Early Fault Detection and Optimal Maintenance Control for Partially Observable Systems Subject to Vibration Monitoring

Due to the advancements in data measurement and computer technology, automated data collection from multiple sensors has become common in recent years. However, very few published papers have dealt with the cost-optimal early fault detection of gearboxes, condition based maintenance policy, and remaining useful life prediction when multiple sensors are used for data collection under both fixed load and varying load.This research has focused on several new developments. An early fault detection scheme has been firstly developed using real multivariate vibration data under varying load. A multivariate Hotelling's T-squared control chart has been applied to detect the early fault occurrence of a gearbox. Furthermore, to investigate maintenance policies triggered by the early fault detection, we have considered the combination of continuous time hidden Markov modeling (HMM) and the optimal Bayesian control technique using vector time series model residuals. Model parameters have been obtained using the Expectation-Maximization (EM) algorithm. An optimal Bayesian maintenance policy represented by a Bayesian control chart has been developed by minimizing the long-run expected average cost per unit time. Prediction of mean residual life using a posterior probability has also been developed in this research. For vibration systems without historical failure data, a hidden semi-Markov model (HSMM), has also been developed for maintenance planning. The evolution of the unknown state process has been described by a hidden, two-state semi-Markov process with a generally distributed sojourn time in the healthy state. The model parameter estimation procedure based on the EM algorithm has been developed. The optimal maintenance scheme in a two-state HSMM framework has also been constructed. We have developed this model considering three different state sojourn time distributions using the same set of real helical gearboxes multivariate data: exponential distribution, phase-type Erlang distribution, and Weibull distribution. The numerical results of parameter estimates and optimal control limits have been compared for these three distributions. Lastly, we present the proposed HSMM models to monitor the health condition of a planetary gearbox degradation system using vibration data from multiple sensors.Ph.D