Application of Hidden Markov Models in Ball Mill Gearbox for Fault Diagnosis

Abstract

In this paper, a ball mill gear reducer was regarded as the research object. Based on the HMM pattern recognition theory, DHMM methods that were used in fault diagnosis had been researched. The vibration signal was required a series transformations which are feature extraction, normalization, scalarization and quantization to get the sequence collections. Then the quantified sequence collections were trained to get the DHMM parameter, or the Viterbi Algorithm which was used for the quantified sequence collections to calculate the maximum probability, thereby the DHMM fault models library was established or the type of fault was recognized. Experiments of five kinds of fault model diagnosis were carried out in this article.