Prognostics and health management of an automated machining process

Abstract

Machine failure modes are presenting a major burden to the operator, the plant, and the enterprise causing significant downtime, labor cost, and reduced revenue. New technologies are emerging over the past years to monitor the machine's performance, detect and isolate incipient failures or faults, and take appropriate actions to mitigate such detrimental events. This paper addresses the development and application of novel Prognostics and Health Management (PHM) technologies to a prototype machining process (a screw-tightening machine). The enabling technologies are built upon a series of tasks starting with failure analysis, testing, and data processing aimed to extract useful features or condition indicators from raw data, a symbolic regression modeling framework, and a Bayesian estimation method called particle filtering to predict the feature state estimate accurately. The detection scheme declares the fault of a machine critical component with user specified accuracy or confidence and given false alarm rate while the prediction algorithm estimates accurately the remaining useful life of the failing component. Simulation results support the efficacy of the approach and match well the experimental data.