A Novel Fault Diagnosis Method Based on SWT and VGG-LSTM Model for Hydraulic Axial Piston Pump

Abstract

Since the hydraulic axial piston pump is the engine that drives hydraulic transmission systems, it is widely utilized in aerospace, marine equipment, civil engineering, and mechanical engineering. Operating safely and dependably is crucial, and failure poses a major risk. Hydraulic axial piston pump malfunctions are characterized by internal concealment, challenging self-adaptive feature extraction, and blatant timing of fault signals. By completely integrating the time-frequency feature conversion capability of synchrosqueezing wavelet transform (SWT), the feature extraction capability of VGG11, as well as the feature memory capability of the long short-term memory (LSTM) model, a novel intelligent fault identification method is proposed in this paper. First, the status data are transformed into two dimensions in terms of time and frequency by using SWT. Second, the depth features of the time–frequency map are obtained and dimensionality reduction is carried out by using the deep feature mining capability of VGG11. Third, LSTM is added to provide the damage identification model for long-term memory capabilities. The Softmax layer is utilized for the intelligent evaluation of various damage patterns and health state. The proposed method is utilized to identify and diagnose five typical states, including normal state, swash plate wear, sliding slipper wear, loose slipper, and center spring failure, based on the externally observed vibration signals of a hydraulic axial piston pump. The results indicate that the average test accuracy for five typical state signals reaches 99.43%, the standard deviation is 0.0011, and the average test duration is 2.675 s. The integrated model exhibits improved all-around performance when compared to LSTM, LeNet-5, AlexNet, VGG11, and other typical models. The proposed method is validated to be efficient and accurate for the intelligent identification of common defects of hydraulic axial piston pumps.

Convolutional Neural Networks: A Survey

“…VGG is a CNN architecture developed by Karen Simonyan and Andrew Zisserman in 2014, which achieved state-of-the-art performance on the ImageNet dataset [133][134][135][136][137]. The VGG architecture consists of multiple layers of small 3 × 3 convolutional filters, followed by max Pool_Lays.…”

Intelligent Fault Diagnosis Methods for Hydraulic Piston Pumps: A Review

“…This method effectively obtained the fault features of HPPs. As shown in Figure 9, Zhu et al [117] explored a new fault identification algorithm combining the synchrosqueezing wavelet transform, VGG11, and long short-term memory, which effectively identified the common fault categories in HPPs. The synchrosqueezing wavelet transform was employed to transform the state data into two dimensions in terms of time and frequency, and then the depth features of the time-frequency map were obtained.…”

Adversarial training of multi-scale channel attention network for enhanced robustness in bearing fault diagnosis

“…This escalation makes it challenging to extract fault feature patterns based on empirically designed methods. With the recent advancements in artificial intelligence technologies, deep learning techniques based on CNNs [3,4], RNNs [5,6], DBNs [7,8], etc have found widespread application in the field of fault diagnosis. These methods have the capability to automatically learn highorder features related to faults from complex signals, demonstrating accuracy levels far superior to traditional approaches [9,10].…”