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- Title
PCA+CHMM在设备性能退化状态识别中的应用研究.
- Authors
钟 飞; 宁 芊; 周新志; 赵成萍
- Abstract
In order to accurately identify the degradation state of mechanical equipment, this paper researched a recognition method of performance degradation state based on PCA( principal component analysis) and CHMM( continuous hidden Markov model). Firstly, it extracted the vibration signal’s time domain, frequency domain and time-frequency domain features in full life cycle, then constructed a new feature set by screening the features, then performed PCA dimensionality reduction for this set. Secondly, it trained a full life cycle CHMM to determine the number of degraded states by using of reduced dimension feature data, and then trained a CHMM for each degraded state, judged the degradation state of the device by comparing the likelihood probability of the observation sequence under each model. Finally, it compared the accuracy of PCA + CHMM and PCA + CHM, PCA + KNN and PCA + CART methods that to identify each degraded state. The results show that the average recognition accuracy of PCA + CHMM is the highest, the recognition effect is good, and it is suitable for the identification of device degraded state.
- Subjects
HIDDEN Markov models; MULTIPLE correspondence analysis (Statistics); PROBABILITY theory; BIOMETRIC identification; OBJECT recognition (Computer vision); DIMENSION reduction (Statistics); ACCURACY
- Publication
Application Research of Computers / Jisuanji Yingyong Yanjiu, 2019, Vol 36, Issue 1, p136
- ISSN
1001-3695
- Publication type
Article
- DOI
10.19734/j.issn.1001-3695.2017.07.0679