Intelligent Fault Diagnosis for Industrial Big Data

2018 ◽  
Vol 90 (8-9) ◽  
pp. 1221-1233 ◽  
Author(s):  
Jia Si ◽  
Yibin Li ◽  
Sile Ma
Keyword(s):  
Big Data ◽  
Fault Diagnosis ◽  
Intelligent Fault Diagnosis ◽  
Industrial Big Data

scholarly journals Industrial Big Data for Fault Diagnosis: Taxonomy, Review, and Applications

IEEE Access ◽  
2017 ◽  
Vol 5 ◽  
pp. 17368-17380 ◽  
Author(s):  
Yan Xu ◽  
Yanming Sun ◽  
Jiafu Wan ◽  
Xiaolong Liu ◽  
Zhiting Song
Keyword(s):  
Big Data ◽  
Fault Diagnosis ◽  
Industrial Big Data

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 Intelligent fault diagnosis system based on big data

2019 ◽  
Vol 2019 (23) ◽  
pp. 8980-8985
Author(s):  
Tianshu Wu ◽  
Shuyu Chen ◽  
Peng Wu
Keyword(s):  
Big Data ◽  
Fault Diagnosis ◽  
Intelligent Fault Diagnosis ◽  
Diagnosis System ◽  
Fault Diagnosis System

scholarly journals An Intelligent Fault Diagnosis Method Using GRU Neural Network towards Sequential Data in Dynamic Processes

Processes ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 152 ◽  
Author(s):  
Jing Yuan ◽  
Ying Tian
Keyword(s):  
Fault Diagnosis ◽  
Sequential Data ◽  
Dynamic Feature ◽  
Intelligent Fault Diagnosis ◽  
Dynamic Features ◽  
Raw Data ◽  
Promising Tool ◽  
Industrial Big Data ◽  
Para Xylene ◽  
Diagnosis Method

Intelligent fault diagnosis is a promising tool to deal with industrial big data due to its ability in rapidly and efficiently processing collected signals and providing accurate diagnosis results. In traditional static intelligent diagnosis methods, however, the correlation between sequential data is neglected, and the features of raw data cannot be effectively extracted. Therefore, this paper proposes a three-stage fault diagnosis method based on a gate recurrent unit (GRU) network. The raw data is divided into several sequence units by first using a moving horizon as the input of GRU. In this way, we can intercept the sequence to get information as needed. Then, the GRU deep network is established through batch normalization (BN) algorithm to extract the dynamic feature from the sequence units effectively. Finally, the softmax regression is employed to classify faults based on dynamic features. Thus, the diagnosis result is obtained with a probabilistic explanation. Two chemical processes validate the proposed method: Tennessee Eastman (TE) benchmark process as well as para-xylene (PX) oxidation process. In the case of TE, the diagnosis results demonstrate the proposed method is superior to conventional methods. Furthermore, in the case of PX oxidation, the result shows that the proposed method also has an exceptional effect with a little historical data.

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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