Fault diagnosis of a wave energy converter gearbox based on an Adam optimized CNN-LSTM algorithm

The complex structure and harsh operating environment of wave energy converters can result in various faults in transmission components. Environmental noise in practical operating situations may obscure the effective information in collected vibration signals, significantly increasing the difficulty of fault diagnosis. This paper presents a fault diagnosis model for the gearbox of the point absorber wave energy converter. The model integrates a convolutional neural network with long short-term memory to realize efficient extraction of local features from signals and enhance the performance in time-series analysis. Moreover, the model incorporates the Adaptive Moment Estimation algorithm to address the situations where gradients within tensors exhibit unstable changes in the model. A rigid-flexible coupled dynamics simulation model is developed to simulate vibration signals used to train and verify the fault diagnosis model. Experimental tests of the proposed model on a vibration dataset acquired from real vibration experiments demonstrate its efficacy in diagnosing various types of faults under interference of operating conditions. Comparative studies with other models demonstrate the superiority of the proposed model in terms of fault feature extraction, learning convergence efficiency, and diagnostic accuracy, indicating that the proposed model can achieve faster and more accurate fault diagnosis of wave energy converter gearboxes.