Advanced Signal Processing Methods for Condition Monitoring

Condition monitoring of induction motors (IM) among with the predictive maintenance concept are currently among the most promising research topics of manufacturing industry. Production efficiency is an important parameter of every manufacturing plant since it directly influences the final price of products. This research article presents a comprehensive overview of conditional monitoring techniques, along with classification techniques and advanced signal processing techniques. Compared methods are either based on measurement of electrical quantities or nonelectrical quantities that are processed by advanced signal processing techniques. This article briefly compares individual techniques and summarize results achieved by different research teams. Our own testbed is briefly introduced in the discussion section along with plans for future dataset creation. According to the comparison, Wavelet Transform (WT) along with Empirical Mode Decomposition (EMD), Principal Component Analysis (PCA) and Park's Vector Approach (PVA) provides the most interesting results for real deployment and could be used for future experiments.

Automated summary

This research article provides a comprehensive overview of condition monitoring techniques for induction motors (IM) and advanced signal processing techniques. The comparison shows that Wavelet Transform (WT) along with Empirical Mode Decomposition (EMD), Principal Component Analysis (PCA), and Park's Vector Approach (PVA) yield the most interesting results for real deployment.