Intelligent fault diagnosis algorithm for fiber optic current transformer

2020 ◽  
Vol 64 (1-4) ◽  
pp. 3-10
Author(s):  
Lihui Wang ◽  
Kai Zhao ◽  
Wenpeng Zhang ◽  
Jian Liu ◽  
Fubin Pang
Keyword(s):  
Fault Diagnosis ◽  
Fiber Optic ◽  
Short Term Memory ◽  
Current Transformer ◽  
Support Vector ◽  
Intelligent Fault Diagnosis ◽  
Signal Prediction ◽  
Residual Signal ◽  
Signal Characteristics ◽  
Diagnosis Algorithm

Affected by environmental factors, the performance of fiber optic current transformer (FOCT) will deteriorate over a long period of time. Intelligent fault diagnosis algorithm of Long-Short Term Memory (LSTM) combing with Support Vector Machine (SVM) is an effective way to deal with FOCT failures. According to the characteristics of LSTM, a signal prediction model in FOCT based on LSTM is proposed by analyzing the historical data. The residual signal can be obtained by the prediction signal and the observed signal. Set the residual threshold to determine whether the FOCT has fault. With the residual signal characteristics, a fault diagnosis model based on SVM is established. By analyzing the residual signal and extracting features, the diagnostic network can realize the pattern recognition and system fault diagnosis. Experiments demonstrate that the drift deviation fault, the ratio deviation fault and the fixed deviation fault can be diagnosed with an accuracy of 94.5%.

scholarly journals A Novel Hierarchical Algorithm for Bearing Fault Diagnosis Based on Stacked LSTM

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Lu Yu ◽  
Jianling Qu ◽  
Feng Gao ◽  
Yanping Tian
Keyword(s):  
Fault Diagnosis ◽  
Short Term Memory ◽  
Operating Conditions ◽  
Support Vector ◽  
Rolling Bearings ◽  
Hierarchical Algorithm ◽  
Bearing Fault Diagnosis ◽  
Rotary Machinery ◽  
Proposed Model ◽  
Series Of Experiments

Faced with severe operating conditions, rolling bearings tend to be one of the most vulnerable components in mechanical systems. Due to the requirements of economic efficiency and reliability, effective fault diagnosis methods for rolling bearings have long been a hot research topic of rotary machinery fields. However, traditional methods such as support vector machine (SVM) and backpropagation neural network (BP-NN) which are composed of shallow structures trap into a dilemma when further improving their accuracies. Aiming to overcome shortcomings of shallow structures, a novel hierarchical algorithm based on stacked LSTM (long short-term memory) is proposed in this text. Without any preprocessing operation or manual feature extraction, the proposed method constructs a framework of end-to-end fault diagnosis system for rolling bearings. Beneficial from the memorize-forget mechanism of LSTM, features inherent in raw temporal signals are extracted hierarchically and automatically by stacking LSTM. A series of experiments demonstrate that the proposed model can not only achieve up to 99% accuracy but also outperform some state-of-the-art intelligent fault diagnosis methods.

scholarly journals A Multichannel Data Fusion Method Based on Multiple Deep Belief Networks for Intelligent Fault Diagnosis of Main Reducer

Symmetry ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 483 ◽  
Author(s):  
Qing Ye ◽  
Changhua Liu
Keyword(s):  
Fault Diagnosis ◽  
Random Forest ◽  
Data Fusion ◽  
Support Vector ◽  
Fusion Method ◽  
Belief Networks ◽  
Deep Belief Networks ◽  
Intelligent Fault Diagnosis ◽  
Comparison Results ◽  
Multichannel Data

Aiming at the problems of poor efficiency of the intelligent fault diagnosis method of the main reducer and the poor effectiveness of multichannel data fusion, this paper proposes a multichannel data fusion method based on deep belief networks and random forest fusion for fault diagnosis. Multiple deep belief networks (MDBNs) are constructed to obtain deep representative features from multiple modalities of multichannel data. Random forest can fuse deep representative features achieved from MDBNs to construct the model of multiple deep belief networks fusion (MDBNF). The proposed method is applied to fault diagnosis of the main reducer and evaluation of the performance. Multiple deep belief network model fusions (MD BN F) are constructed to improve the multichannel data fusion effect. Single sensory data, multichannel data, and two intelligent models based on support vector machine and deep belief networks are used as comparison in the experiments. The results indicate that the classification accuracy of the test set collected by sensor 1 and sensor 2 is 88.35% and 88.73%, respectively. The comparison results show that the method has good convergence. The data fusion of the proposed diagnostic model can effectively improve the correlation between the collected vibration signals and the failure mode, thereby improving the diagnostic performance by nearly 8%, representing improved diagnostic accuracy.

scholarly journals A Novel Fault Diagnosis Algorithm for Rolling Bearings Based on One-Dimensional Convolutional Neural Network and INPSO-SVM

2020 ◽  
Vol 10 (12) ◽  
pp. 4303
Author(s):  
Yang Shao ◽  
Xianfeng Yuan ◽  
Chengjin Zhang ◽  
Yong Song ◽  
Qingyang Xu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Experimental Results ◽  
Learning Ability ◽  
Support Vector ◽  
Intelligent Fault Diagnosis ◽  
Rolling Bearings ◽  
One Dimensional ◽  
Hybrid Framework

Deep learning based intelligent fault diagnosis methods have become a research hotspot in the fields of fault diagnosis and the health management of rolling bearings in recent years. To effectively identify incipient faults in rotating machinery, this paper proposes a novel hybrid intelligent fault diagnosis framework based on a convolutional neural network and support vector machine (SVM). First, an improved one-dimensional convolutional neural network (1DCNN) was adopted to extract fault features, and the state information and intrinsic properties of the raw vibration signals were mined. Second, the extracted features were used to train the SVM, which was applied to classify the fault category. The proposed hybrid framework combined the excellent classification performance of the SVM for small samples and the strong feature-learning ability of CNN network. In order to tune the parameters of the SVM, an improved novel particle swarm optimization algorithm (INPSO) which combined the Tent map and Lévy flight strategy was proposed. Numerical experimental results indicated that the proposed PSO variant had a better performance in searching accuracy and convergence speed. At last, multiple groups of rolling bearing fault diagnosis experiments were carried out and experimental results showed that, with the proposed 1DCNN-INPSO-SVM model, the hybrid framework was capable of diagnosing with high precision for rolling bearings and superior to some traditional fault diagnosis methods.

Rolling Bearing Fault Diagnosis Based on Wavelet Packet Feature Entropy-MFSVM

2010 ◽  
Vol 121-122 ◽  
pp. 813-818 ◽  
Author(s):  
Wei Guo Zhao ◽  
Li Ying Wang
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Rolling Bearing ◽  
Support Vector ◽  
Wavelet Packet Decomposition ◽  
Intelligent Fault Diagnosis ◽  
Fuzzy Support Vector Machine ◽  
Bearing Fault Diagnosis ◽  
Diagnosis Method

On the basis of wavelet packet-characteristic entropy(WP-CE) and multiclass fuzzy support vector machine(MFSVM), the author proposes a new fault diagnosis method of vibrating of hearings，in which three layers wavelet packet decomposition of the acquired vibrating signals of hearings is performed and the wavelet packet-characteristic entropy is extracted，the eigenvector of wavelet packet of the vibrating signals is constructed，and taking this eigenvector as fault sample multiclass fuzzy support vector machine is trained to implement the intelligent fault diagnosis. The simulation result from the proposed method is effective and feasible.

The Support Vector Machine Fault Diagnosis Algorithm Based on Cloud Computing

2014 ◽  
Vol 687-691 ◽  
pp. 761-765
Author(s):  
Chang Hong Zhang ◽  
Si Jia Cheng ◽  
Shu Hao Cao
Keyword(s):  
Support Vector Machine ◽  
Cloud Computing ◽  
Fault Diagnosis ◽  
Large Scale ◽  
Support Vector ◽  
Machine Fault Diagnosis ◽  
Diagnosis Method ◽  
Diagnosis Algorithm ◽  
Machine Fault ◽  
The Way

The paper puts forward the way to solve the problem of SVM training on the large scale firstly, Then perform the experiment to verify the feasibility of scheme. In the last section, SVM fault diagnosis method based on the Mapreduce is put forward.

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