Fault Vector Research Articles

Robust fault tolerant control based on adaptive observer for Takagi-Sugeno fuzzy systems with sensor and actuator faults: Application to single-link manipulator

In this work, we investigate the problem of control for nonlinear systems represented by Takagi-Sugeno (T-S) fuzzy models affected by both sensor and actuator faults subject to an unknown bounded disturbances (UBD). For this, we design an adaptive observer to estimate state, sensor and actuator fault vectors simultaneously despite the presence of external disturbances. Based on this observer, we develop a fault tolerant control (FTC) law not only to stabilize closed loop system, but also to compensate the fault effects. For the observer-based controller design, we propose less conservative conditions formulated in terms of linear matrix inequalities (LMIs). Moreover, both observer and controller gains are calculated via solving a set of LMIs only in single step. Finally, comparative results and an application to single-link flexible joint robot are afforded to prove the efficiency of the proposed design.

FTC in presence of disturbances and un-estimable faults

The problem of fault tolerant control (FTC) with simultaneous faults, which are un-estimable, in presence of disturbance, is considered in this paper. Since this problem is hard, in order to allow its solvability, a class of plants is considered that satisfies an assumption on its outputs. For that class, it is shown that the fault vector can be replaced by a signal of reduced dimension, which is estimable. Then a minimal realization of an FTC controller based on the latter estimation is constructed. An illustrative example is given, which compares the controller of this paper with other controllers.

Almost fault‐tolerant tracking

SummaryA new formulation of the problem of almost fault‐tolerant perfect tracking in presence of disturbances is presented, and necessary and sufficient existence conditions are found, as well as a controller, which consists of a feedback block and two feedforward blocks. The feedback block is aimed for almost disturbance and fault decoupling, and the feedforward blocks are aimed for almost perfect tracking. A two‐step control design procedure such that the feedback block is designed independently of the feedforward blocks is presented. In order to simplify the necessary and sufficient existence conditions, as well as to present a physical interpretation of the existence conditions, the dimension of disturbance and fault vectors is reduced. In order to find a feedback controller, the physical interpretation of the existence conditions is used so that the four‐block‐type problem is transformed to a two‐block‐type problem. Examples are given, which illustrate that the controller of the article is practically realizable.

Adaptive SMO-Based Fault Estimation for Markov Jump Systems With Simultaneous Additive and Multiplicative Actuator Faults

This article studies the problem of fault reconstruction, disturbance estimation, and state estimation for Markovian jump systems (MJSs) with simultaneous additive and multiplicative actuator faults. First, we rewrite the original Markov jump linear systems into an extended form, where the extended vector is composed of an actuator fault vector, a disturbance vector, and a state vector. Then, by proposing a continuous adaptive sliding-mode observer for the extended-form MJSs, the state estimation, actuator fault reconstruction, and disturbance estimation can be achieved. Moreover, the stochastic stability of the overall error plant can be guaranteed. Finally, a simulation example is employed to illustrate the effectiveness of our theoretical results.

Multiple Multiplicative Actuator Fault Detectability Analysis Based on Invariant Sets for Discrete-time LPV Systems

This paper proposes a generalized minimum detectable fault (MDF) computation method based on the set-separation condition between the healthy and faulty residual sets for discrete-time linear parameter varying (LPV) systems with bounded inputs and uncertainties. First, we equivalently transform the multiple multiplicative actuator faults into the form of multiple additive actuator faults, which is beneficial to simplify the problem. Then, by considering the 1-norm of the fault vector, we define the generalized MDF in the case of multiple additive actuator faults, which can be computed via solving a simple linear programming (LP) problem. Moreover, an analysis of the effect of the input vector on the magnitude of the generalized MDF is made. Since the proposed generalized MDF computation method is robust by considering the bounds of inputs and uncertainties, robust fault detection (FD) can be guaranteed whenever the sum of the magnitudes of all occurred faults is larger than the magnitude of the generalized MDF. At the end of this paper, a numerical example is used to illustrate the effectiveness of the proposed method.

Adaptive estimation of component faults and actuator faults for nonlinear one‐sided Lipschitz systems

SummaryFault estimation for classical nonlinear Lipschitz systems has been subject to several research works. So far, much less interest has been given to the more generalized class of systems, namely, one‐sided Lipchitz systems. Dealing with component faults and actuator faults, only very few works have been done to reconstruct these types of faults for this new class of systems. A major limitation of the previous works is that the fault vector to be estimated there does not give any information about the actual faulty physical parameters of the system, so component faults and actuator faults are not distinguishable. In this paper, a set of possible faulty parameters in the system is estimated. Component faults and actuator faults are separated and distinguished. The effectiveness of the proposed method is shown through simulations for a numerical example.

A Generic Block-Level Error Confinement Technique for Memory Based on Principal Component Analysis

Nanoscale CMOS technology has encountered severe reliability issues especially in on-chip memory. Conventional word-level error resilience techniques such as Error Correcting Codes (ECC) suffer from high physical overhead and inability to correct increasingly reported multiple bit flip errors. On the other hands, state-of-the-art applications such as image processing and machine learning loosen the requirement on the levels of data protection, which result in dedicated techniques of approximated fault tolerance. In this work, we introduce a novel error protection scheme for memory, based on feature extraction through Principal Component Analysis and the modular-wise technique to segment the data before PCA. The extracted features can be protected by replacing the fault vector with the averaged confinement vectors. This approach confines the errors with either single or multi-bit flips for generic data blocks, whilst achieving significant savings on execution time and memory usage compared to traditional ECC techniques. Experimental results of image processing demonstrate that the proposed technique results in a reconstructed image with PSNR over 30 dB, while robust against both single bit and multiple bit flip errors, with reduced memory storage to just 22.4% compared to the conventional ECC-based technique.

Fault estimation based on sliding mode observer for Takagi–Sugeno fuzzy systems with digital communication constraints

This work discussed the design of a sliding mode observer for the estimation of actuator faults in a Takagi–Sugeno fuzzy system with digital communication channel. Signals must be quantified before they enter the digital communication channel in network environments. In this work, the fuzzy sliding mode observer was used to offset the effects of quantization and uncertainty. The sliding mode observer can compensate for quantization errors, overcome uncertainty effects, and reconstruct fault vectors if the quantization density was 2−1<σ<1 and a given linear matrix inequality had a feasible solution. Finally, a simulation was performed to verify the effectiveness of the proposed method.

A state augmentation approach to interval fault estimation for descriptor systems

This paper proposes a state augmentation approach to achieve interval fault estimation for descriptor systems with unknown but bounded disturbances and measurement noises. An augmented descriptor system, which is equivalent to the original system, is constructed while the fault vector is considered as an auxiliary state. Based on this augmented system, a robust fault estimation observer is designed to estimate the actuator faults. Under this augmented descriptor form, the conservatism of observer gain matrix can be significantly reduced by introducing more design degrees of freedom. Moreover, interval estimation of actuator faults can be achieved by using zonotopic techniques. Finally, a DC motor model simulation is used to illustrate the effectiveness of the proposed approach.

Dynamic imaging of crystalline defects in lithium-manganese oxide electrodes during electrochemical activation to high voltage

Crystalline defects are commonly generated in lithium-metal-oxide electrodes during cycling of lithium-ion batteries. Their role in electrochemical reactions is not yet fully understood because, until recently, there has not been an effective operando technique to image dynamic processes at the atomic level. In this study, two types of defects were monitored dynamically during delithiation and concomitant oxidation of oxygen ions by using in situ high-resolution transmission electron microscopy supported by density functional theory calculations. One stacking fault with a fault vector b/6[110] and low mobility contributes minimally to oxygen release from the structure. In contrast, dissociated dislocations with Burgers vector of c/2[001] have high gliding and transverse mobility; they lead to the formation, transport and release subsequently of oxygen related species at the surface of the electrode particles. This work advances the scientific understanding of how oxygen participates and the structural response during the activation process at high potentials.

UIO-based Fault Estimation and Accommodation for Nonlinear Switched Systems

This paper investigates the problems of fault estimation and fault accommodation for a class of nonlinear switched systems. First, an augmented switched system is constructed by forming an augmented state vector composed of the state vector and the fault vectors. Then, an unknown input observer (UIO) is designed for the augmented switched system to estimate the augmented state vector. With the assist of the average dwell-time (ADT) method and the switched Lyapunov function technique, sufficient conditions are obtained to guarantee that the error system is globally uniformly asymptotically stable (GUAS) with a prescribed H∞ performance index. An algorithm is provided to show the procedures on how to design the UIO. Moreover, the results are extended to the measurement disturbances case. Based on signal compensation principle, a dynamic output feedback controller is designed to ensure the asymptotical stability of the closed-loop system. Finally, simulation results are presented to demonstrate the proposed technique.

Real-time sensor fault diagnosis method for closed-loop system based on dynamic trend

To solve the common problems of sensor fault detection, fault setting and fault location in the first-order closed-loop control system, a static fault diagnosis model based on the real-time trend of dynamic data flow is constructed. Aiming at the common fixed-value system and servo system in the first-order closed-loop control system, a data processing model based on sliding window is designed by collecting and analysing a large amount of real-time data, and the fault is isolated in the window based on the calibration parameters. Then, the adaptive threshold binarisation method is used to calculate the fault vector, and then the analytical model of fault location is obtained by the linear regression method. Finally, the feasibility and validity of closed-loop sensor fault diagnosis method based on dynamic data flow trend are verified by online simulation of “complex process system innovation experimental platform” based on OPC communication technology.

Fault-Tolerant Control of Time-Delay Markov Jump Systems With &lt;inline-formula&gt; &lt;tex-math notation="LaTeX"&gt; $It\hat{o}$&lt;/tex-math&gt; &lt;/inline-formula&gt; Stochastic Process and Output Disturbance Based on Sliding Mode Observer

This paper focuses on the fault-tolerant control problem of Markov jump systems (MJS) with $It\hat{o}$ stochastic process and output disturbances. Such a problem widely exists in practical systems such as mobile manipulator systems. Since MJS can suitably describe mobile manipulator systems, in this paper, a new approach based on the MJS model is proposed. First, a proportional-derivative sliding mode observer (SMO) and an observer-based controller are designed and synthesized. Two new theorems are derived to ensure the close-loop stochastic stability and the reachability of the sliding mode surface. Compared with the existing works, the system model is more general, which could describe a larger variety of plants or processes. The controller design procedure is simplified by solving the sliding mode parameters and the controller gain simultaneously with only one linear matrix inequality problem. In addition, the augmented fault vector can be reconstructed by employing a descriptor SMO. Simulations are provided to demonstrate the validity of the derived theorems and the effectiveness of the proposed algorithm.

Fault Estimation Sliding-Mode Observer With Digital Communication Constraints

This paper addresses the actuator fault estimation sliding-mode observer (SMO) design problem of linear continuous-time systems over digital communication channels. This problem frequently occurred in a network environment where data has to be quantized before being transmitted via digital communication channels. Traditional observers (linear Luenberger observer, Walcott–ZaK SMO) are not effective to solve this design issue since the effects of signal quantization will degrade estimation performances evidently. In this paper, a new descriptor SMO method is presented to overcome this difficult problem. It is shown that, if the quantizer density is larger than $\sqrt{2}-1$ , the designed observer can compensate quantization errors completely, and the fault vector can be reconstructed despite of signal quantization. Finally, a simulation example with the F-404 aircraft engine model is proposed to demonstrate the effectiveness of the proposed robust digital observer design approach.

Nonlinear Dimension Reduction on Analog Circuit Fault Diagnosis Using L-Isomap

Due to the nonlinear characteristics of analog output signal, the extracted fault feature vectors tend to be high-dimensional and general need to reduce dimension. Focus on this problem, this paper puts forward a feature reduction method for analog circuit fault diagnosis based on wavelet packet energy spectrum and L-Isometric feature mapping (L-Isomap). In our approach, L-Isomap is used to reduce the high-dimensional fault vector of analog circuit and extract the optimized low-dimensional features as the characteristic vector. Compared with Isomap, L-Isomap has better dimension reduction effect and lower time complexity. Several experiments running on a two-stage four-op-amp biquad low-pass filter circuit, and the experiment results validate the effectiveness of our proposed method.

Actuator and sensor faults estimation for discrete-time descriptor linear parameter-varying systems in finite frequency domain

ABSTRACTThis paper investigates the problem of fault estimation for discrete-time descriptor linear parameter-varying systems with actuator faults, sensor faults and external disturbances. First, in order to estimate the actuator and sensor faults simultaneously, an augmented system is constructed, where the auxiliary state vector consists of the original state vector and the sensor fault vector. Then, considering the fact that many faults usually occur in finite frequency ranges, a novel fault estimation observer with finite frequency specifications is designed, where the conservatism can be reduced compared with the traditional fault estimation design methods in full frequency domain. By employing the generalised Kalman–Yakubovich–Popov lemma and two finite frequency H∞ performance indices, the sufficient conditions of the proposed observer are given in terms of linear matrix inequalities. Furthermore, by using slack variables, improved results on the fault estimation observer design are obtained, which are convenient to calculate fault estimation observer parameters for different frequency ranges. An example is presented to illustrate the effectiveness of the proposed method.

Fault-Tolerant Control of Multiarea Power Systems via a Sliding-Mode Observer Technique

This paper is focused on solving the problems of fault estimation and fault-tolerant control for multiarea power systems with sensor failures. First, the estimations of the system states and fault vectors are determined using an improved sliding-mode observer technique. Moreover, a derivative gain and a proportional gain are introduced to design the resultant sliding-mode observer more freely, and a discontinuous input is given to reduce the impact of sensor faults and aggregated uncertainties. Then, based on the obtained state estimates, an integral-type sliding-mode control scheme against faults and disturbances is proposed to ensure that the resultant fault system is asymptotically stable, from which each subsystem states in the multiarea power system can be driven onto the designed sliding-mode surfaces in both the state estimation and error estimation spaces. Finally, a three-area power system is simulated to validate the feasibility of the developed fault-tolerant control scheme.

Evaluation of Fault Diagnosability for Dynamic Systems With Unknown Uncertainties

In this paper, the quantitative fault diagnosability problem for a stochastic dynamic system subjects to unknown uncertainties is proposed. Reliable isolability, reliable detectability, and reliable distinguishability are newly defined for the studied uncertain system. By considering model uncertainties, norm-bounded disturbances, and noises, the quantitative diagnosability problem is originally transferred to an optimization problem. A novel methodology is proposed to quantify the fault diagnosability based on a new sliding window model, which greatly alleviates the computation task. To quantify the disturbance effect on the diagnosability performance, disturbance ratio is defined. Furthermore, the reliable isolability conditions for a fault vector with a specific fault time profile is theoretically analyzed. Effectiveness of the proposed method is verified by a numerical example.

Multiple Feature Vectors Based Fault Classification for WSN Integrated Bearing of Rolling Mill

For rolling mill machines, the operation status of bearing has a close relationship with process safety and production effectiveness. Therefore, reliable fault diagnosis and classification are indispensable. Traditional methods always characterize fault feature using a single fault vector, which may fail to reveal whole fault influences caused by complex process disturbances. Besides, it may also lead to poor fault classification accuracy. To solve the above-mentioned problems, a fault extraction method is put forward to extract multiple feature vectors and then a classification model is developed. First, to collect sufficient data, a data acquisition system based on wireless sensor network is constructed to replace the traditional wired system which may bring dangers during production. Second, the measured signal is filtered by a morphological average filtering algorithm to remove process noise and then the empirical mode decomposition method is applied to extract the intrinsic mode function (IMF) which contains the fault information. On the basis of the IMFs, a time domain index (energy) and a frequency index (singular values) are proposed through Hilbert envelope analysis. From the above analysis, the energy index and the singular value matrix are used for fault classification modeling based on the enhanced extreme learning machine (ELM), which is optimized by the bat algorithm to adjust the input weights and threshold of hidden layer node. In comparison with the fault classification methods based on SVM and ELM, the experimental results show that the proposed method has higher classification accuracy and better generalization ability.

Rolling bearing fault detection based on local characteristic-scale decomposition and teager energy operator

In this paper, a rolling bearing fault detection method based on Local Characteristic-scale Decomposition (LCD) and Teager Energy operator (TEO) is proposed. Vibration signals is related to the bearing fault. However, the vibration signal of rolling bearing is nonlinear and has multiple components, which makes it difficult to analyze the signals by using traditional method such as the fast Fourier transform (FFT). LCD, a recently developed signal decomposition method, is especially capable for dealing with the complex signal by decomposing it into several intrinsic scale components (ISC). Furthermore, to extract fault diagnosis of the components, we used TEO to demodulate each ISC. The energy of fault feature frequencies was extracted as fault vector. The result shows that the method successfully diagnoses bearing fault.