Application of Artificial Intelligence for Failure Prediction of Engine Through Condition Monitoring Technique

Abstract

Engine failure prediction, to date, has become more challenging for adequately diagnosing and assigning appropriate maintenance decision-making processes. This paper investigates the health of an engine through experimental observation using an artificial neural network (ANN). Lubricating oil analysis has been performed for diagnosing quantitative analysis, i.e. wear particle concentration (WPC), severity index (SI), wear severity index (WSI), and percentage of large particles (PLP). An ANN model using a nonlinear autoregressive with exogenous input (NARX) architecture has been employed to predict quantitative outputs. Finally, a data-driven approach by applying an artificial neural network to understand the system degradation from accumulated condition monitoring data is studied. Topology 3–18–4 from NARX (ANN) was optimal in develo** a predictive failure model with regression coefficients (0.9985–0.9999), having an error autocorrelation factor bounded within 95% confidence limit and lowered MSE and MAPE values as 0.00093 and 3.56. The application of neural networks is increasingly attractive and seems to be the right choice for a data-driven diagnostic approach. In addition, the outcomes from the ANN data are validated with the experimental set so that the strength of the model is reflected and a pattern of failure from the historical monitoring of the operating engines is predicted.