Prior Knowledge-Augmented Self-Supervised Feature Learning for Few-shot Intelligent Fault Diagnosis of Machines

2022 ◽  
pp. 1-1
Author(s):  
Tianci Zhang ◽  
Jinglong Chen ◽  
Shuilong He ◽  
Zitong Zhou
Keyword(s):  
Fault Diagnosis ◽  
Prior Knowledge ◽  
Feature Learning ◽  
Intelligent Fault Diagnosis

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.

Intelligent fault diagnosis using an unsupervised sparse feature learning method

2020 ◽  
Vol 31 (9) ◽  
pp. 095903 ◽  
Author(s):  
Chun Cheng ◽  
Weiping Wang ◽  
Haining Liu ◽  
Michael Pecht
Keyword(s):  
Fault Diagnosis ◽  
Feature Learning ◽  
Learning Method ◽  
Intelligent Fault Diagnosis

scholarly journals A K-means clustering method with feature learning for unbalanced vehicle fault diagnosis

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Bo Wang ◽  
Guanwei Wang ◽  
Youwei Wang ◽  
Zhengzheng Lou ◽  
Shizhe Hu ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Prior Knowledge ◽  
Feature Learning ◽  
Data Set ◽  
Efficient Operation ◽  
Content Type ◽  
Key Factor ◽  
Railway System ◽  
Fault Diagnostic ◽  
Low Efficiency

Purpose Vehicle fault diagnosis is a key factor in ensuring the safe and efficient operation of the railway system. Due to the numerous vehicle categories and different fault mechanisms, there is an unbalanced fault category problem. Most of the current methods to solve this problem have complex algorithm structures, low efficiency and require prior knowledge. This study aims to propose a new method which has a simple structure and does not require any prior knowledge to achieve a fast diagnosis of unbalanced vehicle faults. Design/methodology/approach This study proposes a novel K-means with feature learning based on the feature learning K-means-improved cluster-centers selection (FKM-ICS) method, which includes the ICS and the FKM. Specifically, this study defines cluster centers approximation to select the initialized cluster centers in the ICS. This study uses improved term frequency-inverse document frequency to measure and adjust the feature word weights in each cluster, retaining the top τ feature words with the highest weight in each cluster and perform the clustering process again in the FKM. With the FKM-ICS method, clustering performance for unbalanced vehicle fault diagnosis can be significantly enhanced. Findings This study finds that the FKM-ICS can achieve a fast diagnosis of vehicle faults on the vehicle fault text (VFT) data set from a railway station in the 2017 (VFT) data set. The experimental results on VFT indicate the proposed method in this paper, outperforms several state-of-the-art methods. Originality/value This is the first effort to address the vehicle fault diagnostic problem and the proposed method performs effectively and efficiently. The ICS enables the FKM-ICS method to exclude the effect of outliers, solves the disadvantages of the fault text data contained a certain amount of noisy data, which effectively enhanced the method stability. The FKM enhances the distribution of feature words that discriminate between different fault categories and reduces the number of feature words to make the FKM-ICS method faster and better cluster for unbalanced vehicle fault diagnostic.

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