Machine Learning-Based Prognostic Approaches for Construction Equipment Powertrain Systems

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.