Aug 2026· SPE Nigeria Annual International Conference and Exhibition· 0 citations· 13 references
TL;DR
This research proposes an AI-driven, edge-based system for electric motor health monitoring and predictive maintenance using multi-sensor data, and achieves low latency, reduced bandwidth usage, and fast on-site decision-making using TinyML, an edge AI framework.
Abstract
Electric motors are the backbone of industrial productivity, safety, and energy efficiency, making their reliable operation essential to modern manufacturing. As Industry 4.0 reshapes how equipment is managed, predictive maintenance has emerged as a critical capability. Yet traditional condition monitoring still depends largely on routine servicing, periodic inspections, centralised processing, and predefined thresholds – approaches that limit early fault detection and offer little support for real-time monitoring. This research proposes an AI-driven, edge-based system for electric motor health monitoring and predictive maintenance using multi-sensor data. The system integrates vibration, temperature, and electrical current sensors for continuous data acquisition, and a deep learning classifier was developed to predict motor health and detect faults at an early stage. To achieve low latency, reduced bandwidth usage, and fast on-site decision-making, the model was optimised and deployed using TinyML, an edge AI framework. A companion web application supports remote monitoring, real-time visualisation, and maintenance alerts. Using an industrial dataset of 8,000 records, the TinyML model was trained, optimised, and deployed on an Espressif ESP32 microcontroller. Through spectral analysis and 8-bit quantisation, the system achieved a classification accuracy of 92.3% with an inference latency of just 16 ms, while consuming only 13.4 KB of RAM and 95 KB of Flash memory. These results demonstrate that high-speed, decentralised predictive maintenance is feasible on low-power hardware, offering a scalable solution for the energy and manufacturing sectors, reducing unplanned downtime and enabling rapid response to abnormal operating conditions.
An end-to-end predictive maintenance system is proposed for high stress mechanical drivetrain and rotating machinery and the architecture proposed combines an industrial Internet of Things edge sensory network and hybrid machine learning and deep learning pipelines.
Ashish Kumar, Md Mohtab Alam, N. Priya et al.· International journal of com...· 0 citations
An Industrial Internet of Things (IIoT)-based Predictive Maintenance System that integrates smart sensors, edge computing, cloud analytics, artificial intelligence, and digital twin technology is proposed that 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
Industrially used electric motors are crucial parts of industries and process industries, which failure might lead to
significant financial losses due to the unplanned downtime.
Current methods of motor maintenance based on regular inspections and reactive maintenance strategies are unable to detect
the early symptoms of the motor condition deterioration.
This paper describes the development of the Intelligent Industrial Motor Fault Diagnosis and Predictive Maintenance System
using the principles of data fusion from multi-sensors and machine learning for monitoring of the motor condition and its
further faults prediction.
The proposed system includes ESP32 microcontroller along with multiple sensors measuring the important operating parameters,
namely motor current, vibration, rotation speed (RPM), and gyroscope movement. The Wokwi simulation environment has been
created to mimic various operating states of the motor, namely, normal, overloaded, vibrations and fault conditions. The
collected sensor data are used as the training dataset for a machine learning model to automatically predict the state of the motor
health.
The proposed framework provides the ability to use simulation data generation along with machine learning algorithms for a
cost-efficient intelligent monitoring of motor conditions without the need for expensive industrial testing setups. The proposed
methodology is a solid base for future work and can be implemented in industry using IoT remote monitoring and cloud
predictive maintenance.
Piyush Chinde· International Journal for Re...· 0 citations
The rapid evolution of Industry 4.0 has accelerated the adoption of intelligent manufacturing systems requiring real-time monitoring, predictive maintenance, and autonomous decision-making. Traditional cloud-based solutions often suffer from latency, bandwidth limitations, and data privacy concerns, making them unsuitable for time-critical industrial applications. This paper presents an Edge AI-based autonomous monitoring framework that integrates Industrial Internet of Things (IIoT) sensors, edge computing, deep learning models, and cloud platforms for efficient industrial monitoring. Real-time sensor data, including temperature, vibration, pressure, humidity, and power consumption, are processed locally using Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and anomaly detection algorithms to identify equipment faults and optimize operations. Only summarized insights are transmitted to the cloud, reducing communication overhead while enabling scalable enterprise-level analytics. The proposed framework improves prediction accuracy, minimizes downtime, enhances product quality, strengthens cybersecurity, and supports sustainable manufacturing. It provides a scalable and resilient solution for smart factories across industries, enabling intelligent automation, predictive analytics, and efficient autonomous industrial operations.
Narendra Karmarkar· International Journal of Mod...· 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
Motors are crucial elements in the industry, where unexpected failures can interrupt production cycles, reduce profits, and raise safety concerns; and therefore an early anomaly detection in motor behavior is highly appreciated. As an extension of the known internet of things (IoT), industrial IoT or IIoT allows connection of motors and their drive units through distributed sensing platforms capable of acquiring operational data related to power, temperature, vibration, and rotational speed. Once these data are transferred through the available IIoT infrastructure and stored appropriately for later off-line processing, the limited availability of labeled fault data remains a major obstacle in practical industrial applications. As a remedy, this study proposes a semi-supervised anomaly detection framework that relies exclusively on non-intrusive three-phase electrical telemetry. Focusing on a commercial offset printing press, high-frequency power measurements were collected from failure-sensitive dryer motors. Time-domain statistical features, including mean, clearance factor, and shape factor extracted from active power, power factor, and current signals, were employed to train an unsupervised One-Class Support Vector Machine (OC-SVM). Experimental results obtained from 84 hours of real industrial telemetry demonstrated the effectiveness of the proposed approach in modeling normal operating behavior, achieving a Recall of 93.79%, a False Positive Rate of 5.12%, and an F1-Score of 94.59%. The developed framework enables early anomaly detection, lowers maintenance expenses, reduces operational downtime, and enhances overall system reliability, supporting the advancement of smart Industry 4.0 environments.
M. Zeidan, S. Aldalahmeh, Z. Haymoor et al.· IEEE Jordan Conference on Ap...· 0 citations
