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

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.

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

Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering ◽

10.1177/09544070211014852 ◽

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.

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An intelligent fault diagnosis method for rotating machinery based on data fusion and deep residual neural network

Applied Intelligence ◽

10.1007/s10489-021-02555-4 ◽

2021 ◽

Author(s):

Binsen Peng ◽

Hong Xia ◽

Xinzhi Lv ◽

M. Annor-Nyarko ◽

Shaomin Zhu ◽

...

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Data Fusion ◽

Rotating Machinery ◽

Intelligent Fault Diagnosis ◽

Diagnosis Method

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A Generative Adversarial Network-Based Intelligent Fault Diagnosis Method for Rotating Machinery Under Small Sample Size Conditions

IEEE Access ◽

10.1109/access.2019.2947194 ◽

2019 ◽

Vol 7 ◽

pp. 149736-149749 ◽

Cited By ~ 7

Author(s):

Yu Ding ◽

Liang Ma ◽

Jian Ma ◽

Chao Wang ◽

Chen Lu

Keyword(s):

Fault Diagnosis ◽

Sample Size ◽

Small Sample Size ◽

Rotating Machinery ◽

Small Sample ◽

Intelligent Fault Diagnosis ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Diagnosis Method

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A novel unsupervised deep learning network for intelligent fault diagnosis of rotating machinery

Structural Health Monitoring ◽

10.1177/1475921719897317 ◽

2020 ◽

Vol 19 (6) ◽

pp. 1745-1763 ◽

Cited By ~ 8

Author(s):

Xiaoli Zhao ◽

Minping Jia

Keyword(s):

Deep Learning ◽

Fault Diagnosis ◽

Rotating Machinery ◽

Affinity Propagation ◽

Intelligent Fault Diagnosis ◽

Learning Network ◽

Feature Extractor ◽

Unsupervised Deep Learning ◽

Diagnosis Method ◽

Deep Learning Network

Generally, the health conditions of rotating machinery are complicated and changeable. Meanwhile, its fault labeled information is mostly unknown. Therefore, it is man-sized to automatically capture the useful fault labeled information from the monitoring raw vibration signals. That is to say, the intelligent unsupervised learning approach has a significant influence on fault diagnosis of rotating machinery. In this study, a span-new unsupervised deep learning network can be constructed based on the proposed feature extractor (L12 sparse filtering (L12SF)) and the designed clustering extractor (Weighted Euclidean Affinity Propagation) for resolving the issue that the acquisition of fault sample labeled information is burdensome, yet costly. Naturally, the novel intelligent fault diagnosis method of rotating machinery based on unsupervised deep learning network is first presented in this study. Thereinto, the proposed unsupervised deep learning network consists of two layers of unsupervised feature extractor (L12SF) and one layer of unsupervised clustering (Weighted Euclidean Affinity Propagation). L12SF can improve the regularization performance of sparse filtering, and Weighted Euclidean Affinity Propagation can get rid of the traditional Euclidean distance in affinity propagation that cannot highlight the contribution of different features in fault clustering. To make a long story short, the frequency spectrum signals are first entered into the constructed unsupervised deep learning network for fault feature representation; afterward, the unsupervised feature learning and unsupervised fault classification of rotating machinery can be implemented. The superiority of the proposed algorithms and method is validated by two cases of rolling bearing fault dataset. Ultimately, the proposed unsupervised fault diagnosis method can provide a theoretical basis for the development of intelligent unsupervised fault diagnosis technology for rotating machinery.

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Intelligent Fault Diagnosis Method for Coupling Rotating Machinery Based on Deep Convolutional Neural Network

2019 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS) ◽

10.1109/safeprocess45799.2019.9213260 ◽

2019 ◽

Author(s):

Dawei Mu ◽

Li Sheng

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Convolutional Neural Network ◽

Rotating Machinery ◽

Deep Convolutional Neural Network ◽

Intelligent Fault Diagnosis ◽

Diagnosis Method

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A New Intelligent Fault Diagnosis Method of Rotating Machinery under Varying-Speed Conditions Using Infrared Thermography

Complexity ◽

10.1155/2019/2619252 ◽

2019 ◽

Vol 2019 ◽

pp. 1-12 ◽

Cited By ~ 3

Author(s):

Yongbo Li ◽

Xianzhi Wang ◽

Shubin Si ◽

Xiaoqiang Du

Keyword(s):

Support Vector Machine ◽

Fault Diagnosis ◽

Infrared Thermography ◽

Rotating Machinery ◽

Support Vector ◽

Intelligent Fault Diagnosis ◽

Fault Features ◽

Diagnosis Method ◽

Systematic Framework ◽

Machinery Fault Diagnosis

A novel systematic framework, infrared thermography- (IRT-) based method, for rotating machinery fault diagnosis under nonstationary running conditions is presented in this paper. In this framework, IRT technique is first applied to obtain the thermograph. Then, the fault features are extracted using bag-of-visual-word (BoVW) from the IRT images. In the end, support vector machine (SVM) is utilized to automatically identify the fault patterns of rotating machinery. The effectiveness of proposed method is evaluated using lab experimental signal of rotating machinery. The diagnosis results show that the IRT-based method has certain advantages in classification rotating machinery faults under nonstationary running conditions compared with the traditional vibration-based method.

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