Title: Artificial Intelligence Across Disciplines: Research, Innovation, and Intelligent Solutions
Editors: Dr. Subita Bhagat, Dr. A. Balamurugan, Dr. P. Krishna Kumar, and Mrs. S. Nandhini Devi
ISBN: 978-81-69857-83-3
Chapter: 7
DOI: https://doi.org/10.59646/815/07
Author: Dr. J. Anix Joel Singh
Abstract
Predictive maintenance (PdM) within cyber-physical manufacturing architectures has evolved from basic threshold alerts to intelligent computational prognostics powered by Artificial Intelligence (AI). This chapter evaluates the design, deployment, and operational verification of an end-to-end AI-driven predictive maintenance pipeline applied to high-speed computerized numerical control (CNC) milling spindle bearings. Industrial environments generate massive volumes of non-stationary, noisy sensor streams that degrade the reliability of conventional condition-based maintenance models. By integrating high-frequency tri-axial accelerometry, embedded thermocouple metrics, acoustic emissions, and motor current signatures, we formulate a multi-modal temporal framework utilizing deep Convolutional Neural Networks hybridized with Bidirectional Long Short-Term Memory networks (CNN-BiLSTM) alongside continuous Health Index (HI) estimation and dynamic Remaining Useful Life (RUL) regression. The operational pipeline executes continuous signal preprocessing, continuous wavelet transforms, Bayesian hyperparameter optimization, and dynamic failure prediction. Experimental validation demonstrates significant performance improvements: maintenance interventions shift from reactive responses to planned schedules, catastrophic mechanical downtime decreases by 37.4%, overall equipment effectiveness rises by 8.6%, and asset longevity expands through precise algorithmic prognosis.
Keywords: Predictive Maintenance, Smart Manufacturing, Cyber-Physical Systems, Convolutional Neural Networks, Bidirectional Long Short-Term Memory, Remaining Useful Life