An Optimized Clustering Approach Using Simulated Annealing Algorithm with HMM Coordination for Rolling Elements Bearings’ Diagnosis
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Nowadays applying condition-based maintenance technology for detecting the states of machines or components could be anticipated, when the diagnostic and prognostic algorithms are successfully introduced. In particular, for those enduring components like rolling bearings with relative long service life and complex states of development, such like sudden collapse caused by pitting, the diagnosis process is essential for identifying damages and by that way reducing unnecessary costs in many fields of manufacturing industry. This paper presents an improved approach to clarify the defect states of rolling bearings by using optimized clustering method. The simulated annealing k-means algorithm is proposed, which is used to remove isolated noising data points from the main distribution. In addition, of acquiring optimized assignments, and to predict the degradation by setting up hidden Markov model that carries out the diagnosis characteristics. Experiment reveals the correlation between the defect periods and the area of corrosion pits The results are presented to verify the efficiency and effectivity of the approach.
KeywordsDiagnosis Clustering approach Simulated annealing k-means algorithm (SA-k-means) Degradation
This work is partly supported by Natural Science Foundation of China (Grant No. 71471139) and Zhejiang Natural Science Foundation, China (Grant No. LY14E050020).
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