scholarly journals Hydraulic Pump Fault Diagnosis Method Based on EWT Decomposition Denoising and Deep Learning on Cloud Platform

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Wanlu Jiang ◽  
Zhenbao Li ◽  
Sheng Zhang ◽  
Teng Wang ◽  
Shuqing Zhang
Keyword(s):  
Fault Diagnosis ◽  
Frequency Domain ◽  
Time Domain ◽  
Ensemble Empirical Mode Decomposition ◽  
Piston Pump ◽  
Cloud Platform ◽  
Axial Piston Pump ◽  
Mode Decomposition ◽  
Diagnosis Algorithm ◽  
Axial Piston

An axial piston pump fault diagnosis algorithm based on empirical wavelet transform (EWT) and one-dimensional convolutional neural network (1D-CNN) is presented. The fault vibration signals and pressure signals of axial piston pump are taken as the analysis objects. Firstly, the original signals are decomposed by EWT, and each signal component is screened and reconstructed according to the energy characteristics. Then, the time-domain features and the frequency-domain features of the denoised signal are extracted, and features of time domain and frequency domain are fused. Finally, the 1D-CNN model was deployed to the WISE-Platform as a Service (WISE-PaaS) cloud platform to realize the real-time fault diagnosis of axial piston pump based on the cloud platform. Compared with ensemble empirical mode decomposition (EEMD) and complementary ensemble empirical mode decomposition (CEEMD), the results show that the axial piston pump fault diagnosis algorithm based on EWT and 1D-CNN has higher fault identification accuracy.

scholarly journals Fault Diagnosis of Axial Piston Pump Based on Extreme-Point Symmetric Mode Decomposition and Random Forests

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Lei Yafei ◽  
Jiang Wanlu ◽  
Niu Hongjie ◽  
Shi Xiaodong ◽  
Yang Xukang
Keyword(s):  
Machine Learning ◽  
Fault Diagnosis ◽  
Extreme Point ◽  
Random Forests ◽  
Piston Pump ◽  
Support Vector ◽  
Symmetric Mode ◽  
Axial Piston Pump ◽  
Mode Decomposition ◽  
Axial Piston

Aiming at fault diagnosis of axial piston pumps, a new fusion method based on the extreme-point symmetric mode decomposition method (ESMD) and random forests (RFs) was proposed. Firstly, the vibration signal of the axial piston pump was decomposed by ESMD to get several intrinsic mode functions (IMFs) and an adaptive global mean curve (AGMC) on the local side. Secondly, the total energy was selected as the data of feature extraction by analyzing the whole oscillation intensity of the signal. Thirdly, the data were preprocessed and the labels were set, and then, they were adopted as the training and testing set of machine learning samples. Lastly, the RFs model was created based on machine learning service (MLS) to diagnose the faults of the axial piston pump on the cloud. Using the test and verifying the data set for comparative testing, the fault diagnosis precision rates of the model are above 90.6%, the recall rates are more than 90.9%, the F1 score is higher than 90.7%, and the accuracy rate of this model reached 97.14%. A benchmark data simulation of mechanical transmission systems and an experimental data investigation of an axial piston pump are performed to manifest the superiority of the present method by comparing with classification and regression trees (CART) and support vector machine (SVM).

scholarly journals Axial Piston Pump Fault Diagnosis Method Based on Symmetrical Polar Coordinate Image and Fuzzy C-Means Clustering Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Wan-lu Jiang ◽  
Pei-yao Zhang ◽  
Man Li ◽  
Shu-qing Zhang
Keyword(s):  
Fault Diagnosis ◽  
Clustering Algorithm ◽  
Piston Pump ◽  
Axial Piston Pump ◽  
Fuzzy C Means ◽  
Multiple Eigenvalues ◽  
Polar Coordinate ◽  
Fuzzy C Means Clustering ◽  
Diagnosis Method ◽  
Axial Piston

In this paper, a fault diagnosis method based on symmetric polar coordinate image and Fuzzy C-Means clustering algorithm is proposed to solve the problem that the fault signal of axial piston pump is not intuitive under the time-domain waveform diagram. In this paper, the sampled vibration signals of axial piston pump were denoised firstly by the combination of ensemble empirical mode decomposition and Pearson correlation coefficient. Secondly, the data, after noise reduction, was converted into images, called snowflake images, according to symmetric polar coordinate method. Different fault types of axial piston pump can be identified by observing the snowflake images. After that, in order to evaluate the research results objectively, the obtained images were converted into Gray-Level Cooccurrence Matrixes. Their multiple eigenvalues were extracted, and the eigenvectors consisting of multiple eigenvalues were classified by Fuzzy C-Means clustering algorithm. Finally, according to the accuracy of classification results, the feasibility of applying the symmetric polar coordinate method to axial piston pump fault diagnosis has been validated.

scholarly journals An Integrated Deep Learning Method towards Fault Diagnosis of Hydraulic Axial Piston Pump

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6576
Author(s):  
Shengnan Tang ◽  
Shouqi Yuan ◽  
Yong Zhu ◽  
Guangpeng Li
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Image Features ◽  
Learning Ability ◽  
Piston Pump ◽  
Axial Piston Pump ◽  
Intelligent Fault Diagnosis ◽  
Time Frequency ◽  
Hydraulic Axial Piston Pump ◽  
Axial Piston

A hydraulic axial piston pump is the essential component of a hydraulic transmission system and plays a key role in modern industry. Considering varying working conditions and the implicity of frequent faults, it is difficult to accurately monitor the machinery faults in the actual operating process by using current fault diagnosis methods. Hence, it is urgent and significant to investigate effective and precise fault diagnosis approaches for pumps. Owing to the advantages of intelligent fault diagnosis methods in big data processing, methods based on deep learning have accomplished admirable performance for fault diagnosis of rotating machinery. The prevailing convolutional neural network (CNN) displays desirable automatic learning ability. Therefore, an integrated intelligent fault diagnosis method is proposed based on CNN and continuous wavelet transform (CWT), combining the feature extraction and classification. Firstly, CWT is used to convert the raw vibration signals into time-frequency representations and achieve the extraction of image features. Secondly, a new framework of deep CNN is established via designing the convolutional layers and sub-sampling layers. The learning process and results are visualized by t-distributed stochastic neighbor embedding (t-SNE). The results of the experiment present a higher classification accuracy compared with other models. It is demonstrated that the proposed approach is effective and stable for fault diagnosis of a hydraulic axial piston pump.

Research on the Distributings of Vibration Sensors for Fault Diagnosis in Axial Piston Pump

2012 ◽  
Vol 472-475 ◽  
pp. 1155-1159
Author(s):  
Sheng Qiang Wu ◽  
Cai Qin Liu ◽  
Er Ling Cao
Keyword(s):  
Experimental Data ◽  
Fault Diagnosis ◽  
Research Result ◽  
Mechanical Vibration ◽  
Strong Support ◽  
Piston Pump ◽  
Axial Piston Pump ◽  
Vibration Sensors ◽  
Axial Piston ◽  
Feature Frequency

Considering that the distributings of sensors will affect the fault diagnosis results directly, how to distribute vibration sensors for fault diagnosis in axial piston pump are researched. The corresponding feature frequency bands of various faults are analyzed, effects of collecting signals are compared in differrent fixings of vibration sensors, placements scheme of the sensors is proposed. Combined with the experimental data, the best distributings of vibration sensors is obtained. Research result provide a strong support in the pump monitoring and fault diagnosis field and reference in other complex mechanical vibration fault diagnosis field.

scholarly journals Diesel Engine Valve Clearance Fault Diagnosis Based on Improved Variational Mode Decomposition and Bispectrum

Energies ◽  
2019 ◽  
Vol 12 (4) ◽  
pp. 661 ◽  
Author(s):  
Xiaoyang Bi ◽  
Shuqian Cao ◽  
Daming Zhang
Keyword(s):  
Fault Diagnosis ◽  
Diesel Engine ◽  
Frequency Domain ◽  
Time Domain ◽  
Frequency Domain Analysis ◽  
Variational Mode Decomposition ◽  
Vibration Signals ◽  
Engine Valve ◽  
Mode Decomposition ◽  
Reconstructed Signal

The evaluation and fault diagnosis of a diesel engine’s health conditions without disassembly are very important for diesel engine safe operation. Currently, the research on fault diagnosis has focused on the time domain or frequency domain processing of vibration signals. However, early fault signals are mostly weak energy signals, and the fault information cannot be completely extracted by time domain and frequency domain analysis. Thus, in this article, a novel fault diagnosis method of diesel engine valve clearance using the improved variational mode decomposition (VMD) and bispectrum algorithm is proposed. First, the experimental study was designed to obtain fault vibration signals. The improved VMD method by choosing the optimal decomposition layers is applied to denoise vibration signals. Then the bispectrum analysis of the reconstructed signal after VMD decomposition is carried out. The results show that bispectrum image under different working conditions exhibits obviously different characteristics respectively. At last, the diagonal projection method proposed in this paper was used to process the bispectrum image, and the fourth order cumulant is calculated. The calculation results show that three states of the valve clearance are successfully distinguished.

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