Fault diagnosis method based on order analysis and convolutional neural network

Traditional time and frequency domain analysis methods have limitations in dealing with non-stationary signals, and it is difficult to capture the complex features of the signals. As an effective tool for handling unsteady signals, order analysis shows its superiority especially in complex equipment such as rotating machinery. Its unified processing allows analyzing equipment signals under various operating conditions and extracting meaningful features. These features reflect the state and changes of the equipment and provide high-quality input data for classification algorithms. Classification accuracy and robustness can be significantly improved by combining with Convolutional Neural Networks (CNNs), which excel in image classification by automatically extracting important features through a hierarchical structure, avoiding the need for traditional complex feature engineering. Integration of order analysis and CNN fully combines the advantages of both: the former handles unsteady signals, and the latter efficiently classifies feature extraction. Troubleshooting accuracy of 99.8%.