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Fault detection in analog circuits is of great importance to predict the correct operation of the circuit. For this purpose, soft computing techniques such as those based on the application of fuzzy inference systems stand out. However, given the large variability that can exist in analog circuits due to component tolerance, the initial fuzzy inference system (FIS) may not be able to accurately diagnose the different hard faults. This study presents a methodology to diagnose and detect the faults that can occur in analog circuits, which is based on the development of a FIS, starting from a specific fault situation in the analog circuit, and subsequently on the optimization of the membership functions using an evolutionary algorithm so that the adjusted FIS can classify and predict different failure situations. To this end, the application of optimization techniques based on particle swarm optimization (PSO) will be analyzed to develop a FIS capable of predicting different faults. In addition, pattern search algorithm will also be analyzed. A Sallen-Key band-pass filter and a single stage of a small-signal amplifier are used as test circuits. The proposed methodology shows that it is possible to accurately predict the faults that could arise in the circuits under study. [ABSTRACT FROM AUTHOR]

Distribution system voltage profile management at each bus and fault detection and classification are often challenged by complex and changing network configurations. The distribution system voltage profile improvement issue is addressed by placing distributed generation (DG) units at different locations in the network. By placing the DG units at appropriate places in IEEE 33 bus radial distribution networks by a proposed reinforcement learning (RL) algorithm, the voltage profile of each node is improved and power loss in the network is minimized. There is a 69% reduction in active power losses compared to losses without DG. Furthermore, an innovative method for fault detection and classification is developed that uses a convolutional neural network (CNN) cascaded with a long short-term memory network (LSTM) and attention mechanisms (AMs). To extract dynamic information from the data, phasor measurement units (PMUs) placed on different buses are used as input for the CNN architecture. AM strengthens important information. A mapping weight and parameter learning approach allows AM to assign different weights to concentrate on LSTM characteristics and improve learning accuracy. Low and high impedance faults are tested as well as various non-faulty events. The scheme's performance is compared with that of other deep learning techniques through reliability analysis, and the time taken for fault detection (FD) is also determined. [ABSTRACT FROM AUTHOR]

Summary Node faults are often influenced by factors such as physical component failure, communication module errors, battery exhaustion, and environmental factors. Various researchers have contributed to addressing the problem of detecting, diagnosing, and classifying faults in sensor networks. However, computational complexity and fault occurrence probability increase with the size of the network. In this context, an enhanced optimized layered diagnosis framework (OLDF) is proposed for detecting, diagnosing, and classifying sensor node faults. OLDF utilizes a deep belief network (DBN) with chaos quantum‐behaved particle swarm optimization (CQ‐PSO) for efficient diagnosis and classification of various sensor faults in the network. The proposed OLDF consists of two layers. In the first layer, optimized DBN with CQ‐PSO algorithm is employed to determine the fault type of the sensor nodes. Then, the fault type of sensor nodes that are classified in the first layer is given as input into the second layer of the OLDF algorithm to determine the fault severity in the network. Experimentation has been carried out by using the NS3 simulator for the OLDF framework with performance metrics such as classification accuracy, diagnosis accuracy, false positives, fault alarm rate, and average energy consumption. The validity of the proposed framework has been verified through comparisons with existing works such as MFD, INSA, FDRFC, and HFD. From the comparative analysis carried out, it is apparent that the proposed framework achieves a high classification accuracy of 89.94% and diagnosis accuracy of 93.21%, with less false positive rate, and utilizes minimum energy when compared with its counterparts. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive analysis of failures in low-voltage terminal connections within power transformers and proposes a fault classification methodology based on infrared thermography (IRT). Low-voltage terminal connections play a critical role in the reliable operation of power transformers, and their failures can lead to severe operational issues. In this study, we employ IRT as a noninvasive and efficient diagnostic tool to identify and classify various types of failures, including loose connections, overheating, and corrosion. The research involves the collection of infrared thermograms (IRT images) from the low-voltage terminals of power transformers under different operating conditions. The proposed methodology demonstrates its effectiveness in detecting and classifying low-voltage terminal connection failures, thereby enabling timely preventive maintenance and minimizing the risk of transformer malfunctions. This research contributes to enhancing the reliability and longevity of power transformers, reducing downtime, and optimizing maintenance practices in the power industry. [ABSTRACT FROM AUTHOR]

The healthy operation of aircraft engines is crucial for flight safety, and accurate Remaining Useful Life prediction is one of the core technologies involved in aircraft engine prognosis and health management. In recent years, deep learning-based predictive methods within data-driven approaches have shown promising performance. However, for engines experiencing a single fault, such as a High-Pressure Compressor fault, existing deep learning-based predictive methods often face accuracy challenges due to the coupling relationship between different fault modes in the training dataset that includes a mixture of multiple fault modes. In this paper, we propose the FC-AMSLSTM method, a novel approach for Remaining Useful Life prediction specifically targeting High-Pressure Compressor degradation faults. The proposed method effectively addresses the limitations of previous approaches by fault classification and decoupling fault modes from multiple operating conditions using a decline index. Then, attention mechanisms and multi-scale convolutional neural networks are employed to extract spatiotemporal features. The long short-term memory network is then utilized to model RUL estimation. The experiments are conducted using the Commercial Modular Aero-Propulsion System Simulation dataset provided by NASA. The results demonstrate that compared to other prediction models, the FC-AMSLSTM method effectively reduces RUL prediction error for HPC degradation faults under multiple operating conditions. [ABSTRACT FROM AUTHOR]

This study introduces an innovative approach to diagnostics, employing a unique combination of techniques including a stratified group K-fold cross-validation method and a sparse stacked autoencoder (SSAE) alongside LightGBM. By examining signatures derived from motor current, voltage, speed, and torque, the framework aims to effectively detect and classify broken rotor bars (BRBs) within inverter-fed induction machines. In this kind of cross-validation method, class labels and grouping factors are spread out across folds by distributing motor operational data attributes equally over target label stratification and extra grouping information. By integrating SSAE and LightGBM, a gradient-boosting framework, we elevate the precision and efficacy of defect diagnosis. The SSAE feature extraction algorithm proves to be particularly effective in identifying small BRB signatures within motor operational data. Our approach relies on comprehensive datasets collected from motor systems operating under diverse loading conditions, ranging from 0% to 100%. Using a sparse stacked autoencoder, the model lowers the dimensionality and noise of the motor fault data. It then sends the cleaned data to the LightGBM network for fault diagnosis. LightGBM leverages the attributes of the sparse stacked autoencoder to showcase the distinctive qualities associated with BRBs. This integration offers the potential to improve defect identification by furnishing input representations that are both more precise and more concise. The proposed model (SSAE with LightGBM) was trained using 80% of the data, while the remaining 20% was used for testing. To validate the proposed architecture, we evaluate the accuracy, precision, recall, and F1-scores of the results using motor global signals, with the help of confusion matrices with receiver operating characteristic (ROC) curves. Following the training of a new LightGBM model with refined hyperparameters through Bayesian optimization, we proceed to conduct the final classification utilizing the optimal feature subset. Evaluation of the test dataset indicates that the BRBs diagnostic framework facilitates the detection and classification of issues with induction motor BRBs, achieving accuracy rates of up to 99% across all loading conditions. [ABSTRACT FROM AUTHOR]

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AbstractHigh-dimensional data streams are ubiquitous in modern manufacturing, due to their ability to provide valuable information about the industrial system’s performance on a real-time basis. If a shift occurs in a production process, fault diagnosis based on the data streams is of critical importance for identifying the root cause. Existing methods have largely focused on controlling the total missed discovery rate without distinguishing missed signals for positive versus negative components of the shift vector. In practice, however, losses incurred from the two directional shifts can differ substantially, so it is desirable to constrain the proportions of missed signals for positive and negative components at two distinctive levels. In this article, we propose a fault classification procedure that controls the two proportions separately. By formulating the problem as Lagrangian multiplier optimization, we show that the proposed procedure is optimal in the sense that it minimizes the expected number of false discoveries. We also suggest an iterative adjustment algorithm that converges to the optimal Lagrangian parameters. The asymptotic optimality for the data-driven version of our procedure is also established. Theoretical justification and numerical comparison with state-of-the-art methods show that the proposed procedure works well in applications. [ABSTRACT FROM AUTHOR]

To address the issue of diagnosing hard and soft faults in active power factor correction (APFC) power supply, this study analyzes failure modes resulting from aging and malfunction of various sensitive components. The power fault waveform patterns are initially analyzed based on the circuit's THD, current ripple value, and RMS value. The inductor current signals in different fault modes are then utilized to extract and construct time–frequency fusion fault features of the APFC power supply. Finally, these feature quantities are downscaled and optimized using the RF algorithm. The SOA‐KELM model of the APFC converter is proposed, and the feature vectors under different fault modes are used to classify and diagnose faults, achieving hard and soft fault detection of the converter. The experiments show that the method achieves 100% accuracy for hard fault diagnosis and 96.36% accuracy for soft fault diagnosis of the converter, demonstrating high diagnostic accuracy. [ABSTRACT FROM AUTHOR]

AbstractOne important characteristic of modern fault classification systems is the ability to flag the system when faced with previously unseen fault types. This work considers the unknown fault detection capabilities of deep neural network-based fault classifiers. Specifically, we propose a methodology on how, when available, labels regarding the fault taxonomy can be used to increase unknown fault detection performance without sacrificing model performance. To achieve this, we propose to utilize soft label techniques to improve the state-of-the-art deep novel fault detection techniques during the training process and novel hierarchically consistent detection statistics for online novel fault detection. Finally, we demonstrated increased detection performance on novel fault detection in inspection images from the hot steel rolling process, with results well replicated across multiple scenarios and baseline detection methods. [ABSTRACT FROM AUTHOR]

PV(photovoltaic) systems have been deployed more in the recent years to support green energy generation. In the recent era, grid tied PV has been sparked by the emergence of the electricity market and offers an alternative solution to traditional fossil fuel-based electricity generation. A few of the potential impacts of solar PV on grid are false tripping of feeders, unwanted tripping, unnecessary islanding, and blind protection. In this research paper, different artificial intelligence techniques are tested in order to overcome the protection challenges. The PSCAD/EMTDC software package is used to analyze a section of the power system, and an algorithm has been constructed using Python v3.8 and MATLAB 2018. On an IEEE-9 BUS power system connected to a PV source, the Artificial Neural Network, Naive Bayes, Support Vector Machine, Random Forest, and Convolution Neural Network (CNN) algorithms are implemented to classify the faults. The suggested methods are proven effective for both in-zone and out-of-zone problems on power lines interconnected with solar park. The proposed techniques have been validated using total 16,320 internal and external fault cases with a wide range of system parameters alteration. In the proposed system, the effectiveness of several machine learning and deep learning techniques is compared. The obtained results demonstrate that CNN provides greater accuracy in the presence of a PV source, but at the same time, it is appropriate for a large number of data sets. The fault classification accuracy acquired is adequate and demonstrates the adaptability of the proposed approach. [ABSTRACT FROM AUTHOR]

Considering global warming and environmental problems, the importance of renewable energy sources is increasing day by day. In particular, the effects of wind and solar power, which are variable renewable power sources, on the power system necessitate their evaluation in terms of the reliability of the power system. Photovoltaic panels, which enable the conversion of solar power into electrical power with semiconductors, have started to take an important place in global energy investments today. Photovoltaic power plants increase the demand for this energy source with continuous energy conversion depending on sunshine duration and radiation intensity. Among the renewable energy sources, the most easily utilized energy source, regardless of geographical conditions, is the sun. To prevent the energy production of PV power plants from being interrupted, it is necessary to address and analyze all kinds of faults that will affect power production in order to increase the reliability of the system. Academic studies in this field are generally grouped under two topics: classification of faults or modeling of electrical faults. Based on this, in this study, the problems that occur during the installation and operation of photovoltaic systems are classified, and the relevant faults are modeled and simulated in MATLAB Simulink version 23.2 (R2023b). Thus, a scientific approach to the problems of photovoltaic power plant operating conditions has been gained, which will be the basis for academic studies. [ABSTRACT FROM AUTHOR]

This paper proposes a fast protection scheme of a hybrid AC/DC distribution network based on features extraction of the decomposed signals. The signal decomposition is achieved using discrete wavelets transform (DWT). The proposed scheme offers three main functions, fast fault detection for both network sides, AC fault classification with the ability to identify the faulted phase/ phases, and protection coordination between AC and DC sides. In addition, the scheme is based on local measurement so no need for extra-cost for communication infrastructure. The absence of communication reduces the overall cost and, as a most important feature, it reduces the fault clearing time. The system is thoroughly analyzed under many types of faults in both AC and DC sides. Also, different disturbance conditions are considered which used in threshold determination. The simulation results show that the proposed protection scheme was not only able to protect the system but also it offers a generalized fast acting communication-less approach. [ABSTRACT FROM AUTHOR]

Fault detection in analog circuits is of great importance to predict the correct operation of the circuit. For this purpose, soft computing techniques such as those based on the application of fuzzy inference systems stand out. However, given the large variability that can exist in analog circuits due to component tolerance, the initial fuzzy inference system (FIS) may not be able to accurately diagnose the different hard faults. This study presents a methodology to diagnose and detect the faults that can occur in analog circuits, which is based on the development of a FIS, starting from a specific fault situation in the analog circuit, and subsequently on the optimization of the membership functions using an evolutionary algorithm so that the adjusted FIS can classify and predict different failure situations. To this end, the application of optimization techniques based on particle swarm optimization (PSO) will be analyzed to develop a FIS capable of predicting different faults. In addition, pattern search algorithm will also be analyzed. A Sallen-Key band-pass filter and a single stage of a small-signal amplifier are used as test circuits. The proposed methodology shows that it is possible to accurately predict the faults that could arise in the circuits under study.

This paper proposes a fast protection scheme of a hybrid AC/DC distribution network based on features extraction of the decomposed signals. The signal decomposition is achieved using discrete wavelets transform (DWT). The proposed scheme offers three main functions, fast fault detection for both network sides, AC fault classification with the ability to identify the faulted phase/ phases, and protection coordination between AC and DC sides. In addition, the scheme is based on local measurement so no need for extra-cost for communication infrastructure. The absence of communication reduces the overall cost and, as a most important feature, it reduces the fault clearing time. The system is thoroughly analyzed under many types of faults in both AC and DC sides. Also, different disturbance conditions are considered which used in threshold determination. The simulation results show that the proposed protection scheme was not only able to protect the system but also it offers a generalized fast acting communication-less approach.

This research paper presents a proposed system for fault identification, classification and location in transmission lines using a Support Vector Machine (SVM)-based technique in conjunction with a Positive Sequence Analyzer. The objective is to develop an accurate and reliable method for identifying, classifying and locating different fault types in transmission lines. The proposed system leverages the capabilities of SVMs in handling high-dimensional feature spaces and the fault signature extraction capabilities of the Positive Sequence Analyzer. Experimental evaluations are conducted to assess the performance and effectiveness of the proposed system, comparing it with existing fault identification and classification methods. The results demonstrate the superior performance and robustness of the SVM-based technique utilizing the Positive Sequence Analyzer, providing a valuable contribution to fault management and system reliability in transmission line networks.

This study proposes a protection relay using a microcontroller to detect and classify faults in transmission lines based on the Wavelet transform. An experimental model was constructed from an actual 115 kV transmission system prototype. The current signal was observed based on the fault type, phase, and position. Clark’s transform and the discrete Wavelet transform (DWT) were applied to transform signals for analysis. The positive and zero sequences obtained from Clark’s transform were used for fault detection and fault classification, respectively. Moreover, the performances between the DWT and the short-time Fourier Transform (STFT) were compared in terms of accuracy and processing time. In addition, the double-detection technique was used to confirm the accuracy of fault detection. Results show that the proposed method is efficient for fault detection and classification. This finding allows the researcher to choose the appropriate analytical method. Moreover, it can also be used as the basis for overcurrent relay algorithm design in the effort to develop more advanced technologies.

The semiconductor industry, driven by technological advancements, is continuously undergoing process micronization. This micronization has led to an increased complexity in the wafer fabrication process and equipment. Inevitably, this change leads to a rise in defect rates. Furthermore, the traditional manual analysis methods for these defects possess several limitations. Notably, the defect analysis process is time-consuming and heavily relies on the expertise of the workers, making it challenging to achieve consistent analysis results. In modern semiconductor manufacturing sites, a vast amount of sensor data is being generated in real-time from various equipment. These sensor data contain information on the wafer’s condition, the progress of the process, and the operating conditions of the equipment. The purpose of this study is to examine the use of this sensor data in creating a deep learning model that leverages information generated within multi-stage manufacturing processes. The proposed method is designed to automatically classify the defect status of wafers and interpret the causes of these defects. Using the sensor data from two process equipment in semiconductor manufacturing, we achieve better classification performance than other existing methods. Through the interpretation of the classification results, we can identify the sensors and equipment that significantly impact the classification results. The proposed methodology is expected to significantly reduce the effort and reliance on engineers for defect analysis, thus greatly improving production efficiency.

AC microgrids are becoming increasingly important for providing reliable and sustainable power to communities. However, the evolution of distribution systems into microgrids has changed the way they respond to faults and hence their protection requirements. Faults in microgrids could hinder operation stability and damage the system components. The types, locations, and resistances of faults, as well as microgrid operation modes, distributed generation penetration levels, load changes, and system topologies, all affect how the microgrid responds to faults. In order to offer quick restoration and to protect the microgrid components, fault detection and classification are therefore essential for microgrids. In this direction, unconventional methods such as artificial intelligence have been increasing in popularity over the last years. Pattern recognition is a methodology that machine learning as an approach to artificial intelligence is concerned with. The combination of protection with machine learning may be motivating in order to achieve the goal of intelligent operation in the smart grid. In this paper, fault detection, classification and location methods are reviewed for microgrid application. Different methods applied for both fault location and fault classification are being classified by the implemented technique. Such methods are explained and analyzed providing the main advantages and disadvantages of each category. Additionally, the research trends in both fields are analyzed and state–of–the–art methods from each category are thoroughly compared. Finally, the research gaps and future directions are identified.

Based on the signum function (sign of magnitudes) of transient energy, this paper proposes a fault classification approach and algorithm for a monopolar HVDC system. The analytical study here shows that the signum function of variation in transient energy has a zero value individually at both ends on no-fault conditions. Moreover, the summation of the signum functions computed for internal DC faults is zero, whereas the same has a non-zero value for external AC faults. The performance of the proposed algorithm has been extensively evaluated by simulating faults on a CIGRE benchmark system for HVDC monopolar configuration using EMTDC/PSCAD software. Three locations of DC line fault, and AC fault at the two ends of the system have been considered for this evaluation. Five fault resistance values (0, 10, 100, 1000, and 2000 ohms) have been simulated for each fault location. The results conform with the theoretical analysis, and the fault classification by the algorithm is 100% accurate. The time taken to detect and classify a DC fault at the mid-point of the 800-km line is 1.5 ms, and that for line-end faults on the DC line is 3 ms for all values of fault resistance. These results show a marked improvement over those reported earlier in the literature using other techniques. A comparison table is given in the last section to corroborate it.

This paper proposes effective Random Forest (RF)-based fault detection and diagnosis for wind energy conversion (WEC) systems. The proposed technique involved two major steps: feature selection and fault classification. Feature selection pre-processing is an important step to increase the accuracy of the classification algorithm and decrease the dimensionality of a dataset. Therefore, a hybrid feature selection based diagnosis technique, that can preserve the advantages of wrapper and filter algorithms as well as RF model, is proposed. In the first phase, the neighborhood component analysis (NCA) filter algorithm is used to reduce and select only the pertinent features from the original raw data. This phase helps in improving data by removing redundant and unimportant features. In the second step, we applied a wrapper technique called equilibrium optimizer to get optimized features and better classification accuracy. The main idea behind using a hybrid feature selection step is to select a small subset from original data that can achieve maximum classification accuracy and reduce the computational complexity of the RF technique. Then, the sensitive and significant characteristics are transmitted to the RF model for classification purposes. The presented results prove that the proposed methods offer enhanced diagnosis accuracy when applied to WEC systems.

Accurate fault detection and classification help to analyze fault causes and quickly restore faulty phases. Deep learning can automatically extract fault features and identify fault types from the original three-phase voltage and current signals. However, this still imposes challenges such as recognition accuracy and computational complexity. More importantly, high level fault features cannot be extracted in the one-dimensional time series. This paper presents a robust fault classification method based on SA-MobileNetV3 for transmission systems. Considering that the SE (Squeeze-and-Excitation) attention module cannot aggregate the spatial dimension information on the channel, SA (shuffle attention) module is introduced into MobileNetV3, which can effectively fuse the importance of pixels in different channels and in different locations at the same channel. Also, transforming the time series three-phase voltage and current signals into two-dimensional images based on CWT (continuous wavelet transform) makes the proposed method be similar to image recognition, which can mine high level fault features and classify the faults visually. To verify the effectiveness of the method, a 735kV transmission line model is built for data generation through Simulink. Various kinds of fault conditions and factors are considered to verify the adaptability and generalizability. Simulation results show that the method can quickly and accurately identify 11 types of faults, and the accuracy rate is as high as 99.90%. A comparison between the proposed method and other existing techniques shows the superiority of the proposed SA- MobileNetV3, and better anti-noise performance makes it more suitable for real fault signals taken on-site.

This paper proposes a novel unsupervised classification method to solve the problem of mechanical fault diagnosis under the condition of unknown number of fault types. The proposed method combining the three data processing stage. First, the deep encoding neural networks is used to complete the abstract signal feature representation under the unsupervised conditions. Second, the feature dimensionality reduction technique based on manifold learning is used to complete the low-dimensional mapping of the feature space. Third, the spatial clustering based on density criterion is introduced to classify the different fault samples. This paper uses two fault signals dataset to complete the performance verification experiment. The experimental results show that the DMDUC method respectively achieves the classification accuracy of 99.7 % and 100 % on the two datasets. [ABSTRACT FROM AUTHOR]

Solar photovoltaic energy generation has garnered substantial interest owing to its inherent advantages, such as zero pollution, flexibility, sustainability, and high reliability. Ensuring the efficient functioning of PV power facilities hinges on precise fault detection. This not only bolsters their reliability and safety but also optimizes profits and avoids costly maintenance. However, the detection and classification of faults on the Direct Current (DC) side of the PV system using common protection devices present significant challenges. This research delves into the exploration and analysis of complex faults within photovoltaic (PV) arrays, particularly those exhibiting similar I-V curves, a significant challenge in PV fault diagnosis not adequately addressed in previous research. This paper explores the design and implementation of Support Vector Machines (SVMs) and Extreme Gradient Boosting (XGBoost), focusing on their capacity to effectively discern various fault states in small PV arrays. The research broadens its focus to incorporate the use of optimization algorithms, specifically the Bees Algorithm (BA) and Particle Swarm Optimization (PSO), with the goal of improving the performance of basic SVM and XGBoost classifiers. The optimization process involves refining the hyperparameters of the Machine Learning models to achieve superior accuracy in fault classification. The findings put forth a persuasive case for the Bees Algorithm's resilience and efficiency. When employed to optimize SVM and XGBoost classifiers for the detection of complex faults in PV arrays, the Bees Algorithm showcased remarkable accuracy. In contrast, classifiers fine-tuned with the PSO algorithm exhibited comparatively lower performances. The findings underscore the Bees Algorithm's potential to enhance the accuracy of classifiers in the context of fault detection in photovoltaic systems. [ABSTRACT FROM AUTHOR]

The meticulous monitoring and diagnosis of faults in photovoltaic (PV) systems enhances their reliability and facilitates a smooth transition to sustainable energy. This paper introduces a novel application of deep learning for fault detection and diagnosis in PV systems, employing a three-step approach. Firstly, a robust PV model is developed and fine-tuned using a heuristic optimization approach. Secondly, a comprehensive database is constructed, incorporating PV model data alongside monitored module temperature and solar irradiance for both healthy and faulty operation conditions. Lastly, fault classification utilizes features extracted from a combination consisting of a Convolutional Neural Network (CNN) and Bidirectional Gated Recurrent Unit (Bi-GRU). The amalgamation of parallel and sequential processing enables the neural network to leverage the strengths of both convolutional and recurrent layers concurrently, facilitating effective fault detection and diagnosis. The results affirm the proposed technique's efficacy in detecting and classifying various PV fault types, such as open circuits, short circuits, and partial shading. Furthermore, this work underscores the significance of dividing fault detection and diagnosis into two distinct steps rather than employing deep learning neural networks to determine fault types directly. [ABSTRACT FROM AUTHOR]

This paper addresses the critical issue of fault detection and prediction in electric motor machinery, a prevalent challenge in industrial applications. Faults in these machines, stemming from mechanical or electrical issues, often lead to performance degradation or malfunctions, manifesting as abnormal signals in vibrations or currents. Our research focuses on enhancing the accuracy of fault classification in electric motor facilities, employing innovative image transformation methods—recurrence plots (RPs), the Gramian angular summation field (GASF), and the Gramian angular difference field (GADF)—in conjunction with a multi-input convolutional neural network (CNN) model. We conducted comprehensive experiments using datasets encompassing four types of machinery components: bearings, belts, shafts, and rotors. The results reveal that our multi-input CNN model exhibits exceptional performance in fault classification across all machinery types, significantly outperforming traditional single-input models. This study not only demonstrates the efficacy of advanced image transformation techniques in fault detection but also underscores the potential of multi-input CNN models in industrial fault diagnosis, paving the way for more reliable and efficient monitoring of electric motor machinery. [ABSTRACT FROM AUTHOR]

The theme of the paper is to emphasize the detection and classification of faults and their site location in the transmission line using machine learning techniques which help to indemnify the foul-up of the humans in identifying the site and type of occurrence of fault. Moreover, the transient stability is a supreme one in power systems and so the disturbances like faults are required to be separated to preserve the transient stability. In general, the protection of the transmission line includes the installation of relays at both ends of the line that constantly monitor voltages and currents and operate unless a fault occurs on a line. Therefore, this paper intends to introduce a novel transmission line protection model by exploiting the hybrid optimization concept to train the Convolutional Neural Network (CNN). Here, the fault detection, classification and site location are diagnosed by using CNN which is trained and tested by making use of diverse synthetic field data derived from the simulation models of distinct types of transmission lines. Hence, the location and the type of faults will be predicted by the CNN depending on the fault signal characteristics which are optimally trained by a new hybrid algorithm named Chicken Swarm Insisted Spotted Hyena (CSI-SH) Algorithm that hybrids both the concept of Spotted Hyena Optimization (SHO) and Chicken Swarm Optimization (CSO). Finally, the proposed method based on CNN for fault classification and site location of transmission lines is implemented in MATLAB/Simulink and the performances are compared with various measures like classification accuracy, fault detection rate and so on. [ABSTRACT FROM AUTHOR]

As the core components of electric vehicles, the safety of the electric system, including motors, batteries, and electronic control systems, has always been of great concern. To provide early warning of electric-system failure and troubleshoot the problem in time, this study proposes a novel energy-vehicle electric-system failure-classification method, which is named Pearson-ShuffleDarkNet37-SE-Fully Connected-Net (PSDSEF). Firstly, the raw data were preprocessed and dimensionality reduction was performed after the Pearson correlation coefficient; then, data features were extracted utilizing ShuffleNet and an improved DarkNet37-SE network based on DarkNet53; secondly, the inserted squeeze-and-excitation networks (SE-Net) channel attention were able to obtain more fault-related target information; finally, the prediction results of the ShuffleNet and DarkNet37-SE networks were aggregated with a fully connected neural network to output the classification results. The experimental results showed that the proposed PSDSEF-based electric vehicles electric-system fault-classification method achieved an accuracy of 97.22%, which is better than other classical convolutional neural networks with the highest accuracy of 92.19% (ResNet101); the training time is faster than the average training time of the comparative networks. The proposed PSDSEF has the advantage of high classification accuracy and small number of parameters. [ABSTRACT FROM AUTHOR]

Series compensation poses serious challenges to the protection schemes of transmission lines (TLs). Moreover, with the penetration of distributed generation (DG), the conventional protection schemes fail to detect the fault and identify the faulty phase as the backfeed currents from the DG units mask the faulty phase. This work presents an algorithm for the protection of series-compensated TLs (SCTL) integrated with wind farms. The algorithm uses the phase difference between the positive sequence currents at the two ends of the TL to discriminate internal and external faults. The faulty phase for internal fault is identified from the difference in complex power at the two ends of the TL. Extensive simulations with several fault and system parameters assess the reliability and applicability of this technique. [ABSTRACT FROM AUTHOR]

Modern solutions for precise fault localisation in Low Voltage (LV) Distribution Networks (DNs) often rely on costly tools such as the micro-Phasor Measurement Unit (μ PMU), which is potentially impractical for the large number of nodes in LVDNs. This paper introduces a novel fault detection technique using a distribution network digital twin without the use of μ PMUs. The Digital Twin (DT) integrates data from Smart Meters (SMs) and network topology to create an accurate replica. In using SM voltage-magnitude readings, the pre-built twin compiles a database of fault scenarios and matches them with their unique voltage fingerprints. However, this SM-based voltage-only approach shows only a 70.7% accuracy in classifying fault type and location. Therefore, this research suggests using the cables' Currents Symmetrical Component (CSC). Since SMs do not provide direct current data, a Machine Learning (ML)-based regression method is proposed to estimate the cables' currents in the DT. Validation is performed on a 41-node LV distribution feeder in the Scottish network provided by the industry partner Scottish Power Energy Networks (SPEN). The results show that the current estimation regressor significantly improves fault localisation and identification accuracy to 95.77%. This validates the crucial role of a DT in distribution networks, thus enabling highly accurate fault detection when using SM voltage-only data, with further refinements being conducted through estimations of CSC. The proposed DT offers automated fault detection, thus enhancing customer connectivity and maintenance team dispatch efficiency without the need for additional expensive μ PMU on a densely-noded distribution network. [ABSTRACT FROM AUTHOR]

This paper proposes a noise-robust and accurate bearing fault diagnosis model based on time-frequency multi-domain 1D convolutional neural networks (CNNs) with attention modules. The proposed model, referred to as the TF-MDA model, is designed for an accurate bearing fault classification model based on vibration sensor signals that can be implemented at industry sites under a high-noise environment. Previous 1D CNN-based bearing diagnosis models are mostly based on either time domain vibration signals or frequency domain spectral signals. In contrast, our model has parallel 1D CNN modules that simultaneously extract features from both the time and frequency domains. These multi-domain features are then fused to capture comprehensive information on bearing fault signals. Additionally, physics-informed preprocessings are incorporated into the frequency-spectral signals to further improve the classification accuracy. Furthermore, a channel and spatial attention module is added to effectively enhance the noise-robustness by focusing more on the fault characteristic features. Experiments were conducted using public bearing datasets, and the results indicated that the proposed model outperformed similar diagnosis models on a range of noise levels ranging from −6 to 6 dB signal-to-noise ratio (SNR). [ABSTRACT FROM AUTHOR]

To solve the problems of low accuracy, difficult diagnosis and long time consuming of the current fan bearing fault diagnosis, an improved grey wolf optimization (IGWO) algorithm and a support vector machine (SVM) fault diagnosis method are proposed. In order to accurately extract the fault features, the wavelet packet decomposition method in the time-frequency domain analysis is used to extract the fault vibration signal. Take the wavelet packet decomposition energy of the eight frequency bands as the fault feature， the eigenvectors are constructed. Then, the fault model of SVM is established and the parameters of the SVM model are optimized by the IGWO algorithm, which avoids the defects of local optimum and slow convergence. According to the experimental analysis result, the average fault recognition rate of the IGWO algorithm is up to 99.3%, and it can identify fault types more quickly, more efficiently and more accurately, which provides a good support for the development of fault diagnosis.

This paper proposes a feature-based methodology for early bearing fault detection and classification in induction motors through current signals using the digital Taylor–Fourier transform (DTFT) and statistical methods. The DTFT allows the application of narrow bandwidth digital filters located in the spurious current signal components, wherewith it is possible to gain information to detect bearing issues and classify them using statistical methods. The methodology was implemented in MATLAB using the digital Taylor–Fourier transform for three fault types (bearing ball damage, outer-race damage, and corrosion damage) at different powering conditions: power grid source at 60 Hz and adjustable speed drive applied (60 Hz, 50 Hz, 40 Hz, 30 Hz, 20 Hz, and 10 Hz) in loading and unloading conditions. Results demonstrate a classification accuracy between 93–99% for bearing ball damage, 91–99% for outer-race damage, and 94–99% for corrosion damage. [ABSTRACT FROM AUTHOR]

Sparsity-based fault diagnosis methods have achieved great success. However, fault classification is still challenging because of neglected potential knowledge. This paper proposes a combined sparse representation deep learning (SR-DEEP) method for rolling bearing fault diagnosis. Firstly, the SR-DEEP method utilizes prior domain knowledge to establish a sparsity-based fault model. Then, based on this model, the corresponding regularization parameter regression networks are trained for different running states, whose core is to explore the latent relationship between the regularization parameters and running states. Subsequently, the performance of the fault classification is improved by embedding the trained regularization parameter regression networks into the sparse representation classification method. This strategy improves the adaptability of the sparse regularization parameter, further improving the performance of the fault classification method. Finally, the applicability of the SR-DEEP method for rolling bearing fault diagnosis is validated with the CWRU platform and QPZZ-II platform, demonstrating that SR-DEEP yields superior accuracies of 100% and 99.20% for diagnosing four and five running states, respectively. Comparative studies show that the SR-DEEP method outperforms four sparse representation methods and seven classical deep learning classification methods in terms of the classification performance. [ABSTRACT FROM AUTHOR]

Purpose: The global interest of developing monitoring system is increasing due to the continuous challenges in reliability and accuracy. Automatic fault detection and diagnosis of rotating machinery play an important role for the high efficiency and reliability of modern industrial systems. The key point of having high accurate automatic model for fault detection and diagnosis is obtaining defect features and choosing a representative approach for the model. Methods: In this paper, a model is developed based on Mel Frequency Cepstral Coefficients (MFCC) and gammatone cepstral coefficients (GTCC) that are computed for the input signal frames. Additionally, two global representations (feature concatenation and feature statistics) are adopted to feed Support Vector Machine (SVM) and a temporal representation is used with Long Short-Term Memory (LSTM) and Echo State Network (ESN) classification models. To generalize the proposed model, the experiments are evaluated based on two different datasets (PHM09 and DDS), where the PHM09 contains samples of helical and spur gears while the DDS contains samples from parallel and plenary gearboxes. Results: The results show that the proposed SVM model based on feature concatenation can effectively detect faults from gears and outperforms the other existing methods in the state-of-the-art studies. Conclusion: Base on the result of this paper, a global representation by concatenating frame-based features outperforms global statistical and time-series feature representations. [ABSTRACT FROM AUTHOR]

Predictive maintenance is a vital aspect of the industrial sector, and the use of Industrial Internet of Things (IIoT) sensor nodes is becoming increasingly popular for detecting motor faults and monitoring motor conditions. An integrated approach for acquiring, processing, and wirelessly transmitting a large amount of data in predictive maintenance applications remains a significant challenge. This study presents an IIoT-based sensor node for industrial motors. The sensor node is designed to acquire vibration data on the radial and axial axes of the motor and utilizes a hybrid approach for efficient data processing via edge and cloud platforms. The initial step of signal processing is performed on the node at the edge, reducing the burden on a centralized cloud for processing data from multiple sensors. The proposed architecture utilizes the lightweight Message Queue Telemetry Transport (MQTT) communication protocol for seamless data transmission from the node to the local and main brokers. The broker's bridging allows for data backup in case of connection loss. The proposed sensor node is rigorously tested on a motor testbed in a laboratory setup and an industrial setting in a rice industry for validation, ensuring its performance and accuracy in real-world industrial environments. The data analysis and results from both testbed and industrial motors were discussed using vibration analysis for identifying faults. The proposed sensor node is a significant step towards improving the efficiency and reliability of industrial motors through realtime monitoring and early fault detection, ultimately leading to minimized unscheduled downtime and cost savings. [ABSTRACT FROM AUTHOR]

In recent years, photovoltaic (PV) energy production has witnessed overwhelming growth, which has inspired the search for more effective operations. Nevertheless, different PV faults may appear, which leads to various degradation stages. Furthermore, under different irradiance levels, these faults may be misclassified as a healthy mode owing to the high resemblances between them, thus provoking serious challenges in terms of power losses and maintenance costs. Hence, interposing the irradiance variation in grid-connected PV (GCPV) systems modeling is important for monitoring tasks to ensure the effective operation of these systems, to increase their reliability and to prevent false alarms. Therefore, in this paper, a fault detection and diagnosis (FDD) method for the GCPV systems using machine learning (ML) based on principal component analysis (PCA) is proposed in order to ensure the reliability and security of the whole system under irradiance variations. The proposed strategy consists of three main steps: (i) introduce the irradiance variations in PV system modeling because of its great impact on power production; (ii) feature extraction and selection through PCA; and (iii) fault classification using ML techniques. In this study, we generate a database that is used to compare the proposed strategy with the standard strategy (considering a fixed irradiance during FDD), to make, at first, a complete and significant comparative assessment of fault diagnosis and to demonstrate the efficiency of the proposed strategy. The achieved results show the high effectiveness of the proposed one-class classification-based approach to detect and diagnose PV array anomalies, reaching an accuracy up to 99.68%. [ABSTRACT FROM AUTHOR]

During power supply swings, current and voltage waveforms are modulated with additional frequency elements which lead to maloperation of relays. This maloperation may cause cascade tripping of transmission lines, which results in the failure of electrical grid networks. Power Swing Blockers (PSBs) are employed to block and prevent maloperation of distance relays during power swing. Along with power swing, the presence of TCSC compensation in transmission line deteriorates the input signals to relays which make fault classification a complex task. During the PSB blocking period, modulated signals distorted with non-linearities of MOV operation complicate the fault diagnosis functioning. This challenge motivates to develop an algorithm for fault classification during power swings. To address aforementioned challenges, a Wigner Ville energy-based fault classification technique is proposed in this work. A modified 9 bus WSCC system with TCSC compensation is used for testing and validation of the proposed scheme. The scheme is tested for different cases like symmetrical, close-in fault, asymmetrical high resistance fault, and CT saturation, etc. The scheme is also validated against transient conditions like load and capacitor switching. The performance of proposed scheme is compared with existing schemes in literature. The above scheme is also implemented and validated on an Intel Cyclone V SOC FPGA. The results show that the proposed scheme classifies faults accurately during the simulation as well as on hardware platform. [ABSTRACT FROM AUTHOR]

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The on-board equipment of metro communication based train control (CBTC) is facing laborious maintenance problems, and its textual maintenance logs are criticized for having excessively fragmented information, ambiguous semantics and confused categorization, resulting in low classification metrics by traditional textual distributed representation with basic machine learning algorithms. A fault classification method based on bidirectional encoder representations from transformers - convolutional neural network (BERT-CNN) with the focal loss function is proposed to establish the relationship model between the 'fault processing and conclusion' and the 'fault phenomena'. The pre-trained bidirectional encoder representations from transformers (BERT) model is finetuned to fully capture the bidirectional semantics and focus on the keywords to produce better word vectors of the 'fault phenomena'. In order to counteract the classification performance degradation brought by data category imbalance, word vectors are trained using a convolutional neural network (CNN) model with the focal loss function. According to the experimental results conducted by the dataset from an on-board signaling department, the proposed method has the best classification performance among models of BERT-CNN, single BERT and word to vector - CNN (word2vec-CNN) using cross-entropy loss function, and it is also better to correctly classify categories with few samples and contributes to the development of a more comprehensive library of fault cases for intelligent operation and maintenance. [ABSTRACT FROM AUTHOR]

In this paper, the performance analysis of Machine Learning (ML) algorithms for fault analysis in photovoltaic (PV) plants, is given for different algorithms. To make the comparison more relevant, this study is made based on a real dataset. The goal was to use electric and environmental data from a PV system to provide a framework for analysing, comparing, and discussing five ML algorithms, such as: Multilayer Perceptron (MLP), Decision Tree (DT), K-Nearest Neighbors (KNN), Support Vector Machine (SVM) and Light Gradient Boosting Machine (LightGBM). The research findings suggest that an algorithm from the Gradient Boosting family called LightGBM can offer comparable or better performance in fault diagnosis for PV system.

This paper presents a novel approach in which hyperparameter-tuned Machine Learning (ML) classifiers with Optuna is used for fault detection and classification over a power transmission line. In this paper, popular ML models Random Forest (RF), Decision Tree (DT), XGBoost (XGB), and Light Gradient Boosting Machine (LGBM) are used for fault detection and classification. This study uses a two-layer approach for fault detection and classification. An electrical utility and data simulation provided the PMU measurement and recorded data from a simulated grid. The faults had varied impedances and included various fault classes at distinct line locations. The optimal feature from 3-phase current and voltage signals is extracted using Pearson correlation, recursive feature elimination, and univariate feature (t-test) methods. The synthetic minority class oversampling technique (SMOTE) was used to address the issue of imbalanced data. Hyperparameters of the evaluated LGBM classifier is trained with the Optuna. The performance of the proposed classifier is measured in terms of the accuracy, precision, Recall and F1-score metrics. The proposed method outperformed the conventional ML methods.

The presented paper introduces an accurate approach for detecting and classifying parametric or soft faults that affect analog integrated circuits. This technique is based on the use of machine learning algorithm to improve the accuracy and the performance of fault classification process. To achieve this, the real and imaginary frequency responses of output voltage and supply current of the circuits under test (CUT) are used to extract features for both normal and faulty cases. These features are then exploited to train machine learning classifiers, from which the selected one among its equivalents is the quadratic discriminant classifier since it allowed the highest average accuracy score. The faults to be investigated are parametric ones affecting resistors and capacitors values. The proposed approach is validated using three filters circuits that are Sallen-Key band-pass filter, four op-amp biquad high-pass filter, and a leapfrog filter circuit. Obtained results indicate a high classification average accuracy for all circuits that are undergone testing. The proposed approach has provided a highest classification accuracy level comparing to other research works.

Most heating, ventilation, and air-conditioning (HVAC) systems operate with one or more faults that result in increased energy consumption and that could lead to system failure over time. Today, most building owners are performing reactive maintenance only and may be less concerned or less able to assess the health of the system until catastrophic failure occurs. This is mainly because the building owners do not previously have good tools to detect and diagnose these faults, determine their impact, and act on findings. Commercially available fault detection and diagnostics (FDD) tools have been developed to address this issue and have the potential to reduce equipment downtime, energy costs, maintenance costs, and improve occupant comfort and system reliability. However, many of these tools require an in-depth knowledge of system behavior and thermodynamic principles to interpret the results. In this paper, supervised and semi-supervised machine learning (ML) approaches are applied to datasets collected from an operating system in the field to develop new FDD methods and to help building owners see the value proposition of performing proactive maintenance. The study data was collected from one packaged rooftop unit (RTU) HVAC system running under normal operating conditions at an industrial facility in Connecticut. This paper compares three different approaches for fault classification for a real-time operating RTU using semi-supervised learning, achieving accuracies as high as 95.7% using few-shot learning.