Fault Classification Method Research Articles

Automatic Classification of Rotating Rectifier Faults in Brushless Synchronous Machines

This paper presents an advanced automatic fault classification method for detecting rotating rectifier faults in brushless synchronous machines (BSMs). The proposed approach employs a multilayer perceptron (MLP) neural network to classify the operational states of the rotating rectifier, including healthy conditions and common fault types: open-diode (OD), shorted-diode (SD), and open-phase (OP). Key machine measurements, available on an ordinary basis in the industry, such as active power (P), reactive power (Q), stator voltage (U), and excitation current (Ie), are used as inputs for this model, allowing for non-invasive, real-time fault detection. This model achieved an overall classification accuracy of 93.4%, with a precision of 94.9% for fault detection and strong recall performance across multiple fault types. The neural network’s robustness is enhanced by advanced data processing techniques, including Gaussian filtering and class balancing through the synthetic minority over-sampling technique (SMOTE). Experimental testing on a modified 5-kVA BSM setup, where rectifier faults were systematically induced, was used to train the network and validate the model’s performance. This method provides a promising tool for real-time condition monitoring of BSMs, improving machine reliability and minimizing downtime in industrial applications.

Simulation of Faults in the Yeywa-Thaphaywa High Voltage Transmission Line using Fuzzy Logic

Faults in transmission networks frequently undermine the reliability and stability of electrical power supply, causing load shedding and posing risks to power system equipment. Effective fault classification is crucial to minimize power outages and ensure timely restoration. This study presents a fault classification method for transmission lines using fuzzy logic. The classification utilizes three-phase feeder currents, neutral current, and phase voltages as inputs to the Fuzzy Inference System (FIS). The proposed method accurately classifies the various types of faults in a high voltage transmission network, including line-to-ground fault, double line-to-ground fault, line-to-line fault, and three-phase faults. Numerous test cases demonstrate the technique's high accuracy. The simulations are carried out in MATLAB/SIMULINK using Sim Power Systems and the Fuzzy Logic Toolbox (MATLAB 2016a).

Research on tire internal defect identification method based on deep learning

As an important part of automobile, the safety and durability of tire have attracted more and more attention. Tire defect detection is an important link to ensure tire quality, while traditional detection methods have problems such as low efficiency, high false detection rate and high labor intensity. Therefore, this study aims to develop an efficient and accurate tire defect identification and classification technique to improve the efficiency and accuracy of tire inspection. In this paper, based on YOLO (You Only Look Once) v5 algorithm, tire defect recognition and classification are studied. Firstly, the data sets containing various types of tire defects were collected and sorted out, and the data sets were preprocessed. Then, by constructing, training and optimizing the YOLOv5 tire defect recognition model, the fast and accurate recognition of tire defects was realized. Finally, the performance of the model was evaluated through experiments and compared with the existing methods. The experimental results show that the tire defect recognition and classification method based on YOLOv5 proposed in this study has high accuracy. Compared with traditional methods, this method has a significant improvement in detection speed and accuracy.

Application of SPNGO-VMD-SVM in rolling bearing fault diagnosis

Abstract Traditional bearing fault feature extraction and fault classification methods have low recognition accuracy and limited recognition capability in noisy environments. To address this problem, this paper proposes an improved Northern Goshawk Algorithm to optimize the variational modal decomposition (VMD) and support vector machine (SVM) to achieve bearing fault diagnosis. Firstly, to overcome the disadvantages of the Northern Goshawk Algorithm, such as easy fall into local optimal solutions and slow convergence speed, the Sine Cosine Strategy (SCA) and Position Optimisation Search Algorithm (POS) are introduced to optimize the Northern Goshawk Algorithm. The improved algorithm is called SPNGO for short. The superiority of the SPNGO algorithm is proved by comparing different algorithms. Then, SPNGO-VMD is used to adaptively decompose the vibration signals of faulty bearings and generate multiple modal components IMF. The effective IMF components are screened based on the craggy principle to reconstruct the signals. Finally, the reconstructed feature signals are input into SPNGO-SVM for fault classification and compared with other fault diagnosis models. The research results show that the proposed SPNGO-VMD-SVM fault diagnosis model is compared with the data set of Case Western Reserve University and the data set of Xi’an Jiaotong University. The diagnostic accuracy of the two groups of experiments can reach 96.67% and 98.89% respectively, and the intelligent diagnosis of different fault states of rolling bearings can be realized simultaneously.

Defect classification and quantification method based on AC magnetic flux leakage time domain signal characteristics

Pipelines play a crucial role in industries such as petroleum and nuclear power, where non-destructive testing is essential. Magnetic flux leakage testing methods are widely used for pipeline inspection due to their ability to detect both internal and external defects. However, accurately classifying and evaluating the size of such defects poses challenges due to the complex coupling relationship between ferromagnetic materials and defect magnetic fields. This paper proposes a method for defect classification and quantification based on the time domain characteristics of AC magnetic flux leakage signals. Firstly, the paper explores the shielding effects caused by transient high magnetic permeability within the material on signals from internal defects. It analyzes the differences in signals between internal and external defects. Then, based on the nonlinear attributes of defect signals, the paper proposes a defect classification method based on derivatives analysis of the windowed time-domain signal. Moreover, the study finds that rising time and zero-crossing time can be used to assess the depth of internal and external defects separately, which can decouple the width and depth. Finally, experimental validation confirms the effectiveness of defects classification and quantification. This paper provides a feasible method for evaluating the defect of ferromagnetic materials.

An AI-based fault detection and classification method for hybrid parallel HVAC/HVDC overhead transmission lines

In this paper, a novel method is presented to detect and classify the AC, DC, and AC/DC intersystem faults in hybrid parallel HVAC/HVDC overhead transmission lines (HPOTLs) on the same tower. In HPOTLs lines the presence of electromagnetic coupling between the AC and DC conductors induces components in the AC and DC lines which can affect the performance of their protection systems. To tackle these challenges, a protection method based on the decision tree (DT) algorithm and a fault energy index (FEI) is proposed for HPOTLs. The proposed method uses the sending-end voltages and currents of the AC and DC lines and some transformations to calculate the FEIs. The fault detection and classification algorithms and their criteria are derived by the DT algorithm. PSCAD and MATLAB software are used for HPOTL modeling, and voltage and current signals processing, respectively. Simulation results show that the proposed protection method can detect and classify different faults (AC and DC line faults and AC/DC intersystem faults) accurately, and is able to discriminate between transmission line internal and external faults. Simplicity, high speed and accuracy, robustness to the fault resistance and noise, and not needing a telecommunication channel are important features of the proposed method.

Enhanced fault classification in inverter-fed transmission lines using deep learning

Abstract The conventional power transmission system with synchronous generators is protected with phase angle-based approach that decides which type of faut is occurred on the transmission line system. However, the phase angle-based fault classification method is no longer applicable for systems that have integrated Inverter Based Resources (IBR). The control technology employed for grid integration of renewable sources controls the fault current magnitude depending upon Grid code requirements which results in mal operation of relay. A new fault classification scheme is proposed in this paper which aims at precise faulty phase selection using deep learning techniques. Deep learning techniques have gained significance in the field of protection because of the big data availability from Phasor Measurement Units (PMU’s). This paper describes a new approach of fault classification for systems connected to renewables with the application of Long Short-Term Memory (LSTM) Deep learning network.

Enhancing power equipment defect identification through multi-label classification methods

Accurate identification and classification of equipment defects are essential for assessing the health of power equipment and making informed maintenance decisions. Traditional defect classification methods, which rely on subjective manual records and intricate defect descriptions, have proven to be inefficient. Existing approaches often evaluate equipment status solely based on defect grade. To enhance the precision of power equipment defect identification, we developed a multi-label classification dataset by compiling historical defect records. Furthermore, we assessed the performance of 11 established multi-label classification methods on this dataset, encompassing both traditional machine learning and deep learning methods. Experimental results reveal that methods considering label correlations exhibit significant performance advantages. By employing balanced loss functions, we effectively address the challenge of sample imbalance across various categories, thereby enhancing classification accuracy. Additionally, segmenting the power equipment defect classification task, which involves numerous labels, into label recall and ranking stages can substantially improve classification performance. The dataset we have created is available for further research by other scholars in the field.

Research on Welding Defect Classification Methods Based on Convolutional Neural Networks

To address the issues of low detection and identification rates and inaccurate classification of welding defects, this paper proposes a welding defect classification method based on convolutional neural networks. By modifying the ResNet101 backbone network and incorporating a multi-head attention mechanism (MHA), the method enhances the focus on regions of interest in welding defect images, thereby improving the ability to extract defect features. This approach reduces the chances of missed detections and false positives. By analyzing the effects of different optimizers and dynamic factors on recall (M) and precision (N), the optimal optimizer and dynamic factor are identified. The algorithm is validated on the JPEGWD dataset. Experimental results show that the recall (M) reaches 0.9634, representing a 4.88% improvement, and the precision (N) reaches 0.9875, representing a 3.81% improvement. The mean average precision (mAP) is 0.9817, reflecting a 5.98% improvement. These results effectively enhance the recognition rate of welding defects and reduce the probability of missed detections and false positives.

Planetary gearbox fault classification based on tooth root strain and GAF pseudo images

Traditional signal processing methods based on acceleration signals can determine whether a fault has occurred in a planetary gearbox. However, acceleration signals are severely affected by interference, causing difficulties in fault identification. This study proposes a gear fault classification method based on root strain and pseudo images. Firstly, fiber optic sensors are employed to directly acquire strain data from the ring gear root. Next, the strain signals are preprocessed using resampling and a time-domain synchronous averaging algorithm. The processed signals are encoded into two-dimensional images using Gramian Angular Fields (GAF). Then, CN-EfficientNet with contrast learning is proposed to analyze and extract deeper fault features from the image texture features. In the classification experiments for different types of faults, the accuracy reached 96.84%. The results indicate that the method can effectively accomplish the task of fault classification in planetary gearboxes. Comparative experiments with other common classification models further indicate the superior performance of the proposed learning model. Visualization based on Grad-CAM provides interpretability for the fault recognition network’s results and reveals the underlying mechanism for its excellent classification performance.

Fault Monitoring and Analysis of Distributed New Energy Grids Using Simulation Data Models

Data analysis is essential for fault identification and detection in smart grids in order to maintain grid monitoring. Many DL algorithms have been developed recently for data analysis applications related to smart grids. To resolve these challenges, the study suggests a Deep Neural Network (DNN) for data-driven fault location detection and type of fault classification by exploiting the Modified Sand Cat Swarm Optimization (MSCSO) optimization. Here, the DNN is used to diagnose the faults and determine the position of the sites. Numerous synthetic field data sets derived from simulated models of different transmission line types are used for training and testing. The position and type of faults are predicted by the DNN classifier based on the fault signal features, in which the DNN weights are ideally tuned using a novel MSCSO optimization method that is the improved concept of Sand Cat Swarm Optimization (SCSO). Lastly, MATLAB/Simulink is used to implement the suggested DNN-based method for fault classification and its localization in transmission line. An intellectual IEEE 6-node network model is utilized to confirm the efficacy and reliability of these methods. The outcomes demonstrated its effectiveness in giving the system operator precise and detailed analytics for fault identification.

Smart meter fault classification method based on generated distribution optimization under imbalanced dataset

Smart meter fault classification method based on generated distribution optimization under imbalanced dataset

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).

A distinguished deep learning method for gear fault classification using time–frequency representation

Fault diagnosis of gearboxes has attracted increasing interest in recent decades due to their ubiquity and importance in the industry. Modern research trends focus on developing a diagnosis system that works automatically with the application of artificial intelligence. These previous studies have used the Deep Learning (DL) network without adequately addressing noise of the input data, requiring more data to achieve effective training. Thus, this work proposes a novel Transfer Learning method using the time–frequency representation of gear vibration signals, which enables more accurate classification in complex working conditions and reduces necessary input data to train. Using fine-tuning techniques proposed in this paper requires only a limited data set while ensuring acceptable classification results. An experiment test rig within different gear faults and load conditions was set up to evaluate the algorithm’s effectiveness.

Efficient Defect Classification Using Few-Shot Image Generation and Self-Attention Fused Convolution Features

Although deep learning has been proven to significantly outperform most traditional methods in the classification of large-scale balanced image datasets, collecting enough samples for defect classification is extremely time-consuming and costly. In this paper, we propose a lightweight defect classification method based on few-shot image generation and self-attention fused convolution features. We constructed a 4-class dataset using welding seam images collected from a solar cell module packaging production line. To address the issue of limited defect samples, especially for classes with less than 10 images, we implemented two strategies. Geometric enhancement techniques were first used to extend the defective images. Secondly, multi-scale feature fusion Generative Adversarial Networks (GANs) were utilized to further enhance the dataset. We then performed the feature-level fusion of convolution neural networks and self-attention networks, achieving a classification accuracy of 98.19%. Our experimental results demonstrate that the proposed model performs well in small sample defect classification tasks. And, it can be effectively applied to product quality inspection tasks in industrial production lines.

Defect identification method for stainless steel welded pipes based on eddy current testing

Abstract A defect identification and classification method for 316 stainless steel welded pipes is proposed based on eddy current testing technology. Initially, this approach acquires eddy current signals from steel pipes, applies Empirical Mode Decomposition (EMD) to derive Intrinsic Mode Functions (IMFs), chooses the principal IMF based on interrelationships, and extracts time-frequency domain characteristic parameters from the selected IMF. To enhance model recognition efficiency, Principal Component Analysis (PCA) is employed to reduce the dimensionality of the feature vector set. Ultimately, a Support Vector Machine (SVM) is utilized to identify and classify weld defects. The results indicate that this method is highly accurate in identifying defects in 316 stainless steel welded pipes.

ViTR-Net: An unsupervised lightweight transformer network for cable surface defect detection and adaptive classification

As a crucial load-bearing component of the cable-stayed bridge, the cable requires surface defect detection to maintain its safety. The current deep-learning-based stay-cable surface defect detection still faces challenges, including insufficient actual defect samples, cumbersome labeling processes, and an effective method for further evaluation and classification of defects. To address these problems, an unsupervised vision transformer-based lightweight reconstruction network called ViTR-Net is proposed, which only uses normal stay-cable surface images to achieve end-to-end localization of defect areas in the image. ViTR-Net is a generative-based method that uses a hybrid network of convolutional neural networks and visual transformers as a feature extraction network. A feature correction fusion module is designed by integrating differential convolution and attention mechanisms to compensate for potential detail loss caused by feature extraction. Subsequently, a severity index for defects is established to assess the severity of various defects, and the detected defects are adaptively classified using the affinity propagation clustering algorithm. ViTR-Net achieves the highest defect detection accuracy and exquisite defect segmentation results on a realistic cable defect dataset through experimental validation. The proposed model can realize the surface defect assessment and adaptive classification of the stay cable, which has a good application prospect in the actual maintenance process of the stay cable.

Advanced Image Analytics for Mobile Robot-Based Condition Monitoring in Hazardous Environments: A Comprehensive Thermal Defect Processing Framework.

In hazardous environments like mining sites, mobile inspection robots play a crucial role in condition monitoring (CM) tasks, particularly by collecting various kinds of data, such as images. However, the sheer volume of collected image samples and existing noise pose challenges in processing and visualizing thermal anomalies. Recognizing these challenges, our study addresses the limitations of industrial big data analytics for mobile robot-generated image data. We present a novel, fully integrated approach involving a dimension reduction procedure. This includes a semantic segmentation technique utilizing the pre-trained VGG16 CNN architecture for feature selection, followed by random forest (RF) and extreme gradient boosting (XGBoost) classifiers for the prediction of the pixel class labels. We also explore unsupervised learning using the PCA-K-means method for dimension reduction and classification of unlabeled thermal defects based on anomaly severity. Our comprehensive methodology aims to efficiently handle image-based CM tasks in hazardous environments. To validate its practicality, we applied our approach in a real-world scenario, and the results confirm its robust performance in processing and visualizing thermal data collected by mobile inspection robots. This affirms the effectiveness of our methodology in enhancing the overall performance of CM processes.

Multi‐view synergistic enhanced fault recording data for transmission line fault classification

AbstractFault recorded data has been proven to be effective for fault diagnosis of overhead transmission lines. Utilizing deep learning to mine potential fault patterns in fault recording data is an inevitable trend. However, it is usually difficult to obtain massive labeled fault recording data, which results in deep learning‐based fault diagnosis models not being adequately trained. Although data augmentation methods provide ideas for expanding the training data, existing data augmentation algorithms (e.g. random perturbation‐based augmentation) may lead to distortion of multi‐view data, that is, time domain data and frequency domain data of the fault recorded data, which results in the inconsistency of physical properties and statistical distributions of the generated data and the actual recording data, and misguides the training of the models. Hence, this study proposes a transmission line fault classification method via the multi‐view synergistic enhancement of fault recording data. The methodology proposes to start with a synergistic enhancement of multi‐view data such as time and frequency domains of fault recording data, and utilizes contrastive learning to further improve the performance of the fault classification model while ensuring that the generated data is not distorted. Experimental results on three real‐world datasets validate the effectiveness of the proposed method.

Enhancing Fault Identification, Classification and Location Accuracy in Transmission Lines

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.