Construction of a deep sparse filtering network for rotating machinery fault diagnosis

2021 ◽  
pp. 095440702110148
Author(s):  
Chun Cheng ◽  
Wei Zou ◽  
Weiping Wang ◽  
Michael Pecht
Keyword(s):  
Fault Diagnosis ◽  
Back Propagation ◽  
Feature Learning ◽  
Back Propagation Neural Network ◽  
Rotating Machinery ◽  
Fine Tuning ◽  
Local Optimum ◽  
Back Propagation Algorithm ◽  
Intelligent Fault Diagnosis ◽  
Sparse Filtering

Deep neural networks (DNNs) have shown potential in intelligent fault diagnosis of rotating machinery. However, traditional DNNs such as the back-propagation neural network are highly sensitive to the initial weights and easily fall into the local optimum, which restricts the feature learning capability and diagnostic performance. To overcome the above problems, a deep sparse filtering network (DSFN) constructed by stacked sparse filtering is developed in this paper and applied to fault diagnosis. The developed DSFN is pre-trained by sparse filtering in an unsupervised way. The back-propagation algorithm is employed to optimize the DSFN after pre-training. Then, the DSFN-based intelligent fault diagnosis method is validated using two experiments. The results show that pre-training with sparse filtering and fine-tuning can help the DSFN search for the optimal network parameters, and the DSFN can learn discriminative features adaptively from rotating machinery datasets. Compared with classical methods, the developed diagnostic method can diagnose rotating machinery faults with higher accuracy using fewer training samples.

A novel intelligent fault diagnosis method based on variant sparse filtering and back-propagation

2021 ◽  
pp. 095745652110557
Author(s):  
Lifeng Chan ◽  
Chun Cheng
Keyword(s):  
Fault Diagnosis ◽  
Back Propagation ◽  
Feature Learning ◽  
Rotating Machinery ◽  
Learning Ability ◽  
Back Propagation Algorithm ◽  
Intelligent Fault Diagnosis ◽  
Propagation Algorithm ◽  
Sparse Filtering ◽  
Diagnosis Method

Detecting the mechanical faults of rotating machinery in time plays a key role in avoiding accidents. With the coming of the big data era, intelligent fault diagnosis methods based on machine learning models have become promising tools. To improve the feature learning ability, an unsupervised sparse feature learning method called variant sparse filtering is developed. Then, a fault diagnosis method combining variant sparse filtering with a back-propagation algorithm is presented. The involvement of the back-propagation algorithm can further optimize the weight matrix of variant sparse filtering using label data. At last, the developed diagnosis method is validated by rolling bearing and planetary gearbox experiments. The experiment results indicate that the developed method can achieve high accuracy and good stability in rotating machinery fault diagnosis.

scholarly journals An intelligent fault diagnosis method of rotating machinery using L1-regularized sparse filtering

2018 ◽  
Vol 20 (8) ◽  
pp. 2839-2854 ◽  
Author(s):  
Weiwei Qian ◽  
Shunming Li ◽  
Jinrui Wang ◽  
Zenghui An ◽  
Xingxing Jiang
Keyword(s):  
Fault Diagnosis ◽  
Rotating Machinery ◽  
Intelligent Fault Diagnosis ◽  
Sparse Filtering ◽  
Diagnosis Method

scholarly journals A Novel Deep Learning Method for Intelligent Fault Diagnosis of Rotating Machinery Based on Improved CNN-SVM and Multichannel Data Fusion

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1693 ◽  
Author(s):  
Gong ◽  
Chen ◽  
Zhang ◽  
Zhang ◽  
Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Back Propagation ◽  
Rotating Machinery ◽  
Fault Classification ◽  
Support Vector ◽  
Intelligent Fault Diagnosis ◽  
K Nearest Neighbor

Intelligent fault diagnosis methods based on deep learning becomes a research hotspot in the fault diagnosis field. Automatically and accurately identifying the incipient micro-fault of rotating machinery, especially for fault orientations and severity degree, is still a major challenge in the field of intelligent fault diagnosis. The traditional fault diagnosis methods rely on the manual feature extraction of engineers with prior knowledge. To effectively identify an incipient fault in rotating machinery, this paper proposes a novel method, namely improved the convolutional neural network-support vector machine (CNN-SVM) method. This method improves the traditional convolutional neural network (CNN) model structure by introducing the global average pooling technology and SVM. Firstly, the temporal and spatial multichannel raw data from multiple sensors is directly input into the improved CNN-Softmax model for the training of the CNN model. Secondly, the improved CNN are used for extracting representative features from the raw fault data. Finally, the extracted sparse representative feature vectors are input into SVM for fault classification. The proposed method is applied to the diagnosis multichannel vibration signal monitoring data of a rolling bearing. The results confirm that the proposed method is more effective than other existing intelligence diagnosis methods including SVM, K-nearest neighbor, back-propagation neural network, deep BP neural network, and traditional CNN.

scholarly journals BP Neural Network Algorithms for Fault Diagnosis of Microwave Components

2019 ◽  
Vol 95 ◽  
pp. 04008
Author(s):  
Gao Kun ◽  
Wang Aimin ◽  
Ge Yan
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Network Model ◽  
Neural Network Model ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Main Trend ◽  
Intelligent Fault Diagnosis ◽  
The Neural Network ◽  
Training Samples

Intelligent diagnosis is the main trend of modern fault diagnosis technology. The emergence of artificial neural network technology provides a new way for this kind of intellectualization. Aiming at the problem of microwave module fault diagnosis, an intelligent fault diagnosis method based on BP(Back Propagation) neural network is proposed in this paper. In this paper, the process of determining the neural network model and the operation flow of BP algorithm are introduced, and the network is trained with training samples. By applying the neural network model to an AQ module for testing, the feasibility, accuracy and efficiency of the fault diagnosis of the microwave module are verified, which provides a new method for intelligent fault diagnosis of this kind of microwave module.

Fault Diagnosis of Power Transformers Based on Membrane Computing Optimizing Neural Network

2012 ◽  
Vol 214 ◽  
pp. 740-744
Author(s):  
Zhi Jian Yuan ◽  
Hao Deng ◽  
Wen Jun Liu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Membrane Computing ◽  
Power Transformer ◽  
Back Propagation ◽  
Optimization Procedure ◽  
Back Propagation Neural Network ◽  
Gas Content ◽  
Back Propagation Algorithm ◽  
Strict Requirement

Presently, dissolved gas content analysis and fault diagnosis are the important segments of power transformer. As to the problem of the back propagation algorithm of neural network commonly used lies in the optimization procedure getting easily stacked into the minimal value locally and strict requirement on the initial value, a fault diagnostic method is presented, based on the membrane computing optimizing back propagation neural network. Throughout the process, compromise is satisfactorily reached among the network complexity, the convergence and the generalization ability. The results of diagnosis test show that the algorithm proposed has high classification accuracy, which proves its robustness and effectiveness.

Sign in / Sign up

Export Citation Format

Share Document