Research on Intelligent Fault Diagnosis Technique of Complex Equipment

2014 ◽  
Vol 945-949 ◽  
pp. 1098-1101
Author(s):  
Rui Zhu ◽  
Ming Ji Huang ◽  
Guo Bao Ding ◽  
Shuai Jia
Keyword(s):  
Fault Diagnosis ◽  
Decision Tree ◽  
Intelligent Fault Diagnosis ◽  
Intelligent Diagnosis ◽  
Complex Equipment ◽  
Circuit Decomposition ◽  
Diagnosis Technique

Aiming at the actual demand of complex equipment fault diagnosis, this paper made the fault intelligent diagnosis technology of a certain type of equipment as research object, analyzed the characteristics of equipment and its faults, presented four strategy to solve the problem: Circuit-decomposition, the decision tree, confirm key component using FMECA and establish model by PSPICE.And proving it by actual circuit.

Research on fault diagnosis method of rapier loom based on the fusion of expert system and fault tree

2021 ◽  
pp. 1-13
Author(s):  
Yanjun Xiao ◽  
Furong Han ◽  
Yvheng Ding ◽  
Weiling Liu
Keyword(s):  
Fault Diagnosis ◽  
Expert System ◽  
Knowledge Base ◽  
Fault Tree ◽  
Maintenance Cost ◽  
Intelligent Fault Diagnosis ◽  
Tree Model ◽  
Intelligent Diagnosis ◽  
Diagnosis Method ◽  
System Knowledge

The safety and stability of the rapier loom during operation directly impact the quality of the fabric. Therefore, it is of great significance to carry out fault diagnosis research on rapier looms. In order to solve the problems of low diagnosis efficiency, untimely diagnosis, and high maintenance cost of existing rapier looms in manual troubleshooting of loom failures. This paper proposes a new intelligent fault diagnosis method for rapier looms based on the fusion of expert system and fault tree. A new expert system knowledge base is formed by combining the dynamic fault tree model with the expert system knowledge base. It solves the problem that the traditional expert system cannot achieve precise positioning in the face of complex fault types. Construct the rapier loom’s fault diagnosis model, build the intelligent diagnosis platform, and finally realize the intelligent fault diagnosis of the rapier loom. Experimental results show that the algorithm can quickly diagnose and locate rapier loom faults. Compared with the current intelligent diagnosis algorithm, the algorithm structure is simplified, which provides a theoretical basis for the broad application of intelligent fault diagnosis on rapier looms.

Study on CBR-Based Automobile Engine Intelligent Fault Diagnosis Technique

2011 ◽  
Vol 460-461 ◽  
pp. 637-641 ◽  
Author(s):  
Pei Feng Sun ◽  
Yong Ni
Keyword(s):  
Fault Diagnosis ◽  
High Technology ◽  
Case Based Reasoning ◽  
Complex Structures ◽  
Intelligent Fault Diagnosis ◽  
Automobile Engine ◽  
Diagnosis System ◽  
Fault Diagnosis System ◽  
Key Techniques ◽  
Diagnosis Technique

It is difficult to do the fault diagnosis on the modern car engines which have high technology and complex structures. In this study, a case-based-reasoning (CBR) based automobile engine intelligent fault diagnosis system was proposed against this problem. The system’s structure and its mechanism of fault diagnosis were introduced. The key techniques to implement the system were analyzed, including the case establishment, the case search, the case learning and the maintenance of case library. The proposed system gave a new way to establish an efficient automobile engine fault diagnosis system.

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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