Rotating Machinery Fault Diagnosis Based on Wavelet Fuzzy Neural Network
نویسندگان
چکیده
According to complicated fault characteristic of rotating machinery, its fault diagnosis based on wavelet fuzzy neural network (WFNN) which combines wavelet packet analysis and fuzzy neural network is put forward. By using it, the fuzzy fault diagnosis of rotating machinery is realized. All the arithmetic process of WFNN is realized through the computer. The results of simulation and test indicate that this method has obvious advantage for dealing with multi-coupled fault situation, the diagnosis method is simple, quick and has high correctness of fault diagnoses, proving that the diagnosis method is effective and providing a theoretical basis and new way for the fault diagnosis of rotating machinery.
منابع مشابه
Intelligent Diagnosis of Rotating Machinery Faults-A Review
(2002) Intelligent diagnosis of rotating machinery faults-A review. The task of condition monitoring and fault diagnosis of rotating machinery faults is both significant and important but is often cumbersome and labour intensive. Automating the procedure of feature extraction, fault detection and identification has the advantage of reducing the reliance on experienced personnel with expert know...
متن کاملBearing Fault Detection Based on Maximum Likelihood Estimation and Optimized ANN Using the Bees Algorithm
Rotating machinery is the most common machinery in industry. The root of the faults in rotating machinery is often faulty rolling element bearings. This paper presents a technique using optimized artificial neural network by the Bees Algorithm for automated diagnosis of localized faults in rolling element bearings. The inputs of this technique are a number of features (maximum likelihood estima...
متن کاملA Novel Intelligent Fault Diagnosis Approach for Critical Rotating Machinery in the Time-frequency Domain
The rotating machinery is a common class of machinery in the industry. The root cause of faults in the rotating machinery is often faulty rolling element bearings. This paper presents a novel technique using artificial neural network learning for automated diagnosis of localized faults in rolling element bearings. The inputs of this technique are a number of features (harmmean and median), whic...
متن کاملIntelligent Methods for Condition Diagnosis of Plant Machinery
In the case of condition diagnosis of the plant machinery, particularly rotating machinery, the utilization of vibration signals is effective in the detection of faults and the discrimination of fault type, because the signals carry dynamic information about the machine state. Condition diagnosis depends largely on the feature analysis of vibration signals, so it is important that the feature o...
متن کاملRecent Research Results on Intelligent Methods for Conditon Diagnosis of Rotating Machinery
This paper reports several intelligent diagnostic approaches for rotating machinery based on artificial intelligence methods and feature extraction of vibration signals. That is: the diagnosis method based on wavelet transform, rough sets and neural network; the diagnosis method based on sequential fuzzy inference; diagnosis approach by possibility theory and certainty factor model; the diagnos...
متن کامل