An intelligent fault diagnosis approach based on Dempster-Shafer theory for hydraulic valves

Ji, Xiancheng; Ren, Yan; Tang, Hesheng; Shi, Chong; Xiang, Jiawei

doi:10.1016/j.measurement.2020.108129

Cited by 49 publications

(22 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Other critical equipment in hydraulic systems are solenoid-controlled valves. Ji et al 42 proposed that the most common types of faults in these valves are oil leaks. As previously mentioned, the function of accumulators in a hydraulic circuit is to store the required pressurized liquid under pressure.…”

Section: System Architecture Of Digital Twinmentioning

confidence: 99%

A digital twin framework for aircraft hydraulic systems failure detection using machine learning techniques

Kosova

Ünver

2022

Proceedings of the Institution of Mechanical Engineers, Part C:

View full text Add to dashboard Cite

Since the last decade, aircraft systems, such as flight control and landing gear, have been requiring increasing power, and consequently, the complexity of hydraulic aircraft systems has escalated. Inevitably, this complexity has resulted in the need for the troubleshooting of hydraulic aircraft systems that are dispersed around an aircraft and supply power to critical flight systems. This study proposes a novel digital twin-based health monitoring system for aircraft hydraulic systems to enable diagnostics of system failures early in the design cycle using machine learning (ML) methods. The scope of the systems is limited to hydraulic systems at the aircraft level using 20 failure scenarios. The support vector machine and several ensemble learning algorithms of ML methods were used to identify these failures. A comparison of the ML methods revealed that the random forest algorithm performed superior to the other ML algorithms. The developed digital twin framework for hydraulic system of aerial vehicle platforms, can help researchers and engineers to evaluate diagnostics systems early in the design phase.

show abstract

Section: System Architecture Of Digital Twinmentioning

confidence: 99%

A digital twin framework for aircraft hydraulic systems failure detection using machine learning techniques

Kosova

Ünver

2022

Proceedings of the Institution of Mechanical Engineers, Part C:

View full text Add to dashboard Cite

show abstract

“…Chen et al adopted a stacked self-encoding algorithm and realized the fault diagnosis of electrohydraulic servo valves through layer-by-layer greedy training [14]. Based on the Dempster-Shafer theory, Ji et al solved the problem of information source conflicts and used a CNN, LSTM, random forest (RF) and other methods to realize the fault diagnosis of electrohydraulic servo valves [15]. Chao et al proposed a multi-sensor fusion method using a CNN.…”

Section: Introductionmentioning

confidence: 99%

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

Gao

sun

et al. 2023

Meas. Sci. Technol.

View full text Add to dashboard Cite

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%.

show abstract

“…With the rapid development of artificial intelligence (AI) technology, deep learning (DL) has been widely applied in the field of mechanical fault diagnosis, and a series of intelligent fault diagnosis methods have been developed based on different AI models [1][2][3][4][5], such as the convolutional neural network (CNN) [6], deep belief network [7] and long short-term memory network [8], etc. These intelligent fault diagnosis methods can automatically extract the potential fault feature information from the mechanical monitoring data and intelligently identify * Author to whom any correspondence should be addressed.…”

Section: Introductionmentioning

confidence: 99%

A game theory enhanced domain adaptation network for mechanical fault diagnosis

Sun¹,

Liu²,

Liu³

et al. 2022

Meas. Sci. Technol.

Self Cite

View full text Add to dashboard Cite

Transfer learning (TL) technology have been successfully applied to address the domain adaptation (DA) problem in machinery fault diagnosis. However, partial DA problem is more suitable for industrial applications, where the target data only covers a subset of the source classes, which makes it difficult to know where to transfer the target data. To overcome this problem, a novel game theory enhanced domain adaptation network (GT-DAN) is proposed in this paper. Based on different metrics, including Maximum mean discrepancy (MMD), Jensen-Shannon (JS) divergence and Wasserstein distance, three attention matrices are constructed to describe the distribution discrepancies between the source domain and the target domain, and the optimal coordination between these attention matrices is achieved by the combination weighting based on game theory to generate the optimal probability weights, which can guide to filter out the irrelevant source examples in domain adaptation. Two experiments show that the proposed GT-DAN is superior to the existing methods in the partial DA diagnosis performance.

show abstract

An intelligent fault diagnosis approach based on Dempster-Shafer theory for hydraulic valves

Cited by 49 publications

References 30 publications

A digital twin framework for aircraft hydraulic systems failure detection using machine learning techniques

A digital twin framework for aircraft hydraulic systems failure detection using machine learning techniques

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

A game theory enhanced domain adaptation network for mechanical fault diagnosis

Contact Info

Product

Resources

About