Online Bearing Fault Diagnosis Based on Packet Loss Influence-Inspired Retransmission Mechanism

Zhe Tong, Wei Li, Enrico Zio, Bo Zhang, Gongbo Zhou
2022 Mathematics  
Vibration response has been extensively used for fault diagnosis to ensure the smooth operation of mechanical systems. However, the data for vibration condition monitoring may be misconstrued due to channel quality issues and external disturbances. In particular, data packet losses that often occur during transmission can cause spectral structure distortion, and as multiple sensing nodes are often employed for condition monitoring, the differences in the spectral structure distortions for
more » ... ent sensing nodes can be significant. While retransmission can reduce packet loss, it is difficult to achieve good performance under the complex conditions. Excessive or insufficient retransmission of data streams can result in unacceptable delays or errors for online fault diagnosis. In this paper, we propose a Packet Loss Influence-inspired Retransmission Mechanism (PLIRM) to address this problem and improve the online diagnostic efficiency. First, we devise a scheme for zero padding based on packet loss model (ZPPL) to preserve intrinsic properties of frequency domain. Then, we formulate a dynamic retransmission scheme generated based on the optimal packet loss mode to minimize the effects of spectral structure distortions. To ensure that the data stream that is most sensitive to a fault will be preferentially transmitted, we apply a priority setting trick using maximum mean discrepancy (MMD) to evaluate the spectral structure discrepancies between a data stream and the historical datasets. We evaluate the retransmission scheme using a fault diagnosis model based on K-nearest neighbor (KNN) for timely online bearing fault diagnosis. Extensive experimental results showed that the proposed method can accurately identify the bearing faults in a timely manner, outperforming competitive approaches under packet loss condition.
doi:10.3390/math10091422 fatcat:mwoxcufskja27irj55wazoyvzu