Jun 2026· Journal of Electrical Engineering· Vol 1, pp. 54-67· 0 citations
TL;DR
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.
Abstract
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.
Predictive maintenance has become a key application of the Industrial Internet of Things (IIoT), helping industries improve equipment reliability and operational efficiency. Conventional maintenance approaches, such as reactive and preventive maintenance, often result in unexpected equipment failures, increased maintenance costs, and inefficient use of resources. By integrating real-time sensor monitoring with machine learning techniques, predictive maintenance enables early detection of potential faults, allowing maintenance activities to be performed only when necessary. This study presents a real-time predictive maintenance framework for Industrial IoT systems using machine learning. The proposed solution collects live sensor data, including temperature, vibration, and pressure, from industrial equipment. The data is preprocessed and analyzed using Python-based tools before being fed into machine learning models to identify anomalies and predict potential equipment failures. The system provides timely maintenance recommendations, minimizing unplanned downtime and improving overall equipment performance. Experimental results demonstrate that the proposed framework achieves high fault prediction accuracy, enhances system reliability, reduces maintenance costs, and supports data-driven decision-making in industrial environments.
Keywords—Industrial Automation, Machine Learning, Predictive Maintenance, Sensor Networks, Equipment Failure Prediction.
Govind D. More, Shreyas Hon, Piyush Kotkar et al.· International Journal of Cre...· 0 citations
This research presents an internet of things (IoT-based) system for real-time monitoring and control of a three-phase induction motor that enables continuous monitoring, early fault detection, and predictive maintenance, thereby improving overall operational efficiency and reducing downtime.
Y. S. Pawar, Sandip Rahane, A. Thakare et al.· Bulletin of Electrical Engin...· 0 citations
The rapid adoption of Industry 4.0 technologies has transformed modern manufacturing by enabling intelligent monitoring and automation of industrial equipment. However, unexpected machine failures continue to cause production downtime, increased maintenance costs, and reduced operational efficiency. Predictive maintenance has emerged as an effective strategy to address these challenges by forecasting equipment failures before they occur. This paper proposes an Industrial Internet of Things (IIoT)-based Predictive Maintenance System that integrates smart sensors, edge computing, cloud analytics, artificial intelligence, and digital twin technology. The proposed framework continuously collects machine parameters such as vibration, temperature, pressure, current consumption, and acoustic signals through connected sensors. The collected data is analyzed using machine learning algorithms to identify anomalies, estimate remaining useful life (RUL), and generate maintenance recommendations. A digital twin model provides a virtual representation of industrial assets, enabling real-time simulation and performance evaluation. Experimental results demonstrate significant improvements in fault detection accuracy, equipment availability, and maintenance efficiency while reducing downtime and operational expenses. The proposed system contributes to the development of intelligent and self-optimizing industrial environments aligned with Industry 4.0 objectives.
Gajula Prasad Gajula Prasad, Bolloju Divya Sri Bolloju Divya Sri, Dr B Ramprasad Dr B Ramprasad· International Journal of Sci...· 0 citations
Unplanned failures of induction motors impose serious operational and financial penalties on industrial facilities, yet the fault signatures that precede such failures are detectable well in advance through careful sensor instrumentation and data-driven analysis. This paper presents an end-to-end Internet-of-Things (IoT) predictive maintenance scheme based on two off-the-shelf sensors: a DS18B20 one-wire digital thermometer and 2 piezo vibration sensor modules, with an ESP32 edge device for running the full machine learning pipeline offline, independent from any cloud services. Four operating scenarios are considered: healthy condition, BPFO (bearing outer race fault), misaligned shaft, and rotor imbalance. Based on 1-second sampling intervals, 17 descriptors are derived, including statistics in the time domain, Fourier harmonic peaks, energy ratios between different frequency bands, and temperature gradients measured across all sensors. A dual-stage feature selection method using mutual information (MI) score and Random Forest mean decrease impurity (MDI) ranking reduces the number of features to the 10 most relevant descriptors, reducing the computational complexity by 41% at the expense of 5.6% F1-macro. On a balanced 600-sample synthetic dataset, the resulting Random Forest classifier attains 87.3% hold-out accuracy, 91.0±1.9% five-fold cross-validation accuracy, and a macro area-under-the-ROC-curve of 0.980. End-to-end inference takes just 39 ms on the ESP32, easily meeting the 200 ms requirement for real-time alerting.
Akash Mastud, Dhiraj Vaidya, Azaroddin Sayyed et al.· International Conference on...· 0 citations
Predictive maintenance (PdM) in Industrial Internet of Things (IIoT) environments plays a vital role in minimizing unplanned downtime, improving operational efficiency, and spreading equipment lifespan. This paper presents a Machine Learning (ML)-based predictive maintenance basis deployed on Google Cloud AI Platform for real-time monitoring and fault prediction of manufacturing milling machine devices. The proposed system develops sensor-generated operational data, including torque, rotational speed, temperature, and tool wear, to train and evaluate multiple ML models such as Decision Tree, K-Nearest Neighbors (KNN), Gradient Boosting, Support Vector Machine (SVM), Gaussian Naïve Bayes, and Logistic Regression. The confirmed models, the Decision Tree classifier reached the highest accuracy of 99.40%, with strong cross-validation and AUC performance, indicating larger capability in detection machine failures. By fit in cloud-based AI services, the framework ensures scalable model deployment, high availability, and efficient real-time predictive analytics for manufacturing applications. Experimental findings reveal important improvements in prediction accuracy and conservation cost reduction associated to conventional reactive maintenance approaches. The study confirms the efficiency of combining IIoT sensor analytics, ML, and cloud-based AI structure for intelligent and proactive industrial conservation systems.
More Praveen, A. Lakshman, V.Jyothi et al.· International Conference Com...· 0 citations
Unexpected equipment failures are known to be one of the most critical causes of production losses. Such failures can result in increased maintenance cost, compromise safety of operators and increase operational inefficiency. Therefore, there is a growing interest to monitor equipment during normal operation and predict possible failure before it actually occurs. This paper describes a smart Industrial Internet of Things (IIoT) framework to monitor industrial equipment in real time using ensemble learning for failure prediction. The framework initially collects a variety of real time operating parameters of industrial equipment such as temperature, vibration, speed, torque, pressure, etc from various industrial sensors. The collected data is then preprocessed using techniques such as treatment of missing values, noise removal, feature scaling, handling of class imbalance and feature selection to select most relevant features. The preprocessed data is then fed into various machine learning models such as random forest, support vector machine, gradient boosting and multi layer perceptron. A stacking-based ensemble model is used to combine the predictions of individual models to improve the overall prediction accuracy and stability. The final output of the framework is a classification of equipment condition into normal or failure-prone, along with a probability of failure. Shapley Additive explanations (SHAP) is used to provide both global and local interpretability to the predictions made by the model. The performance of the model is evaluated using various metrics such as accuracy, precision, recall, F1-score, area under receiver operating characteristic curve, false-alarm rate and inference time. The framework has potential to provide better failure detection using combination of IIoT monitoring and explainable AI, and provide understandable reasons for failure to maintenance personnel responsible for maintenance.
Enoch Success Boakai, P. A. Mary, R. Singh et al.· International journal of com...· 0 citations