Empirical mode reconstruction: Preserving intrinsic components in data augmentation for intelligent fault diagnosis of civil aviation hydraulic pumps

Meng, Linghui; Zhao, Minghang; Cui, Zhiquan; Zhang, Xingming; Zhong, Shisheng

doi:10.1016/j.compind.2021.103557

Cited by 32 publications

(4 citation statements)

References 34 publications

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“…This method reduces the influence of external interference on frequency band acquisition and has a good decomposition effect on fault signals. Meng et al [34,35] proposed a fault identification approach based on empirical mode decomposition to address the problem of the number of fault samples being less than the number of healthy samples in a civil aviation hydraulic pump. This method can enhance sample data and retain the inherent components of enhanced samples, ascribing the enhanced sample data and original data similar features and preventing overfitting.…”

Section: Data-driven Fault Diagnosis Methodsmentioning

confidence: 99%

Intelligent Fault Diagnosis Methods for Hydraulic Piston Pumps: A Review

Zhu,

Wu,

Tang

et al. 2023

JMSE

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As the modern industry rapidly advances toward digitalization, networking, and intelligence, intelligent fault diagnosis technology has become a necessary measure to ensure the safe and stable operation of mechanical equipment and effectively avoid major disaster accidents and huge economic losses caused by mechanical equipment failure. As the “power heart” of hydraulic transmission systems, hydraulic piston pumps (HPPs) occupy an important position in aerospace, navigation, national defense, industry, and many other high-tech fields due to their high-rated pressure, compact structure, high efficiency, convenient flow regulation, and other advantages. Faults in HPPs can create serious hazards. In this paper, the research on fault recognition technology for HPPs is reviewed. Firstly, the existing fault diagnosis methods are described, and the typical fault types and mechanisms of HPPs are introduced. Then, the current research achievements regarding fault diagnosis in HPPs are summarized based on three aspects: the traditional intelligent fault diagnosis method, the modern intelligent fault diagnosis method, and the combined intelligent fault diagnosis method. Finally, the future development trend of fault identification methods for HPPs is discussed and summarized. This work provides a reference for developing intelligent, efficient, and accurate fault recognition methods for HPPs. Moreover, this review will help to increase the safety, stability, and reliability of HPPs and promote the implementation of hydraulic transmission technology in the era of intelligent operation and maintenance.

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Section: Data-driven Fault Diagnosis Methodsmentioning

confidence: 99%

Intelligent Fault Diagnosis Methods for Hydraulic Piston Pumps: A Review

Zhu,

Wu,

Tang

et al. 2023

JMSE

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show abstract

“…Therefore, Adam is selected in the improved AlexNet model. (6) Considering that the state category of the pre-diagnosed axial piston pump is five, the output layer is set as five. Softmax function is used in the classification stage.…”

Section: Bayesian Optimization Algorithmmentioning

confidence: 99%

“…The hydraulic piston pump is an essential power element in the hydraulic system and can realize the transformation between mechanical energy and hydraulic energy [1][2][3]. Because of the advantages of its small size, a high power/weight ratio and easy variable adjustment, the axial piston pump has been diffusely utilized in marine, aerospace and mining equipment, under working conditions containing high efficiency and high pressure [4][5][6]. The operational process of the axial piston pump is a composite motion of rotary motion and linear motion, with multiple friction pairs and large friction amplitude.…”

Section: Introductionmentioning

confidence: 99%

A Data-Driven Diagnosis Scheme Based on Deep Learning toward Fault Identification of the Hydraulic Piston Pump

Zhu

Zhou

Tang

et al. 2023

JMSE

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The piston pump is the significant source of motive force in a hydraulic transmission system. Owing to the changeable working conditions and complex structural characteristics, multiple friction pairs in the piston pump are prone to wear and failure. An accurate fault diagnosis method is a crucial guarantee for system reliability. Deep learning provides a great insight into the intelligent exploration of machinery fault diagnosis. Hyperparameters are very important to construct an effective deep model with good performance. This research fully mines the feature component from vibration signals, and converts the failure recognition into a classification issue via establishing a deep model. Furthermore, Bayesian algorithm is introduced for hyperparameter optimization as it considers prior information. An adaptive convolutional neural network is established for typical failure pattern recognition of an axial piston pump. The proposed method can automatically complete fault classification and represents a higher accuracy by experimental verification. Typical failures of an axial piston pump are intelligently diagnosed with reduced subjectivity and preprocessing knowledge. The proposed method achieves an identification accuracy of more than 98% for five typical conditions of an axial piston pump.

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“…Popular data-driven fault diagnosis includes fault diagnosis methods based on artificial intelligence [7][8][9][10][11][12] and signal processing [13,14]. Both methods can also be combined for fault diagnosis [15,16].…”

Section: Introductionmentioning

confidence: 99%

A New Method for Fault Diagnosis of Hydraulic System Based on Improved Empirical Wavelet Transform and Kernel Extreme Learning Machine

Chen,

Kelimu,

Yang

2023

IEEE Access

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To address the problem that multivariable faults in hydraulic systems are difficult to diagnose accurately in a short time, a new signal processing method combining an improved empirical wavelet transform with a kernel extreme learning machine optimized by the Pelican optimization algorithm is proposed. First, the problem of unreasonable spectral partitioning of the empirical wavelet transform is solved using an improved k-means clustering method, which upgrades the empirical wavelet transform in the maximum scale space and then adaptively decomposes the acquired pressure signal to obtain a series of subsignal components, thus reducing the dimensionality of multivariate faults and saving computation time. Second, 17 features of each sub-signal component are calculated and input to the kernel extreme learning machine for training, and the sequence-forward selection strategy is used to select the optimal features from the kernel extreme learning machine to ensure the basic accuracy and efficiency of prediction. Finally, a Pelican optimization algorithm improved kernel extreme learning machine algorithm is proposed for fast classification of faults. Experiments show that the method can diagnose a variety of faults in hydraulic systems quickly and accurately, with a diagnostic accuracy of 97.24 percent, which is better than other methods.INDEX TERMS Improved empirical wavelet transform, hydraulic systems, multivariable fault diagnosis, pelican optimization algorithm, kernel extreme learning machine.

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Empirical mode reconstruction: Preserving intrinsic components in data augmentation for intelligent fault diagnosis of civil aviation hydraulic pumps

Cited by 32 publications

References 34 publications

Intelligent Fault Diagnosis Methods for Hydraulic Piston Pumps: A Review

Intelligent Fault Diagnosis Methods for Hydraulic Piston Pumps: A Review

A Data-Driven Diagnosis Scheme Based on Deep Learning toward Fault Identification of the Hydraulic Piston Pump

A New Method for Fault Diagnosis of Hydraulic System Based on Improved Empirical Wavelet Transform and Kernel Extreme Learning Machine

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