An early warning method of degradation for mechanical facilities based on data self-organization mining technology

Key words:  data self-organization mining  hidden Markov model (HMM)  group method of data handling (GMDH)  early warning of degradation

Abstract:

Data self-organization mining technology was introduced during facility condition monitoring process, and an early warning method of degradation for facilities was developed. Hidden Markov model (HMM) was used to identify and assess the early degradation state of the facility, and the predictive model was further developed to predict the future degradation trend. In the case study, the proposed method was applied to bearings in the rotating machinery. The results show that the effectiveness, objectivity and accuracy of this method are validated by the test results. The predictive states are consistent with the actual situation, and the relative error is only 3.1%. In this way, the early warning of the degradation states can be given to make engineer carry out appropriate maintenance strategies effectively and timely, which can avoid production and economic losses due to unplanned shutdown of machine.