Analysing ML, DL Approaches for Real-Time Maintenance Forecasting in Industrial Scenarios

Pranita Bhosale, Sangeeta Jadhav

2025

Abstract

Predictive maintenance became increasingly crucial in large machines like extruder machines to ensure optimal performance & prevent costly downtimes. Monitoring temperature is particularly critical in extruder machines as it directly impacts product quality. To address this, real-time data from a plastic extruder machine equipped with four temperature sensors taken into account to ensure precise temperature control for high-quality output. The study analysed a dataset comprising 19679 rows of data, stored in an Excel sheet, using a range of ML and DL algorithms. Primary focus was evaluating performance of these algorithms in predictive maintenance tasks. Among the algorithms tested, the Probabilistic Neural Network, a type of ML algorithm, demonstrated promising results. PNN achieved accuracy of 99.70%. PNN showed several advantages when compared to other popular algorithms such as Backpropagation Neural Network, Convolutional Neural Network, Support Vector Machine, Long Short Term Memory, and Bidirectional LSTM. PNN requires minimal parameter tuning compared to complex algorithms like LSTM & Bi-LSTM, simplifying implementation process. In conclusion, the research highlights the effectiveness of the PNN algorithm in predictive maintenance tasks for extruder machines based on temperature sensor data. Its performance and simplicity makes it a promising choice for real-time maintenance prediction, offering potential cost savings and operational efficiency improvements in industrial settings.

Paper Citation

in Harvard Style

Bhosale P. and Jadhav S. (2025). Analysing ML, DL Approaches for Real-Time Maintenance Forecasting in Industrial Scenarios. In Proceedings of the 3rd International Conference on Futuristic Technology - Volume 1: INCOFT; ISBN 978-989-758-763-4, SciTePress, pages 764-770. DOI: 10.5220/0013585300004664

in Bibtex Style

@conference{incoft25,
author={Pranita Bhosale and Sangeeta Jadhav},
title={Analysing ML, DL Approaches for Real-Time Maintenance Forecasting in Industrial Scenarios},
booktitle={Proceedings of the 3rd International Conference on Futuristic Technology - Volume 1: INCOFT},
year={2025},
pages={764-770},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013585300004664},
isbn={978-989-758-763-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Futuristic Technology - Volume 1: INCOFT
TI - Analysing ML, DL Approaches for Real-Time Maintenance Forecasting in Industrial Scenarios
SN - 978-989-758-763-4
AU - Bhosale P.
AU - Jadhav S.
PY - 2025
SP - 764
EP - 770
DO - 10.5220/0013585300004664
PB - SciTePress