Predictive Maintenance of Sensors: A Machine Learning Approach for Proactive Anomaly Detection

Abstract

Sensor-based monitoring systems form the backbone of modern industrial and safety-critical infrastructures. Failures in sensors can cause unexpected downtime, economic loss, and hazardous conditions in automated environments. This research proposes a hybrid machine learning framework for predictive maintenance that integrates regression, classification, and clustering techniques to estimate the Remaining Useful Life (RUL) of sensors, predict maintenance requirements, and detect anomalies. The developed model leverages supervised learning for RUL and maintenance prediction, combined with K-Means clustering for unsupervised anomaly detection. A user-centric Streamlit dashboard enables real-time visualization and operational decision-making. The system functions completely offline, ensuring data privacy in restricted environments. Experiments on a balanced simulated dataset demonstrate accurate anomaly detection and effective predictive insights, highlighting the framework’s potential for adoption in real-world industrial systems.

Introduction

In Industry 4.0, sensors are vital for data collection, process optimization, automation, and safety. However, sensor degradation and drift can lead to inaccurate readings, causing operational downtime, financial loss, and safety risks. Traditional reactive or preventive maintenance strategies are inefficient, prompting the adoption of predictive maintenance, which uses machine learning to forecast failures and schedule timely interventions.

This study proposes a hybrid predictive maintenance framework combining regression, classification, and clustering models to:

• Predict Remaining Useful Life (RUL) of sensors,

• Classify maintenance needs,

• Detect anomalies via K-Means clustering,

• Provide an interactive offline Streamlit dashboard for visualization and decision support.

The methodology includes comprehensive data preprocessing (handling missing values, scaling, outlier retention, feature engineering), and a model architecture where:

• Regression predicts RUL,

• Classification flags sensors needing maintenance,

• Clustering identifies anomalous behavior,

• A hybrid rule fuses outputs for robust anomaly detection.

The framework integrates data acquisition, preprocessing, model inference, hybrid evaluation, and visualization in a seamless workflow, using a three-tier architecture: data layer, business logic layer, and presentation layer. This approach enhances reliability, interpretability, and proactive maintenance compared to traditional threshold-based systems.

Conclusion

This research demonstrates a complete predictive maintenance framework that leverages machine learning for sensor anomaly detection and Remaining Useful Life (RUL) prediction. By integrating regression, classification, and clustering models within a Streamlit-based dashboard, the system bridges predictive intelligence with usability. The results indicate that the framework achieves a balanced trade-off between accuracy, interpretability, and offline functionality. It provides a scalable foundation for integrating real sensor data in future industrial implementations.

References

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Copyright

Copyright © 2025 Rohan Khirswal, Swastik Arora, Rudra Arora, Soumya Ranjan, Ms. Preeti Kalra. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.