Deep Sequence Modeling of Dump Truck Sensor Data for Fuel
Efficiency and Engine Health Prediction
Raed Majeed1,∗, Hiyam Hatem2
1Department of Computer Information Systems, College of Computer Science and Information Technology,
University of Sumer, Dhi-qar, Iraq
2Department of Computer Science, College of Computer Science and Information Technology, University of
Sumer, Dhi-qar, Iraq
Emails: raed.m.muttasher@gmail.com; hiamhatim2005@gmail.com
Abstract
The Fourth Industrial Revolution represents a shift to a more connected, digital world across all industries,
including mining. The application of smart sensors will reduce site risks and fuel consumption, reduce equip-
ment breakdowns, improve preventative maintenance, and improve equipment efficiency, including dump truck
engines. Dump truck fuel efficiency is influenced by a number of real-world factors, including driver behav-
ior, road and weather conditions, and vehicle specifications. Additionally, potential engine failures and other
aspects can impact vehicle outcomes. By using dynamic on-road data to predict fuel consumption per trip,
the industry can effectively minimize the expense associated with driving evaluations. Furthermore, analysis
of data provides valuable insights into identifying the underlying causes of fuel consumption by analyzing
input parameters. This paper proposes and evaluates novel models for predicting dump truck fuel consump-
tion and engine failures in open-pit mining. These models combine the power of features derived from data
collected locally by dump truck sensors and their analysis. The fuel consumption prediction architecture for
open-top mining trucks using an improved Long Short-Term Memory (LSTM) model and a double-layer thick
Deep Neural Network (DNN) forms the basis of the model design, which consists of two separate compo-
nents. Multi-delay Recurrent Neural Network (RNN) models have been found to be efficient and accurate.
The RNN architecture is applied to capture the cyclic components and complex rules in engine consumption
data. This research relied on essential factors (route, vehicle speed, engine revolutions, and engine load). The
proposed model outperforms existing models, achieving (MAE=0.0210), (RMSE=0.0294), (MSE=0.0009),
and accuracy (R²=0.9842), demonstrating that it can produce highly accurate predictions.
Keywords: Recurrent Neural Networks (RNNs); Fuel Consumption; Engine Failures; Deep Learning (DL);
Mining Dump Trucks