The automotive industry is being transformed by the application of artificial intelligence and big data analysis. In particular, predictive analytics is becoming a powerful tool for anticipating component failure. This key area of research provides automotive industry manufacturers with lower warranty expenses and incremental service parts revenue while rewarding customers with higher uptime. These benefits are particularly important for commercial vehicles operations such as bus and truck fleets, since analytics led predictive maintenance can prevent inconvenient and costly interruptions of vehicle mission. Accurate prediction models for component failures are especially challenging. This paper describes one such effort for failure prediction of transit bus NOx (Nitrogen Oxides) sensors.
Stringent emissions regulations have made diesel exhaust aftertreatment systems mandatory in nearly all global markets, and NOx sensors play a critical role in control and diagnostic algorithms used by these systems. NOx is measured before and after the SCR (Selective Catalytic Reduction) system with two different components namely Engine out (EO) NOx and System out (SO) NOx. Due to differences in operating conditions and failure rates these two components were studied separately. The results of different Machine Learning algorithms were obtained and compared to get the optimal predictions. Moreover, early life and late life failures were also studied separately to differentiate between random and wear-out failure modes. Highlights of the paper are: - the data collection process, feature engineering and feature selection process, as well as explainable AI (Artificial Intelligence) built on top of the machine learning model. Efforts were also taken to keep the approach generic and not become too component specific so that it can easily be replicated for predicting other failures on other product lines or of different components.
Predictive Analytics, Nox Sensor Failure, Explainable AI, Decile Analysis, Machine Learning, XGBOOST, Preventive maintenance
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