Explainable Predictive Maintenance: Forecasting and Anomaly Detection of Diagnostic Trouble Codes for Truck Fleet Management

Abstract:

Predictive Maintenance involves monitoring a vehicle’s Diagnostic Trouble Codes (DTCs) to identify potential anomalies before they escalate into major problems, enabling maintenance teams to proactively conduct necessary repairs or maintenance and prevent critical breakdowns. 

This thesis aims to explore and compare various approaches of data analytics and machine learning methods for finding patterns and abnormalities to forecast the next DTC (with a specific emphasis on predicting Suspect Parameter Number (SPN) and Failure Mode Identifier (FMI) codes) in the sequence and using anomaly detection methods to understand how dangerous the predicted DTC is. It also aims to make the forecasted model interpretable using Explainable AI techniques for maintenance professionals to have a clear understanding of the underlying factors influencing predictions. 

The dataset is provided by Elektrobit Automotive GmbH and contains tabular time series data.

Research Objectives 

1. Investigating strategies for enhancing predictive maintenance models through effective data pre-processing, feature selection, and handling an imbalanced dataset. 
2. Comparing various model architectures for effective forecasting of the DTC. 
3. Designing and evaluating anomaly detection strategies to distinguish between dangerous and non-dangerous forecasted DTC. 
4. Assessing Explainable AI approaches in improving the explainability of forecasted DTC prediction models. 

Thesis Outline

The thesis involves the following key steps: 

• Step 1: Literature review and theoretical framework development. 
• Step 2: Data pre-processing, and analysis. 
• Step 3: Design and develop model architectures for our use case. 
• Step 4: Build Explainable AI based framework for the models. 
• Step 5: Evaluate and compare the results of the models.
• Step 6: Thesis writing and final presentation preparation.

Through an in-depth exploration of data analytics and machine learning, this thesis seeks to elevate predictive maintenance by investigating effective strategies, model architectures, anomaly detection, and Explainable AI for Diagnostic Trouble Codes. The theoretical framework, grounded in a comprehensive literature review, will guide the study’s key steps, leading to actionable insights for proactive vehicle maintenance.

References 

• A. B. Hafeez, E. Alonso, and A. Ter-Sarkisov. Towards sequential multivariate fault prediction for vehicular predictive maintenance. In 2021 20th IEEE International Conference on Ma chine Learning and Applications (ICMLA), pages 1016–1021, Pasadena, CA, USA, 2021. doi: 10.1109/ICMLA52953.2021.00167. 
• Abdul Basit Hafeez, Eduardo Alonso, and Atif Riaz. DTCEncoder: A Swiss Army Knife Architecture for DTC Exploration, Prediction, Search and Model Interpretation. In 2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA), pages 519– 524, 2022. 
• Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention Is All You Need. CoRR, abs/1706.03762, 2017. 
• M. Yang, J. Moon, S. Yang, H. Oh, S. Lee, Y. Kim, and J. Jeong. Design and Implementation of an Explainable Bidirectional LSTM Model Based on Transition System Approach for Cooperative AI-Workers. Appl. Sci., 12:6390, 2022. doi: 10.3390/app12136390.