Fault diagnosis of water injection pump via wavelet-enhanced attention guided Inception-LSTM networks

Abstract

Accurate fault diagnosis of water injection pump is essential for ensuring operational safety and efficiency in oil and gas exploitation. However, traditional diagnostic methods often struggle with non-stationary vibration signals and severe category imbalance in complex industrial environments. To address these challenges, this paper proposes a multi-level Inception-long short-term memory (Inception-LSTM) network integrated with wavelet packet decomposition (WPD) and efficient channel attention (ECA), termed the multi-level Inception-LSTM network with WPD and ECA (MILN-WE). The proposed framework first employs WPD to decompose complex vibration signals into fine-grained frequency sub-bands, capturing subtle fault characteristics. Subsequently, a multi-scale Inception module is utilized to extract diverse spatial features, while an LSTM layer captures the long-term temporal dependencies of the signals. The integration of the ECA mechanism further enhances the model’s ability to focus on critical diagnostic information. The effectiveness of MILN-WE is validated using a private oilfield water injection pump dataset and a public rotating machinery dataset. Experimental results demonstrate that the proposed model achieves higher diagnostic accuracy and robustness compared to state-of-the-art methods, particularly under conditions of strong noise interference and data imbalance. Specifically, on the private oilfield water injection pump dataset, the model achieved an accuracy of 99.38%, improving upon traditional convolutional neural network (CNN) and class-balanced-CNN models by 6.05% and 3.24%, respectively. This study provides a high-precision and robust solution for the intelligent predictive maintenance of critical energy equipment, offering significant theoretical and practical value for industrial health monitoring systems.

Keywords

Water injection pump / unbalanced data / convolutional neural network / long short-term memory network / attention mechanism

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