Fault diagnosis of hydraulic system in large forging hydraulic press

Fu, Xiang; Liu, Bin; Zhang, Yucun; Lian, Li-na

doi:10.1016/j.measurement.2013.12.010

Cited by 38 publications

(24 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…FTC of an electro hydraulic servo axis was studied by Beck and Mark Another technique based on system models and observers have been widely used in various applications [6,7,14,[22][23][24]. In EHA applications, several researches [13,25,26] This paper is sketched as follows: in Section 2, the overall description of the EHA apparatus has been introduced at first.…”

Section: In the Modern Industry Electro-hydraulic Actuators (Ehas) Hmentioning

confidence: 99%

Modeling and fault tolerant control of an electro-hydraulic actuator

Nahian

Dinh

Chowdhury

et al. 2016

Int. J. Precis. Eng. Manuf.

View full text Add to dashboard Cite

Section: In the Modern Industry Electro-hydraulic Actuators (Ehas) Hmentioning

confidence: 99%

Modeling and fault tolerant control of an electro-hydraulic actuator

Nahian

Dinh

Chowdhury

et al. 2016

Int. J. Precis. Eng. Manuf.

View full text Add to dashboard Cite

“…External leakage Δc e and return-line pressure deviation Δp s− are not considered in the fault modeling since the former can be detected from the cylinder surface, whereas the latter is not common because of low pressure. As in any model-based method, uncertainties in the RSS model cannot be avoided and are accounted for in the disturbance d in (6). The parameter uncertainties in the RSS model include V ± , β e , and τ , which generally cannot be measured exactly.…”

Section: Model Uncertainties In Fault Modelingmentioning

confidence: 99%

“…3, the input to the observer is the voltage u from the controller, and the measured and estimated observer output vectors are y = [ẋ p p + p − ] T and y = [x pp+p− ] T , respectively. g(x) is a nonlinear term, C is the parameter matrix defined in (6), and N, G, L, T, and E are unknown matrices of appropriate dimensions to be designed such thatx(t) → 0 as t → ∞. The following assumptions are made in the design of the observers and algorithm.…”

Section: A Fault Modelingmentioning

confidence: 99%

See 1 more Smart Citation

Model-Based Fault Detection and Isolation Scheme for a Rudder Servo System

Lee

Zhou

et al. 2015

IEEE Trans. Ind. Electron.

View full text Add to dashboard Cite

This paper offers a model-based fault detection and isolation (FDI) scheme for a rudder servo system (RSS) that manages ship navigation. Inherent nonlinearities and unknown external disturbances present great challenges in applying FDI technologies to RSS in practice. This paper presents the derivation of the state equations and commonly encountered fault types of RSS from physical laws, and the design of five nonlinear unknown input observers (NUIOs) capable of eliminating the influences of unknown disturbance while detecting and isolating faults. In the NUIOs, no boundary assumption is made on the disturbance, and their parameter matrices are obtained using a linear-matrix-inequality method. Along with an algorithm that programs the logic rules for FDI, the model-based FDI scheme has been implemented on an actual RSS test rig for validating the RSS model and experimentally evaluating it in a real-world environment; both actuator and sensor faults are considered. Based on experimental tests, an adaptive threshold method is introduced to improve decision making and hence effectively eliminate false alarms. Experimental results show that the model-based scheme is efficient and can be used for online FDI.Index Terms-Adaptive threshold, fault detection, fault isolation, nonlinear unknown input observer (NUIO), rudder servo system (RSS). NOMENCLATURE Capital letters ASystem matrix of RSS model. B, CInput and output matrices of RSS model, respectively. C iOutput matrix of RSS model obtained by deleting ith row of C. DDisturbance matrix of RSS model. DDisturbance matrix of RSS model obtained by adding e i to D. E Matrix of the observer for RSS output term.

show abstract

“…The sealing characteristic of the hydraulic system structure results in hydraulic faults being concealed and easily affected by random factors. The mapping relationship between the signal characteristics and the system state is also complex [18,19]. Therefore, choosing a right diagnosis method is very critical.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Weak Fault in Hydraulic System Based on Multi-scale Permutation Entropy of Fault-Sensitive Intrinsic Mode Function and Deep Belief Network

Huang

Wang

et al. 2019

Entropy

View full text Add to dashboard Cite

With the aim of automatic recognition of weak faults in hydraulic systems, this paper proposes an identification method based on multi-scale permutation entropy feature extraction of fault-sensitive intrinsic mode function (IMF) and deep belief network (DBN). In this method, the leakage fault signal is first decomposed by empirical mode decomposition (EMD), and fault-sensitive IMF components are screened by adopting the correlation analysis method. The multi-scale entropy feature of each screened IMF is then extracted and features closely related to the weak fault information are then obtained. Finally, DBN is used for identification of fault diagnosis. Experimental results prove that this identification method has an ideal recognition effect. It can accurately judge whether there is a leakage fault, determine the degree of severity of the fault, and can diagnose and analyze hydraulic weak faults in general.

show abstract

Fault diagnosis of hydraulic system in large forging hydraulic press

Cited by 38 publications

References 14 publications

Modeling and fault tolerant control of an electro-hydraulic actuator

Modeling and fault tolerant control of an electro-hydraulic actuator

Model-Based Fault Detection and Isolation Scheme for a Rudder Servo System

Analysis of Weak Fault in Hydraulic System Based on Multi-scale Permutation Entropy of Fault-Sensitive Intrinsic Mode Function and Deep Belief Network

Contact Info

Product

Resources

About