owned by several parties for a range of reasons, such as compliance and legal concerns, preventing discovery and insight-driven IIoT innovation. Notably, valuable and even vital information often remains unutilized as the rise and adoption of AI and IoT in parallel with the concerns and challenges associated with privacy and security. This adversely influences inter- and intra-organization collaborative use of IIoT data. To tackle these challenges, this article leverages emerging multi-party technologies, privacy-enhancing techniques (e.g., Federated Learning), and AI approaches to present a holistic, decentralized architecture to form a foundation and cradle for a cross-company collaboration platform and a federated data space to tackle the creeping fragmented data landscape. Moreover, to evaluate the efficiency of the proposed reference model, a collaborative predictive diagnostics and maintenance case study is mapped to an edge-enabled IIoT architecture. Experimental results show the potential advantages of using the proposed approach for multi-party applications accelerating sovereign data sharing through Findable, Accessible, Interoperable, and Reusable (FAIR) principles.

In order to build an effective condition monitoring (CM) model for the target wind turbines (WTs) with few operational data, an approach based on the feature transfer learning and a modified generative adversarial network is proposed. First, a large amount of labelled data from WTs are analyzed to construct a CM model with the aid of an autoencoder. This forms the knowledge of CM for WTs in the source domain. Second, a generative adversarial network is trained to build a mapping relationship between the features of different WTs. Third, the health status of the target WT is determined by analyzing the data collected from it online based on the proposed approach. Two case studies are conducted to verify that the proposed method can transfer the CM knowledge from source WT to target WT and achieve good performance in the CM of target WT.

Ball screw preload is an important factor in maintaining repeatably, rigidity, and in reducing or eliminating backlash in feed drive systems. Ball screw feeds drives are used in computer numerical control (CNC) machine tools to manufacture high-quality, precision parts. Many fault detection and condition monitoring (CM) methods have been proposed for measuring and detecting loss of preload, however, most of these methods require external sensors. Ideally, sensors, measurements, and methods integral to a CNC machine tool could be used to eliminate the extra cost and complexity of external sensors. A sensor-less method of estimating levels of preload using the mode probability of interacting multiple models (IMMs) is proposed. This method calculates a weighted sum which utilizes the mode probability of models representing different levels of preload, along with an activation function and weighing factor, to estimate the current level of preload. Unlike many other methods used for detecting levels of preload, this method requires only a system model and data collected by the CNC systems, while requiring no external sensors. The proposed method was shown to be robust and able to accurately and quickly predict preload levels under many different testing conditions. This method demonstrated a high degree of prediction accuracy (95%) which is comparable to, or better than other methods in the literature. In addition to being a novel method for preload detection, this work is also a novel implementation of IMM for fault detection, as it has not yet been applied to fault detection in feed drives.

Condition monitoring of insulated gate bipolar transistor (IGBT) modules is an effective way to improve the transient performance and reliability of modular multilevel converters (MMC). This article proposes a novel bond wire failure monitoring method for the multichip IGBT modules in the MMC half-bridge submodule (SM) structure. The collector voltage undershoot VCA(np) of the complementary IGBT switch is measured during the turn-off switching transition of the controlled IGBT switch. VCA(np) is sensitive to the induced voltage over the stray inductance of the IGBT bond wires and provides high sensitivity as a health indicator. The non-intrusive measurement technique is demonstrated using a half-bridge circuit during the turn-off transition of the controlled IGBT, while its complementary switch (i.e., the device under test) is in the off state. Theoretical analysis and experimental results show that the parametric drift due to wire bonding failures can be effectively monitored with high specific relative sensitivity and granularity. To ensure its effectiveness in practical applications, the influence of load current, SM capacitor voltage, and junction temperature is discussed. In addition, a readout circuit is designed featuring the integrated desaturation detection and voltage peak detection, which offers both the short-term overcurrent fault protection and long-term bond wire aging monitoring functions. [ABSTRACT FROM AUTHOR]

Gate oxide degradation, which considerably affects turn-on/-off dynamics of the switch, embraces a large percentage of chip-related failure modes both in silicon and silicon carbide power MOSFETs. The gate oxide layer is thinner in silicon carbide power MOSFETs in comparison to their silicon-based counterparts. Consequently, the problem of gate oxide degradation has become a more crucial impediment in achieving reliable performance in silicon carbide power MOSFETs. This problem is even more severe in high-frequency applications due to higher EMI signature and complicated and costly measurement. In this article, a reliable fully analog cost-effective gate oxide degradation condition monitoring technique is proposed and validated. High-order harmonics magnitudes of drain–source voltage are used to produce a dc signal as the aging precursor of the gate oxide region. Using a dedicated degradation setup, the credibility of the developed condition monitoring technique was examined at different rates of gate oxide degradation for 650-V/22-A silicon carbide discrete MOSFET. In 200-kHz, 217-V switch operation, the proposed precursor showed 68% change in comparison to its initial value. This brings a high-resolution assessment on the reliability level of the switch gate oxide region. [ABSTRACT FROM AUTHOR]

The healthy operation conditions of wind turbines (WTs) with insufficient data need attention, but they face the problems of data imbalance and lack of labels. Aiming at the condition monitoring (CM) of these WTs, a CM method based on transfer learning and one-class classification (OCC) is proposed. This method uses the source WT data to help learn information about monitoring data from the target WT. First, the data of the target and source WTs are preprocessed to construct a training set. Second, to improve the TrAdaBoost algorithm for the OCC task, a novel weighting method based on an autoencoder (AE)—an unsupervised one-class classifier—is designed to assign weights for samples in the source and target domain dynamically and then the CM model of the target WT is established. Finally, its effectiveness is verified by using the monitoring data to detect the generator fault of the target WT. The comparison with the nontransfer method shows that the proposed method can significantly reduce the number of false alarms and can issue early warnings. [ABSTRACT FROM AUTHOR]

Hybrid dc-link capacitors serve a critical role in cascaded two-stage systems, such as the solid-state transformer (SST) units being developed for medium-voltage utility grids. The capacitors need to absorb ripple current in a wide spectrum, which induces aging stresses. This article presents a technique to combine lifetime extension in design with condition monitoring (CM). It is found that the excessive high-frequency ripple current typically flows through electrolytic capacitors (E-caps). A dedicated transformer-based sensor is then designed, and its primary-side inductance acts as an external impedance to reduce the high-frequency current in the E-cap loop. On the other hand, the voltage induced by high-frequency current measured on the secondary side of the sensor can be used as an indicator of equivalent series resistance (ESR) degradation for CM. The integrated design features low complexity, pure passivity, and versatility, and the universal gauging principle for the sensor is elaborated. In the end, a 5-kW prototype of an SST unit is built to verify the performance of the proposed solution. The experimental results indicate a noticeable reduction in thermal and electrical stress, corresponding to a 40% extension of the E-cap lifetime, and the CM model can accurately track the change of the ESR. [ABSTRACT FROM AUTHOR]

The existing supervisory control and data acquisition (SCADA) system continuously collects data from wind turbines (WTs), which provides a basis for condition monitoring (CM) of WTs. However, due to the complexity and high dimension and nonlinearity of data, effective modeling of spatial-temporal correlations among the data still becomes a primary challenge. In this paper, we propose a novel CM approach based on the m ultidirectional s patial- t emporal f eature a ggregation n etworks (MSTFAN) to accurately evaluate the performance and hence diagnose the faults of the turbines. Firstly, the data collected from various sensors are formulated into graph-structured data at each timestamp. Spatial-temporal network constructed by combing a graph attention network (GAT) and a temporal convolutional network (TCN) is used to extract spatial-temporal features of the data. Then, a bi-directional long short-term memory (BiLSTM) neural network is adopted to further study long-term spatial-temporal dependency of the extracted features. Finally, the condition score is obtained and the streaming peaks over threshold (SPOT) is applied to determine the abnormal threshold for early warning of the fault occurrence. Experiments on datasets from real-world wind farms demonstrate that the proposed approach can detect the early abnormal situation of the WTs, and outperform other established methods. [ABSTRACT FROM AUTHOR]

With the applications of Machine Learning (ML) in condition monitoring (CM) of wind turbines (WTs), regression-based approaches are mainly for WT power curve modelling to evaluate its performance. A fitted performance curve (PC) of power versus wind speed is prevailing and straightforward for WT performance monitoring. As WT operations could be affected by multiple parameters. It is difficult to identify the fault type merely by a power curve. Particularly at the rotor side of doubly fed induction generator (DFIG), there are miscellaneous electrical disturbances that have their distinct effects on current signals. Hence, by merely processing the rotor currents, a PC of standard deviation of currents versus wind speed can be employed for CM of rotor-side faults. A non-parametric regression model, Gaussian process regression (GPR), is used to predict the PC under normal operation, and afterwards, Fuzzy Inference System (FIS) is applied for fault diagnosis. Therefore, the paper proposes a hybrid GPR and FIS approach towards condition monitoring (CM) and fault diagnosis at the rotor side of a DFIG by merely processing rotor currents through various signal processing techniques, where miscellaneous electrical disturbances can be recognized and localized. The comparative results show that the hybrid approach has superiority over two common ML classifiers, support vector machine (SVM) and decision tree (DT), for fault diagnosis under both test cases including drift faults and excluding drift faults. By disregarding drift faults, this hybrid approach is validated to be efficient for a real-time application with overall promising performance metrics in terms of accuracy (98.29%), dependability (98.12%), and security (100%). The diagnosed results on individual faults and research shortcomings are also discussed. [ABSTRACT FROM AUTHOR]

Automated continuous condition monitoring of industrial electrical machines to identify internal faults has become one of the critical research areas for the past decade. Among various defects, early-stage identification of insulation failure in stator winding is of notable demand as it often occurs and accounts for 37% of the overall motor failures. Identifying the current signature at its embryonic stage will effectively improve industrial machinery's downtime and repair costs. Recent advances in computational performance and sensor technology concede advanced systems for achieving these goals. The design of an AI-based fault signature analyzer (FSA) has been developed in this paper. FSA uses real-time stator current data in the time and frequency domain from healthy and faulty induction motors to train the various AI-based machine learning classifiers to identify health conditions using wavelets. Comparing machine learning algorithms such as artificial neural network, random forest, fuzzy logic, neuro-fuzzy logic, K-nearest neighbors is performed, and various performance attributes are quantified. A reliable, automatic fault signature from a motor current is thus analyzed using the fusion of a wavelet-based feature extraction technique and a capable knowledge-based efficient artificial intelligence (AI) approach. [ABSTRACT FROM AUTHOR]

The wear of tension members in timing belts results in a change in the pre-tensioning force. Therefore, a sensing timing belt was developed to measure the pre-tensioning force. For this purpose, a sensor system is integrated into a tooth, with which the acceleration is measured and sent to an external receiver via Bluetooth. In this publication, the detectability of the natural frequencies under different operating conditions is investigated. The acquired data is separated into span specific segments, as each trum segment has different natural frequencies. This paper will analyse occurring disturbance factors and evaluate the detectability of the natural frequencies. [ABSTRACT FROM AUTHOR]

Over the past few years, the power electronic converters have gained significant attraction among researchers, especially as an interface between distributed generation (DG) systems and the grid. Hence, it is imperative to establish a path for enhancing the operation of power electronic converters, and improve their reliability. Further, it is identified that for a solar photovoltaic (PV) inverter the power module construction intricacy and the complex operating conditions may degrade the reliability of these modules, affecting the functional efficiency of the overall grid-connected PV systems (GCPS). These constraints are considered to have a serious impact on the safety and failure cost especially associated with the grid-connected PV inverters (GCPIs). Therefore, it becomes crucial to have a clear understanding on the health monitoring strategies and reliability aspects corresponding to GCPIs. Consequently, it is also necessary to arrange all the information regarding major failure modes and categorize their root-cause analysis specific to GCPIs. In light of the above requirements, this paper contributes to the current research in making the GCPS more robust, efficient and reliable. The review identifies a comprehensive list of various failure modes in the inverter power modules and capacitors, and provides a broad view of their detection and localization approaches available in the literature.

This article proposes a condition monitoring scheme for the dc-link capacitors used in ac/dc/ac pulsewidth-modulation converters based on the signal injection, which can estimate capacitance in real time when both the rectifier and the inverter are in normal operation. First, a low-frequency signal is injected into the dc-link voltage, and its effects on the total harmonic distortion (THD) of the input current are analyzed. Accordingly, a suitable injection frequency is selected to keep the THD of input current within limits prescribed by the IEEE standard. Then, a novel calculation method of capacitor current without any dc-link current sensor is given, where the converter system is still in normal operation and different inverter operations are both taken into consideration. Finally, capacitance estimation based on the slide discrete Fourier transform is introduced. At the end of this article, experiments have been done to validate the correctness of the conclusion and effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

A failure of insulation is one of the main reasons for electric machine outage. Online condition monitoring (CM) of an incipient degradation of turn insulation could eliminate the potential fault at the early stage, which is attractive but challenging. To improve the sensitivity and availability of CM, a noninvasive technique is presented in this article for the inverter-fed electric machine utilizing the existing high-frequency (HF) switching oscillations during pulsewidth modulation operation. First, the tail component sensitive to turn insulation is selected among the dominant oscillation modes in switching current, which can penetrate the winding of machine within an adequate frequency band (e.g., several hundreds of kHz). Then, a noncontact and simple HF sensor is developed for switching oscillation current capture. The CM system, procedure, and turn insulation state indicator are devised. After that, experimental work is carried out on a 3-kW permanent magnet synchronous machine test rig. The results show good sensitivity (about 2%) to the local incipient degradation of turn insulation with insusceptibility to the operating conditions. Finally, further discussion is given to demonstrate the performance of the method. [ABSTRACT FROM AUTHOR]

Prognostic and condition-based maintenance of lithium-ion batteries is a fundamental topic, which is rapidly expanding since a long battery lifetime is required to ensure economic viability and minimize the life cycle cost. Remaining useful life (RUL) estimation is an essential tool for prognostic and health management of batteries. In this article, a hybrid approach based on both condition monitoring and physic model is presented to improve the accuracy and precision of RUL estimation for lithium-ion battery. An artificial intelligence estimation method based on recurrent neural network (RNN) is integrated with a state-space estimation technique, which is typical of filtering-based approach. The state-space estimation is used to generate a big dataset for the training of the RNN. Some additional deep layers are used to improve the prediction of nonlinear trends (typical of batteries), while the performance optimization of the RNN is ensured using a genetic algorithm. The performances of the proposed method have been tested using a battery degradation dataset from the data repository of Prognostics Center of Excellence at NASA. Two different degradation models are compared, the widely known empirical double exponential model and an innovative single exponential model that allows to ensure optimal performance with fewer parameters required to be estimated. [ABSTRACT FROM AUTHOR]

Generally, the internal states of most engineering systems are inaccessible to sensors under operational conditions, permitting indirect measurements and failures to be observed discretely. For this type of system, a class of proportional hazard (PH) models with multistate process have been proposed for performing prognostics. However, existing studies use a constant degradation mechanism with a very limited number of states, which may fail to model the actual deterioration of the system. Therefore, this article presents a novel PH model that utilizes a nonconstant degradation mechanism with a large number of states to perform health prognostics. A matrix partition-based approximation method is developed to include all the possible state transitions and to estimate important health quantities of the system, such as conditional reliability (CR), mean remaining useful life (MRUL), and remaining useful life distribution (RULD). An online prognostic scheme based on this matrix approach is presented to provide an update with the latest health quantities of the system. The proposed approach provides appealing computational features and avoids difficulties in calculating uncountable transitions for PH model-based prognostics. The effectiveness of the proposed method is demonstrated via a case of helical gearboxes under indirect monitoring and random failure conditions. [ABSTRACT FROM AUTHOR]

Bond wire degradation is one of the most common failure modes for wire-welded packaging insulated-gate bipolar transistor (IGBT) modules. This article proposes a novel bond wire detection method based on IGBT turn-on gate voltage overshoot. The degree of bond wires lift-off will change the stray inductance of the gate charge loop circuit, and therefore has a strong influence on turn-on gate voltage overshoot before miller platform, which can be used as an effective bond wire fault indicator. A double pulse test platform is built to verify the resolution and sensitivity of the proposed precursor for bond wire degradation. The dependences on bus voltages, load currents, junction temperatures are discussed and tested. The experimental results show that the gate turn-on voltage overshoot of IGBT increases with the increasing severity of bond wire faults, which agrees with the theoretical analysis. The proposed turn-on gate voltage overshoot-based method has a high sensitivity for identifying bond wire fault at the incipient stage. It offers an effective technique for detecting bond wire degradation for practical applications. [ABSTRACT FROM AUTHOR]

Online monitoring of dc-link capacitor's equivalent series resistance (ESR) could provide valuable and timely information for power converter failure prognosis and predictive maintenance. The conventional ESR estimation methods are mainly based on pulsewidth modulation (PWM) voltage and current ripples (ESR≈Δuc/Δic), but the performance is affected by operating conditions (especially at light-load), quasi-resonance effect and sensor amplitude error. To overcome these difficulties, an online ESR monitoring method is proposed using the damping characteristic of dc capacitor switching ringings. The intrinsic high-frequency (HF) resonance incidents between the power device fast switching and converter parasitic parameters are utilized in this article. First, the mechanism of dc capacitor switching ringing is analyzed by the HF resonance equivalent circuit. The analytic relationship between the damping characteristic and the ESR is established. Then, the condition monitoring (CM) scheme, noncontact HF sensor, and half-power bandwidth algorithm for ESR estimation are presented. Finally, an experimental study is carried out to validate the feasibility and effectiveness. The damping of switching ringing is inherently sensitive to the ESR of dc capacitor under the condition of resonance. Both theoretical analysis and experimental results confirm that the proposed method can achieve mΩ-level accuracy and good robustness under different conditions. [ABSTRACT FROM AUTHOR]

The high investment cost, which is in majority introduced by the sensor costs, is one of the barriers for adopting Predictive Maintenance/Condition-Based Maintenance strategy in the industry. To lower this barrier, the use of cost-effective sensors for such an application is, therefore, a necessity. This paper presents experiences in setting-up two different remote vibration monitoring systems using low-cost MEMS accelerometers available on the market in two different industrial settings. Some technical challenges and the state-of-the-art algorithms used to analyze the measured raw vibration signals are presented. The installed vibration monitoring systems have successfully detected faults on two different critical assets, namely an induction motor and an expander machine. [ABSTRACT FROM AUTHOR]

Capacitors are widely used in dc links of power electronic converters to balance power, suppress voltage ripple, and store short-term energy. Condition monitoring (CM) of dc-link capacitors has great significance in enhancing the reliability of power converter systems. Over the past few years, many efforts have been made to realize CM of dc-link capacitors. This article gives an overview and a comprehensive comparative evaluation of them with emphasis on the application objectives, implementation methods, and monitoring accuracy when being used. First, the design procedure for the CM of capacitors is introduced. Second, the main capacitor parameters estimation principles are summarized. According to these principles, various possible CM methods are derived in a step-by-step manner. On this basis, a comprehensive review and comparison of CM schemes for different types of dc-link applications are provided. Finally, application recommendations and future research trends are presented. [ABSTRACT FROM AUTHOR]

An important and strategic instrument in energy market is wind turbine (WT). WT consists of many components, whose operation and maintenance of them are very sensitive due to their unstable dynamic and environmental conditions. Condition monitoring (CM) and fault prognosis approaches are typical methods in order to decrease the production cost of energy and WT downtime. In this paper, a new combinatory system of CM and fault prognosis based on feature extraction and prediction algorithm is proposed. Firstly, 3 phase current signals are collected. In the next step, proper features selection is done in time and frequency domain. The CM operation is based on a feature amplitude value crossing of an alarm threshold. The fault prognosis is conducted with improved support vector machine (SVM) method. The least squares (LS) type of SVM method is used for fault prediction and particle swarm optimization (PSO) is used for the optimum selection of SVM and kernel parameters. When the trend of fault symptom crosses through the specific alarm threshold, the prognostics unit is activated and remaining useful life (RUL) prediction is performed using PSO-LSSVM. For a scenario of the fault, generator rotor winding failure based on rotor electrical asymmetry is created with different percentages in the doubly-fed induction generator. The effectiveness of the proposed prognosis structure is evaluated using a WT simulation setup. For this setup, a 2.5 Mw WT experimental data and equipment parameters are used. The statistical investigations show the precision of PSO-LSSVM in RUL prediction. The different simulations confirm that improved condition-based maintenance performances can be obtained. [ABSTRACT FROM AUTHOR]

In the era of globalization, manufacturing industries are facing intense pressure to prevent unexpected breakdowns, reduce maintenance cost and increase plant availability. Induction motors are the most sought-after prime movers in modern-day industries due to their robustness. Recently, research has picked up a fervent pace in the area of fault diagnosis of electrical machines. This paper presents the application of Support Vector Machine (SVM) and Artificial Neural Network (ANN)-based system to diagnose the vibration and Instantaneous Power (IP)-based responses of rolling element bearings and broken rotor bars in an induction motor. The dimensionality of the extracted features was reduced using Principal Component Analysis (PCA) and thereafter the selected features were ranked in order of relevance using the Sequential Floating Forward Selection (SFFS) method for reducing the size of input features and finding the most optimal feature set. A comparative analysis of the effectiveness of SVM and ANN is carried out using statistical parameters extracted from vibration and IP signals. The highest accuracy of 92.5% and 98.2% was achieved for vibration and IP signatures, respectively, using the proposed SFFS-based feature selection technique and ANN classification method. The results reveal that ANN has better performance than SVM and the proposed strategy can be used for automatic recognition of machine faults. The use of this type of intelligent system helps in avoiding unwanted and unplanned system shutdowns due to the failure of the motor. [ABSTRACT FROM AUTHOR]

System downtime and unplanned outages massively affect plant productivity; therefore, the reliability, availability, maintainability, and safety (RAMS) disciplines, together with fault diagnosis and condition monitoring (CM), are mandatory in energy applications. This article focuses on the optimization of a maintenance plan for the yaw system used in an onshore wind turbine (WT). A complete reliability-centered maintenance (RCM) procedure is applied to the system to identify which maintenance action is the optimal solution in terms of cost, safety, and availability. The scope of the research is to propose a new customized decision-making diagram inside the RCM assessment to reduce the subjectivity of the procedure proposed in the standard and save the cost by optimizing maintenance decisions, making the projects more cost-efficient and cost-effective. This article concludes by proposing a new diagnostic method based on a data-driven CM system to efficiently monitor the health and detect damages in the WT by means of measurements of critical parameters of the tested system. This article highlights how a reliability analysis, during the early phase of the design, is a very helpful and powerful means to guide the maintenance decision and the data-driven CM. [ABSTRACT FROM AUTHOR]

This letter proposes a novel capacitor condition monitoring method for modular multilevel converters (MMCs) based on reference submodules (SMs). It significantly enhances the capacitor monitoring accuracy by making full use of the SM voltage sensor measurement range. Accuracy comparison between the existing method and the proposed method is conducted to quantify the improvement of accuracy. Moreover, its operation principle and practical implementation considerations are presented. Finally, a three-phase MMC platform is built to experimentally verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This letter proposes a new submodule capacitor condition monitoring method for modular multilevel converters (MMCs) based on the dc-side start-up procedure. During this process, the MMC can be simplified into an RC charging circuit, and the submodule capacitance can be estimated by investigating its relationship with the phase current and submodule voltage. Several practical issues related to the implementation, advantages, and limitations of the proposed method are presented as well. In order to validate the method, a series of experiments with different submodule capacitances are conducted based on a three-phase MMC platform. Experimental results confirm the effectiveness and feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

Condition monitoring (CM) has been playing an important role in the operation and maintenance of industrial equipment. However, the changeable environments where some equipment, such as wind turbines (WTs) are installed, have put a negative effect on CM. To deal with this problem, this article proposes a CM method of WTs based on assembled multidimensional membership functions (MFs) using a fuzzy inference system. First, multidimensional MFs, which are formed by fusing environment factor into conventional MFs, are proposed to reduce the negative effect from changeable environments. Second, the input data are properly divided into segments and the segments are classified into four types to distinguish the effects of different data on CM. Different weights are assigned to each segment and the corresponding membership of each segment is calculated separately. Then, these memberships are assembled for fuzzy inference. Finally, based on the assembled multidimensional MFs, a new CM architecture is established. Four groups of experiments were carried out to evaluate the proposed method with the data collected from a wind farm in northern China. The experiments results show that the proposed method can not only detect anomalies at an early stage but also effectively decrease the false alarms and missing detections. [ABSTRACT FROM AUTHOR]

In this paper, based on acoustic emission (AE) monitoring and digital filtering technology, the low and high frequency components in the switching mechanical stress wave (MSW) of insulated gate bipolar transistor (IGBT) device were discovered for the first time. Then, the effects of turn-off current on the low and high frequency components were studied. The obviously linear relationship was found between the low frequency component and the turn-off current, and the low frequency component could closely follow the change of turn-off current, showing that the low frequency component was strongly related with turn-off current. The high frequency component contained two pulse waves with the inverse phase, single frequency and high similarity, and it was weakly linear related with or not linear related the turn off current, and it was insensitive to the change of turn-off current, proving that the turn-off current was not the main cause of the high frequency component. Besides, the repeatability test was performed on another IGBT device, showing good repeatability for the experimental results. [ABSTRACT FROM AUTHOR]

Operation and maintenance costs of wind power plants represent a significant share of the total expenditure of a wind farm project. It is therefore vital to optimize maintenance activities in order to reduce costs while improving wind turbine reliability and availability. The induction generator is a major contributor to failure rates and downtime of wind turbines, where doubly fed induction generators (DFIGs) are the dominant variable-speed technology employed. Actual data from operating wind turbines is seldom presented in the scientific literature. This paper analyzes two in-service DFIGs from wind turbines operating in a Spanish wind power plant. One of the generators under study reported excessive temperature on the rotor windings, while the other did not. In order to achieve a diagnosis for the reported excess of temperature, current signature analyses were performed on both machines. Fault-related frequency components were identified in the current spectra of the faulty machine, and were compared against the healthy one. A diagnosis was achieved for the faulty machine: Dynamic eccentricity caused by a rotor fault was identified as the cause of the excessive temperature in the rotor windings. [ABSTRACT FROM AUTHOR]

Recently, condition monitoring (CM) and fault detection and diagnosis (FDD) techniques for rotating machinery (RM) have witnessed substantial advancements in recent decades, driven by the increasing demand for enhanced reliability, efficiency, and safety in industrial operations. CM of valuable and high-cost machinery is crucial for performance tracking, reducing maintenance costs, enhancing efficiency and reliability, and minimizing mechanical failures. While various FDD methods for RM have been developed, these predominantly focus on signal processing diagnostics techniques encompassing time, frequency, and time-frequency domains, intelligent diagnostics, image processing, data fusion, data mining, and expert systems. However, there is a noticeable knowledge gap regarding the specific review of image-based CM and FDD. The objective of this research is to address the aforementioned gap in the literature by conducting a comprehensive review of image-based intelligent techniques for CM and fault FDD specifically applied to induction motors (IMs). The focus of the study is to explore the utilization of image-based methods in the context of IMs, providing a thorough examination of the existing literature, methodologies, and applications. Furthermore, the integration of image-based techniques in CM and FDD holds promise for enhanced accuracy, as visual information can provide valuable insights into the physical condition and structural integrity of the IMs, thereby facilitating early FDD and proactive maintenance strategies. The review encompasses the three main faults associated with IMs, namely bearing faults, stator faults, and rotor faults. Furthermore, a thorough assessment is conducted to analyze the benefits and drawbacks associated with each approach, thereby enabling an evaluation of the efficacy of image-based intelligent techniques in the context of CM and FDD. Finally, the paper concludes by highlighting key issues and suggesting potential avenues for future research. [ABSTRACT FROM AUTHOR]

Accurate condition monitoring has been a major challenge among railroad management authorities as they work to minimize collisions that lead to fatalities or damage to railroads infrastructure. Hence, research and technological developments in railroad maintenance and inspection are vital. Several research studies, on the inspection and defect detection techniques for railroads have been conducted by scholars. Despite the significant advancements made in this area, extensive studies are still required to enhance the accuracy of prognostic methods for railroad structural health monitoring (SHM) and condition monitoring (CM). Distributed acoustic sensing (DAS) has been recognized as a promising measurement method because of its quick sensing capabilities over long distances and for massive structures. DAS systems are classified according to the optical sensing quality and sensing range. As DAS produces large noisy datasets, in the case of railroad applications, algorithms for precise real-time and reliable analysis are essential. Meanwhile, data-driven and machine learning (ML) methods for defect detection have emerged as valuable approaches for SHM. Engineers and stakeholders can use ML algorithms to examine the large volumes of data produced by SHM systems to identify patterns and behaviors that might not appear in manual inspections. To support more precise and accurate maintenance and inspection for railroad systems, methodologies used to detect, identify, and characterize abnormal conditions in railroads using DAS and signal processing approaches for DAS large size and noisy signals, must be reviewed. Accordingly, in this literature survey, the applications of DAS methods for railroad CM are investigated. Among the variety of DAS methods, optical time domain reflectometry (OTDR) is reviewed in more details, since it is the most common approach in distributed fiber optic sensing. In addition, different OTDR-based DAS research for train tracking and railroad SHM are reviewed, and a comprehensive summary of different railroad defects is provided for further investigation. In all, this review paper provides a comprehensive background on distributed fiber optic sensing, the importance and challenges in railroad continuous CM, and the state-of-the-art application and future roadmap for the application of DAS in the railroad industry. [ABSTRACT FROM AUTHOR]

All electric powered machines offer the possibility of extracting information and calculating Key Performance Indicators (KPIs) from the electric current signal. Depending on the time window, sampling frequency and type of analysis, different indicators from the micro to macro level can be calculated for such aspects as maintenance, production, energy consumption etc. On the micro-level, the indicators are generally used for condition monitoring and diagnostics and are normally based on a short time window and a high sampling frequency. The macro indicators are normally based on a longer time window with a slower sampling frequency and are used as indicators for overall performance, cost or consumption. The indicators can be calculated directly from the current signal but can also be based on a combination of information from the current signal and operational data like rpm, position etc. One or several of those indicators can be used for prediction and prognostics of a machine’s future behavior. This paper uses this technique to calculate indicators for maintenance and energy optimization in electric powered machines and fleets of machines, especially machine tools.

The key function of rotating machine condition monitoring (CM) is to detect structural changes during machine operations. This paper presents a novel statistical time-frequency analysis method for this purpose. In particular, frequency spectrum is extracted from the machine condition signals based on periodogram estimation. Undirected weighted graph is then constructed from the resulting periodograms, where the so-called median graph is introduced and adopted to describe the normal machine status. Statistical analysis is performed to investigate newly observed data with respect to the median graph for change decision making. The proposed method has been applied to three different engineering applications to evaluate its effectiveness: load CM; early bearing failure detection; and speed CM. The results were compared with some benchmark methods reported in the literature, where significant improvements of the proposed method were demonstrated, indicating its good potentials in engineering applications. [ABSTRACT FROM AUTHOR]

The condition monitoring (CM) of large mechanical equipment has previously been carried out based on the characteristics of the testing techniques, the test objects and the environmental conditions, without any type of guiding strategy Therefore, in order to solve the problems encountered, this study first reviews the current definition, classification and common techniques of CM and puts forward a new definition for the method. Secondly, the study introduces the theory, content and implementation procedures of a proposed health management of mechanical systems (HMMS) monitoring method. Then, based on the above, the conditions of the proposed HMMS monitoring method are examined and the implementation procedures of the method are presented. It is concluded that CM should be carried out under the guidance of the HMMS theory and the key is to develop reasonable health management items and conditions for the monitoring parameters, as well as selecting the appropriate monitoring methods. Finally, two experimental applications of the proposed CM method based on the HMMS theory are carried out. The results obtained verify the feasibility of the proposed method, which provides a good reference for condition monitoring of similar equipment in the future. [ABSTRACT FROM AUTHOR]

This paper presents data-driven approaches to improve the active power output of wind turbines based on estimating their health condition. The main procedure includes estimations of fault degree and health condition level, and optimal power dispatch control. The proposed method can adjust the active power output of individual turbines according to their health condition and can thus optimize the total energy output of a wind farm. In the paper, extreme learning machine algorithm and Bonferroni interval are applied to estimate fault degree, while an analytic hierarchy process is used to estimate the health condition level. A scheme for power dispatch control is formulated based on the estimated health condition. Models have been identified from supervisory control and data acquisition data acquired from an operational wind farm, which contains temperature data of gearbox bearing and generator winding. The results show that the proposed method can maximize the operation efficiency of the wind farm while significantly reducing the fatigue loading on the faulty wind turbines. [ABSTRACT FROM AUTHOR]

Condition monitoring of electric drives play a significant role for a safe working environment. Induction motors are widely used in industries and any fault in it leads to interruption in the process or complete shutdown of the equipment. In this paper, acoustic-based fault detection in a three-phase induction motor is done by estimating the torque from the acoustic signals released by the machine. The fault detection is possible as acoustic emission is different for faults such as single phasing, bearing cage damage, and broken rotor bars. The acoustic signals are processed using rational-dilation wavelet transform (RADWT) technique to extract the fault features and thereby diagnose the fault type. The torque estimation is done using multiple regression method by extracting the energy possessed in the processed acoustic signal and the faults are diagnosed precisely. An experimental setup comprising of a three-phase induction motor with brake drum loading is used to validate this approach. The RADWT has adjustable frequency resolution in comparison with other wavelet methods. When high Q-factor filters are employed in the RADWT, better representation of different faults are obtained in the decomposed sub-bands. In addition, characteristic frequencies of different faults are calculated analytically and validated by observing the frequencies in the FFT spectrum of acoustic fault signals. [ABSTRACT FROM AUTHOR]

Condition monitoring (CM) has been considered as a promising technique to improve the reliability of insulated gate bipolar transistors (IGBTs). Among various condition parameters, switching time is a good health status indicator to detect IGBT failures. However, on-line monitoring of the IGBT high-speed switching time is still difficult in practice due to the requirement of the extremely high sampling rate for signal acquisition. To overcome the technical difficulty, this paper provides an innovative compressed sensing (CS) method to achieve equivalent sampling performance for high-speed IGBT switching time monitoring with a lower sampling rate. By utilizing the sparse characteristics of an IGBT switching signal, the sampling rate in the CS method could be far less than the traditional Nyquist sampling rate. To clarify the method, the CM mechanism using IGBT switching time is first analyzed. Then, three key points in the CS method are studied, i.e., the selection of sparsifying basis, the design of a measurement matrix, and the implementation of a reconstruction algorithm. Finally, experiments are carried out to investigate the performance of the CS method for on-line CM. Experimental results show that the IGBT turn-offtime at different temperatures can be accurately monitored with a highly compressed sampling rate, which validates the feasibility and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]