Intelligent fault diagnosis of mechanical equipment under varying working condition via iterative matching network augmented with selective Signal reuse strategy

2020 ◽  
Vol 57 ◽  
pp. 400-415
Author(s):  
Kaiyu Zhang ◽  
Jinglong Chen ◽  
Tianci Zhang ◽  
Shuilong He ◽  
Tongyang Pan ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Working Condition ◽  
Mechanical Equipment ◽  
Intelligent Fault Diagnosis ◽  
Matching Network

scholarly journals Big Data Technology Application in Mechanical Intelligent Fault Diagnosis

2021 ◽  
Vol 2066 (1) ◽  
pp. 012064
Author(s):  
Huiteng Cao
Keyword(s):  
Data Mining ◽  
Information Technology ◽  
Big Data ◽  
Fault Diagnosis ◽  
Machining Process ◽  
Mechanical Equipment ◽  
Data Mining Algorithm ◽  
Intelligent Fault Diagnosis ◽  
Mining Algorithm ◽  
Big Data Technology

Abstract With the rapid implementation of made in China 2025 plan and the rapid development and application of information technology such as artificial intelligence, big data technology, industrial Internet of things and 5G, information technology has been integrated into every link of the whole life management cycle of mechanical products, such as tool condition detection and mechanical fault diagnosis in machining process. Based on this, the purpose of this study is to study the application of big data technology in mechanical intelligent fault diagnosis. In the process of this study, the decision number algorithm and data mining algorithm are used to study the experiment, and some mechanical faults in the past are analyzed and studied. Summary of the experimental results show that the use of decision number algorithm and data mining algorithm in the experiment has achieved good results, through these methods and big data technology, we can quickly diagnose the fault of mechanical equipment, accurately locate the fault location of mechanical equipment. Mechanical intelligent fault diagnosis based on big data technology can improve the efficiency of fault diagnosis, reduce enterprise costs and improve economic performance.

scholarly journals Domain Adaptation Network with Double Adversarial Mechanism for Intelligent Fault Diagnosis

2021 ◽  
Vol 11 (17) ◽  
pp. 7983
Author(s):  
Kun Xu ◽  
Shunming Li ◽  
Ranran Li ◽  
Jiantao Lu ◽  
Xianglian Li ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Domain Adaptation ◽  
Wasserstein Distance ◽  
Diagnostic Methods ◽  
Mechanical Equipment ◽  
Intelligent Fault Diagnosis ◽  
Sample Distribution ◽  
Feature Extractor ◽  
Long Time ◽  
Diagnosis Method

Due to the mechanical equipment working under variable speed and load for a long time, the distribution of samples is different (domain shift). The general intelligent fault diagnosis method has a good diagnostic effect only on samples with the same sample distribution, but cannot correctly predict the faults of samples with domain shift in a real situation. To settle this problem, a new intelligent fault diagnosis method, domain adaptation network with double adversarial mechanism (DAN-DAM), is proposed. The DAN-DAM model is mainly composed of a feature extractor, two label classifiers and a domain discriminator. The feature extractor and the two label classifiers form the first adversarial mechanism to achieve class-level alignment. Moreover, the discrepancy between the two classifiers is measured by Wasserstein distance. Meanwhile, the feature extractor and the domain discriminator form the second adversarial mechanism to realize domain-level alignment. In addition, maximum mean discrepancy (MMD) is used to reduce the distance between the extracted features of two domains. The DAN-DAM model is verified by multiple transfer experiments on some datasets. According to the transfer experiment results, the DAN-DAM model has a good diagnosis effect for the domain shift samples. Moreover, the diagnostic accuracy is generally higher than other mainstream diagnostic methods.

An intelligent fault diagnosis method for rolling bearings based on feature transfer with improved DenseNet and joint distribution adaptation

2021 ◽  
Author(s):  
Chenhui Qian ◽  
Quansheng Jiang ◽  
Yehu Shen ◽  
Chunran Huo ◽  
Qingkui Zhang
Keyword(s):  
Fault Diagnosis ◽  
Joint Distribution ◽  
Marginal Distribution ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Mechanical Equipment ◽  
Intelligent Fault Diagnosis ◽  
Rolling Bearings ◽  
Bearing Fault Diagnosis ◽  
Diagnosis Method

Abstract Mechanical intelligent fault diagnosis is an important method to accurately identify the health status of mechanical equipment. Traditional fault diagnosis methods perform poorly in the diagnosis of rolling bearings under complex conditions. In this paper, a feature transfer learning model based on improved DenseNet and joint distribution adaptation (FT-IDJ) is proposed. With this model, we apply it to implement rolling bearing fault diagnosis. A lightweight DenseNet model is firstly proposed to extract the transferable features of the raw vibration signal. Furthermore, the parameters in the DenseNet are constrained by the domain adaptive regularization term and pseudo label learning. The marginal distribution discrepancy and the conditional distribution discrepancy of the learned transferable features are reduced by this way. The proposed method is validated by the diagnosis experiments with CWRU and Jiangnan University rolling bearing datasets. The experimental results showed that the proposed FT-IDJ has higher classification accuracy than DAN and other eight methods, which demonstrated its effectively learning transferable features from auxiliary data.

Intelligent fault diagnosis method for common rail injectors based on hierarchical weighted permutation entropy and pair-wise feature proximity feature selection

2021 ◽  
pp. 107754632110105
Author(s):  
Yun Ke ◽  
Chong Yao ◽  
Enzhe Song ◽  
Liping Yang ◽  
Quan Dong
Keyword(s):  
Fault Diagnosis ◽  
High Frequency ◽  
Recognition Rate ◽  
Low Frequency ◽  
Intelligent Manufacturing ◽  
Common Rail ◽  
Permutation Entropy ◽  
Mechanical Equipment ◽  
Intelligent Fault Diagnosis ◽  
Diagnosis Method

It is of great significance for intelligent manufacturing to study diagnosis methods to realize the diagnosis of mechanical equipment faults. Multiscale weighted permutation entropy is an effective method recently proposed to measure the complexity and dynamic changes of dynamic systems. To solve the shortcoming of multiscale weighted permutation entropy that does not consider high-frequency components, this article proposes hierarchical weighted permutation entropy, which can comprehensively and accurately reflect the low-frequency and high-frequency information of the time series. The simulation signal verifies the effectiveness and superiority of hierarchical weighted permutation entropy. Then, a novel intelligent fault diagnosis method for common rail injectors based on hierarchical weighted permutation entropy and pair-wise feature proximity is proposed. Finally, the proposed method is applied to the common rail injector fault data, and the results verify the effectiveness of the proposed method. Compared with other methods, this method has a higher fault recognition rate and stronger robustness.

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.

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