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Modified Stacked Convolutional Auto-Encoder for Hydraulic Pump Fault Diagnosis with Unbalanced Data

As one of the key technologies in PHM, fault diagnosis is the foundation of real-time health monitoring. The quality of fault features often determines the upper limit of fault diagnosis capabilities. However, the monitoring data of hydraulic pumps are usually composed of numerous unlabeled samples and few, unbalanced labeled samples, which severely limits the applications of intelligent data-driven diagnosis approaches. Therefore, for the typical non-ideal data condition of sample imbalance between different failure modes, this paper proposes a modified stacked convolutional auto-encoder as an end-to-end data-driven approach for diagnosing hydraulic pumps using vibration signals only. Firstly, a hyperparameter-free signal conversion method is used to convert the time series signals into images. Then, several convolutional layers and transposed convolutional layers are stacked to form two auto-encoders, and the unlabeled samples are used to extract high-order local features via a stable training process. Finally, Focal loss is extended to multi-classes to fine-tune the fully connected layers via the unbalanced labeled samples. The proposed method is verified with an experimental hydraulic pump test rig. And the detailed process of reconstruction and multi-layer feature self-learning is analyzed and visualized to reveal the robustness of the high-order local features extracted by the proposed method.