Jul 2026· IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies· pp. 7-13· 0 citations· 20 references
Abstract
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.
Electrical Submersible Pumps (ESPs) are critical artificial lift assets whose unexpected failure causes significant non-productive time and workover costs, with unplanned shutdowns lasting up to several weeks. Existing monitoring systems rely on reactive, threshold-based alarms applied to surface measurements. These traditional methods cannot detect incipient faults—such as early-stage gas locking, progressive impeller erosion, and developing bearing friction—and struggle to distinguish between faults that produce overlapping, single-channel signal signatures. While the governing equations of ESP operation are well established, their direct application to fault detection remains impractical in the field. Fault-relevant parameters like effective fluid density are not directly measurable from surface instrumentation, and differentiating noisy speed measurements to recover analytical quantities amplifies uncertainty to impractical levels. To address these limitations, this paper presents MLSensor, an edge-deployed machine learning framework that bridges physics-based understanding with data-driven implementation. To overcome the scarcity of labelled field data, a multi-domain Digital Twin was developed in OpenModelica, coupling Kloss motor dynamics with Affinity Law hydraulics. This twin generated twelve labelled simulation runs across three fault types at three severity levels, incorporating Gaussian sensor noise at 10–45 dB SNR to mimic real-world conditions. A 46-element feature vector, including four novel cross-modal electrical-hydraulic decoupling features (such as the highly discriminative decouplingIQ, was extracted per 2-second window. A Random Forest classifier achieved a 100% precision, recall, and F1-score classification accuracy under a temporal train/test split, validating the discriminative completeness of the cross-modal feature representation on simulation data. Finally, the trained model was deployed on an ESP32 microcontroller, achieving a 427 µs average inference latency while utilizing only 23% of program flash and 6% of SRAM, proving the viability of cloud-independent, real-time edge inference.
T. M. Busoye, Q. A. Jokomba, O. M. Busoye et al.· SPE Nigeria Annual Internati...· 0 citations
A novel self-supervised strategy for effective single-machine training based on classifying the distance between the monitored machine and each microphone sensor of a multi-channel recording system is introduced, providing a cost-efficient and privacy-preserving alternative while delivering competitive detection performance.
Erich Malan, Valentino Peluso, A. Calimera et al.· IEEE Access· 0 citations
The proposed IoT-MDS-EDFIM-IGANN framework is efficient, accurate, and cost-effective solution for induction motor fault diagnosis and combines advanced preprocessing, class balancing, feature extraction, and optimization to achieve reliable predictive maintenance and promote operational reliability of industrial induction motor systems.
G. Rayappan, V. Duraisamy, D. Somasundareswari· Journal of Vibration Enginee...· 0 citations
This paper proposes a knowledge-based input configuration to inform deep learning models for both electrical and mechanical fault diagnosis, rather than increasing model complexity, and confirms that, while conventional feature processing techniques perform well for electrical fault diagnosis, only the proposed FFT-informed input effectively captures both electrical and mechanical fault patterns.
Jingyi Yan, Hariram Arni, Bin Jou et al.· Measurement· 1 citation
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
A structured methodology for ML-based PdM frameworks, covering data-driven, physics-based, and hybrid approaches, including supervised, unsupervised, and deep learning models is proposed, offering valuable insights for developing efficient and scalable PdM solutions.
Sithik Shah· International Journal of App...· 0 citations
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