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Diagnosis and Prognosis of Degradation Process via Hidden Semi-Markov Model
- Source :
- IEEE/ASME Transactions on Mechatronics. 23:1456-1466
- Publication Year :
- 2018
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2018.
-
Abstract
- The intelligent estimation of degradation state and the prediction of remaining useful life (RUL) are important for the maintenance of industrial equipment. In this study, the degradation process of equipment is modeled as an improved hidden semi-Markov model (HSMM), in which the dependence of durations of adjacent degradation states is described and modeled in the HSMM. To avoid underflow problem in computing the forward and backward variables, a modified forward–backward algorithm is proposed in the HSMM. Based on the improved algorithm, online estimation of degradation state and the distribution of RUL can be obtained. Case studies on tool wearing diagnosis and prognosis have verified the effectiveness of this model.
- Subjects :
- 0209 industrial biotechnology
Industrial equipment
Arithmetic underflow
Computer science
020208 electrical & electronic engineering
Feature extraction
Improved algorithm
02 engineering and technology
computer.software_genre
Computer Science Applications
020901 industrial engineering & automation
Control and Systems Engineering
0202 electrical engineering, electronic engineering, information engineering
Hidden semi-Markov model
Data mining
Electrical and Electronic Engineering
Degradation process
Hidden Markov model
computer
Degradation (telecommunications)
Subjects
Details
- ISSN :
- 1941014X and 10834435
- Volume :
- 23
- Database :
- OpenAIRE
- Journal :
- IEEE/ASME Transactions on Mechatronics
- Accession number :
- edsair.doi...........2229d9876c2a4836c8bb6504aca23795