Machine Condition Prediction Based on Adaptive Neuro-Fuzzy and High-Order Particle Filtering

—Machine prognosis is a significant part of Condition-Based Maintenance (CBM) and intends to monitor and track the time evolution of the fault so that maintenance can be performed or the task be terminated to avoid a catastrophic failure. A new prognostic method is developed in this paper using adaptive neuro-fuzzy inference systems (ANFIS) and high-order particle filtering. The ANFIS is trained via machine historical failure data. The trained ANFIS and its modeling noise constitute an m th-order hidden Markov model to describe the fault propagation process. The high-order particle filter uses this Markov model to predict the time evolution of the fault indicator in the form of a probability density function (pdf). An on-line update scheme is developed to adapt the Markov model to various machine dynamics quickly. The performance of the proposed method is evaluated by using the testing data from a cracked carrier plate and a faulty bearing. The results show that it outperforms classical condition predictors.