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Machine Learning-Based Prognostic Approaches for Construction Equipment Powertrain Systems

Authors:

Zafer Yigit, Håkan Forsberg, Masoud Daneshtalab

Research group:

Heterogeneous systems - hardware software co-design

Publication Type:

Conference/Workshop Paper

Venue:

36th IEEE Intelligent Vehicles Symposium

Abstract

Construction equipment has important roles in industries such as construction and mining. Any downtime because of failures increase cost. Traditional diagnostic systems detect failures only after they occur, making it difficult to take precautions and prolonging repair times. This paper is the first to address the analysis of machine learning powered Prognostic and Health Management (PHM) systems specifically for predicting failures in diesel engine air intake systems, focusing on two common issues: air leakage and Exhaust Gas Recirculation (EGR) blockage. This study compares various machine learning and deep learning models for anomaly detection and fault classification using real world sensor data from controlled engine tests. The results demonstrate that ensemble and neural network-based machine learning methods, such as Random Forest, XGBoost, and LSTM, achieve highly successful predictions for anomaly detection and fault classification.

Bibtex

@inproceedings{Yigit7189,
author = {Zafer Yigit and H{\aa}kan Forsberg and Masoud Daneshtalab},
title = {Machine Learning-Based Prognostic Approaches for Construction Equipment Powertrain Systems},
month = {June},
year = {2025},
booktitle = {36th IEEE Intelligent Vehicles Symposium},
url = {http://www.es.mdu.se/publications/7189-}
}