Current Status and Applications for Hydraulic Pump Fault Diagnosis: A Review

Yang, Yue; Liu, Ding; Xiao, Jinhua; Fang, Guinan; Li, Jia

doi:10.3390/s22249714

Cited by 14 publications

(9 citation statements)

References 141 publications

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“…SL and DL are the two typical subdomains. SVM, which originates from statistical learning theory, can be used for supervised learning, unsupervised learning, and semi-supervised learning, and it has an outstanding ability for both linear and nonlinear signals [50]. Therefore, it has been widely studied in the field of fault diagnosis.…”

Section: Fault Diagnosismentioning

confidence: 99%

Intelligent fault diagnosis methods for hydraulic components based on information fusion: review and prospects

Guan,

Ren,

Tang

et al. 2024

Meas. Sci. Technol.

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Hydraulic component faults have the characteristics of nonlinear time-varying signal, strong concealment, and difficult feature extraction, etc. Timely and accurately fault diagnosis of hydraulic components is helpful to curb economic losses and accidents, so researches have carried out a lot of research on hydraulic components. Information fusion technology can combine multi-source data from multiple dimensions to mine fault data characteristics, which effectively improves the accuracy and reliability of fault diagnosis results. However, there is currently a lack of a comprehensive and systematic review in this domain. Therefore, in this paper, the hydraulic components information fusion fault diagnosis technologies are summarized and analyzed, including the main process information fusion fault diagnosis and the research status of information fusion fault diagnosis of hydraulic system. The methods and techniques involved in the fusion process, data source and fusion method of fault diagnosis of hydraulic components information fusion are described and summarized. The problems of information fusion in fault diagnosis of hydraulic components are analyzed, the solutions are discussed, and the research ideas of improving information fusion fault diagnosis are put forward. Finally, digital twin technology is introduced, and the advantages and research status of intelligent fault diagnosis based on digital twin are summarized. On this basis, the intelligent fault diagnosis of hydraulic components based on information fusion is summarized, and the challenges and future research ideas of applying information fusion and digital twin to intelligent fault diagnosis of hydraulic components are put forward and analyzed comprehensively.

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Section: Fault Diagnosismentioning

confidence: 99%

Intelligent fault diagnosis methods for hydraulic components based on information fusion: review and prospects

Guan,

Ren,

Tang

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…Hydraulic systems are widely used in engineering machinery, metallurgy, mining and other fields because of their fast response, strong load resistance, small size, large driving force, and wide speed range. Component and hydraulic oil in a hydraulic system work in a closed oil circuit, and it is difficult to troubleshoot [ 1 ]. The failure of the hydraulic system leads to the decline in product quality or the interruption of production activities.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Hydraulic Components Based on Multi-Sensor Information Fusion Using Improved TSO-CNN-BiLSTM

Zhang,

Zheng,

Liu

et al. 2024

Sensors

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In order to realize the accurate and reliable fault diagnosis of hydraulic systems, a diagnostic model based on improved tuna swarm optimization (ITSO), optimized convolutional neural networks (CNNs), and bi-directional long short-term memory (BiLSTM) networks is proposed. Firstly, sensor selection is implemented using the random forest algorithm to select useful signals from six kinds of physical or virtual sensors including pressure, temperature, flow rate, vibration, motor power, and motor efficiency coefficient. After that, fused features are extracted by CNN, and then, BiLSTM is applied to learn the forward and backward information contained in the data. The ITSO algorithm is adopted to adaptively optimize the learning rate, regularization coefficient, and node number to obtain the optimal CNN-BiLSTM network. Improved Chebyshev chaotic mapping and the nonlinear reduction strategy are adopted to improve population initialization and individual position updating, further promoting the optimization effect of TSO. The experimental results show that the proposed method can automatically extract fusion features and effectively utilize multi-sensor information. The diagnostic accuracies of the plunger pump, cooler, throttle valve, and accumulator are 99.07%, 99.4%, 98.81%, and 98.51%, respectively. The diagnostic results of noisy data with 0 dB, 5 dB, and 10 dB signal-to-noise ratios (SNRs) show that the ITSO-CNN-BiLSTM model has good robustness to noise interference.

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“…The rolling bearing serves as a fundamental component in the mechanical equipment transmission system [1]. It is also one of the most easily damaged components, which makes the equipment have potential safety hazards and leads to accidents [2,3].…”

Section: Introductionmentioning

confidence: 99%

Generalized nonlinear hybrid-norm parallel sparse filtering for bearing fault diagnosis under complex interference

Wang,

Jia,

Zhang

et al. 2024

Meas. Sci. Technol.

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Considerable attention has been garnered by the bearing fault diagnostic method, founded on sparse feature learning, for its exceptional robustness and adaptability to noise. Being an effective sparse learning method, parallel sparse filtering (PSF) has found successful applications in intelligent fault diagnosis. However, influenced by mechanical structural collisions and complex transmission paths, the obtained bearing signals come with abnormal impacts and noise, resulting in a reduced reliability of the PSF. Additionally, PSF constrains features and samples with l_(1⁄2) norm, which is significantly disturbed by noise. To address these issues, generalized nonlinear hybrid-norm parallel sparse filtering (GNHNPSF) is proposed. Firstly, the Sigmoid nonlinear function is examined to suppress the amplitude of the anomalous shock. Subsequently, the hybrid-norm constraints, which involve the application of a l_(1⁄2) norm to the column features of the input signal and a l_(1⁄3) norm to the row features, can be employed to effectively extract distinctive information from the original data. The GNHNPSF is demonstrated to improve the accuracy of bearing fault diagnosis under complex interference, as evidenced by the results of simulation and experiments.

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Current Status and Applications for Hydraulic Pump Fault Diagnosis: A Review

Cited by 14 publications

References 141 publications

Intelligent fault diagnosis methods for hydraulic components based on information fusion: review and prospects

Intelligent fault diagnosis methods for hydraulic components based on information fusion: review and prospects

Fault Diagnosis of Hydraulic Components Based on Multi-Sensor Information Fusion Using Improved TSO-CNN-BiLSTM

Generalized nonlinear hybrid-norm parallel sparse filtering for bearing fault diagnosis under complex interference

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