Abstract
Clustering methods have been widely applied to the fault diagnosis of mechanical system, but the characteristic that the number of cluster needs to be determined in advance limits the application range of the method. In this paper, a novel clustering method combining the adaptive resonance theory (ART) with the similarity measure based on the Yu’s norm is presented and applied to the fault diagnosis of rolling element bearings, which can be adaptive to generate the number of cluster by the vigilance parameter test. Time-domain features, frequency-domain features, and time series model parameters are extracted to demonstrate the fault-related information about the bearings, and then considering the irrelevance or redundancy of some features many salient features are selected by an improved distance discriminant technique and input into the proposed clustering method to diagnose the faults of bearings. The experiment results confirmed that the proposed clustering method can diagnose the fault categories accurately and has better diagnosis performance compared with fuzzy ART and Self-Organizing Feature Map (SOFM).
Highlights
In order to decrease the downtime on production machinery and to increase reliability against possible failures, some important machinery is equipped with condition monitoring systems, but how to be intelligent to classify the data samples collected by the condition monitoring system is challenging
Artificial neural networks (ANNs) used as an intelligent classification tool have been widely applied in the fault diagnosis field of machine conditions which are treated as classification problems based on learning pattern from empirical data modeling in complex mechanical processes and systems [1]
Before application of the proposed clustering method to the fault diagnosis of bearings, time-domain statistical characteristics features, frequency-domain statistical characteristics features, and AR time series model parameters are extracted to characterize the fault-related information of bearing
Summary
In order to decrease the downtime on production machinery and to increase reliability against possible failures, some important machinery is equipped with condition monitoring systems, but how to be intelligent to classify the data samples collected by the condition monitoring system is challenging. To the best of our knowledge, the clustering method using similarity measure based on the Yu’s norm is seldom applied in the fault diagnosis of mechanical system. It is preferable to make use of the similarity measure based on the Yu’s norm to develop a new diagnostic method which is capable of learning from the process data steam by identifying the different fault categories automatically; namely, the novel diagnostic method can be adaptive to generate the number of cluster nodes according to the number of faults in real time. In the learning process when a new sample is input into the ART network, it can attempt to categorize the sample by comparing it with the stored weight vectors of existing cluster node which represented a category. That sample x which belongs to Cm can be determined by the following: S
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