Fault diagnosis in a hydraulic directional valve using a two-stage multi-sensor information fusion

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.

Section: Signal Processingmentioning

confidence: 99%

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

Guan,

Ren,

Tang

et al. 2024

“…Currently, the model-based and data-driven methods for control valve fault diagnosis have become dominant [7]. The model-based methods detect features by establishing fault mechanism models.…”

Section: Introductionmentioning

confidence: 99%

“…Han et al [2] proposed a canonical variate analysis model and utilized the square of the residual Mahalanobis distance as a diagnosis index to improve the model performance. However, many important parameters of control valves are hard to measure in complex systems, such as the flow coefficient and fluid characteristics, and it is difficult to achieve effective fault diagnosis using such an imperfect model [7].…”

Section: Introductionmentioning

confidence: 99%

Improved DBO-VMD and optimized DBN-ELM based fault diagnosis for control valve

Zhang,

Han

et al. 2024

Control valves play a vital role in process production. In practical applications, control valves are prone to blockage and leakage faults. At the small control valve openings, the vibration signals exhibit the drawbacks of significant interference and weak fault characteristics, which causes subpar fault diagnosis performance. To address the issue, a diagnostic model based on optimized Variational Mode Decomposition (VMD) and improved Deep Belief Network-Extreme Learning Machine (DBN-ELM) is proposed. Firstly, good point set population initialization, nonlinear convergence factor, and adaptive Gaussian-Cauchy mutation strategies are applied in the dung beetle optimization algorithm (DBO) to escape local optima. Then, the improved DBO (IDBO) is used to optimize VMD parameters to obtain a series of modal components. Next, the generalized dispersion entropy (GDE) is formed by the combination of Generalized Gaussian Distribution and refined composite multiscale fluctuation-based dispersion entropy. The maximum correlation coefficient modal components are applied to extract generalized dispersion entropy. Finally, the IDBO is applied to optimize the parameters of the DBN-ELM network to improve the classification performance of control valve faults. The comparative experiment results demonstrate that the proposed model can extract effective features and the diagnostic accuracy reaches 99.87%.

“…Electrohydraulic control valves have become increasingly digital, integrated and intelligent to meet the requirements of Industry 4.0 and the development of automation, digital technology and communication technology [1]. Because hydraulic valves usually work in harsh conditions and are severely disturbed by various paths, it is difficult to detect internal faults in hydraulic valves using traditional hydraulic testing techniques, such as pressure or flow sensors [2]. Therefore, research on intelligent fault diagnosis methods for servo valves is of great significance for improving the service quality, reducing the operation and maintenance costs of hydraulic systems and realizing hydraulic intelligence.…”

Section: Introductionmentioning

confidence: 99%

“…Tang Shengnan et al used Bayesian optimization (BO) algorithm to automatically select parameters, and built an adaptive model named convolutional neural network (CNN)-BO based on Gaussian process BO, which can accurately complete the intelligent fault diagnosis of hydraulic pumps [4]. Shi et al proposed a two-stage multisensor information fusion method to diagnose the internal fault of hydraulic changeover valves [2] by using a vibration signal analysis method instead of the traditional hydraulic test method to solve problems such as the difficulty in obtaining the fault status of hydraulic valves and the low accuracy of the fault diagnosis of hydraulic valves. Guo, FY and others proposed a fault diagnosis method for reciprocating compressor valves based on a transfer learning convolutional neural network, focusing on the problem of the valve fault status for reciprocating compressors, and the fault recognition rate reached 98.32% [5].…”

Section: Introductionmentioning

confidence: 99%

Multisource electrohydraulic servo valve fault status diagnostic algorithm based on a message propagation mechanism

Gao

sun

et al. 2023

Fault identification of electrohydraulic servo valves is crucial to maintain the reliability and safety of high-precision electrohydraulic servo systems. Because the nonlinear characteristics and fault characteristics of electrohydraulic servo systems under noise conditions are implicit, it is difficult to obtain a large number of fault data of electrohydraulic servo valves. Therefore, an electrohydraulic servo valve fault diagnosis model based on characteristic distillation is proposed in this paper. First, the original fault data model is obtained based on an electrohydraulic servo valve fault test platform, the data are standardized, and the data of more than one cycle are extracted using a combination of down sampling and a sliding window for data enhancement. Second, a neural network fault diagnosis algorithm based on stack graph convolution is proposed, which is suitable for detecting different types of states (normal state, wear state, stuck state and coil short-circuit state) of electrohydraulic servo valves. The accuracy of the test set fluctuates between 0.7 and 1.0. Then, because there is a certain relationship between the characteristic smoothing phenomenon of a stack graph convolution model and the number of layers, a multilayer stack graph convolution model is bound to have problems such as model degradation. Therefore, a residual model is introduced into the stack model to improve the convergence speed of the model during the optimization process. The results show that the average accuracy of this method is 100%.