Latent degradation indicator estimation using condition monitoring information

Asset health prediction is imperative to optimal asset management. Online and offline inspections can provide useful information for predicting asset health. The information from an asset health inspection can be divided into two types. (1) Direct indicators which directly determine failures (e.g. the thickness of a brake pad, or the wear in a component) and (2) indirect indicators which are not related to failures directly (e.g. vibration signals or oil analysis results). The direct indicators can provide more precise reference for the maintenance strategy determination. However, these direct degradation indicators are often technically or economically impossible to inspect frequently and accurately. The indirect indicators, on the other hand, can be acquired more easily using various condition monitoring techniques. This paper proposes two continuous state space models to estimate and predict direct degradation indicators using indirect degradation indicators. The two continuous state space models adopt the Wiener process and the Gamma process respectively. The Expectation Maximization (EM) algorithms based on the modified Kalman smoother and the modified particle smoother are used to estimate the parameters of the proposed models. The application process of the EM algorithms and the characteristics of the state space models are illuminated through a simulation study. Finally, a case study using the data from an accelerated test of a gear box is conducted to justify the feasibility of the proposed models.