A review on fault prognostics in integrated health management

Abstract

Integrated Health Management (IHM) is an advanced technology which integrated artificial intelligence with advanced test and information technologies. Having gone through fault detection, isolation and reconfiguration and immerged with the state of arts reasoning technologies, IHM monitors and controls the function of critical systems and components in order to ensure safe and efficient operation. An IHM system usually comprises seven functional modules, namely data acquisition, signal/feature extraction, condition assessment, diagnostics, prognostics, decision reasoning and human interface. Among them, fault prognostics are not only the core of IHM, but also an important guarantee to reduce the costs of life-cycle maintenance, and to improve system security. Fault prognostics is the process to project the current health state of equipment into the future taking into account estimates of future usage profiles. It may report health status at a future time, or may estimate the remaining useful lifetime (RUL) of a machine given its projected usage profile. In recent years, fault prognostics are under unprecedented attentions. And it is becoming the most challenging research area which is so-called crystal ball of IHM. Based on the theory, methods and routes adopted in the practical application, fault prognostics is generally fallen into three main categories, namely model-based approaches, knowledge-based approaches and data-based approaches. Then, based on the analysis of some typical applications on each approaches, the strengths and weaknesses of each approach are further discussed. Finally, according to the current research situation at home and abroad, the future development trend of fault prognostics is also presented.