Abstract
Industrial machinery failures can lead to production downtime, financial loss, and safety risks. This project proposes a hybrid predictive maintenance system that estimates the Remaining Useful Life (RUL) of machines using multivariate time-series sensor data. Sequential operational readings are analyzed using deep learning models along with statistical and relational feature extraction to capture degradation behaviour over time. The extracted features are integrated through a fusion-based prediction model to generate reliable lifespan estimates. To improve interpretability, the system also includes a language-model-based explanation module that converts prediction outputs into human-readable maintenance guidance. An interactive dashboard is developed for real-time prediction, visualization, and monitoring. The proposed system aims to support proactive maintenance planning and reduce unexpected industrial equipment failures.
Keywords
Predictive Maintenance, Remaining Useful Life, Industrial Monitoring, Time-Series Analysis, Deep Learning, Sensor Modeling, Multimodal Fusion, Explainable AI.









Introduction
Modern industrial systems rely heavily on the continuous operation of machinery to maintain productivity, safety, and efficiency. Unexpected equipment failures can result in production downtime, financial loss, and operational risks. Traditional maintenance approaches such as reactive repair or fixed-interval preventive servicing do not effectively utilize real-time machine condition data, often leading to inefficient maintenance planning.
With the rapid growth of industrial automation and sensor deployment, large volumes of operational data are now available from machines. These multivariate sensor measurements provide valuable insight into machine health and degradation behaviour over time. Predictive maintenance systems aim to analyze such data to estimate the Remaining Useful Life (RUL) of machinery, allowing maintenance to be scheduled proactively before failure occurs.
This project proposes a hybrid predictive maintenance framework that combines deep learning, statistical sensor analysis, relational modeling, and explainable artificial intelligence to estimate machine lifespan and provide interpretable maintenance guidance.

Objectives
• To develop a predictive maintenance system for estimating Remaining Useful Life using sensor data.
• To analyze sequential operational measurements and identify degradation behaviour.
• To implement deep learning models for temporal pattern extraction.
• To extract statistical features representing machine operating conditions.
• To model dependencies among sensor variables for improved system understanding.
• To combine multiple predictive components using a fusion-based approach.
• To generate human-readable maintenance explanations using language-model assistance.
• To design an interactive dashboard for real-time prediction and visualization.