Application of Machine Learning to Engine Air System Failure Prediction

Abstract

<div class="section abstract"><div class="htmlview paragraph">With the capability of avoiding failure in advance, failure prediction model is important not only to end users, but also to the service engineers in vehicle industry. This paper proposes an approach based on anomaly detection algorithms and telematic data to predict the failure of the engine air system with Turbo charger. Firstly, the relationship between air system and all obtained features are analyzed by both physical mechanism and data-wise. Then, the features including altitude, air temperature, engine output power, and charger pressure are selected as the input of the model, with the sampling interval of 1 minute. Based on the selected features, the healthy state for each vehicle is defined by the model as benchmark. Finally, the ‘Medium surface’ is determined for specific vehicle, which is a hyperplane with the medium points of the healthy state located at, to detect the minor weakness symptom (sub-health state). The precisions of our model are 89.1% and 92.9% on training dataset and field test dataset, respectively. The average time gap between prediction and failure is 45 days, which means the model can help our end users and service engineers take actions to avoid sever failure 45 days in advance. There are also two applied cases of the model to real vehicles. In both cases, the model successfully detects the sub-health state and helps service engineers repair the air system in advance. After the repair, the state indicator was recovered to the normal level, which can be a validation of our model. The algorithm could be generalized to failure of other vehicle parts with similar conditions and save money and time for our customers.</div></div>