Fault diagnosis of a hydraulic actuator circuit using neural networks—an output vector space classification approach

Crowther, William; Edge, K A; Burrows, C. R.; Atkinson, R M; Woollons, D J

doi:10.1243/0959651981539299

Cited by 35 publications

(13 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A technology for the diagnosis of a hydraulic system fault, which uses an artificial intelligence (AI) pattern recognition method as its main body and combines various feature extraction methods, has dominated the focus and development trend in this field. Abbott [46] designed a fault diagnosis approach of expert for a hydraulic system used on the NASA space shuttle; Crowther et al [47] established a neural network identification model for the hydraulic system of a second-order hydraulic actuator. Amin et al [48] combined multi-feature fusion and fuzzy decision theory to study the on-line health monitoring of hydraulic pumps.…”

Section: Fault Diagnosis Of Hydraulic System Based On Artificial Intementioning

confidence: 99%

Signal-Based Intelligent Hydraulic Fault Diagnosis Methods: Review and Prospects

Dai

Tang

Huang

et al. 2019

Chin. J. Mech. Eng.

View full text Add to dashboard Cite

Hydraulic systems have the characteristics of strong fault concealment, powerful nonlinear time-varying signals, and a complex vibration transmission mechanism; hence, diagnosis of these systems is a challenge. To provide accurate diagnosis results automatically, numerous studies have been carried out. Among them, signal-based methods are commonly used, which employ signal processing techniques based on the state signal used for extracting features, and further input the features into the classifier for fault recognition. However, their main deficiencies include the following: (1) The features are manually designed and thus may have a lack of objectivity. (2) For signal processing, feature extraction and pattern recognition are conducted using independent models, which cannot be jointly optimized globally. (3) The machine learning algorithms adopted by these methods have a shallow architecture, which limits their capacity to deeply mine the essential features of a fault. As a breakthrough in artificial intelligence, deep learning holds the potential to overcome such deficiencies. Based on deep learning, deep neural networks (DNNs) can automatically learn the complex nonlinear relations implied in a signal, can be globally optimized, and can obtain the high-level features of multi-dimensional data. In this paper, the main technology used in an intelligent fault diagnosis and the current research status of hydraulic system fault diagnosis are summarized and analyzed. The significant prospect of applying deep learning in the field of intelligent fault diagnosis is presented, and the main ideas, methods, and principles of several typical DNNs are described and summarized. The commonality between a fault diagnosis and other issues regarding typical pattern recognition are analyzed, and research ideas for applying DNNs for hydraulic fault diagnosis are proposed. Meanwhile, the research advantages and development trend of DNNs (both domestically and overseas) as applied to an intelligent fault diagnosis are reviewed. Furthermore, the fault characteristics of a complex hydraulic system are summarized and discussed, and the key problems and possible research ideas of applying DNNs to an intelligent hydraulic fault diagnosis are presented and comprehensively analyzed.

show abstract

Section: Fault Diagnosis Of Hydraulic System Based On Artificial Intementioning

confidence: 99%

Signal-Based Intelligent Hydraulic Fault Diagnosis Methods: Review and Prospects

Dai

Tang

Huang

et al. 2019

Chin. J. Mech. Eng.

View full text Add to dashboard Cite

show abstract

“…Heron and Huges [51] developed a novel contaminant monitoring scheme to examine the cleanliness level of fluid in a hydraulic system, for in the presence of solid contaminants in the fluid, the friction between working elements of hydraulic components increases and the system operates in the critical zone. Crowther et al [52] built a neural network model for a hydraulic actuation system, investigating the lack of supply pressure, internal leakage in the actuator, and dynamic friction load. Skormin and Apone [53] developed a failure prediction procedure, detecting and utilizing trends exhibited by parameter estimation.…”

Section: Advantages and Disadvantages Of Eha/ieha Unitsmentioning

confidence: 99%

Review of Development Stages in the Conceptual Design of an Electro-Hydrostatic Actuator for Robotics

Karanović

Jocanović

Jovanović

2014

APH

View full text Add to dashboard Cite

“…Moreover, the limited human resource of expert personnel restricts a widespread application of the fault diagnosis techniques. As such, in order to help inexperienced operators to quickly make an objective and accurate decision on machinery running conditions, a wide variety of automated classification paradigms have been introduced for fault classification such as expert systems (ESs), fuzzylogic inference [25,26], NNs [6,[27][28][29][30][31][32][33][34][35], and SVMs [36][37][38][39][40]. ESs may be the earliest attempt made to automate fault diagnosis, which documents the expertise of human experts into a computer system and emulates human reasoning process.…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis based on optimized node entropy using lifting wavelet packet transform and genetic algorithms

Zhang

Xiong

Liu

et al. 2010

Proceedings of the Institution of Mechanical Engineers, Part I:

View full text Add to dashboard Cite

The current paper presents a novel scheme for bearing fault diagnosis based on lifting wavelet packet transform (LWPT), sample entropy (SampEn), support vector machines (SVMs), and genetic algorithms (GAs). In the proposed scheme, bearing vibration signals were first decomposed into different frequency sub-bands through a four-level LWPT, resulting in a total of 31 node signal components throughout all layers of the LWPT decomposition tree. The SampEns of all 31 components were then calculated as an original feature pool to characterize the complexity of the bearing vibration signals within the corresponding frequency bands. For selecting the most informative features thus reducing the number of features, a GA was applied to simultaneously pick out the salient features and optimize the parameters of SVMs so as to avoid the curse of dimensionality, alleviate computational burden, and improve the subsequent classification. Experiments were conducted on an induction motor with respect to various bearing faults and a range of fault severities. As an example here, three salient features were selected from the original 31 features, with the dimension of features reduced dramatically. The selected three features were then presented into the optimized SVM to identify various bearing conditions. The scheme was compared with the widely used WPT-Energy method and with a neural network classifier with respect to feature extractions and fault classifications, respectively. The results are in favour of the proposed scheme in terms of the feature dimension, number of support vectors, robustness to the data partition, and classification rate.

show abstract

Fault diagnosis of a hydraulic actuator circuit using neural networks—an output vector space classification approach

Cited by 35 publications

References 20 publications

Signal-Based Intelligent Hydraulic Fault Diagnosis Methods: Review and Prospects

Signal-Based Intelligent Hydraulic Fault Diagnosis Methods: Review and Prospects

Review of Development Stages in the Conceptual Design of an Electro-Hydrostatic Actuator for Robotics

Fault diagnosis based on optimized node entropy using lifting wavelet packet transform and genetic algorithms

Contact Info

Product

Resources

About