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Optimizing Simulation and Predicting Engine Behavior of a Wheel Loader Through LSTM Neural Network Modeling: A Simulation-Based Approach

Authors:

Abdulkarim Habbab, Anas Fattouh, Bobbie Frank , Koteshwar Chirumalla, Markus Bohlin

Research groups:

Industrial AI Systems
Digital and Circular Industrial Services

Publication Type:

Conference/Workshop Paper

Venue:

The Second SIMS EUROSIM Conference on Modelling and Simulation

Abstract

In recent years, the demand for efficient modeling and simulation techniques to analyze complex systems has intensified across various industries. However, existing methodologies often struggle to adequately capture the intricate temporal dependencies present in sequential data, particularly in heavy-duty construction equipment like wheel loaders. This paper addresses this gap by exploring the application of Long Short-Term Memory (LSTM) neural networks to optimize and enhance the simulation of engine behavior in wheel loaders. Through a comprehensive analysis of LSTM architectures and the utilization of data collected from MATLAB-based models and real-time sensors, this research aims to improve simulation time by accurately predicting engine performance parameters such as fuel consumption and torque output. Additionally, this approach holds promise for enhancing operational efficiency, optimizing maintenance schedules, and mitigating equipment downtime, thereby offering valuable insights into wheel loader engine behavior. Through experimentation and validation against field data, the efficacy of LSTM networks in improving predictive capabilities for managing wheel loader engine performance across diverse operational conditions is demonstrated, contributing to the advancement of heavy equipment engineering practices.

Bibtex

@inproceedings{Habbab6935,
author = {Abdulkarim Habbab and Anas Fattouh and Bobbie Frank and Koteshwar Chirumalla and Markus Bohlin},
title = {Optimizing Simulation and Predicting Engine Behavior of a Wheel Loader Through LSTM Neural Network Modeling: A Simulation-Based Approach},
month = {September},
year = {2024},
booktitle = {The Second SIMS EUROSIM Conference on Modelling and Simulation},
url = {http://www.ipr.mdu.se/publications/6935-}
}