Lee, JM Kim, SJ Hwang, Y Song, CS 2024-01-21T06:15:21Z 2024-01-21T06:15:21Z 2021-09-05 2004-09-22 0022-460X https://pubs.kist.re.kr/handle/201004/137221 Hidden Markov Model (HMM) has been actively studied in speech recognition since 1960s and increasingly used in many other fields. However, its application to mechanical engineering has been very limited. HMM is not only very accurate and robust in analyzing signals but also can be a very powerful method of predicting target system's condition change. In this paper, continuous HMM (CHMM) has been tuned to be used in mechanical signal analysis and applied to diagnose of various mechanical signals including rotor fault signals. The results show HMM's big potential as an intelligent condition monitoring tool based on its accuracy, robustness, and forecasting ability. (C) 2003 Elsevier Ltd. All rights reserved. English ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD Diagnosis of mechanical fault signals using continuous hidden Markov model Article 10.1016/j.jsv.2003.08.021 1 JOURNAL OF SOUND AND VIBRATION, v.276, no.3-5, pp.1065 - 1080 JOURNAL OF SOUND AND VIBRATION 276 3-5 1065 1080 scie scopus 000223528000029 2-s2.0-3242733167 Acoustics Engineering, Mechanical Mechanics Acoustics Engineering Mechanics Article Hidden Markov Model Diagnosis rotor fault signal condition monitoring forecasting