Research on fault diagnosis of hydraulic pump using convolutional neural network

Yan, Jiahe; Zhu, Hao; Yang, Xiaoqiang; Cao, Youhui; Shao, Lei

doi:10.21595/jve.2016.16956

Cited by 22 publications

(16 citation statements)

References 11 publications

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“…The satisfied accuracy of 99.79%, 99.481%, and 100% were acquired respectively, with a superiority to some other DL based fault diagnosis methods. Yan et al also used CNN based diagnostic approach for hydraulic pump, and the accuracy achieved around more than 90% even under changing speeds [116]. In order to explore unknown failure mechanism of axial piston pumps, a new CNN was constructed on the basis of minimum entropy deconvolution, which was employed to preprocess the raw signal to enhance the stability of feature learning and classification performance.…”

Section: Cnn-based Fault Diagnosis For Pumpmentioning

confidence: 99%

Convolutional Neural Network in Intelligent Fault Diagnosis Toward Rotatory Machinery

2020

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Rotating machinery is of vital importance in the field of engineering, including aviation and navigation. Its failure will lead to severe loss to personnel safety and the stability of the equipment system. It is a long way to investigate the relevant fault diagnosis method, especially the intelligent fault diagnosis method on the basis of deep learning. In consideration of the limitations of traditional fault diagnosis approaches based on shallow layer network structure, the methods based on deep neural network (DNN) are worthy of thorough exploration. As a common DNN with special structure, deep convolutional neural network is of great concern in intelligent fault diagnosis due to its advantages in processing nonlinear problems. This review will play an emphasis on convolutional neural network (CNN). The basic structure and principle are introduced. The applications of CNN-based fault diagnosis method in rotating machinery are summarized and analyzed. Furthermore, the diagnosis performance and potential mechanism from different CNN methods are discussed. In the end, this review is highlighted on the challenges and the potential key points in research on novel intelligent fault diagnosis strategies. The corresponding analysis and discussion will provide some references and lay the foundation for the investigation in related fields. INDEX TERMS Deep learning, convolutional neural network, intelligent fault diagnosis, rotating machinery.

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Section: Cnn-based Fault Diagnosis For Pumpmentioning

confidence: 99%

Convolutional Neural Network in Intelligent Fault Diagnosis Toward Rotatory Machinery

2020

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“…Compared to 2D CNN models, 1D fault diagnosis models work with raw sensor data that are described as 1D time series and can avoid preprocessing with the aforementioned techniques. 1D CNN techniques for induction motors [ 25 , 26 ], pumps [ 27 ] and rolling element bearings [ 28 , 29 ] are developed. Further on, the authors of [ 30 ] presented multi-channels 1D CNN (MC-DCNN) for human activity classification that is modified by [ 31 ] for 3 axis vibration data input with input size of 6400 × 1 × 3.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Fault Diagnosis of Rotary Machinery by Convolutional Neural Network with Automatic Hyper-Parameters Tuning Using Bayesian Optimization

Kolar

Lisjak

Pająk

et al. 2021

Sensors

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Intelligent fault diagnosis can be related to applications of machine learning theories to machine fault diagnosis. Although there is a large number of successful examples, there is a gap in the optimization of the hyper-parameters of the machine learning model, which ultimately has a major impact on the performance of the model. Machine learning experts are required to configure a set of hyper-parameter values manually. This work presents a convolutional neural network based data-driven intelligent fault diagnosis technique for rotary machinery which uses model with optimized hyper-parameters and network structure. The proposed technique input raw three axes accelerometer signal as high definition 1-D data into deep learning layers with optimized hyper-parameters. Input is consisted of wide 12,800 × 1 × 3 vibration signal matrix. Model learning phase includes Bayesian optimization that optimizes hyper-parameters of the convolutional neural network. Finally, by using a Convolutional Neural Network (CNN) model with optimized hyper-parameters, classification in one of the 8 different machine states and 2 rotational speeds can be performed. This study accomplished the effective classification of different rotary machinery states in different rotational speeds using optimized convolutional artificial neural network for classification of raw three axis accelerometer signal input. Overall classification accuracy of 99.94% on evaluation set is obtained with the CNN model based on 19 layers. Additionally, more data are collected on the same machine with altered bearings to test the model for overfitting. Result of classification accuracy of 100% on second evaluation set has been achieved, proving the potential of using the proposed technique.

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“…The traditional diagnostic approaches used for fault tolerance are based on mechanism and handcrafted feature extraction [1]. These schemes have complicated feature extraction and feature selection mechanisms.…”

Section: Introductionmentioning

confidence: 99%

Analysis on transformer vibration signal recognition based on convolutional neural network

Hou¹,

Cai²

2021

J. vibroeng.

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In order to study the relationship between the transformer vibration and the operation state, the wavelet analysis method and the convolutional neural network method were used to analyze the transformer vibration signal. This paper proposes a transformer based on convolution neural network-based surface vibration signal feature extraction method. The result show that the convolution of neural network in different station transformer surface vibration signal classification has a lot of advantage, as the integration of feature extraction and classification recognition process together can effectively classify vibration signal recognition processing. This method is feasible for classification and identification by providing an accuracy value of 92.74 %. The future perspective of this research will focus on a generalized network model and parameters through experimentation for further investigation of accuracy and efficiency of this method.

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Research on fault diagnosis of hydraulic pump using convolutional neural network

Cited by 22 publications

References 11 publications

Convolutional Neural Network in Intelligent Fault Diagnosis Toward Rotatory Machinery

Convolutional Neural Network in Intelligent Fault Diagnosis Toward Rotatory Machinery

Intelligent Fault Diagnosis of Rotary Machinery by Convolutional Neural Network with Automatic Hyper-Parameters Tuning Using Bayesian Optimization

Analysis on transformer vibration signal recognition based on convolutional neural network

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