Fault Diagnosis and Prognosis of Bearing Based on Hidden Markov Model with Multi-Features
Weiguo Zhao, Tiancong Shi, Liying Wang
Abstract
Abstract A new approach to achieve fault diagnosis and prognosis of bearing based on hidden Markov model (HMM) with multi-features is proposed. Firstly, the time domain, frequency domain, and wavelet packet decomposition are utilized to extract the condition features of bearing vibration signals, and the PCA method is merged into multi-features to reduce their dimensionality. Then the low-dimensional features are processed to obtain the scalar probabilities of each bearing condition, which are multiplied to generate the observed values of HMM. The results reveal that the established approach can well diagnose fault conditions and achieve the remaining life estimation of bearing.
Topics & Concepts
Hidden Markov modelBearing (navigation)Pattern recognition (psychology)Computer scienceFault (geology)Artificial intelligenceMarkov chainCurse of dimensionalityMachine learningGeologySeismologyMachine Fault Diagnosis TechniquesFault Detection and Control SystemsEngineering Diagnostics and Reliability