Pump station units under prolonged high-load operation are prone to mechanical faults that threaten the safe and stable operation of water diversion projects. Existing diagnostic methods often face challenges in adaptive parameter optimization of variational mode decomposition (VMD), modal aliasing, and insufficient spatiotemporal feature representation. To address these issues, this study proposes an intelligent fault diagnosis framework based on an improved VMD–convolutional neural network (CNN)–long short-term memory (LSTM)-coupled model. The main contributions are as follows. 1) A dedicated parameter optimization strategy is proposed by enhancing the sparrow search algorithm (SSA) with an Osprey-inspired exploration mechanism and a Cauchy mutation operator (resulting in OCSSA). This approach adaptively optimizes VMD parameters, thus overcoming the limitations of manual tuning and local optima. 2) The optimal intrinsic mode function (IMF) is selected based on envelope entropy to effectively mitigate modal aliasing and noise interference. 3) A CNN–LSTM hybrid architecture is constructed to achieve joint spatiotemporal modeling—CNN extracts local spatial features, while LSTM captures temporal dependencies—addressing the shortcomings of single models in comprehensive feature representation. Fault classification is completed via a fully connected layer and a softmax function. Experimental results show that under 5 dB low signal-to-noise ratio (SNR) conditions, the proposed model achieves 80.95% diagnostic accuracy for typical faults such as rotor misalignment and rubbing—a 12.72 percentage point improvement over the baseline CNN–LSTM model—while maintaining competitive training efficiency. Under 20 dB SNR, the accuracy further reaches 97.50%. The model significantly reduces misdiagnosis rates for complex coupled faults, demonstrating superior robustness and engineering applicability. This integrated framework offers a reliable and deployable solution for the intelligent maintenance of pump station units.
Zhang et al. (Fri,) studied this question.