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| Rolling bearing performance degradation state recognition based on basic scale entropy and GG fuzzy clustering |
| WANG Bing1, HU Xiong1, LI Hongru2, SUN Dejian1 |
1. College of Logistics Engineering, Shanghai Maritime University, Shanghai 201306, China;
2. Department of Missile Engineering, Army Engineering University (Shijiazhuang Campus),Shijiazhuang 050003, China |
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Abstract A method based on basic scale entropy and GG clustering was proposed to solve the problem of bearing performance degradation state recognition. The evolution law of the basic scale entropy in bearing performance degradation process was analyzed firstly, and its monotonicity and sensitivity were emphasized. Considering the continuity of bearing degradation state on time scale, a 3D degradation feature vector was constructed with basic scale entropy, its root mean square and degradation time, and GG fuzzy clustering method was used to divide different stages of bearing performance degradation state to realize bearing performance degradation state recognition. Bearing full lifetime test data of IEEE PHM 2012 was adopted to do example analysis, and the results were compared with those using FCM and GK algorithms. The results showed that the proposed method’s clustering effect is better and its time aggregation degree is higher in the same degradation state; the method can provide an effective way for bearing performance degradation state recognition.
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Received: 26 October 2017
Published: 28 February 2019
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