Integrated Equipment Health Prognosis Considering Crack Initiation Time Uncertainty and Random Shock

Abstract

With integrated equipment health prognosis, both physical models and condition monitoring data are utilized to achieve more accurate prediction of equipment remaining useful life (RUL). In this paper, an integrated prognostics method is proposed to account for two important factors which were not considered before, the uncertainty in crack initiation time (CIT) and the shock in the degradation. Prognostics tools are used for RUL prediction starting from the CIT. However, there is uncertainty in CIT due to the limited capability of existing fault detection tools, and such uncertainty has not been explicitly considered in the literature for integrated prognosis. A shock causes a sudden damage increase and creates a jump in the degradation path, which shortens the total lifetime, and it has not been considered before in the integrated prognostics framework either. In the proposed integrated prognostics method, CIT is considered as an uncertain parameter, which is updated using condition monitoring data. To deal with the sudden damage increase and reduction of total lifetime, a virtual gradual degradation path with an earlier CIT is introduced in the proposed method. In this way, the effect of shock is captured through identifying an appropriate CIT. Examples of gear prognostics are given to demonstrate the effectiveness of the proposed method.

A review on diagnostic and prognostic approaches for gears

“…In practical cases, a sudden shock on the gear tooth surface may suddenly increase the damage size and creates a jump in the degradation path that reduces the gear life. In a further extension of the work proposed in Zhao et al, 276 Zhao et al 279 address this problem by proposing a hybrid prognostics model which considers the uncertainty of crack initiation time and random shock.…”

Remaining useful life prediction based on a modified SVR method with health stage division, cluster sampling and similarity matching

“…The operating reliability of mechanical equipment significantly influences the sustainability and competitiveness of manufacturing industry. As the reliability of mechanical equipment decreases with the extension of the operating time, prognostics and health management (PHM) techniques attract more and more attentions [1][2][3]. It determines the optimal maintenance time, inspection interval, spare parts order quantity and other logistics management strategies through efficient and low-cost diagnostic and prognostic activities to achieve the lowest economic cost or equipment failure risk.…”