Health Indicator of Diesel Generator to Support Predictive Maintenance Strategy on Railway

The rail industry requires robust technology to support environmentally friendly and sustainable transportation, of which diesel generators are a key component. Currently, maintenance approaches for railway diesel generators tend to use preventive strategies that are less efficient and costly. Therefore, this study proposes the estimation of diesel generator health indicators to support predictive maintenance by utilizing multivariate stochastic approaches and exponential-based degradation models. This degradation model contains deterministic and stochastic parameters estimated using Bayes filter. In addition, to improve forecasting accuracy, this approach involves more than one type of measurement variable data and applies one of the multivariate analysis techniques, namely principal component analysis (PCA). The PCA variables are then used to determine the health indicators of diesel generators. As a case study, this research was conducted using data from a diesel generator on a power car. The results show that the health indicator is obtained when the tresshold has been reached.