Sensor Fault Reconstruction Research Articles

Sensor fault reconstruction for wind turbine benchmark model using a modified sliding mode observer

<p><span lang="EN-GB">This paper proposes a fault diagnosis scheme applied to a wind turbine system. The technique used is based on a modified sliding mode observer (SMO), which permits the reconstruction of actuator and sensor faults. A wind turbine benchmark with a real sequence of wind speed is exploited to validate the proposed fault detection and diagnosis scheme. Rotor speed, generator speed, blade pitch angle, and generator torque have different orders of magnitude. As a result, the dedicated sensors are susceptible to faults of quite varying magnitudes, and estimating simultaneous sensor faults with accuracy using a classical SMO is difficult. To address this issue, some modifications are made to the classic SMO. In order to test the efficiency of the modified SMO, several sensor fault scenarios have been simulated, first in the case of separate faults and then in the case of simultaneous faults. The simulation results show that the sensor faults are isolated, detected, and reconstructed accurately in the case of separate faults. In the case of simultaneous faults, with the proposed modification of SMO, the faults are precisely isolated, detected, and reconstructed, even though they have quite different amplitudes; thus, the relative gap does not exceed 0.08% for the generator speed sensor fault.</span></p>

A practical observer for state and sensor fault reconstruction of a class of fractional‐order nonlinear systems

A practical observer for state and sensor fault reconstruction of a class of fractional‐order nonlinear systems

In-situ sensor calibration for building HVAC systems with limited information using general regression improved Bayesian inference

Sensors in building heating, ventilation and air-conditioning systems (HVACs) play important roles in maintaining indoor environmental quality and energy consumption. Owing to the repeatedly varied outdoor working environment and indoor users’ demand, sensor faults could be inevitable in the lifespan. To allow HVACs worked at fault-tolerant way, previous studies developed the in-situ sensor calibration method via energy conservation equations and Bayesian inference (EC-BI). However, the practical application may encounter challenges like limited-variable information, low-quality data and increasing risks of calibration uncertainty by indirect information supplement. These cause increasing in-situ calibration complexity and modeling costs. To address these challenges, this study proposed a general regression improved Bayesian inference (BI) in-situ sensor calibration strategy. The multiple linear regression (MLR) was utilized as a typical example of general regression method to improve the BI method. The proposed MLR-BI method was validated using both simulated and practical data of two building HVAC systems in two case studies. The principle component analysis (PCA)-based sensor fault reconstruction method was used for comparison under five fault conditions covering both single and simultaneous faults. Five variable scenarios were considered to validate the effectiveness of MLR-BI on HVACs with the limited variable information. Results indicated that the calibration accuracy of MLR-BI is over 99% under four conditions of the simulated case 1, which is about 6% and 8% higher than PCA and EC-BI respectively. For all the three variable scenarios of the simulated case 1, the calibration accuracy of MLR-BI is 99.65% on average. Especially in the four-variable scenario with limited variable information, MLR-BI shows the average calibration accuracy of 99.75% while PCA obtains 79.46% and EC-BI fails to work because of variable limitation. For the fault condition of the limitted-variable practical case 2, MLR-BI still outperforms the other two and obtains 97.1% calibration accuracy in two practical scenarios.

Linear Fault Estimators for Nonlinear Systems: An Ultra-Local Model Design

This paper addresses the problem of robust process and sensor fault reconstruction for nonlinear systems. The proposed method augments the system dynamics with an approximated internal linear model of the combined contribution of known nonlinearities and unknown faults - leading to an approximated linear model in the augmented state. We exploit the broad modeling power of ultra-local models to characterize this internal dynamics. We use a linear filter to reconstruct the augmented state (simultaneously estimating the state of the original system and the sum of nonlinearities and faults). Having this combined estimate, we can simply subtract the analytic expression of nonlinearities from that of the corresponding estimate to reconstruct the fault vector. Because the nonlinearity does not play a role in the filter dynamics (it is only used as a static nonlinear output to estimate the fault), we can avoid standard restrictive assumptions like globally (one-sided) Lipschitz nonlinearities and/or the need for Lipschitz constants to carry out the filter design. The filter synthesis is posed as a mixed H2/H∞ optimization problem where the effect of disturbances and model mismatches is minimized in the H∞ sense, for an acceptable H2 performance with respect to measurement noise.

Simultaneous Sensor and Actuator Fault Reconstruction by Using a Sliding Mode Observer, Fuzzy Stability Analysis, and a Nonlinear Optimization Tool

This paper proposes a Takagi–Sugeno (TS) fuzzy sliding mode observer (SMO) for simultaneous actuator and sensor fault reconstruction in a class of nonlinear systems subjected to unknown disturbances. First, the nonlinear system is represented by a TS fuzzy model with immeasurable premise variables. By filtering the output of the TS fuzzy model, an augmented system whose actuator fault is a combination of the original actuator and sensor faults is constructed. An performance criteria is considered to minimize the effect of the disturbance on the state estimations. Then, by using two further transformation matrices, a non-quadratic Lyapunov function (NQLF), and fmincon in MATLAB as a nonlinear optimization tool, the gains of the SMO are designed through the stability analysis of the observer. The main advantages of the proposed approach in comparison to the existing methods are using nonlinear optimization tools instead of linear matrix inequalities (LMIs), utilizing NQLF instead of simple quadratic Lyapunov functions (QLF), choosing SMO as the observer, which is robust to the uncertainties, and assuming that the premise variables are immeasurable. Finally, a practical continuous stirred tank reactor (CSTR) is considered as a nonlinear dynamic, and the numerical simulation results illustrate the superiority of the proposed approach compared to the existing methods.

Observer-Based Fault Reconstruction and Fault-Tolerant Control for Nonlinear Systems Subject to Simultaneous Actuator and Sensor Faults

In this article, we pay our attention on exploring observer-based actuator fault and sensor fault reconstruction associated with fault-tolerant control (FTC) for nonlinear systems, which are approximated by the Takagi–Sugeno (T-S) fuzzy method. By designing a brand-new unknown input observer (UIO), unknown state, sensor, and actuator faults can be reconstructed simultaneously, where some constraints imposed on the actuator fault such as the first derivative of fault being equal to zero required in the previous results are not needed in our work. With the support of this estimation information, a FTC scheme is well established, by which the system may recover its performance even in the occurrence of faults. Another contribution of the developed method is that all the stability criteria are deduced via fuzzy Lyapunov functions. It makes the obtained conditions more relaxed than the ones derived by quadratic Lyapunov functions. Finally, simulation results conducted on two practical dynamics are provided to show the validity of the achieved procedure.

Current Sensor Fault Reconstruction and Compensation of an AC-Voltage Sensorless Controlled Single Phase Grid Connected Converter using PI-Observers

Current Sensor Fault Reconstruction and Compensation of an AC-Voltage Sensorless Controlled Single Phase Grid Connected Converter using PI-Observers

A Novel Adaptive Observer-Based Fault Reconstruction and State Estimation Method for Markovian Jump Systems

The issues of actuator fault reconstruction, sensor fault reconstruction, and state estimation are considered in this article for Markovian jump systems, using a novel adaptive observer approach. First, by utilizing coordinate transformation technology, the original plant is decoupled into two subplants. The actuator faults and sensor faults are separated in different subplants. Then, two novel observers are proposed, respectively. With the two proposed adaptive observers, state estimation, sensor faults, and actuator faults reconstruction can be achieved. Finally, a numerical example is employed to verify the effectiveness of the theoretical results and to make comparisons with some existing fault reconstruction schemes.

Sensor Fault Reconstruction based on Adaptive Sliding Mode Observer for Forklift Fault-Tolerant Control System

For the problem of multiple sensor fault detection and reconstruction in the forklift fault-tolerant control system, a sliding mode observer (SMO) with adaptive regulation law is proposed. Based on the three-degree-of-freedom (3-DOF) model of forklift, a linear state equation with output disturbance is designed as its equivalent sensor fault model. The sensor fault is converted into an actuator fault by defining an auxiliary state variable as an output signal filter. Then the SMO-based method of sensor fault detection and reconstruction is given. Without knowing the upper bound of an unknown fault, an adaptive sliding mode observer (ASMO) can also be effective through the adaptive algorithm. Finally, experimental results further verify the effectiveness of the method, and provide a foundation for forklift fault-tolerant control.

A Hybrid Sensor Fault Diagnosis for Maintenance in Railway Traction Drives.

Due to the importance of sensors in railway traction drives availability, sensor fault diagnosis has become a key point in order to move from preventive maintenance to condition-based maintenance. Most research works are limited to sensor fault detection and isolation, but only a few of them analyze the types of sensor faults, such as offset or gain, with the aim of reconfiguring the sensor in order to implement a fault tolerant system. This article is based on a fusion of model-based and data-driven techniques. First, an observer-based approach, using a Sliding Mode observer, is utilized for sensor fault reconstruction in real time. Then, once the fault is detected, a time window of sensor measurements and sensor fault reconstruction is sent to the remote maintenance center for fault evaluation. Finally, an offline processing is carried out to discriminate between gain and offset sensor faults, in order to get a maintenance decision-making to reconfigure the sensor during the next train stop. Fault classification is done by means of histograms and statistics. The technique here proposed is applied to the DC-link voltage sensor in a railway traction drive and is validated in a hardware-in-the-loop platform.

Robust sliding mode observer design for simultaneous fault reconstruction in perturbed Takagi-Sugeno fuzzy systems using non-quadratic stability analysis

Appearing faults in a practical system is dispensable, and if it is not compensated, it results in poor system performance or even dysfunction of the system. The fault detection has become a promising challenging issue to guarantee the safety and reliability of systems. In this paper, a novel fuzzy-based sliding mode observer for the simultaneous actuator and sensor fault reconstruction of nonlinear systems subjected to external disturbance is proposed. The proposed approach employs the Takagi-Sugeno fuzzy model, sliding mode observer and non-quadratic Lyapunov function. First, by filtering the system output, a fictitious system whose actuator faults are the original actuator and sensor faults is constructed. Then, by considering the performance criteria, the effect of disturbance on the state estimations is minimized. It is proved that the estimations asymptotically converge to their actual values for non-perturbed systems. In the process of designing the observer gains, some transformation matrices are obtained by solving linear matrix inequalities. The proposed approach has some superiority over the existing methods. First, considering the non-quadratic Lyapunov function leads to relaxed results and good estimation performance. Second, using the sliding mode observer makes the proposed approach insensitive to the uncertainties and unknown inputs and determines the shape and size of the fault. Third, assuming the premise variables are immeasurable makes the presented approach more applicable. In conclusion, two practical systems are considered and simulation results illustrate the merits of the proposed approach in comparison with the recent methods from the fast and precise fault detection performance viewpoints.

Robust Reconstruction of Current Sensor Faults for PMSM Drives in the Presence of Disturbances

The reliability and availability of a permanent magnet synchronous motor (PMSM) drive system are of increasing importance in industrial applications. Occurrence of current sensor faults in the system can lead to system degradation. Therefore, early detection of the faults has become an important problem. However, various exogenous disturbances seriously affect the accuracy of the faults detection. This paper presents a method of reconstructing current sensor faults for a PMSM drive containing unknown disturbances. An equivalent-input-disturbance approach is devised to reject the effects of unknown disturbances on system performance and reconstruction of faults And an integral observer is designed to approximately separate faults from the measured output to eliminate the interference to disturbance rejection. Then, a sliding-mode observer is designed for the disturbance-rejected system with current sensor faults. And the faults are reconstructed based on sliding-mode equivalent control methodology. The presented scheme has good performance in terms of faults reconstruction and is robust to time-varying unknown disturbances. Simulations and experiments validate the effectiveness of the method.

Robust Sensor Fault Reconstruction via a Bank of Second-Order Sliding Mode Observers for Aircraft Engines

This paper deals with sensor faults of aircraft engines under uncertainties using a bank of second-order sliding mode observers (SMOs). In view of the effect of inevitable uncertainties on the fault reconstruction, a method combining H ∞ concepts and linear matrix inequalities (LMIs) is proposed, in which a scaling matrix is designed to minimize the gain of the transfer function matrix from uncertainty to reconstruction. However, robust design generally requires that engine outputs outnumber faults. In the case where the above-mentioned requirement is not satisfied, a bank of sliding mode observers is proposed to ensure the degrees of freedom available in robust design. In specific, each observer corresponds to a certain sensor with the hypothesis that the corresponding sensor will not have faults, to create one degree of design freedom for each observer. After fault occurrence, a large estimation error is expected in the observers with wrong hypothesis, and then a logic module is designed to detect sensor faults and obtain the optimal robust sensor fault reconstruction at the same time. The proposed approach is applied to a nonlinear engine component-level-model (CLM) simulation platform, and a numerical study is performed to validate the effectiveness.

Sensor fault diagnosis for bridge monitoring system using similarity of symmetric responses

To ensure high quality data being used for data mining or feature extraction in the bridge structural health monitoring (SHM) system, a practical sensor fault diagnosis methodology has been developed based on the similarity of symmetric structure responses. First, the similarity of symmetric response is discussed using field monitoring data from different sensor types. All the sensors are initially paired and sensor faults are then detected pair by pair to achieve the multi-fault diagnosis of sensor systems. To resolve the coupling response issue between structural damage and sensor fault, the similarity for the target zone (where the studied sensor pair is located) is assessed to determine whether the localized structural damage or sensor fault results in the dissimilarity of the studied sensor pair. If the suspected sensor pair is detected with at least one sensor being faulty, field test could be implemented to support the regression analysis based on the monitoring and field test data for sensor fault isolation and reconstruction. Finally, a case study is adopted to demonstrate the effectiveness of the proposed methodology. As a result, Dasarathy\'s information fusion model is adopted for multi-sensor information fusion. Euclidean distance is selected as the index to assess the similarity. In conclusion, the proposed method is practical for actual engineering which ensures the reliability of further analysis based on monitoring data.

Fault Detection and Reconstruction for Discrete Nonlinear Systems via Takagi-Sugeno Fuzzy Models

Observer-based actuator fault detection and sensor fault reconstruction for a class of discrete-time nonlinear systems with actuator and sensor faults are investigated in this paper. A descriptor Takagi-Sugeno (T-S) fuzzy model is employed to construct observer-based systems for the purpose of fault detection and sensor fault reconstruction. Two methods for observer design are proposed. In the first method, the observer gains are computed off-line. In the second method, the observer gains are computed on-line at each iteration. The observer designs are formulated using linear matrix inequalities. Sufficient conditions for the existence of the observer-based fault detection and sensor fault reconstruction systems are provided. Comparative simulation study to illustrate the validity of the proposed methods is performed.

Robust integral-observer-based fault estimation for Lipschitz nonlinear systems with time-varying uncertainties

This paper addresses the problem of state estimation and sensor fault reconstruction conjointly for a class of nonlinear systems with time-varying uncertainties for which the nonlinear characteristic satisfies the Lipschitz circumstance. A hybrid approach based on an integral observer and sliding-mode theory has been proposed in order to model sensor fault as a virtual actuator one. For the augmented model, the observer matching condition is not satisfied. To overcome this problem, a new method, which improves the design approach and enhances the rapidity of the fault estimation convergence, has been proposed. The fault estimation error effect is minimized by integrating the [Formula: see text] disturbance attenuation level. The proposed design is formulated and derived as a linear matrix inequality problem. Parameters of this observer are calculated through the linear matrix inequality technique. The proposed method has been validated through an example of a single-link manipulator robot. Simulation results show that this approach can estimate the state and the sensor fault successfully, despite the time-varying uncertainties and the presence of unknown inputs.

DC-Link Voltage and Catenary Current Sensors Fault Reconstruction for Railway Traction Drives.

Due to the importance of sensors in control strategy and safety, early detection of faults in sensors has become a key point to improve the availability of railway traction drives. The presented sensor fault reconstruction is based on sliding mode observers and equivalent injection signals, and it allows detecting defective sensors and isolating faults. Moreover, the severity of faults is provided. The proposed on-board fault reconstruction has been validated in a hardware-in-the-loop platform, composed of a real-time simulator and a commercial traction control unit for a tram. Low computational resources, robustness to measurement noise, and easiness to tune are the main requirements for industrial acceptance. As railway applications are not safety-critical systems, compared to aerospace applications, a fault evaluation procedure is proposed, since there is enough time to perform diagnostic tasks. This procedure analyses the fault reconstruction in the steady state, delaying the decision-making in some seconds, but minimising false detections.

Robust Output Disturbance, Actuator and Sensor Faults Reconstruction Using H∞ Sliding Mode Descriptor Observer for Uncertain Nonlinear Boiler System

This paper deals with a robust sliding mode descriptor observer for estimation of states and reconstruction of actuator and sensor faults simultaneously in the presence of uncertainties, measurement noise and output disturbances. In the proposed method, the well-known H∞ concepts are used for minimizing the effects of the uncertainty in the estimation of the states and reconstruction of the faults by using the Linear Matrix Inequalities (LMIs). By considering the sensor fault and output disturbance as auxiliary state vectors, an augmented system is established. As a case study, an industrial boiler is considered and the proposed robust observer illustrates the robust performance on estimation of its states and reconstruction of its faults in the presence of uncertainties.

Faults reconstruction for output time-delay systems: sliding mode observer approach

This paper addresses actuator and sensor faults reconstruction based on a new sliding mode observer (SMO) for output time-delay systems. Therefore, using the Lyapunov-Krasovskii functional, the stability of the estimation error dynamics is guaranteed. Also, the linear matrix inequality (LMI) technique is applied to express the observer synthesis. After that, the proposed SMO is used to obtain actuator and sensor faults reconstruction. To show the validity and the applicability of the proposed approaches, a numerical example is provided.

Simultaneous actuator and sensor fault estimation for discrete‐time Lipschitz nonlinear systems in finite‐frequency domain

SummaryThis paper addresses the problem of actuator and sensor fault reconstruction simultaneously with finite‐frequency specifications for discrete‐time Lipschitz nonlinear systems. First, an augmented system description is given by extending the sensor fault as an auxiliary state vector. Then, a multiobjective unknown input proportional‐integral observer is designed, and the nonlinear error dynamics are transformed into a linear parameter‐varying system based on the use of a reformulated Lipschitz property. Sufficient conditions for the design of a fault estimator with 2 finite‐frequency performances are derived in terms of linear matrix inequalities where a slack variable is introduced to reduce the conservativeness of the design. The proposed method can provide less restrictive linear matrix inequality conditions and get a better fault estimation performance than the existing one in the full frequency domain. Simulation results are given to show the effectiveness of the proposed techniques.