ArticleSensors (Basel, Switzerland)2020
Intelligent Diagnosis towards Hydraulic Axial Piston Pump Using a Novel Integrated CNN Model.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Fault Diagnosis Method of Plunger Pump Based on Meta-Learning and Improved Multi-Channel Convolutional Neural Network Under Small Sample Condition.Sensors (Basel, Switzerland) · 2025Article
- Fault Diagnosis Method for Centrifugal Pumps in Nuclear Power Plants Based on a Multi-Scale Convolutional Self-Attention Network.Sensors (Basel, Switzerland) · 2025Article
- A Robust Deep Learning Ensemble-Driven Model for Defect and Non-Defect Recognition and Classification Using a Weighted Averaging Sequence-Based Meta-Learning Ensembler.Sensors (Basel, Switzerland) · 2022Article
- Current Status and Applications for Hydraulic Pump Fault Diagnosis: A Review.Sensors (Basel, Switzerland) · 2022Review
- A Hard Voting Policy-Driven Deep Learning Architectural Ensemble Strategy for Industrial Products Defect Recognition and Classification.Sensors (Basel, Switzerland) · 2022Article
- Approaching OPC UA Publish-Subscribe in the Context of UDP-Based Multi-Channel Communication and Image Transmission.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
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
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.