Leveraging Variable Frequency Drive Data for Nondestructive Testing and Predictive Maintenance in Industrial Systems
Nondestructive testing (NDT) has a crucial role in ensuring the reliability and safety of industrial systems. However, traditional methods typically rely on external sensors, which can lead to increased costs and added complexity. The current study examined an alternative approach using variable-fre...
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MDPI AG
2025-03-01
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| Series: | NDT |
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| Online Access: | https://www.mdpi.com/2813-477X/3/2/7 |
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| author | Carl Lee Tolbert |
| author_facet | Carl Lee Tolbert |
| author_sort | Carl Lee Tolbert |
| collection | DOAJ |
| description | Nondestructive testing (NDT) has a crucial role in ensuring the reliability and safety of industrial systems. However, traditional methods typically rely on external sensors, which can lead to increased costs and added complexity. The current study examined an alternative approach using variable-frequency drive (VFD) data for real-time fault detection and predictive maintenance. Most VFDs continuously monitor essential parameters such as motor speed, torque, efficiency, and power consumption, facilitating sensorless condition monitoring that helps detect early-stage motor and apparatus faults without additional hardware. To improve diagnostic capabilities, calculated metrics such as apparent power, efficiency, torque, and energy consumption can deliver more profound insights into system performance, assisting in identifying potential failure patterns. A Python-based data acquisition and visualization system was developed and implemented as an example of a potential solution, enabling centralized monitoring, anomaly detection, and historical data analysis. Future advancements in artificial intelligence and machine learning could further refine automated fault detection by utilizing historical VFD data to predict system failures accurately. By integrating VFD-based diagnostics into NDT, industries can develop scalable, cost-effective, intelligent testing and maintenance solutions that improve reliability and asset management in modern systems. |
| format | Article |
| id | doaj-art-e6275daa4deb405b87a805837100bbd7 |
| institution | DOAJ |
| issn | 2813-477X |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | NDT |
| spelling | doaj-art-e6275daa4deb405b87a805837100bbd72025-08-20T03:16:24ZengMDPI AGNDT2813-477X2025-03-0132710.3390/ndt3020007Leveraging Variable Frequency Drive Data for Nondestructive Testing and Predictive Maintenance in Industrial SystemsCarl Lee Tolbert0Institute for Globally Distributed Open Research and Education (IGDORE), USANondestructive testing (NDT) has a crucial role in ensuring the reliability and safety of industrial systems. However, traditional methods typically rely on external sensors, which can lead to increased costs and added complexity. The current study examined an alternative approach using variable-frequency drive (VFD) data for real-time fault detection and predictive maintenance. Most VFDs continuously monitor essential parameters such as motor speed, torque, efficiency, and power consumption, facilitating sensorless condition monitoring that helps detect early-stage motor and apparatus faults without additional hardware. To improve diagnostic capabilities, calculated metrics such as apparent power, efficiency, torque, and energy consumption can deliver more profound insights into system performance, assisting in identifying potential failure patterns. A Python-based data acquisition and visualization system was developed and implemented as an example of a potential solution, enabling centralized monitoring, anomaly detection, and historical data analysis. Future advancements in artificial intelligence and machine learning could further refine automated fault detection by utilizing historical VFD data to predict system failures accurately. By integrating VFD-based diagnostics into NDT, industries can develop scalable, cost-effective, intelligent testing and maintenance solutions that improve reliability and asset management in modern systems.https://www.mdpi.com/2813-477X/3/2/7nondestructive testingvariable frequency drivepredictive maintenanceindustrial monitoringPythonreliability engineering |
| spellingShingle | Carl Lee Tolbert Leveraging Variable Frequency Drive Data for Nondestructive Testing and Predictive Maintenance in Industrial Systems NDT nondestructive testing variable frequency drive predictive maintenance industrial monitoring Python reliability engineering |
| title | Leveraging Variable Frequency Drive Data for Nondestructive Testing and Predictive Maintenance in Industrial Systems |
| title_full | Leveraging Variable Frequency Drive Data for Nondestructive Testing and Predictive Maintenance in Industrial Systems |
| title_fullStr | Leveraging Variable Frequency Drive Data for Nondestructive Testing and Predictive Maintenance in Industrial Systems |
| title_full_unstemmed | Leveraging Variable Frequency Drive Data for Nondestructive Testing and Predictive Maintenance in Industrial Systems |
| title_short | Leveraging Variable Frequency Drive Data for Nondestructive Testing and Predictive Maintenance in Industrial Systems |
| title_sort | leveraging variable frequency drive data for nondestructive testing and predictive maintenance in industrial systems |
| topic | nondestructive testing variable frequency drive predictive maintenance industrial monitoring Python reliability engineering |
| url | https://www.mdpi.com/2813-477X/3/2/7 |
| work_keys_str_mv | AT carlleetolbert leveragingvariablefrequencydrivedatafornondestructivetestingandpredictivemaintenanceinindustrialsystems |