A Continuous Gaussian Mixture HMM Based Acoustic Fault Diagnosis Scheme for Bearings

Plentiful significant information about the operation status of bearings, which is potential for the fault diagnose after processed properly, is contained in their acoustic signals. In this paper, a new fault diagnosis scheme using acoustic signals is proposed for the bearings by introducing continuous Gaussian mixture hidden Markov model (CGHMM) method, in which the data processing error due to vector quantization is avoided, and therefore the diagnosis precision is improved. Besides, a clustering algorithm and a scaled coefficient algorithm are introduced for parameters initiation and the forward and backward algorithms to simplify the complexity in the computation and improve the training and recognizing speed and diagnosis precision. At last, experiment results of a diagnosis precision achieved to 98.75% demonstrated the feasibility and potential for applications of the presented scheme.