scholarly journals Microgrid Fault Detection and Classification: Machine Learning Based Approach, Comparison, and Reviews

Energies ◽  
2020 ◽  
Vol 13 (13) ◽  
pp. 3460 ◽  
Author(s):  
Shahriar Rahman Fahim ◽  
Subrata K. Sarker ◽  
S. M. Muyeen ◽  
Md. Rafiqul Islam Sheikh ◽  
Sajal K. Das
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Classification Model ◽  
Superior Performance ◽  
Fault Classification ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Fault Detection And Classification ◽  
Proposed Model ◽  
The Individual

Accurate fault classification and detection for the microgrid (MG) becomes a concern among the researchers from the state-of-art of fault diagnosis as it increases the chance to increase the transient response. The MG frequently experiences a number of shunt faults during the distribution of power from the generation end to user premises, which affects the system reliability, damages the load, and increases the fault line restoration cost. Therefore, a noise-immune and precise fault diagnosis model is required to perform the fast recovery of the unhealthy phases. This paper presents a review on the MG fault diagnosis techniques with their limitations and proposes a novel discrete-wavelet transform (DWT) based probabilistic generative model to explore the precise solution for fault diagnosis of MG. The proposed model is made of multiple layers with a restricted Boltzmann machine (RBM), which enables the model to make the probability reconstruction over its inputs. The individual RBM layer is trained with an unsupervised learning approach where an artificial neural network (ANN) algorithm tunes the model for minimizing the error between the true and predicted class. The effectiveness of the proposed model is studied by varying the input signal and sampling frequencies. A level of considered noise is added with the sample data to test the robustness of the studied model. Results prove that the proposed fault detection and classification model has the ability to perform the precise diagnosis of MG faults. A comparative study among the proposed, kernel extreme learning machine (KELM), multi KELM, and support vector machine (SVM) approaches is studied to confirm the robust superior performance of the proposed model.

Support vector machine and K-nearest neighbour for unbalanced fault detection

2014 ◽  
Vol 20 (1) ◽  
pp. 65-75 ◽  
Author(s):  
Ashkan Moosavian ◽  
Hojat Ahmadi ◽  
Babak Sakhaei ◽  
Reza Labbafi
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Experimental System ◽  
Performance Comparison ◽  
Fault Classification ◽  
Support Vector ◽  
Rotating Machines ◽  
Vibration Signals ◽  
Content Type ◽  
Intelligent Approach

Purpose – The purpose of this paper is to develop an appropriate approach for detecting unbalanced fault in rotating machines using KNN and SVM classifiers. Design/methodology/approach – To fulfil this goal, a fault diagnosis approach based on signal processing, feature extraction and fault classification, was used. Vibration signals were acquired from a designed experimental system with three conditions, namely, no load, balanced load and unbalanced load. FFT technique was applied to transform the vibration signals from time-domain into frequency-domain. In total, 29 feature parameters were extracted from FFT amplitude of the signals. SVM and KNN were employed to classify the three different conditions. The performances of the two classifiers were obtained under different values of their parameter. Findings – The experimental results show the potential application of SVM for machine fault diagnosis. Practical implications – The results demonstrate that the proposed approach can be used effectively for detecting unbalanced condition in rotating machines. Originality/value – In this paper, an intelligent approach for unbalanced fault detection was proposed based on supervised learning method. Also, a performance comparison was made between KNN and SVM in fault classification. In addition, this approach gave a high level of classification accuracy. The proposed intelligent approach can be used for other mechanical faults.

Fault Classification Model of Rotor Based on Support Vector Machine

2011 ◽  
Vol 66-68 ◽  
pp. 1982-1987
Author(s):  
Wei Niu ◽  
Guo Qing Wang ◽  
Zheng Jun Zhai ◽  
Juan Cheng
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Rotating Machinery ◽  
Classification Model ◽  
Fault Classification ◽  
Support Vector ◽  
Vibration Signals ◽  
Good Classification ◽  
Speed Change ◽  
Diagnosis Model

The vibration signals of rotating machinery in operation consist of plenty of information about its running condition, and extraction and identification of fault signals in the process of speed change are necessary for the fault diagnosis of rotating machinery. This paper improves DDAG classification method and proposes a new fault diagnosis model based on support vector machine to solve the problem of restricting the rotating machinery fault intelligent diagnosis due to the lack of fault data samples. The testing results demonstrate that the model has good classification precision and can correctly diagnose faults.

Rolling bearing fault detection approach based on improved dispersion entropy and AFSA optimized SVM

2020 ◽  
pp. 002072092094058
Author(s):  
Wuqiang Liu ◽  
Jinxing Shen ◽  
Xiaoqiang Yang
Keyword(s):  
Fault Detection ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Classification Model ◽  
Coarse Grained ◽  
Fault Classification ◽  
Support Vector ◽  
Artificial Fish Swarm Algorithm ◽  
Artificial Fish Swarm

The support vector machine (SVM) does not have a fixed parameter selection method and the manual selection of parameters is difficult to determine the validity, which affects the accuracy of recognition. simultaneously, The existing coarse-grained approach cannot effectively analyze the high-frequency components of time series. In view of the shortcomings of the above method, we put forward a new technique of rolling bearings for fault detection, which combines wavelet packet dispersion entropy (WPDE) and artificial fish swarm algorithm (AFSA) optimize support vector machines (AFSA-SVM). First of all, wavelet packet is devoted to decompose the original vibration signal into components of different frequency bands. Secondly, the dispersion entropy (DE) are calculated for each of the obtained frequency band components to acquire more comprehensive and complete fault information. Afterward, Input feature samples into the SVM model for training, and AFSA is used to optimize the parameters of SVM to obtain the optimal value so as to establish the best classification model. Finally, the prepared test set is input into AFSA-SVM for fault classification. The achievement of bearing detection experiments show that this approach can accurately and quickly identify fault types.

Gear Fault Diagnosis Based on Wavelet-Support Vector Machines

2010 ◽  
Vol 33 ◽  
pp. 450-453 ◽  
Author(s):  
Jie Zhao ◽  
Chun Hua Li
Keyword(s):  
Fault Diagnosis ◽  
Support Vector Machines ◽  
Classification Model ◽  
Fault Classification ◽  
Small Samples ◽  
Support Vector ◽  
Noise Interference ◽  
Vector Machines ◽  
Gear Fault ◽  
Vibration Noise

According to the characteristics of gear vibration noise large and fault diagnosis complex, the paper proposes the method of gear fault classification based on wavelet analysis - Support Vector Machines (SVM). This method effectively eliminates the noise interference of the gear signals. The classification model of gear diagnosis applicable to small samples is established and the result of simulation shows that the model can correctly realize gear fault.

Fault Diagnosis for Shaft System of Hydropower Unit Based on LS-SVM

2013 ◽  
Vol 325-326 ◽  
pp. 660-664
Author(s):  
Ye Zhou ◽  
Shu Tang ◽  
Luo Ping Pan ◽  
Ping Ping Li
Keyword(s):  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Least Square ◽  
Classification Model ◽  
Fault Classification ◽  
Support Vector ◽  
Shaft System ◽  
Condition Monitoring System ◽  
Energy Components

In this paper, shaft monitoring data in condition monitoring system of hydropower units was used to build the fault classification model based on the least square support vector machine (LS-SVM). By the wavelet packet signal decomposition for unit vibration signal, setting the signal energy components as the study sample, learning of fault diagnosis classifier was conducted, to achieve the diagnosis of common faults in shaft running of hydropower unit.

Classification and Location of Transformer Winding Deformations using Genetic Algorithm and Support Vector Machine

2021 ◽  
Vol 14 ◽  
Author(s):  
Zhenhua Li ◽  
Junjie Cheng ◽  
A. Abu-Siada
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Fault Location ◽  
Classification Model ◽  
Fault Classification ◽  
Support Vector ◽  
Fault Type ◽  
Svm Model ◽  
Transformer Winding

Background: Winding deformation is one of the most common faults that an operating power transformer experiences over its operational life. Thus it is essential to detect and rectify such faults at early stages to avoid potential catastrophic consequences to the transformer. At present, methods published in the literature for transformer winding fault diagnosis are mainly focused on identifying fault type and quantifying its extent without giving much attention to the identification of fault location. Methods: This paper presents a method based on a genetic algorithm and support vector machine (GA-SVM) to improve the faults’ classification of power transformers in terms of type and location. In this regard, a sinusoidal sweep signal in the frequency range of 600 kHz to 1MHz is applied to one terminal of the transformer winding. A mathematical index of the induced current at the head and end of the transformer winding under various fault conditions is used to extract unique features that are fed to a support vector machine (SVM) model for training. Parameters of the SVM model are optimized using a genetic algorithm (GA). Results : The effectiveness of mathematical indicators to extract fault type characteristics and the proposed fault classification model for fault diagnosis is demonstrated through extensive simulation analysis for various transformer winding faults at different locations. Conclusion : The proposed model can effectively identify different fault types and determine their location within the transformer winding, and the diagnostic rate of the fault type and fault location are 100% and 90%, respectively.

scholarly journals Automatic fault detection of sensors in leather cutting control system under GWO-SVM algorithm

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0248515
Author(s):  
Ke Luo ◽  
Yingying Jiao
Keyword(s):  
Fault Detection ◽  
Principal Component ◽  
Support Vector ◽  
Svm Classifier ◽  
Gray Wolf ◽  
Existing Problems ◽  
Proposed Model ◽  
Svm Algorithm ◽  
Gray Wolf Optimizer ◽  
The Individual

The purposes are to meet the individual needs of leather production, improve the efficiency of leather cutting, and increase the product’s competitiveness. According to the existing problems in current leather cutting systems, a Fault Diagnosis (FD) method combining Convolutional Neural Network (CNN) and the Support Vector Machine (SVM) of Gray Wolf Optimizer (GWO) is proposed. This method first converts the original signal into a scale spectrogram and then selects the pre-trained CNN model, AlexNet, to extract the signal scale spectrogram’s features. Next, the Principal Component Analysis (PCA) reduces the obtained feature’s dimensionality. Finally, the normalized data are input into GWO’s SVM classifier to diagnose the bearing’s faults. Results demonstrate that the proposed model has higher cutting accuracy than the latest fault detection models. After model optimization, when c is 25 and g is 0.2, the model accuracy can reach 99.24%, an increase of 66.96% compared with traditional fault detection models. The research results can provide ideas and practical references for improving leather cutting enterprises’ process flow.

scholarly journals A fault diagnosis method based on the Support Vector Machine in rod pumping systems

2021 ◽  
Vol 2125 (1) ◽  
pp. 012004
Author(s):  
Juanni Li ◽  
Jun Shao
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Classification Model ◽  
Petroleum Engineering ◽  
Fault Classification ◽  
Support Vector ◽  
Training Time ◽  
Sucker Rod ◽  
Artificial Intelligence Technology ◽  
Diagnosis Method

Abstract Monitoring the working status of the sucker rod pump is an important part in petroleum engineering. With the development of artificial intelligence technology, more methods have been applied to the fault diagnosis of rod pumping systems. An evolutional fault diagnosis method based on Support Vector Machine (SVM) in sucker rod pumping systems is proposed. Fourier descriptors and Light Field compression algorithm are used in this method to extract the graphic features of the indicator diagram. SVM is used to build fault classification model. This method is verified experimentally through data of indicator diagrams and the results show that it has a shorter training time and higher accuracy.

scholarly journals Vibration Analysis of Gearbox Fault Diagnosis using DWT and Statistical Features

2021 ◽  
Author(s):  
Setti Suresh ◽  
◽  
Srinivas M ◽  
Naidu VPS ◽  
◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Discrete Wavelet Transform ◽  
Time Domain ◽  
Vibration Analysis ◽  
Fault Classification ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Statistical Features ◽  
Reconstructed Signal

The gearbox is one of the critical subsystems in any rotating machinery, which plays a significant role in machine-driven power transmission in terms of change in speed and torque. It plays a vital role in patching different industrial functionalities. The advent of developing different gear technologies and the requirement to fulfil the desired mechanical benefits lead to add more importance to the gearbox health condition monitoring from various types of fault occurrences at an earlier stage. This study presents the vibration analysis of gearbox fault diagnosis using discrete wavelet transform (DWT) and statistical features. It is observed that, using wavelet reconstruction in the fault diagnosis better fault classification is achieved. The fault diagnosis has presented with an emphasis in time-domain followed by two different approaches. The approach-1 is illustrated as windowing of raw signal, feature extraction and feature classification using support vector machine (SVM). In the approach-2, after windowing the raw signal - each window of original vibration signal has converted into wavelet coefficients reconstructed signals (without leaving the time domain) using discrete wavelet transform (DWT) at different levels of decomposition followed by approach-1. The fault diagnostic accuracy of SVM is presented with 100 Monte-Carlo -runs to validate the consistency in the accomplished result. By observing the success rates in two approaches, it is clear that approach-2 with wavelet coefficient’s reconstructed signal is providing better classification accuracy, which can be practically deployed to diagnose the gearbox fault.

Drug Target Group Prediction with Multiple Drug Networks

2020 ◽  
Vol 23 (4) ◽  
pp. 274-284 ◽  
Author(s):  
Jingang Che ◽  
Lei Chen ◽  
Zi-Han Guo ◽  
Shuaiqun Wang ◽  
Aorigele
Keyword(s):  
Drug Target ◽  
Low Cost ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Support Vector ◽  
Multiple Drug ◽  
Property A ◽  
Multiple Networks ◽  
Proposed Model ◽  
The One

Background: Identification of drug-target interaction is essential in drug discovery. It is beneficial to predict unexpected therapeutic or adverse side effects of drugs. To date, several computational methods have been proposed to predict drug-target interactions because they are prompt and low-cost compared with traditional wet experiments. Methods: In this study, we investigated this problem in a different way. According to KEGG, drugs were classified into several groups based on their target proteins. A multi-label classification model was presented to assign drugs into correct target groups. To make full use of the known drug properties, five networks were constructed, each of which represented drug associations in one property. A powerful network embedding method, Mashup, was adopted to extract drug features from above-mentioned networks, based on which several machine learning algorithms, including RAndom k-labELsets (RAKEL) algorithm, Label Powerset (LP) algorithm and Support Vector Machine (SVM), were used to build the classification model. Results and Conclusion: Tenfold cross-validation yielded the accuracy of 0.839, exact match of 0.816 and hamming loss of 0.037, indicating good performance of the model. The contribution of each network was also analyzed. Furthermore, the network model with multiple networks was found to be superior to the one with a single network and classic model, indicating the superiority of the proposed model.

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