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

Abstract. The traditional condition monitoring methods, such as threshold-driven vibration warning systems and periodic check-up programs, do not have the discriminative ability needed to differentiate between incipient faults signatures and normal operational variation. This paper proposes 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, which are normal operation, inner-race bearing fault, outer-race bearing fault, and gear tooth spalling. Vibration signals are acquired at 12 kHz using tri-axial accelerometers, pre-processed through adaptive band-pass filtering and Z-score normalization, and subsequently transformed into a 47-dimensional feature space spanning time-domain statistics (RMS, kurtosis, skewness, crest factor), fast Fourier transform (FFT) spectral amplitudes, and discrete wavelet transform (DWT) energy coefficients. A two-stage feature selection pipeline—combining mutual information ranking with recursive feature elimination reduces the feature space to 18 optimal descriptors. An ensemble of random forest with Bayesian-optimization in cross validation of 10 folds and stratified cross-validation reach a mean accuracy of 98.7 percent, which is statistically better than support vector machine (SVM) and Artificial Neural Network (ANN) baselines by 4.1 and 2.9 percentage points respectively. The proposed framework demonstrates strong generalisability across variable operating speeds and load conditions, 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

Induction motors account for approximately 40–45% of global industrial electricity consumption. Unplanned downtime due to stator faults, bearing failures, and rotor imbalances costs manufacturing enterprises an estimated $15–20 billion annually. This paper presents a complete Internet of Things (IoT) based predictive maintenance system for real-time condition monitoring of three-phase induction motors. The proposed system integrates low-cost wireless vibration sensors, thermal imaging modules, and current signature analyzers connected via a LoRaWAN gateway to a cloud-based analytics platform. A two-year study was conducted across 48 industrial motors (5.5–250 kW) operating in a German automotive parts factory. Using machine learning algorithms (random forest and long short-term memory networks), the system achieved 94.7% accuracy in fault prediction with an average warning time of 312 hours before critical failure. The results demonstrate a 67% reduction in unplanned downtime, a 53% decrease in maintenance costs, and a payback period of 1.2 years. The proposed solution prevents 82 tonnes of CO₂ equivalent per facility per year by reducing production stoppages and avoiding premature motor replacement. This scalable approach can be implemented across various industrial sectors.

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

Minimizing unplanned downtime is critical for maintaining productivity in modern manufacturing. While combining sensor networks with machine learning provides a practical way to detect mechanical failures early, conventional data-driven diagnostics often fail during highly transient stamping operations. This failure stems from severe spectral smearing and signal distortions caused by fluctuating process loads and variable operating speeds. To address these limitations, we present a field-tested fault diagnosis (FD) framework deployed in an active automotive components plant. Over a twelve-month observation period, we collected raw vibration and process data from two operational transfer presses, building a comparative dataset that captures both localized gear damage and healthy baseline dynamics. After preprocessing the data to isolate signal anomalies, we systematically evaluated the diagnostic performance of six algorithms: SVM, Random Forest, Naive Bayes, k-NN, Decision Trees, and Logistic Regression. By integrating angle-based position data from a high-resolution encoder, the developed framework successfully pinpointed specific defective gear teeth. Ultimately, the Random Forest model outperformed the others, delivering the most robust detection accuracy under real-world factory conditions. These 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.