Multistage centrifugal pumps (MCPs) are critical components in industrial systems, where early and reliable fault diagnosis remains challenging due to nonstationary operating conditions, noise contamination, and limited fault sensitive information in single domain representations. To address these issues, this paper proposes a physics guided fused (PGF) image learning framework with enhanced squeeze excitation (ESE) attention, for intelligent MCP fault diagnosis. First, a physics guided window selection strategy identifies the most informative signal segments by jointly considering energy concentration, impulsiveness, and fault related frequency band characteristics. From each selected segment, a PGF image is constructed by integrating a physics guided Mel spectrogram, a Gramian Angular Difference Field (GADF), and a Cross Interaction Map (CIM) that explicitly models their mutual dependency. This fused image captures complementary time frequency, nonlinear temporal, and interaction level fault characteristics in a unified representation. In addition, a low dimensional physics feature vector is extracted from each signal segment and injected into an ESE attention mechanism to adaptively recalibrate convolutional feature responses based on physical signal behavior. The proposed framework is validated on a real industrial MCP dataset under three operating pressures of 3 bar, 3.5 bar, and 4 bar, covering multiple fault conditions. Experimental results demonstrate consistently high diagnostic performance across all pressure levels, achieving accuracy of greater than 99% across all pressure bars with macro average F1 scores exceeding 0.99. These results confirm the robustness and generalization capability of the proposed physics guided fused image and attention learning framework for real world MCP fault diagnosis.
Ullah et al. (2026) studied this question.