Modeling and Experimental Study for Online Measurement of Hydraulic Cylinder Micro Leakage Based on Convolutional Neural Network

Guo, Yuan; Yin-chuan, Zeng; Lian-dong, FU; Chen, Xinyuan

doi:10.3390/s19092159

Cited by 20 publications

(11 citation statements)

References 27 publications

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“…To highlight the effectiveness of the proposed model, four conventional data-driven techniques named the Bagtree, the support vector regression (SVR), the BP neural network [35], and the ELM are employed as a control group to simulate the complete characteristic curve of the pump turbine. The Bagtree model is constructed using MATLAB's "Bag" function.…”

Section: Parameters Settingmentioning

confidence: 99%

An Improved Autoencoder and Partial Least Squares Regression-Based Extreme Learning Machine Model for Pump Turbine Characteristics

et al. 2019

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Complete characteristic curves of a pump turbine are fundamental for improving the modeling accuracy of the pump turbine in a pump turbine governing system. In view of the difficulty in modeling the "S" characteristic region of the complete characteristic curves in the pump turbine, a novel Autoencoder and partial least squares regression based extreme learning machine model (AE-PLS-ELM) was proposed to describe the pump turbine characteristics. First, a mathematical model was formulated to describe the flow and moment characteristic curves. The improved Suter transformation was employed to transfer the original curves into WH and WM curves. Second, the ELM-Autoencoder technique and the partial least squares regression (PLSR) method were introduced to the architecture of the original ELM network. The ELM-Autoencoder technique was employed to obtain the initial weights of the Autoencoder based extreme learning machine (AE-ELM) model. The PLS method was exploited to avoid the multicollinearity problem of the Moore-Penrose generalized inverse. Lastly, the effectiveness of the proposed AE-PLS-ELM model has been verified using real data from a pumped storage unit in China. The results demonstrated that the AE-PLS-ELM model can obtain better modeling accuracy and generalization performance than the traditional models and, thus, can be exploited as an effective and sufficient approach for the modeling of pump turbine characteristics.

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Section: Parameters Settingmentioning

confidence: 99%

An Improved Autoencoder and Partial Least Squares Regression-Based Extreme Learning Machine Model for Pump Turbine Characteristics

et al. 2019

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“…Using the multiple hidden layers stacked hierarchically, a deep learning model can realize the highly complicated transformation and abstraction of raw signals [13,14]. Guo et al adopted a convolutional neural network (CNN) that employed raw strain signals to output the internal leakage of the hydraulic cylinder [15]. However, the strain caused by microflows is extremely small under low pressure, that is, for pressure values lower than 7 MPa, and the abovementioned method cannot effectively diagnose the internal leakage in such cases.…”

Section: Introductionmentioning

confidence: 99%

Internal Leakage Diagnosis of a Hydraulic Cylinder Based on Optimization DBN Using the CEEMDAN Technique

Zhang

Chen

2021

Shock and Vibration

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Internal leakage diagnosis in a hydraulic cylinder is a key technique for the maintenance of hydraulic systems. However, it is difficult to diagnose the internal leakage under different low loads. To solve this problem, a novel fault diagnosis method based on the optimization deep belief network (DBN) combined with the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) technique is proposed to treat the collected AE signals. The raw AE signals are decomposed into a set of intrinsic mode functions (IMFs) by using CEEMDAN. Subsequently, according to the decreasing order of the Pearson correlation coefficient values, the first five IMFs are selected for signal reconstruction to suppress the abnormal interference from noise. The reconstructed signals are regarded as the input of the optimization DBN, and the particle swarm optimization simulated annealing (PSOSA) algorithm is adopted to identify the four internal leakage levels. The experimental results show that the proposed method exhibits a higher classification accuracy than other methods under different low loads. This result validates the effectiveness and superiority of the proposed approach to realize internal leakage diagnoses under different low loads.

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“…For example, ZHOU et al [19] proposed a hybrid intelligent method that integrates kernel principal component analysis and cascade support vector data description for microleakage detection of pipeline. GUO et al [20] proposed an internal micro-leakage detection method for hydraulic cylinder. LANG et al [21] used the method based on the method of characteristics theory to detect the micro-leakage of the oil and gas pipeline.…”

Section: Introductionmentioning

confidence: 99%

A data-driven early micro-leakage detection and localization approach of hydraulic systems

Cai

Yang

Liu

et al. 2021

J. Cent. South Univ.

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Modeling and Experimental Study for Online Measurement of Hydraulic Cylinder Micro Leakage Based on Convolutional Neural Network

Cited by 20 publications

References 27 publications

An Improved Autoencoder and Partial Least Squares Regression-Based Extreme Learning Machine Model for Pump Turbine Characteristics

An Improved Autoencoder and Partial Least Squares Regression-Based Extreme Learning Machine Model for Pump Turbine Characteristics

Internal Leakage Diagnosis of a Hydraulic Cylinder Based on Optimization DBN Using the CEEMDAN Technique

A data-driven early micro-leakage detection and localization approach of hydraulic systems

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