Synergy Between Resource-Efficient Data Transmission and Precision-Adaptive Fault Diagnosis for High-Frequency Signals

Abstract

Real-time transmission of high-frequency signals in online fault diagnosis challenges the limited bandwidth. However, reducing the volume of data transmission will compromise the data quality and drop the accuracy of fault diagnosis. This article synergizes data transmission and fault diagnosis to simultaneously achieve high transmission reduction ratio and fault diagnostic accuracy. First, a novel long sequence dual prediction scheme (L-DPS) is proposed to reduce the high-frequency data transmission online while ensuring the data precision. Second, a resource-efficient transformer model is proposed to improve the precision and speed of long sequence prediction in L-DPS, thus effectively improving its transmission reduction ratio and applicable frequency. Finally, a precision-adaptive fault diagnosis model is proposed to tackle precision differences in the transmission-restored data, thus effectively improving the accuracy of fault diagnosis. Experiments based on the real-world dataset confirm that the solution can cope with high-frequency data up to 20.69 KHz and achieve 94.16% transmission reduction and 99.43% fault diagnosis accuracy.