NDT 2025 , 3 , 7
7of 12
example and the initial Python code snippet (Figure 2), external sensors for additional data (e.g., vibration) can easily be added.
Figure 2. Initial setup of Python code snippet. Note: The Python code was created in PyCharm 2024.3.3 (Professional Edition), Build #PY-243.24978.54, built on 12 February 2025. Matplotlib and data visualization capabilities developed by Python provide the benefit of real-time graphical representations of VFD performance [20]. Instead of manually reviewing log files, engineers can utilize live plots to track anomalies, optimize motor performance, and trigger alerts when the parameters deviate from the expected values shown in Figure 2. By integrating data acquisition, computation, and visualization, Python offers a cost-effective, scalable, and highly customizable solution for VFD monitoring and NDT applications [19,20]. Figure 3 shows the Python code that creates the dashboard, as displayed in Figures 4 and 5. The complete Python code is available in a text file at the link provided under Supplementary Materials.
Figure3. Calculated metrics.
The NDT dashboard and Python code presented serve as the foundation for condition monitoring, enabling the addition of thresholds and alarms based on the monitored applica- tion for trending or acute occurrences, together with reliability considerations (as predictive maintenance) such as the remaining useful life (RUL). Integrating RUL estimation into the NDT dashboard enhances predictive maintenance by providing real-time insights into the health of VFD and rotating equipment. By leveraging historical VFD data and degradation trends, the dashboard can calculate RUL using ML models such as linear degradation, regression-based estimation, or deep learning for sequential failure prediction [8,9]. The Python implementation would utilize VFD time-series data—current, failure thresholds, and degradation rates in this case—to compute RUL dynamically [6]. Future research can explore hybrid AI models, combining Weibull analysis, Bayesian networks, and deep learning to improve prediction accuracy and reliability in industrial applications [5]. Addi- tionally, integrating edge computing for RUL estimation would enable real-time analytics
Made with FlippingBook interactive PDF creator