A Hydraulic Pump Fault Diagnosis Method Based on the Modified Ensemble Empirical Mode Decomposition and Wavelet Kernel Extreme Learning Machine Methods

Li, Zhenbao; Jiang, Wanlu; Zhang, Sheng; Sun, Yu; Zhang, Shuqing

doi:10.3390/s21082599

Cited by 27 publications

(17 citation statements)

References 36 publications

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“…Gao et al (2018) proposed a denoising method based on the Walsh transform with multi-sensor strategy, and the feasibility of the proposed method is validated by numerical and experimental investigations. In addition to methods based on wavelet analysis, other types of methods have also been introduced to the fault diagnosis of pumps, i.e., Symbolic Perceptually Important Point (SPIP) and Hidden Markov Model (HMM) (Jia et al, 2018), Sparse Representation (Han, 2019), and Mode Decomposition (Lan et al, 2018;Li Z. et al, 2021;Liu et al, 2021). However, although it is effective to extract information in time and frequency domains using these methods, the criterion to justify whether pumps are normal or fault is often set artificially, which restricts their further applications.…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis of a mixed-flow pump under cavitation condition based on deep learning techniques

Tan¹,

Qiu³

et al. 2023

Front. Energy Res.

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Deep learning technique is an effective mean of processing complex data that has emerged in recent years, which has been applied to fault diagnosis of a wide range of equipment. In the present study, three types of deep learning techniques, namely, stacked autoencoder (SAE) network, long short term memory (LSTM) network, and convolutional neural network (CNN) are applied to fault diagnosis of a mixed-flow pump under cavitation conditions. Vibration signals of the mixed-flowed pump are collected from experiment measurements, and then employed as input datasets for deep learning networks. The operation status is clarified into normal, minor cavitation, and severe cavitation conditions according to visualized bubble density. The techniques of FFT and dropout algorithms are also applied to improve diagnosis accuracy. The results show that the diagnosis accuracy based on SAE and LSTM networks is lower than 50%, while is higher than 68% when using CNN. The maximum accuracy can reach 87.2% by mean of a combination of CNN, BN, MLP, and using frequency domain data by FFT as inputs, which validates the feasibility of applying CNN in mixed-flow pumps.

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Section: Introductionmentioning

confidence: 99%

Fault diagnosis of a mixed-flow pump under cavitation condition based on deep learning techniques

Tan¹,

Qiu³

et al. 2023

Front. Energy Res.

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“…In actual production, the liquid component carried by the centrifugal pump usually contains solid particles such as sediment and corrosive components. These components frequently cause damage such as the cavitation, corrosion, and abrasion of the impeller in close contact with the liquid, which intensifies the vibration of the pump unit and reduces the overall hydraulic performance and reliability of the pump [15][16][17]. Therefore, the choice of centrifugal pump impeller, which is the research object of this paper, has far-reaching practical significance.…”

Section: Introductionmentioning

confidence: 99%

Multi-Sensor Data Driven with PARAFAC-IPSO-PNN for Identification of Mechanical Nonstationary Multi-Fault Mode

Chen

Xiong

et al. 2022

Machines

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Data analysis has wide applications in eliminating the irrelevant and redundant components in signals to reveal the important informational characteristics that are required. Conventional methods for multi-dimensional data analysis via the decomposition of time and frequency information that ignore the information in signal space include independent component analysis (ICA) and principal component analysis (PCA). We propose the processing of a signal according to the continuous wavelet transform and the construction of a three-dimensional matrix containing the time–frequency–space information of the signal. The dimensions of the three-dimensional matrix are reduced by parallel factor analysis, and the time characteristic matrix, frequency characteristic matrix, and spatial characteristic matrix are obtained with tensor decomposition. Through the comparative analysis of the simulation and the experiment, the time characteristic matrix and the frequency characteristic matrix can accurately characterize the normal and fault states of the mechanical equipment. On this basis, the authors established a probabilistic neural network classification model optimized by the improved particle swarm algorithm (IPSO). The parallel factor (PARAFAC) decomposition algorithm can extract features from the centrifugal pump experimental data for normal and multiple fault states, establish the mapping relationship of different fault features of the centrifugal pump in time, frequency, and space, and import the fault features into the model classification. The above measures can significantly improve the fault identification rate and accuracy for a centrifugal pump.

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“…Support vector machine (SVM) [ 1 ], k-nearest neighbor (kNN) [ 2 ], naive Bayes [ 3 , 4 ], decision tree [ 5 ], logistic regression [ 6 ], and many other classifiers with high accuracy appear and have attracted much attention. Huang et al [ 7 ] proposed extreme learning machine (ELM) which is a better classifier with powerful nonlinear fitting and approximation capabilities [ 8 , 9 ] and has been widely studied and applied in brain-computer interfaces [ 10 , 11 ], medical diagnosis [ 12 , 13 ], fault diagnosis [ 14 ], hyperspectral [ 15 ], and other fields.…”

Section: Introductionmentioning

confidence: 99%

TSTELM: Two-Stage Transfer Extreme Learning Machine for Unsupervised Domain Adaptation

Zang

et al. 2022

Computational Intelligence and Neuroscience

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As a single-layer feedforward network (SLFN), extreme learning machine (ELM) has been successfully applied for classification and regression in machine learning due to its faster training speed and better generalization. However, it will perform poorly for domain adaptation in which the distributions between training data and testing data are inconsistent. In this article, we propose a novel ELM called two-stage transfer extreme learning machine (TSTELM) to solve this problem. At the statistical matching stage, we adopt maximum mean discrepancy (MMD) to narrow the distribution difference of the output layer between domains. In addition, at the subspace alignment stage, we align the source and target model parameters, design target cross-domain mean approximation, and add the output weight approximation to further promote the knowledge transferring across domains. Moreover, the prediction of test sample is jointly determined by the ELM parameters generated at the two stages. Finally, we investigate the proposed approach in classification task and conduct experiments on four public domain adaptation datasets. The result indicates that TSTELM could effectively enhance the knowledge transfer ability of ELM with higher accuracy than other existing transfer and non-transfer classifiers.

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A Hydraulic Pump Fault Diagnosis Method Based on the Modified Ensemble Empirical Mode Decomposition and Wavelet Kernel Extreme Learning Machine Methods

Cited by 27 publications

References 36 publications

Fault diagnosis of a mixed-flow pump under cavitation condition based on deep learning techniques

Fault diagnosis of a mixed-flow pump under cavitation condition based on deep learning techniques

Multi-Sensor Data Driven with PARAFAC-IPSO-PNN for Identification of Mechanical Nonstationary Multi-Fault Mode

TSTELM: Two-Stage Transfer Extreme Learning Machine for Unsupervised Domain Adaptation

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