Fault Classification Research Articles

Hydraulic system fault diagnosis decoupling method based on 2D time-series modeling and self-attention fusion

Hydraulic systems play a pivotal and extensive role in mechanics and energy. However, the performance of intelligent fault diagnosis models for multiple components is often hindered by the complexity, variability, strong hermeticity, intricate structures, and fault concealment in real-world conditions. This study proposes a new approach for hydraulic fault diagnosis that leverages 2D temporal modeling and attention mechanisms for decoupling compound faults and extracting features from multisample rate sensor data. Initially, to address the issue of oversampling in some high-frequency sensors within the dataset, variable frequency data sampling is employed during the data preprocessing stage to resample redundant data. Subsequently, two-dimensional convolution simultaneously captures both the instantaneous and long-term features of the sensor signals for the coupling signals of hydraulic system sensors. Lastly, to address the challenge of feature fusion with multisample rate sensor data, where direct merging of features through maximum or average pooling might dilute crucial information, a feature fusion and decoupling method based on a probabilistic sparse self-attention mechanism is designed, avoiding the issue of long-tail distribution in multisample rate sensor data. Experimental validation showed that the proposed model can effectively utilize samples to achieve accurate fault decoupling and classification for different components, achieving a diagnostic accuracy exceeding 97% and demonstrating robust performance in hydraulic system fault diagnosis under noise conditions.

A second hit by PFOS exposure exacerbated developmental defects in medaka embryos with a history of ancestral BPA exposure

Bisphenol-A (BPA), a known endocrine-disrupting chemical (EDC) in plastics and resins, has been found to induce heritable health effects in fish and mammals, affecting directly exposed individuals and indirectly their progenies in subsequent generations. It is not clearly understood if subsequent generations of the BPA-exposed ancestors have increased sensitivity to the second hit by the chemicals of emerging concern. To understand this, the present study examined the effects of developmental exposure to perfluorooctanesulfonic acid (PFOS), which has been a global contaminant recently, in embryos whose ancestors were exposed to BPA. Two lineages of medaka (Oryzias latipes) were established: 1) the BPA lineage in which the F0 generation was exposed to 10 μg/L BPA during early development and 2) the control lineage with no BPA exposure in the F0 generation. These lineages were raised up to the F4 generation without further exposure. The embryos of the F4 generation were exposed to PFOS at 0, 0.002, 0.02, 0.2, 2, and 20 mg/L concentrations. Early developmental defects resulting in mortality, delayed hatching, teratogenic phenotypes, and altered gene expression were examined in both lineages. The expression level of genes encoding DNA methyltransferases and genes responsible for oxidative stress defense were determined. Following environmentally relevant PFOS exposure, organisms with a history of BPA exposure displayed significant changes in all categories of developmental defects mentioned above, including increased expression of genes related to oxidative stress, compared to individuals without BPA exposure. The present study provides initial evidence that a history of ancestral BPA exposure can alter sensitivity to developmental disorders following the second hit by PFOS exposure. The variable of ancestral BPA exposure could be considered in mechanistic, medical, and regulatory toxicology, and can also be applied to holistic environmental equity research.

Fault classification and localization of multi-machine-based ieee benchmark test case power transmission lines using optimizable weighted extreme learning machine

Accurate fault diagnosis in transmission lines is crucial to ensure the reliability and stability of power grids. Conventional approaches often rely on expert knowledge or complex feature extraction methods, which are subjective and time-consuming. Additionally, many existing approaches use separate sub-algorithms for fault classification and localization. These are operating independently and sequentially. This research work proposes an innovative method for fault classification and localization in transmission lines using phasor measurement unit (PMU) data. The proposed method employs a Weighted Extreme Learning Machine (WELM) algorithm, which uses the variable data distribution across different fault classes through a weighted approach. The PMU data is generated by simulation using an IEEE 9-bus test system in the MATLAB simulation environment. The Maximal Overlap Discrete Wavelet Transform-based feature extraction technique is applied to derive input feature data to facilitate fault classification and localization. The WELM classifier is also optimized using the Grey Wolf Optimization (GWO) algorithm. The resulting GWO-optimized WELM (GWO-WELM) model, when trained on PMU data, achieves a remarkable fault classification accuracy of 99.83 % and fault localization accuracy of 95.48 %, respectively. These results demonstrate that the GWO-WELM model outperforms commonly used classifiers. Moreover, the proposed model shows robustness by accurately classifying the noisy data with a signal-to-noise ratio (SNR) of 10 dB (achieving 91.5 % accuracy in classification and 88.1 % accuracy in localization, respectively).

Power fault analysis and classification based on correlation analysis and random forest algorithm

The electric power system is one of the indispensable infrastructures in modern society, and its stable operation is crucial for guaranteeing economic development and social security. By calculating the correlation coefficient and drawing the correlation heat map, we find that there is a correlation between the voltage at each location and the occurrence of electrical faults, and at the same time, there is also a certain correlation between various types of electrical faults and the voltage at each location. These results indicate that there are complex and close interrelationships between different locations and between different types in the power system. In order to better identify and classify different types of electrical faults, we used the random forest model for prediction. The model is used to classify whether an electrical fault occurs or not and to determine its category. The prediction results show that the Random Forest achieved 99.63% in classifying whether an electrical fault occurred or not, and 91.26% in classifying the category of electrical faults. This shows that the Random Forest algorithm model is able to detect electrical faults well, as well as judge the category of electrical faults. In summary, this paper provides insight into the operation of the power system by studying the correlation between the voltage at various locations in the power system and the different types of electrical faults and using the random forest model to make predictions. These findings are of great significance in ensuring the stable operation of power systems.

Wavelet scattering and multiclass support vector machine (WS_MSVM) for effective fault classification in transformers: a real-time experimental approach

The dependable operation of the protective system is affected by residual magnetism and magnetic bias during the activation of the transformer. Challenges arise in distinguishing between inrush current and fault current when the transformer circuit breaker switching angle and remnant flux attain specific values. This situation can lead to a lack of sensitivity or failure in the operation of the transformer protection system. Thus, this paper proposes an effective approach for classifying different faults in transformers (internal and non-internal faults) using wavelet scattering and multiclass support vector machine (MSVM) technique (WS_MSVM). The wavelet scattering method begins by transmitting the data through a sequence of wavelet transforms, nonlinear operations, and averaging processes. This series of operations aims to generate low-variance representations of time series data. Wavelet time scattering produces signal representations that remain unaffected by shifts in the input signal, while still preserving the ability to distinguish between internal and non-internal faults. Internal faults are those occurring within the zones of the current transformer (CT) connected on both sides of the transformer. On the other hand, non-internal faults consist of magnetizing inrush current, sympathetic inrush current, and external faults (faults occurring outside the CT zones). The efficacy of the proposed WS_SVM is evaluated on the real-time experimental results obtained from the differential path of a laboratory transformer. The experimental data generated over one power frequency cycle under diverse operating conditions is employed in MATLAB to assess the effectiveness of the proposed WS_SVM algorithm.

A noise generative network to reduce the gap between simulation and measurement signals in mechanical fault diagnosis

Data-driven artificial intelligence models play an important role in mechanical fault diagnosis. Generally, it is difficult to collect relative complete fault samples, which limits the application of artificial intelligence models for complex mechanical systems. To address this issue, numerical model-based or called physical model-based fault sample generation methods attracted many attentions but still an open problem: the difference in fault samples between numerical simulation and measurement of a physical system needs to be well decreased. Therefore, a noise generative network (NGN) for mechanical fault classification is developed. First, the NGN is trained using the simulation and measurement normal samples. The simulation fault samples are further fed into the trained NGN to obtain more solid simulation fault samples with minor differences from measurements of mechanical systems with faults. Second, deep convolutional neural network is trained by solid simulation fault samples, and the test samples of unknown fault types will be finally recognized. Finally, validation experiments using rotation vector reducers, bearings, and rotors classified test samples with average accuracies of 99.5%, 95.7%, and 99.9%, respectively. They indicate that the proposed NGN model effectively reduces the difference between the simulation and measurement samples to lead more precision results for fault classification in mechanical systems.

Intelligent fault diagnosis of rolling bearings in strongly noisy environments using graph convolutional networks

SummaryRolling bearings often function under complex and non‐stationary conditions, where significant noise interference complicates fault diagnosis by obscuring fault characteristics. This paper presents an innovative fault diagnosis technique using graph convolutional networks (GCN) to address these challenges. Vibration signals are first transformed into the frequency domain through fast Fourier transform (FFT), creating a detailed graph where nodes and edges encapsulate fault signals. The GCN method then extracts complex node features from this graph, enabling a classifier, comprising a fully connected layer and Softmax function, to accurately identify fault types. Experimental results demonstrate the superior performance of the proposed GCN‐based fault diagnosis method, achieving an accuracy of 99.79%. This significantly surpasses traditional machine learning methods (85.4%), deep learning models (92.3%), and other graph neural network approaches (94.1%). Notably, the method shows exceptional resilience to noise, maintaining high accuracy even with 20% added noise, underscoring its robustness for practical industrial applications. The transformation of vibration signals into the frequency domain using FFT, followed by constructing a detailed graph structure, enables the GCN to effectively capture and represent intricate fault characteristics, thus enhancing accurate fault classification. These findings highlight the method's practical applicability and potential for deployment in advanced industrial settings characterized by high noise levels and complexity.

Optimization of Variational Mode Decomposition-Convolutional Neural Network-Bidirectional Long Short Term Memory Rolling Bearing Fault Diagnosis Model Based on Improved Dung Beetle Optimizer Algorithm

(1) Background: Rolling bearings are important components in mechanical equipment, but they are also components with a high failure rate. Once a malfunction occurs, it will cause mechanical equipment to malfunction and may even affect personnel safety. Therefore, studying the fault diagnosis methods for rolling bearings is of great significance and is also a current research hotspot and frontier. However, the vibration signals of rolling bearings usually exhibit nonlinear and non-stationary characteristics, and are easily affected by industrial environmental noise, making it difficult to accurately diagnose bearing faults. (2) Methods: Therefore, this article proposes a rolling bearing fault diagnosis model based on an improved dung beetle optimizer (DBO) algorithm-optimized variational mode decomposition-convolutional neural network-bidirectional long short-term memory (VMD-CNN-BiLSTM). Firstly, an improved DBO algorithm named CSADBO is proposed by integrating multiple strategies such as chaotic mapping and cooperative search. Secondly, the optimal parameter combination of VMD was adaptively determined through the CSADBO algorithm, and the optimized VMD algorithm was used to perform modal decomposition on the bearing vibration signal. Then, CNN-BiLSTM was used as the model for fault classification, and hyperparameters of the model were optimized using the CSADBO algorithm. (3) Results: Finally, multiple experiments were conducted on the bearing dataset of Case Western Reserve University, and the proposed method achieved an average diagnostic accuracy of 99.6%. (4) Conclusions: Experimental comparisons were made with other models to verify the effectiveness of the proposed model. The experimental results show that the proposed model based on an improved DBO algorithm optimized VMD-CNN-BiLSTM can effectively be used for rolling bearing fault diagnosis, with high diagnostic accuracy, and can provide a theoretical reference for other related fault diagnosis problems.

Performance Evaluation of a Decentralized Learning Architecture for PCB Defect Classification

Printed circuit board (PCB) defect detection, which is an important task in industrial factories, receives great attention from both researchers and practitioners. To achieve high detection accuracy, the traditional training method requires data collection from multiple industrial factories. However, in practice, factories possess their own data and do not want to share the private data with other participants. Therefore, we introduce a decentralized learning method that makes use the knowledge of clients in the system. By leveraging the federated learning technique, a consensus global detection model can be produced while maintaining data privacy. We have conducted extensive experiments to evaluate the detection performance under various learning methods: federated learning, centralized learning, and local learning. We also compare the detection performance of two well-known detection models: YOLOv5 and YOLOv8. The experimental results show that the federated learning based method yields better detection performance than the local learning.

Fault detection and classification in overhead transmission lines through comprehensive feature extraction using temporal convolution neural network

AbstractFaults in transmission lines cause instability of power system and result in degrading end users sophisticated equipment. Therefore, in case of fault and for the quick restoration of problematic phases, reliable and accurate fault detection and classification techniques are required to categorize the faults in a minimum time. In this work, 500 kV transmission line (Jamshoro‐New Karachi), Sindh, Pakistan has been modeled in MATLAB. The discrete wavelet transform (DWT) has been used to extract features from the transient current signal for different faults in 500 kV transmission line under various parameters such as fault location, fault inception angle, ground resistance and fault resistance and time series data has been obtained for fault classification. Moreover, the temporal convolutional neural network (TCN) is used for fault classification in 500 kV transmission network due to its robust framework. From simulation results, it is found that faults in 500 kV transmission line are classified with 99.9% accuracy. Furthermore, the simulation results of the TCN model compared to bidirectional long short‐term memory (BiLSTM) and Gated Recurrent Unit (GRU) and it has been found that TCN model is capable of classifying faults in 500 kV transmission line with high accuracy due to its ability to handle long receptive field size, less memory requirement and parallel processing due to dilated causal convolutions. Through this work, the meantime to repair of 500 kV transmission line can be reduced.

Few-shot bearing fault diagnosis by semi-supervised meta-learning with graph convolutional neural network under variable working conditions

Aiming at the problems of low accuracy and weak generalization ability in fault diagnosis caused by complex working conditions and limited fault samples of bearings, a few-shot bearing fault diagnosis method by semi-supervised meta-learning with simplifying graph convolutional neural network (semi-meta-sgc) is proposed. Firstly, the time domain signal is converted into the frequency domain by fast Fourier transform (FFT). Secondly, considering each spectrum sample as a node, a k-nearest neighbor (KNN) graph is constructed according to the adjacencies between nodes, thus transforming the bearing fault classification into a graphical node classification. Then, relying on meta-learning, graph convolutional neural network, and semi-supervised learning, a high-accuracy node classifier is obtained by using a small number of training samples to achieve the classification of bearing faults under variable working conditions. Finally, the proposed method is verified by four cases and compared with other methods. The results show that this method has higher recognition accuracy and generalization performance.

Detection and Classification of Physical and Electrical Fault in PV Array System by Random Forest-Based Approach

The importance of solar photovoltaic (PV) systems has increased over the past ten years due to the solar PV industry's explosive growth. To ensure the reliable, safe, and efficient operation of residential PV systems, fault detection is crucial. Early classification of faults can improve PV system performance and reduce damage and energy loss. Many recent studies have focused on classifying and detecting PV faults but most of them are limited to specific reasons like Real-world data can be restricted, unbalanced, or include noise, all of which may decrease the effectiveness of ML models. This paper proposes a method for identifying and classifying both physical and electrical faults in the PV array system applying a machine learning (Random Forest) model to that is trained using a synthetic photovoltaic training database. Make use of a synthetic PV database opens the door to a more precise, effective, and scalable PV system by addressing the limitations of real-world data. MATLAB is used to create a synthetic database while scikit-learn tool in Jupyter Notebook is used to train an ML model are the two main steps in this paper. The performance of the proposed model is compared with the existing ML model and achieves the most effective algorithm offering higher accuracy in detection of 98.6% and classification accuracy is 94.2% for both physical and electrical faults after being successfully tested on real-world datasets and trained on historical data from the PV array system (PV Database).

Microgrid Fault Detection and Classification using HHT and Decision Tree

Microgrid Fault Detection and Classification using HHT and Decision Tree

Digital Twin Modeling for Hydropower System Based on Radio Frequency Identification Data Collection

The safe and steady operation of hydropower generation systems is crucial for electricity output in the grid. However, hydropower stations have complicated interior structures, making defect detection difficult without disassembly inspections. The application of digital modeling to hydropower stations will effectively promote the intelligent transformation of hydropower stations as well as reduce the maintenance costs of the system. This study provides a model of the power generating and transmission system for hydropower plants, with an emphasis on primary equipment and measured data. The model utilizes PSCAD to digitalize state response in hydropower plants with various short-circuit faults. The fault information is identified and learned using the Adaptive Time–Frequency Memory (AD-TFM) deep learning model. It is demonstrated that our proposed method can effectively obtain the fault information through radio frequency identification (RFID). The accuracy of the traditional method is 0.90, while the results for AD-TFM show a fault classification accuracy of 0.92, which is more than enough to identify multiple fault types compared to the existing methods.

Feature selection based on snake optimizer for defect or damage classification in composite rolls via ultrasonic Rayleigh waves

Defects or damages on the surface or subsurface of composite rolls directly affect the quality of the rolled products, and their periodic inspection and accurate identification can provide a reference for the repair or replacement of composite rolls. Ultrasonic Rayleigh waves can non-destructively assess the surface or subsurface of composite rolls, during which similar original signals are perhaps generated for the defects or damages with different types or sizes, making it difficult to distinguish their types. Machine learning is proposed to solve these problems, and feature selection based on the ultrasonic Rayleigh waves mainly utilizes traditional methods such as ReliefF. However, these unsupervised methods have not been combined with specific classification algorithms. In this study, swarm intelligence optimization algorithms, including snake optimizer (SO), dung beetle optimizer (DBO), and grasshopper optimization algorithm (GOA), are investigated based on the support vector machine (SVM). Note that the utilized original signals are received on the left and right sides of defects or damages, and they are processed by segmentation, fast Fourier transform (FFT), and wavelet packet decomposition to build original feature sets. It is illustrated that Rayleigh waves passing through the defects or damages carry more valuable information about the types. In contrast, the ultrasonic waves reflected from the defects or damages can provide information about the types that the former does not contain. Furthermore, for the intelligent classification of defects or damages in composite rolls using ultrasonic Rayleigh waves, SO is more suitable for SVM and has certain advantages.

A new relaying approach for protecting TCSC compensated transmission line connected to DFIG based wind farm

In modern power systems, Thyristor Controlled Series Capacitor (TCSC)-compensated transmission lines are crucial in transporting bulk power from large wind farms. However, the performance of conventional distance relays is adversely impacted by the typical fault characteristics of the Doubly Fed Induction Generator (DFIG) and TCSC. To address this challenge, this paper presents a new relaying algorithm such as Complete Ensemble Empirical Mode Decomposition with Adaptive Noise-based Dominant Mode Algorithm-Hilbert Transform (CEEMDAN-DMA-HT) for fault detection and CEEMDAN assisted Enhanced Jaya Optimization-based Random Vector Functional Link Network (EJAYA-RVFL) for fault classification. In proposed fault detection algorithm, the differential current from both ends of the line is subjected to Complete Ensemble Empirical Mode Decomposition with Adaptive Noise, leading to the extraction of distinct intrinsic mode functions (IMFs). Employing the Dominant Mode Algorithm, the IMF with the highest Pearson correlation coefficient, referred to as the dominant IMF, is identified. Subsequently, a comparison between this dominant mode IMF and the original input signal is conducted after passing the obtained signal through the Hilbert Transform (HT), enabling effective fault detection. The proposed classifier leverages a straightforward feature set that quantifies the energy of each phase and the ground. These features serve as inputs for the proposed classifier, facilitating the categorization of different fault types. To evaluate the effectiveness of the proposed relaying algorithm, several fault and non-fault scenarios are simulated on a test power system using MATLAB/Simulink. The results demonstrate that; the proposed fault detection and classification algorithm are able to detect and classify faults in less than a half-cycle. The proposed method gives 100% fault accuracy for fault detection and 99.97 % accuracy for fault classification. Furthermore, the proposed classifier achieves the performance metrics like Precision (0.998), Recall (0.997) and F1-Score (0.996), providing quantitative insights into its accuracy and dependability. Finally, a comparative analysis is carried out with existing approaches.

An intelligent compound fault diagnosis method using generalized zero-shot model of bearing

Abstract Compound fault occurrence has been unpredictable, especially in industrial scenarios where it is difficult to collect a large number of labeled samples for compound fault. Based on this, this paper proposes a generative generalized zero-shot learning(GZSL) model aimed at synthesizing compound fault features through training with single fault samples. These synthesized features are then used for the recognition of compound fault. Firstly, in order to construct an accurate and effective semantic vector, the semantic generation module and discriminator are utilized to generate the semantics of compound fault. Secondly, a feature extraction module based on CNN is designed to extract various fault features from the two-dimensional time-frequency diagram. Finally, a fault semantic matching module is designed to match the feature vectors of compound faults with the generated fault semantic vectors. This enables the identification of unseen compound fault by computing their maximum similarity. The experimental results demonstrate that the proposed method achieved H scores of 75.83 and 69.24 on two real fault datasets, ensuring the correct classification of compound fault to the greatest extent possible.

Development of overcurrent relay based on wavelet transform for fault detection in transmission line

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. Moreover, the performance of fault detection based on the output signals of Clark’s transform (alpha sequence, beta sequence, and zero sequence current) was compared to the performance of the alternative proposed fault detection method, which is based on the combining factor between alpha and beta sequence current. In addition, the influence of DWT level on fault analysis is also considered and is 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.

Dynamic infrared scanning thermography based on CNN: a novel large-scale honeycomb defect detection and classification technique

Dynamic infrared scanning thermography based on CNN: a novel large-scale honeycomb defect detection and classification technique

BearingFM: Towards a foundation model for bearing fault diagnosis by domain knowledge and contrastive learning

Monitoring bearing failures in production equipment can effectively prevent finished product quality issues and unplanned factory downtime, thereby reducing supply chain uncertainties and risk. Therefore, monitoring bearing failures in production equipment is important for improving supply chain sustainability. Due to the generalization limitations of neural network models, specific models must be trained for specific tasks. However, in real industrial scenarios, there is a severe lack of labeled samples, making it difficult to deploy fault diagnosis models across massive amounts of equipment in workshops. In order to solve the above issue, this paper proposes a cloud-edge-end collaborative semi-supervised learning framework, which provides multi-level computing power and data support for building a foundation model. A data augmentation method based on the bearing fault mechanism is proposed, which effectively preserves the inherent essential characteristics in vibration signals by normalizing frequency and adding noise in specific frequency bands. A novel contrastive learning model is designed, which narrows the distances between positive samples and widens the distances between negative samples in the high-dimensional space through cross comparisons in the time dimension and knowledge dimension, thereby extracting the most essential characteristics from the unlabeled signals. Multiple sets of experiments conducted on four datasets demonstrate that the proposed approach achieves an approximately 98% fault classification accuracy with only 1.2% labeled samples.