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efficiency can be used to identify early-stage failures such as bearing wear, misalignment, and insulation degradation. This approach not only reduces hardware costs but also streamlines condition monitoring, making predictive diagnostics more accessible and scalable across various industrial sectors. The real-time monitoring system described in this review and application illustrates how Python-based data acquisition aids in extracting, analyzing, and visualizing vital VFD parameters. With fieldbus protocols such as Modbus TCP/IP, essential drive data can be continuously logged and integrated into SCADA or cloud-based platforms for remote monitoring. Moreover, calculated metrics such as efficiency, torque, and energy consumption offer deeper insights into motor performance and operational health. The key benefits of VFD-integrated NDT include reduced costs due to minimized reliance on external sensors, real-time condition monitoring for continuous diagnostics, scalability across diverse industrial applications, and AI-driven predictive analytics for automated fault detection and maintenance planning. In addition, AI and ML are promising technologies for improving VFD-based diag- nostics. By training models on historical VFD data, AI-driven systems can identify failure patterns before they develop into critical issues, facilitating proactive maintenance strate- gies. Future research should focus on advancing deep learning techniques for VFD-based anomaly detection, exploring multi-sensor fusion by integrating VFD data with vibra- tion and thermal imaging, and expanding IIoT predictive maintenance frameworks for large-scale industrial applications. Ultimately, the expanded role of VFD data in NDT signifies a transformative shift in industrial reliability management. By utilizing real-time diagnostics, predictive analytics, and scalable automation, industries can achieve greater efficiency, reduced downtime, and improved equipment longevity. As industrial automation continues to evolve, VFD- integrated NDT solutions will be vital in advancing condition monitoring technologies and ensuring the reliability of essential assets.
Supplementary Materials: The Python code created and used in this paper can be found at https: //doi.org/10.6084/m9.figshare.28620839.v1 (accessed on 18 November 2024).
Funding: This research received no external funding. Institutional Review Board Statement: Not applicable. Data Availability Statement: Not applicable. Conflicts of Interest: The author declares no conflicts of interest. Abbreviations The following abbreviations are used in this manuscript:
AI Artificial intelligence CNN Convolutional neural network DC Direct current DCS Distributed control system GAN Generative adversarial network GMM Gaussian mixture model HART Highway addressable remote transducer IIoT Industrial Internet of Things IP Internet protocol ML Machine learning MQTT Message queuing telemetry transport NDT Nondestructive testing
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