Fault Classification Method Research Articles

Fault detection and classification in solar based distribution systems in the presence of deep learning and social spider method

This research proposes an intelligent method for fault detection and classification (FDC) in solar based distribution systems using Generative Adversarial Networks (GANs) and Social Spider method. The suggested method is constructed using the combination of GANs and Social Spider method, which is a hybrid system to increase its capability for the classification purposes. The GANs model is used to detect fault signatures in the system, while the Improved Social Spider method is used to reinforce its training process. The proposed method is evaluated on the big data gathered using the digital twin of a solar based distribution system for different situations of operation. The results show that GANs can detect fault signatures with high accuracy and the Improved Social Spider method can classify the fault types with high accuracy. The proposed method compared with other existing methods and the results show that the suggested method outperforms the existing methods considering accuracy, recall, precision and speed. The proposed method can be used for FDC in solar based distribution systems, and can be developed to other distribution systems.

Intelligent Machinery Fault Diagnosis Method Based on Adaptive Deep Convolutional Neural Network: Using Dental Milling Cutter Malfunction Classifications as an Example

Intelligent machinery fault diagnosis is one of the key technologies for the transformation and competitiveness of traditional factories. Complex production environments make it difficult to maintain good prediction performance using traditional methods. This paper proposes a deep convolutional neural network combined with an adaptive environmental noise method to achieve robust fault classification. The proposed method uses six-dimensional physical signals for data fusion and feature fusion, extracts obvious features and enhances subtle features, and uses continuous wavelets and Gramian angular fields to transform signals with different physical and frequency characteristics into time–frequency maps and two-dimensional images. The fusion technology of different signals can provide comprehensive features for fault prediction, improving upon the blind spots of traditional methods to extract features, and then perform prediction and classification through deep convolutional neural networks. In the experiment, the tool failure classification of the dental milling machine is used as a verification case. The results show that the prediction accuracy of the proposed method is nearly 100%, much better than other comparison methods. In addition, white noise was added in the experiment to verify the noise immunity of the model. The results show that the accuracy of the proposed method is 99%, which is better than other comparison methods in terms of accuracy and robustness, proving the effectiveness of the proposed method for fault diagnosis and classification.

Multiple Defect Classification Method for Green Plum Surfaces Based on Vision Transformer

Green plums have produced significant economic benefits because of their nutritional and medicinal value. However, green plums are affected by factors such as plant diseases and insect pests during their growth, picking, transportation, and storage, which seriously affect the quality of green plums and their products, reducing their economic and nutritional value. At present, in the detection of green plum defects, some researchers have applied deep learning to identify their surface defects. However, the recognition rate is not high, the types of defects identified are singular, and the classification of green plum defects is not detailed enough. In the actual production process, green plums often have more than one defect, and the existing detection methods ignore minor defects. Therefore, this study used the vision transformer network model to identify all defects on the surfaces of green plums. The dataset was classified into multiple defects based on the four types of defects in green plums (scars, flaws, rain spots, and rot) and one type of feature (stem). After the permutation and combination of these defects, a total of 18 categories were obtained after the screening, combined with the actual situation. Based on the VIT model, a fine-grained defect detection link was added to the network for the analysis layer of the major defect hazard level and the detection of secondary defects. The improved network model has an average recognition accuracy rate of 96.21% for multiple defect detection of green plums, which is better than that of the VGG16 network, the Desnet121 network, the Resnet18 network, and the WideResNet50 network.

Fault classification using convolutional neural networks and color channels for time-frequency analysis of acoustic emissions

We present a novel method for real-time fault classification using the time history of acoustic emissions (AEs) recorded from a lab-scale gas turbine operating under normal and faulty conditions across multiple turbine speeds. Time-frequency features are extracted using the continuous wavelet transform, and for each signal, the root mean square (RMS) and kurtosis are calculated. We employ a color mapping technique to combine the time-frequency and statistical features into a single red–green–blue (RGB) image. The red channel is mapped to the time-frequency data, whereas the green and blue channels are mapped to the RMS and kurtosis, respectively. Subsequently, a deep convolutional neural network is trained on the generated images to classify the gas turbine condition. We show that the proposed model can form an online monitoring system using AEs to classify multiple running conditions at various turbine speeds. The methodology not only achieves real-time classification of faults but also minimizes the human intervention in identifying these faults. The datasets and codes used in this paper will be openly available.

Exploring the potential of a machine learning-based methodology for fault classification in inverter-based resource interconnection lines

Considering the increasing penetration of Inverter-Based Resources (IBRs) in electrical power systems, and the observed impacts on protection and fault diagnosis functions due to their atypical operational characteristics, this paper proposes a fault classification methodology that presents high efficiency for IBR interconnection lines. The fault classification methodology, which combines Discrete Wavelet Transform (DWT) with Machine Learning (ML) tree or rule-based methods, is presented and evaluated. The generalization ability of this methodology as well as the influence of the number and representativeness of training instances on the percentage of correct fault classifications are scrutinized. The proposed methodology was also evaluated for systems with different voltage levels, noisy signals, and considering variations in fault types, locations, inception angles, and resistances, in addition to different grid short circuit levels and IBR controls. Finally, comparisons are made with state-of-the-art fault classification methods to prove the impact of IBRs on these methods and to highlight the superiority of the proposed methodology. For the case studies, two systems with different voltage levels and typical connection topologies of IBRs to the grid are modeled in the PSCAD software, and several contingency scenarios are simulated.

Fault Classification in Power Distribution System with Distributed Generation

The primary aim of Power System is to provide uninterrupted power supply to consumers but the performance of Power System is frequently affected by Power Distribution System faults. In order to maintain the continuity of supply and to improve the efficiency of Power System the Power Distribution System fault should be rapidly diagnosed and treated accurately. This paper describes the development of Wavelet-Artificial Neural Network method for classification of Faults in Power Distribution System with Distributed Generation. The disturbances characterized are events from an IEEE 14 bus test system with Distributed Generation. The main purpose of the algorithm is to classify the unsymmetrical faults, single-line-to-ground fault (LG), double line fault (LL) and double line to ground fault (LLG). Along with this some other parameters are also discussed in this work like impact of DG on voltage regulation, impact of DG on harmonics, impact of DG on losses and the impact on short circuit level of the network

Integrated Fault Detection, Classification and Section Identification (I-FDCSI) Method for Real Distribution Networks Using μPMUs

This paper presents a rules-based integrated fault detection, classification and section identification (I-FDCSI) method for real distribution networks (DN) using micro-phasor measurement units (μPMUs). The proposed method utilizes the high-resolution synchronized realistic measurements from the strategically installed μPMUs to detect and classify different types of faults and identify the faulty section of the distribution network. The I-FDCSI method is based on a set of rules developed using expert knowledge and statistical analysis of the generated realistic measurements. The algorithms mainly use line currents per phase reported by the different μPMUs to calculate the minimum and maximum short circuit current ratios. The algorithms were then fine-tuned with all the possible types and classes of fault simulations at all possible sections of the network with different fault parameter values. The proposed I-FDCSI method addresses the inherent challenges of DN by leveraging the high-precision measurements provided by μPMUs to accurately detect, classify, and sectionalise faults. To ensure the applicability of the developed IFDCSI method, it is further tested and validated with all the possible real-time events on a real distribution network and its performance has been compared with the conventional fault detection, classification and section identification methods. The results demonstrate that the I-FDCSI method has a higher accuracy and faster response time compared to the conventional methods and facilitates faster service restoration, thus improving the reliability and resiliency indices of DN.

Optimal Methods for Fault Detection and Classification

Detecting fault in transmission line is very important in order to have a well-functioned power system. This is due to the fact that the system will be distorted if there is fault in the transmission line. Occurrence of fault causes the significant difference in terms of the value of current or voltage in the system. There are a few approaches that can be used in order to detect and classify fault in the transmission line. Two methods of fault detection and classification have been used to be analyzed in order to identify both method accuracy and reliability. The two methods are the Wavelet Transform method and the Fuzzy Logic based method. Both methods show their own advantages and disadvantages after simulation have been done. These methods are later being utilized by combining both to create a better version of fault detection and classification method. In this paper, a combined method of Wavelet Transform and Fuzzy Logic based for fault detection and classification model for power systems is developed and simulated. This combined method is later compared to other method under the same category but different perspective and aspect namely the Radial Basis Function Neural Network. Fuzzy Logic Based method and Radial Basis Function Neural Network falls under Artificial Intelligence category for fault classification method. However, the approach used for both method is significantly different.

A CNN-Architecture-Based Photovoltaic Cell Fault Classification Method Using Thermographic Images

Photovoltaic (PV) cells are a major part of solar power stations, and the inevitable faults of a cell affect its work efficiency and the safety of the power station. During manufacturing and service, it is necessary to carry out fault detection and classification. A convolutional-neural-network (CNN)-architecture-based PV cell fault classification method is proposed and trained on an infrared image data set. In order to overcome the problem of the original dataset’s scarcity, an offline data augmentation method is adopted to improve the generalization ability of the network. During the experiment, the effectiveness of the proposed model is evaluated by quantifying the obtained results with four deep learning models through evaluation indicators. The fault classification accuracy of the CNN model proposed here has been drawn by the experiment and reaches 97.42%, and it is superior to that of the models of AlexNet, VGG 16, ResNet 18 and existing models. In addition, the proposed model has faster calculation, prediction speed and the highest accuracy. This method can well-identify and classify PV cell faults and has high application potential in automatic fault identification and classification.

Identification of overhead line fault traveling wave and interference clutter based on convolution neural network and random forest fusion

High-speed traveling wave acquisition devices often use a mutation start algorithm with a low threshold value, which can collect a large number of interference clutters. If the devices automatically screen out a flashover fault traveling wave from a massive traveling wave, the traveling wave fault location performance could be improved. In this paper, a fault and interference classification method based on the random forest algorithm, which uses a convolutional neural network as a supervised feature extractor of traveling wave data, is proposed. First, one-dimensional traveling wave data are mapped to a two-dimensional matrix by dividing the information section, and a gray image is used to characterize it. Next, a two-dimensional convolution neural model of traveling wave data is constructed to realize the self-learning of traveling wave data characteristics, and the waveform feature sequence of traveling wave data is obtained. Then, on this basis, a random forest algorithm is used to realize automatic identification and screening of flashover fault traveling waves. Finally, a large number of tests on the simulation and measured data show that the proposed combined algorithm based on the feature extraction and classification of stochastic forest algorithm has better recognition effect of fault and interference than the traditional support vector machine, single stochastic forest algorithm, and convolutional neural network.

Shunt faults detection and classification in electrical power transmission line systems based on artificial neural networks

PurposeThis study aims to focus on an adaptive method for fault detection and classification of fault types that trigger in three-phase transmission lines using artificial neural networks (ANNs). The proposed scheme can detect and classify several types of faults, including line-to-ground, line-to-line, double-line-to-ground, triple-line and triple-line-to-ground faults.Design/methodology/approachThe fundamental components of three-phase current and voltage were used as inputs in the ANNs. An analysis of the impact of variations in the fault resistance, fault type and fault inception time was conducted to evaluate the ANNs performance. The survey compares the performance of the multi-layer perceptron neural network (MLPNN) and Elman recurrent neural network trained with the backpropagation learning technique to improve each of the three phases of the fault detection and classification process. A detailed analysis validates the choice of the ANNs architecture based on the variation in the number of hidden neurons in each step.FindingsThe mean square error, root mean square error, mean absolute error and linear regression are measured to improve the efficiency of the ANN models for both fault detection and classification. The results indicate that the MLPNN can detect and classify faults with a satisfactory performance.Originality/valueThe smart adaptive scheme is fast and accurate for fault detection and classification in a single circuit transmission line when faced with different conditions and can be useful for transmission line protection schemes.

Hydraulic Rock Drill Fault Classification Using X−Vectors

Hydraulic rock drills are widely used in drilling, mining, construction, and engineering applications. They typically operate in harsh environments with high humidity, large temperature differences, and vibration. Under the influence of environmental noise and operational patterns, the distributions of data collected by sensors for different operators and equipment differ significantly, which leads to difficulty in fault classification for hydraulic rock drills. Therefore, an intelligent and robust fault classification method is highly desired. In this paper, we propose a fault classification technique for hydraulic rock drills based on deep learning. First, considering the strong robustness of x−vectors to the features extracted from the time series, we employ an end−to−end fault classification model based on x−vectors to realize the joint optimization of feature extraction and classification. Second, the overlapping data clipping method is applied during the training process, which further improves the robustness of our model. Finally, the focal loss is used to focus on difficult samples, which improves their classification accuracy. The proposed method obtains an accuracy of 99.92%, demonstrating its potential for hydraulic rock drill fault classification.

Impedance Ground Faults Detection and Classification Method for DC Microgrid

Impedance Ground Faults Detection and Classification Method for DC Microgrid

Fault diagnosis of EHA with few-shot data augmentation technique

As an emerging object in aerospace actuators, electro-hydrostatic actuator (EHA) has the advantages of heavy load capacity and high reliability. An EHA fault diagnosis method based on a few-shot data augmentation technique is proposed to diagnose and isolate possible faults. The sensitive parameters of typical failure modes are demonstrated based on the mathematical model of EHA. By converting multi-dimensional experimental data into two-dimensional grayscale data and extracting local features, the time series characteristics and correlation between different signals can be highlighted. The Wasserstein deep convolutional generative adversarial network (WDCGAN) is used to enhance the EHA small sample data. The diagnostic model WDCGAN-stacked denoised auto encoder (SDAE) combined with WDCGAN and SDAE is proposed to differentiate between multiple types of EHA failures. Compared with the five commonly used fault classification methods, the proposed method can effectively identify the typical fault modes of EHA, with the highest accuracy of fault classification and strong feature extraction ability.

A fault diagnosis method for building electrical systems based on the combination of variational modal decomposition and new mutual dimensionless

The fault diagnosis of building electrical systems are of great significance to the safe and stable operation of modern intelligent buildings. In this paper, it has many problems, such as various fault types, inconspicuous fault characteristics, uncertainty of fault type and mode, irregularity, unstable signal, large gap between fault data classes, small gap between classes and nonlinearity, etc. A method of building electrical system fault diagnosis based on the combination of variational mode decomposition and mutual dimensionless indictor (VMD-MDI) and quantum genetic algorithm-support vector machine (QGA-SVM) is proposed. Firstly, the method decomposes the original signal through variational modal decomposition to obtain the optimal number of Intrinsic Mode Function(IMF) containing fault feature information. Secondly, extracts the mutual dimensionless indicator for each IMF. Thirdly, the optimal penalty coefficient C of the support vector machine and the parameter gamma (gamma) in the radial basis kernel function are selected by the quantum genetic algorithm. Finally, SVM optimized by the QGA is used to identify and classify the faults. By applying the proposed method to the experimental platform data of building electrical system, and compared with the traditional feature extraction method Empirical Mode Decomposition (EMD), Singular Value Decomposition(SVD), Local Mean Decomposition(LMD). And compared with traditional SVM, Genetic Algorithm optimized Support Vector Machine (GA-SVM), One-Dimensional Convolutional Neural Network (1DCNN) for fault classification methods. The experimental results show that the method has better effect and higher accuracy in fault diagnosis and classification of building electrical system. Its average test accuracy can reach 91.67%.

Deep Learning-Based Fault Diagnosis for Marine Centrifugal Fan

Abstract Marine centrifugal fans usually work in harsh environments. Their vibration signals are non-linear. The traditional fault diagnosis methods of fans require much calculation and have low operating efficiency. Only shallow fault features can be extracted. As a result, the diagnosis accuracy is not high. It is difficult to realize the end-to-end fault diagnosis. Combining the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and lightweight neural network, a fault classification method is proposed. First, the CEEMDAN can decompose the vibration signal into several intrinsic modal functions (IMF). Then, the original signals can be transformed into 2-D images through pseudo-colour coding of the IMFs. Finally, they are fed into the lightweight neural network for fault diagnosis. By embedding a convolutional block attention module (CBAM), the ability of the network to extract critical feature information is improved. The results show that the proposed method can adaptively extract the fault characteristics of a marine centrifugal fan. While the model is lightweight, the overall diagnostic accuracy can reach 99.3%. As exploratory basic research, this method can provide a reference for intelligent fault diagnosis systems on ships.

Defect classification for specular surfaces based on deflectometry and multi-modal fusion network

Automated defect inspection for specular surfaces is still a challenge in the manufacturing industry because of their specular reflection property. Deflectometry provides surface information based on the captured fringe patterns through the reflection of the specular surfaces and has been widely applied in defect detection for specular surfaces. Conventional methods combined deflectometry with machine learning approaches, but the hand-crafted features need to be defined for each specific task. Combined with the deep neural network, the input images are obtained from deflectometry, and the network completes the identification of the defects. Nevertheless, conventional deep-learning-based defect inspection methods approached the problem as a binary classification, or only certain obvious defects can be correctly classified. In this study, we generated and released, for the first time, to the best of our knowledge, the benchmark dataset named SpecularDefect9 with various defects for specular surfaces, and the classification accuracy of some kinds of defects may be low with only one kind of input image. To classify all kinds of defects accurately, the proposed method applied the light intensity contrast map combined with the original captured fringe pattern as the input of the network, and a fusion network was introduced to extract features from multi-modal inputs. Experimental results based on the released benchmark dataset verified the effectiveness and robustness of the proposed multi-modal defect classification method.

Transient Fault Signal Identification of AT Traction Network Based on Improved HHT and LSTM Neural Network Algorithm

This paper aims to address the difficult to pinpoint fault cause of the full parallel AT traction power supply system with special structure. The fault characteristics are easily covered up, and high transition impedance only affects the singularity of the wavehead, making the traveling waves hard to identify. Moreover, the classification accuracy of the traditional time-frequency analysis method is not sufficiently high to distinguish precisely. In this paper, a fault classification method of traction network based on single-channel improved Hilbert–Huang transform and deep learning is proposed. This method extracts effective fault features directly from the original fault signals and classifies the fault types at the same time. The accuracy of data categorization is increased by directly applying the Hilbert–Huang transform to fault signals to extract transient fault features and produce one-dimensional feature data, which are analyzed by the time-frequency energy spectrum. Using the similarity recognition method of long-short-term memory neural network, the extracted high-frequency one-dimensional feature data are trained and tested to classify fault signals more accurately. In order to verify the effectiveness of this method, several kinds of short-circuit and lightning strike faults are continuously simulated and verified in this paper. Considering various fault conditions and factors, the proposed improved HHT+LSTM method is compared with the LSTM method for direct processing of the original signals. The improved HHT + LSTM classification algorithm achieves an accuracy of 99.99%.

A Robust Fault Classification Method for Streaming Industrial Data Based on Wasserstein Generative Adversarial Network and Semi-Supervised Ladder Network

With the development of modern information technology, the collection, storage and transmission of information in the process industry has been gaining popularity. However, the massive streaming industrial data obtained in real time has some non-ideal characteristics, such as lack of labels and missing values, which greatly increases the difficulty of process monitoring in process industry. Therefore, a robust semi-supervised fault classification method is proposed in this paper. First, Wasserstein generative adversarial network (WGAN) and enhanced minimal gated unit (EMGU) are integrated to complete the missing data imputation of the incomplete unlabeled streaming industrial data, and then semi-supervised ladder network (SLN) is trained with the imputed unlabeled data and complete labeled data for fault classification. A case study on the hot rolling process demonstrates that the proposed method shows outstanding modeling and classification performance in lack of labeled data and missing data, compared to the other state-of-art deep learning methods.

An Imbalanced Data Augmentation and Assessment Method for Industrial Process Fault Classification With Application in Air Compressors

An Imbalanced Data Augmentation and Assessment Method for Industrial Process Fault Classification With Application in Air Compressors