Abstract
As a critical part of a hydraulic transmission system, a hydraulic axial piston pump plays an indispensable role in many significant industrial fields. Owing to the practical undesirable working environment and hidden faults, it is challenging to precisely and effectively detect and diagnose the varying fault in the engineering. Deep learning-based technology presents special strengths in processing mechanical big data. It can simultaneously complete the feature extraction and classification, and achieve the automatic information learning. The popular convolutional neural network (CNN) is exploited for its potent ability of image processing. In this paper, a novel combined intelligent method is developed for fault diagnosis towards a hydraulic axial piston pump. First, the conversion of signals to images is conducted via continuous wavelet transform; the effective feature is preliminarily extracted from the transformed time-frequency images. Second, a novel deep CNN model is constructed to achieve the fault classification. To disclose the potential learning in the disparate layers of the CNN model, the visualization of reduced features is performed by employing t-distributed stochastic neighbor embedding. The effectiveness and stability of the proposed model are validated through the experiments. With the proposed method, different fault types can be precisely identified and high classification accuracy is achieved in a hydraulic axial piston pump.
Highlights
Hydraulic transmission systems are broadly used in state-of-the-art machinery because of their strengths in terms of high energy, quick response, easy control, high output force [1,2,3]
As the pivotal energy conversion component of hydraulic transmission system, a hydraulic axial piston pump plays a crucial role in guaranteeing the stability of the system in many fields
On the basis of LeNet 5, an improved convolutional neural network (CNN) model is employed for intelligent fault diagnosis of the hydraulic pump
Summary
Hydraulic transmission systems are broadly used in state-of-the-art machinery because of their strengths in terms of high energy, quick response, easy control, high output force [1,2,3]. Deep learning (DL)-based methods effectively overcome the disadvantages of the common learning model in the feature extraction and can automatically extract the useful information from raw input data [18,19,20]. Based on an adversarial idea, a deep semi-supervised learning model was employed for fault diagnosis of the transmission and bearing [31]. The researches on the deep model-based intelligent fault diagnosis have been concentrated on the applications in the bearing, gearing and gearbox. In light of complicated structures, changeable operation conditions and challenging data acquisition, the accurate and effective fault diagnosis is immensely difficult for a hydraulic axial piston pump. This work is based on the deep model-based fault diagnosis approach of research hydraulic axial piston pump.
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