Adaptive Wavelet Transform and SVM-based Fault Diagnosis with PSO Optimization in Industrial IoT

This paper proposes an innovative joint algorithm based on adaptive wavelet transform (AWT) and support vector machine (SVM) to diagnose data equipment faults in the industrial Internet of Things and optimize the model parameters through particle swarm optimization (PSO) technology. Firstly, adaptive wavelet transform is used to extract time-frequency features of sensor data by adaptively adjusting the wavelet transform according to the wavelet basis function, thereby realizing the extraction of the time-frequency characteristics of the signal. Secondly, an improved support vector machine is used to classify feature data. At last, PSO is applied to improve the precision and efficiency of classification. The experiment results show that the new algorithm has higher precision, computing speed and faster response than the conventional single algorithm. The experimental results show that the proposed AWT-SVM-PSO algorithm achieves an average accuracy improvement of 13% over traditional methods, with the classification accuracy of different fault modes reaching 98%, and the response time is shortened from 300 milliseconds to 200 milliseconds. This project's research results will effectively improve industrial equipment's fault diagnosis capability and provide reliable support for large-scale data processing and real-time monitoring.