Wavelet coherence-aware multi-branch deep ensemble for fault identification in centrifugal pumps.

Abstract

Reliable fault diagnosis of centrifugal pumps is challenging due to the nonstationary nature of vibration signals, weak early-stage laboratory fault signatures, and overlapping characteristics among different mechanical defects. This study proposes a wavelet coherence-aware multi-branch deep ensemble framework that integrates physically meaningful time-frequency coupling with complementary deep feature learning. Multi-channel vibration signals are transformed into two-dimensional wavelet coherence maps to emphasize localized inter-sensor phase-consistent structures induced by mechanical processes. Three lightweight and architecturally diverse convolutional neural networks are trained in parallel to extract fine-scale, global, and compact structural features. Their outputs are fused through a probabilistic soft-voting strategy to improve robustness and decision stability. The framework is evaluated on vibration datasets collected from a PMT-4008 centrifugal pump test bench under three operating pressures (3.0, 3.5, and 4.0 bar). The results demonstrate consistent and reliable fault discrimination across all investigated conditions, with strong class separability confirmed by Receiver Operating Characteristic analysis and feature-space visualization. These findings demonstrate the effectiveness of the proposed framework for centrifugal pump fault diagnosis within the investigated experimental setup and operating conditions.