Automobile Predictive Maintenance Using Deep Learning

Sanjit Dash, Jibitesh Mishra, Satyam Raj, Rahul Agarwal

OAI: oai:igi-global.com:279274 • DOI: 10.4018/IJAIML.20210701.oa7

There are three types of maintenance management policy Run-tofailure (R2F), Preventive Maintenance (PvM) and Predictive Maintenance (PdM). In both R2F and PdM we have the data related to the maintenance cycle. In case of Preventive Maintenance (PvM) complete information about maintenance cycle is not available. Among these three maintenance policies, predictive Maintenance (PdM) is becoming a very important strategy as it can help us to minimize the repair time and the associated cost with it. In this paper we have proposed PdM, which allows the dynamic decision rules for the maintenance management. PdM is achieved by training the machine learning model with the datasets. It also helps in planning of maintenance schedules. We specially focused on two models that are Binary Classification and Recurrent Neural Network. In Binary Classification we classify whether our data belongs to the failure class or the non failure class. In Binary Classification the number of cycles is entered and classification model predicts whether it belongs to the failure/non failure class.

Computer Science and Information Technology Artificial Intelligence Machine Learning