Intelligent Fault Diagnosis of Gear Box Based on BSS and SVM

2013 ◽  
Vol 818 ◽  
pp. 218-223 ◽  
Author(s):  
Wei Jin Ma ◽  
Rui Xiang Yu ◽  
Jun Yuan Wang ◽  
Xiao Fei Wan
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Blind Source Separation ◽  
Source Separation ◽  
Vibration Signal ◽  
Support Vector ◽  
Intelligent Fault Diagnosis ◽  
Basic Model ◽  
The Common ◽  
Gear Box

The vibration signal of gear box shows the information of its running state. The thesis explains the basic model and its algorithm of blind source separation, simulates the common fault of gear box in the condition of laboratory, disposing the fault signals of gear box by blind source separation and intelligently identifying the faulty condition of gear box by the method of support vector machine (SVM) after extracting eigenvector, which achieves success.

Study of Intelligent Fault Diagnosis Method for Turbo-Generator Unit Based on Support Vector Machine and Knowledge

2014 ◽  
Vol 543-547 ◽  
pp. 1057-1063
Author(s):  
Xiao Wen Deng ◽  
Yu Jiong Gu ◽  
Li Ping Fang ◽  
Zhao Xu Ren ◽  
Ya Peng Han
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Vibration Signal ◽  
Signal Spectrum ◽  
Support Vector ◽  
Intelligent Fault Diagnosis ◽  
Knowledge Reasoning ◽  
Turbo Generator ◽  
Generator Unit ◽  
Low Efficiency

For the low efficiency and poor accuracy of turbo-generator Unit s fault diagnosis, this paper divided the common 18 kinds of vibration fault into four categories, and took advantage of support vector machine to distinguish the fault cluster for early fault diagnosis according to the characteristics of vibration signal spectrum. For different fault cluster, different fault pattern recognition model was established. With the use of certain symptom group and weighted fuzzy logic, this article engaged in knowledge reasoning to obtain the specific fault recognition mode. Besides, the searching methods of fault cause, fault influence and troubleshooting measures in the knowledge base were proposed, which made the diagnosis process more meticulous and comprehensive. Case analysis shows that it is feasible to use this method to develop a system for intelligent fault diagnosis of turbo-generator unit, which is valuable for further study in more depth.

scholarly journals Mechanical Fault Diagnosis for HV Circuit Breakers Based on Ensemble Empirical Mode Decomposition Energy Entropy and Support Vector Machine

2015 ◽  
Vol 2015 ◽  
pp. 1-6 ◽  
Author(s):  
Jianfeng Zhang ◽  
Mingliang Liu ◽  
Keqi Wang ◽  
Laijun Sun
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Circuit Breaker ◽  
Vibration Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Support Vector ◽  
Time Segment ◽  
Vibration Signals ◽  
Equal Time ◽  
Mode Decomposition

During the operation process of the high voltage circuit breaker, the changes of vibration signals can reflect the machinery states of the circuit breaker. The extraction of the vibration signal feature will directly influence the accuracy and practicability of fault diagnosis. This paper presents an extraction method based on ensemble empirical mode decomposition (EEMD). Firstly, the original vibration signals are decomposed into a finite number of stationary intrinsic mode functions (IMFs). Secondly, calculating the envelope of each IMF and separating the envelope by equal-time segment and then forming equal-time segment energy entropy to reflect the change of vibration signal are performed. At last, the energy entropies could serve as input vectors of support vector machine (SVM) to identify the working state and fault pattern of the circuit breaker. Practical examples show that this diagnosis approach can identify effectively fault patterns of HV circuit breaker.

The Use of Blind-Source-Separation Algorithm for Mechanical Signal Separation and Machine Fault Diagnosis

2003 ◽  
Author(s):  
Peter W. Tse ◽  
Jinyu Zhang
Keyword(s):  
Fault Diagnosis ◽  
Blind Source Separation ◽  
Condition Monitoring ◽  
Source Separation ◽  
Simulated Data ◽  
Vibration Signal ◽  
Advantages And Disadvantages ◽  
Machine Fault Diagnosis ◽  
Machine Fault ◽  
Mechanical Components

Vibration based machine fault diagnosis is widely adopted in machine condition monitoring. Since a machine is usually composed of many mechanical components, during the machine running, each component will generate its vibration and transmit to other components thru the shaft or linkages. Hence, the vibration signal collected from a sensor is the aggregation of all generated vibrations. To enhance the accuracy in vibration based machine fault diagnosis, the vibration generated by each component must be isolated and identified. In this paper, the performance of blind-source-separation (BSS) in separating various mixed sources is discussed. The BSS based method of second order statistics (SOS) has been applied to separate the aggregated vibration signals generated from a number of mechanical components. To verify the effectiveness of the BSS based SOS, a number of experiments were conducted using both simulated data and vibration generated form the industrial machines. The results show that the BSS possesses the ability to separate both artificially and naturally mixed signals. Such ability is definitely welcome in the fields of condition monitoring and maintenance. Moreover, the paper also discusses the advantages and disadvantages of the algorithm in the applications of machine fault diagnosis and future improvements.

Fault Diagnosis for Electrical Control System of Automobile Based on Support Vector Machine

2014 ◽  
Vol 666 ◽  
pp. 203-207
Author(s):  
Jian Hua Cao
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Control System ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Experimental Results ◽  
Support Vector ◽  
Electrical Control ◽  
Diagnosis Method ◽  
The Common

This paper is to present a fault diagnosis method for electrical control system of automobile based on support vector machine. We collect the common fault states of electrical control system of automobile to analyze the fault diagnosis ability of electrical control system of automobile based on support vector machine. It can be seen that the accuracy of fault diagnosis for electrical control system of automobile by support vector machine is 92.31%; and the accuracy of fault diagnosis for electrical control system of automobile by BP neural network is 80.77%. The experimental results show that the accuracy of fault diagnosis for electrical control system of automobile of support vector machine is higher than that of BP neural network.

Rolling Bearing Fault Diagnosis Based on Wavelet Packet Feature Entropy-MFSVM

2010 ◽  
Vol 121-122 ◽  
pp. 813-818 ◽  
Author(s):  
Wei Guo Zhao ◽  
Li Ying Wang
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Rolling Bearing ◽  
Support Vector ◽  
Wavelet Packet Decomposition ◽  
Intelligent Fault Diagnosis ◽  
Fuzzy Support Vector Machine ◽  
Bearing Fault Diagnosis ◽  
Diagnosis Method

On the basis of wavelet packet-characteristic entropy(WP-CE) and multiclass fuzzy support vector machine(MFSVM), the author proposes a new fault diagnosis method of vibrating of hearings，in which three layers wavelet packet decomposition of the acquired vibrating signals of hearings is performed and the wavelet packet-characteristic entropy is extracted，the eigenvector of wavelet packet of the vibrating signals is constructed，and taking this eigenvector as fault sample multiclass fuzzy support vector machine is trained to implement the intelligent fault diagnosis. The simulation result from the proposed method is effective and feasible.

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