Multi-Sensor Fusion and Frequency-Domain Analysis for Predictive Maintenance of Industrial Induction Motors

Smart Predictive Maintenance for a DC Motor Using IoT and Machine Learning

Machine learning-driven fault detection in rotating machinery: A predictive maintenance framework

A new predictive maintenance model that incorporates multi-domain feature extraction, and hybrid feature selection approach and optimized ensemble classifier to facilitate high-fidelity fault detection in four operational modes is proposed, affirming its suitability for deployment in industrial cyber-physical monitoring systems.

Cold-Start Motor Condition Monitoring Using Multi-Sensor Signals and Pseudo-Supervised Machine Learning.

As predictive maintenance transitions from the data-centric paradigm of Industry 4.0 to the sustainable, human-centric framework of Industry 5.0, diagnosing servo motor conditions faces the dual challenges of data scarcity and a profound lack of labeled fault samples. To address this cold-start problem, we present a pseudo-supervised machine learning framework evaluated on a custom five-channel dataset comprising 199 servo motor telemetry samples (current, voltage, temperature, humidity, and vibration). The methodology integrates hard structural partitioning (k-means) and soft posterior confidence estimation (Gaussian Mixture Models) to characterize operating modes without prior annotation. Concurrently, an Isolation Forest model quantifies anomaly intensity and establishes a dynamic quantile-based threshold. A critical innovation of this research is the deterministic risk mapping derived from engineering priors; it defines the "high-risk" (abnormal) state by inversely weighting the physical safety margins of the sensors. This mechanism strictly maps unsupervised clusters to binary pseudo-labels. These labels are subsequently used to supervise downstream discriminators (Random Forest and Support Vector Machine). The final online diagnostic outputs a score-level fusion of the classifier probability and the GMM posterior, gated by the anomaly threshold. Quantitative evaluation demonstrates that the Random Forest model achieved a perfect F1 score of 1.000, while the comparative SVM yielded an F1 score of 0.997, proving the framework to be a robust, interpretable, and highly accurate solution for cold-start industrial health monitoring.

IMPLEMENTATION OF IOT-BASED PREDICTIVE MAINTENANCE FOR INDUCTION MOTORS IN INDUSTRIAL ENTERPRISES

This paper presents a complete Internet of Things (IoT) based predictive maintenance system for real-time condition monitoring of three-phase induction motors that prevents production stoppages and avoiding premature motor replacement.

Multi-Sensor Vibration-Based Classification of Shaft Misalignment Severity Using Interpretable Machine Learning: An Experimental Study

This work investigates the application of data driven techniques for classification of misalignment severity using vibration measurements acquired from multiple sensor locations. Experiments were conducted under four operating conditions representing healthy operation and three levels of shaft misalignment at several motor speeds and loads. A set of time-domain and frequency-domain features was extracted and used to train Random Forest classifier. Separate models were developed for two individual sensors as well as for a combined multi-sensor feature set. While overall accuracies were comparable (0.879-0.895), cross-condition validation using Leave-Condition-Out (LCO) revealed that the sensors exhibit different sensitivities to operational changes. Analysis of prediction disagreement showed that 12% of signal segments were correctly identified by only one of the sensors, particularly at the lowest operating speed (20 Hz). This suggests that multi-sensor fusion enhances reliability in scenarios where individual sensor signals are weak. To improve interpretability, SHAP-based explanation techniques were applied to the fusion model. The analysis showed that several physically meaningful features - such as spectral bandwidth, RMS value, vibration range, and form factor - play a dominant role in the classification process and exhibit clear relationships with fault severity. The results demonstrate that while sensor fusion provides marginal gains in average accuracy, it effectively reduces diagnostic “blind spots”, offering a more robust framework for automated misalignment evaluation within the studied operational range.

Comparative Evaluation of Machine Learning Algorithms for Fault Diagnosis in Automotive Press Lines

The results show that the proposed Condition Monitoring (CM) approach significantly reduces resource waste and prevents costly downtime, offering a practical and scalable asset management model for industrial applications.