Abstract
The acoustic-based internal defect detection is essential to ensure the quality of arc magnets efficiently. Swarm decomposition (SWD) is conducive to processing acoustic signals, but it is still confronted with threshold optimization problems. Especially, the existing optimization methods for the SWD thresholds are merely available for a single signal with exclusive characteristics, instead of the various signals with similar characteristics. Therefore, a threshold-optimized SWD using grey wolf optimizer (GWO) is proposed to solve these issues and applied to detect the internal defects of arc magnets. In this method, a fitness function is designed to indicate the relationship between the SWD thresholds and the overall decomposition effect of similar signals. The minimum value of it corresponds to the threshold setting yielding the optimal decomposition. GWO is used for searching such a minimum value, and the obtained optimal threshold setting allows SWD to decompose any signal into a series of oscillatory components. The frequency information in the two oscillatory components with the highest energy ratio is extracted as the internal defect features. Random forest is carried out to identify these features. Experimentally, the detection accuracy reaches above 97%, and the detection speed per single arc magnet does not exceed 3.4 seconds. The proposed method cannot only determine the unified threshold setting of SWD for similar signals but also achieve an accurate, rapid detection for the internal defects.
Highlights
Arc magnet is a tile-shaped magnetic segment, which is used to form a constant magnetic field within motors [1]. e quality of arc magnets plays a decisive role in the performance and life of motors
We propose an acoustic signal analysis method for detecting the internal defects of arc magnets based on the optimized Swarm decomposition (SWD), in which grey wolf optimizer (GWO) is used to determine the optimal Pth and StDth thresholds of SWD. e fitness function used for GWO is designed to denote the relationship between the SWD thresholds and the overall optimal decomposition performance of similar signals. e experimental results demonstrated that the proposed method realizes a unified optimization of SWD thresholds for the various similar signals and offers the reliable performance to detect the existence of internal defects of arc magnets rapidly and accurately
In order to fairly compare the characteristics of signals that are likely to be of different magnitudes, each signal of selected samples is normalized by the proportion of the signal amplitude to its maximum value
Summary
Arc magnet is a tile-shaped magnetic segment, which is used to form a constant magnetic field within motors [1]. e quality of arc magnets plays a decisive role in the performance and life of motors. The internal defect detection of arc magnets is important to ensure the performance and safety of motors. E commonly used detection strategy is to determine whether the internal defects exist through the sensitive human hearing when an arc magnet collides with a mental block, since internal defects are able to change the acoustic and vibration characteristics of an arc magnet being excited [3]. Such a detection method has low efficiency and poor accuracy, and, more importantly, is prone to be errors because of
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