FAULT DIAGNOSIS OF HYDRAULIC SERVO SYSTEM USING THE UNSCENTED <scp>K</scp>ALMAN FILTER

Li, Hongmei; Liu, Dawei; Lu, Chen; Wang, Xuan

doi:10.1002/asjc.860

Cited by 28 publications

(16 citation statements)

References 18 publications

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“…In addition to kernel methods, many learning methods have also been widely studied, such as the gray relational analysis (GRA) [22], Kalman filtering [23], and non-negative matrix factor [24]. However, GRA needs to determine the optimal values of the indicators first, and thus is subjective.…”

Section: Main Flow Of Fault Diagnosis 211 Signal Processing and Feamentioning

confidence: 99%

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

Dai

Tang

Huang

et al. 2019

Chin. J. Mech. Eng.

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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.

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Section: Main Flow Of Fault Diagnosis 211 Signal Processing and Feamentioning

confidence: 99%

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

Dai

Tang

Huang

et al. 2019

Chin. J. Mech. Eng.

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“…A variety of statistical tools, such as generalized likelihoods [93], χ 2 testing [94], cumulative sum algorithms [95] and multiple hypothesis test [96] [97]. The unscented Kalman filter (UKF), depending on a more accurate stochastic approximation, i.e., unscented transform, can better capture the true mean and covariance leading to better diagnosis performance [98,99]. Adaptive Kalman filters can be employed to tune process noise covariance matrix, or measurement noise covariance matrix in order to obtain satisfactory fault diagnosis [100,101].…”

Section: B Stochastic Fault Diagnosis Methodsmentioning

confidence: 99%

A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part I: Fault Diagnosis With Model-Based and Signal-Based Approaches

Gao

Cecati

Ding

2015

IEEE Trans. Ind. Electron.

2,412

937

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Northumbria University has developed Northumbria Research Link (NRL) to enable users to access the University's research output. Copyright © and moral rights for items on NRL are retained by the individual author(s) and/or other copyright owners. Single copies of full items can be reproduced, displayed or performed, and given to third parties in any format or medium for personal research or study, educational, or not-for-profit purposes without prior permission or charge, provided the authors, title and full bibliographic details are given, as well as a hyperlink and/or URL to the original metadata page. The content must not be changed in any way. Full items must not be sold commercially in any format or medium without formal permission of the copyright holder. The full policy is available online: http://nrl.northumbria.ac.uk/policies.html This document may differ from the final, published version of the research and has been made available online in accordance with publisher policies. To read and/or cite from the published version of the research, please visit the publisher's website (a subscription may be required.) IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS Abstract-With the ever increase of complexity and expense of industrial systems, there is less tolerance for performance degradation, productivity decrease and safety hazards, which greatly stimulates to detect and identify any kinds of potential abnormalities and faults as early as possible, and implement realtime fault-tolerant operation for minimizing performance degradation and avoiding dangerous situations. During the last four decades, fruitful results were reported about fault diagnosis and fault-tolerant control methods and their applications in a variety of engineering systems. The three-part survey paper aims to give a comprehensive review for real-time fault diagnosis and fault tolerant control with particular attention on the results reported in the last decade. In the first-part review, fault diagnosis approaches and their applications are reviewed comprehensively from model-based and signal-based perspectives, respectively.

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“…Therefore, it is essential to construct advanced fault detection and isolation (FDI) and fault-tolerant control (FTC) techniques for augmenting the system to ensure the safety, reliability, and sustainability of the systems. The benefits of FDI or simply fault diagnosis (FD) and FTC techniques for industrial systems have been recognized [5][6][7][8][9][10][11]. The FD algorithm primarily consists of making a binary decision between fault and no fault.…”

Section: Introductionmentioning

confidence: 99%

Experimental Study of Sensor Fault-Tolerant Control for an Electro-Hydraulic Actuator Based on a Robust Nonlinear Observer

Nguyen¹,

2019

Energies

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Electro-hydraulic actuators (EHAs) have been widely used in modern industries. However, sensor faults and actuator faults in EHA systems can arise due to aging during operation, making the system unstable and unsafe. To solve these issues, fault-tolerant control (FTC) techniques for EHA systems have been studied intensively. In this paper, an FTC is proposed and developed for the mini motion package (MMP) EHA system. First, a mathematical model of the MMP system is formulated and improved to provide position tracking control using a well-known proportional-integral-derivative (PID) controller. Second, an unknown input observer (UIO) reconstruction is performed to estimate the states, disturbances, and sensor faults so that an asymptotically stable control error can be obtained by a linear matrix inequality (LMI) optimization algorithm through Lyapunov’s stability condition. Third, the FTC designed for the nonlinear discrete-time system is formed from fault compensation based on a residual logic signal to implement the fault compensation process and ensure stability and tracking performance with respect to minimizing impacts of disturbances and sensor faults. Here, residual is defined by the difference between state response and state estimation. Finally, numerical simulations and experiments of the MMP system are presented to illustrate the efficiency of the proposed FTC technique.

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FAULT DIAGNOSIS OF HYDRAULIC SERVO SYSTEM USING THE UNSCENTED KALMAN FILTER

Cited by 28 publications

References 18 publications

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

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

A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part I: Fault Diagnosis With Model-Based and Signal-Based Approaches

Experimental Study of Sensor Fault-Tolerant Control for an Electro-Hydraulic Actuator Based on a Robust Nonlinear Observer

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