Fault diagnosis research of laser gyroscope based on optimized-kernel extreme learning machine

Traditional diagnostic models for laser gyroscopes, widely which are commonly employed as high-precision angular velocity sensors in aerospace applications, often encounter challenges in terms of reliability and accuracy. These challenges arise from difficulties in feature extraction, high computational costs, and lengthy training times. In light of these challenges, the present study proposes a new method for diagnosing faults in laser gyroscopes using the Kernel Extreme Learning Machine (KELM). Specifically, the proposed method utilizes Wavelet Packet Decomposition (WPD) to efficiently extract features from the laser gyroscope signal, which are then used as input for our diagnostic model. Furthermore, the KELM model is trained for fault diagnosis. Afterward, we utilize the Improved Dung Beetle Optimizer (IDBO) algorithm to optimize its parameters for improved optimization performance. According to the experimental results, our proposed IDBO-KELM model demonstrates a 3.68% improvement in diagnostic accuracy compared to traditional approaches. Additionally, it offers the advantages of shorter training time and increased precision.