A Hybrid CNN-BiLSTM Deep Learning Framework for Predictive Maintenance in Industry 4.0 Using Multivariate Time-Series Sensor Data

Abstract

Unplanned equipment failure remains one of the costliest problems in modern manufacturing, driving research toward Predictive Maintenance (PdM) strategies that anticipate failures before they occur using real-time sensor data. This paper proposes a hybrid deep learning framework combining Convolutional Neural Networks (CNN) for local spatial feature extraction and Bidirectional Long Short-Term Memory (BiLSTM) networks with a temporal attention mechanism for capturing long-range degradation trends in multivariate time-series sensor data. The framework is designed to estimate Remaining Useful Life (RUL) and detect early fault signatures in Industry 4.0 environments equipped with IoT-based condition-monitoring sensors. We formulate the problem as a windowed sequence-to-value regression task, describe a sliding-window feature pipeline, and benchmark the framework's data pipeline against classical machine learning regressors (Random Forest, Gradient Boosting, Support Vector Regression, and a Multi-Layer Perceptron) on a run-to-failure sensor dataset. Experimental results show that ensemble tree-based models achieve strong baseline performance (RMSE as low as 9.96 cycles, R2 = 0.946), establishing a validated pipeline and baseline for the full CNN-BiLSTM-Attention model. The proposed architecture, evaluation protocol, and ablation plan are presented in full so the framework can be reproduced and extended on industrial-scale datasets such as NASA C-MAPSS or live plant telemetry.