Robust Sensor Fault Research Articles

Simultaneous Robust State and Sensor Fault Estimation of Autonomous Vehicle via Synthesized Design of Dynamic and Learning Observers

Simultaneous Robust State and Sensor Fault Estimation of Autonomous Vehicle via Synthesized Design of Dynamic and Learning Observers

Advancing Sensor Data Integrity with Deep Learning-Based Fault Detection

In the realm of modern data-driven systems, accurate and reliable sensor measurements are imperative for informed decision-making and system integrity. The objective of this project is to develop a robust sensor fault detection methodology leveraging the power of deep learning techniques. The ubiquity of sensors such as temperature and humidity sensors has led to a critical need for discerning between accurate readings and erroneous data, thereby enhancing the reliability of these measurements. This study addresses the prevailing challenge of sensor reliability by introducing a data-driven approach that harnesses deep learning algorithms to detect sensor faults promptly and accurately. A comprehensive dataset comprising sensor readings under diverse conditions forms the foundation of this investigation. Multiple cutting-edge deep learning architectures and techniques are systematically explored and evaluated against this dataset to identify the most efficient and precise sensor fault detection method. The outcomes of this research contribute significantly to advancing the state-of-the-art in sensor fault detection, with implications for a wide array of applications reliant on sensor measurements. By harnessing the capabilities of deep learning, this project presents a tangible solution to the challenge of accurately identifying faulty sensor data in real-time, thereby bolstering the dependability and efficacy of sensor-driven systems. Ultimately, this work underscores the potential of integrating advanced machine learning techniques in ensuring the reliability and precision of sensor data, heralding a new era of robust and trustworthy sensor-enabled environments.

Robust state and sensor fault estimation for switched nonlinear systems based on asynchronous switched fuzzy observers

SummaryThis article aims to address state and sensor fault estimation issue for a class of continuous‐time nonlinear switched systems, modelled as Takagi‐Sugeno (T‐S) models with nonlinear consequent parts and subject to norm bounded disturbances. This modelling approach helps to prevent Unmeasured Premise Variables (UPVs) problem. The primary contribution of this study consists of proposing robust asynchronous switched observers to simultaneously estimate state and sensor faults under state‐dependent switching. Based on a candidate multiple Lyapunov function, the design of the proposed observer is formulated in terms of Linear Matrix Inequalities (LMIs) conditions. These conditions are dwell‐time‐independent and less conservative compared with similar previous studies, thanks to some usual relaxation techniques and the incremental quadratic constraints applied on unmeasured nonlinear consequent parts. Another contribution consists to perform the estimation of the attraction domain of the estimation error using an optimization procedure. In order to illustrate the effectiveness of the proposed design approach, two simulation examples are considered. The first one concerns the conservatism reduction brought by our proposal compared with previous studies, while the second one is dedicated to show through an illustrative example the performance of the proposed switched T‐S observers under mismatching switching laws.

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.

A Novel Adaptive Sensor Fault Estimation Algorithm in Robust Fault Diagnosis.

The paper deals with a robust sensor fault estimation by proposing a novel algorithm capable of reconstructing faults occurring in the system. The provided approach relies on calculating the fault estimation adaptively in every discrete time instance. The approach is developed for the systems influenced by unknown measurement and process disturbance. Such an issue has been handled with applying the commonly known H∞ approach. The novelty of the proposed algorithm consists of eliminating a difference between consecutive samples of the fault in an estimation error. This results in a easier way of designing the robust estimator by simplification of the linear matrix inequalities. The final part of the paper is devoted to an illustrative example with implementation to a laboratory two-rotor aerodynamical system.

Robust Sensor Fault Estimation for Control Systems Affected by Friction Force

The paper presents an observer-based estimation of sensor fault for control systems affected by friction force. In such systems, the non-linearity of friction force leads to deteriorating sensor fault estimation capability of the observer. Hence, the challenge is to design an observer capable of attaining robust sensor fault estimation while avoiding the effects of friction. To overcome the highlighted challenge, an Unknown Input Observer (UIO) is designed to decouple the effects of friction as well as to estimate the state and sensor fault.The benefit of proposing UIO is to guarantee robust sensor fault estimation despite the highly non-linear disturbance in the form of friction. The gains of the UIO are computed through a single–step linear matrix inequality. Finally, an inverted pendulum simulation is presented to demonstrate the novel approach's performance effectiveness. Index Terms—Robust fault estimation, Fault-Tolerant control, unknown input observer, Friction force, estimation/decoupling approach.

Robust Sensor Fault Estimation for Control Systems Affected by Friction Force

The paper presents an observer-based estimation of sensor fault for control systems affected by friction force. In such systems, the non-linearity of friction force leads to deteriorating sensor fault estimation capability of the observer. Hence, the challenge is to design an observer capable of attaining robust sensor fault estimation while avoiding the effects of friction. To overcome the highlighted challenge, an Unknown Input Observer (UIO) is designed to decouple the effects of friction as well as to estimate the state and sensor fault.The benefit of proposing UIO is to guarantee robust sensor fault estimation despite the highly non-linear disturbance in the form of friction. The gains of the UIO are computed through a single–step linear matrix inequality. Finally, an inverted pendulum simulation is presented to demonstrate the novel approach's performance effectiveness. Index Terms—Robust fault estimation, Fault-Tolerant control, unknown input observer, Friction force, estimation/decoupling approach.

Fault-tolerant control with high-order sliding mode for manipulator robot

<p>The safety of manipulators systems is receiving a lot of attention these days. Faults have the ability to carry out harmful activities that harm equipment, the environment, or persons. As a result, it is critical to detect and diagnose problems as soon as possible, as well as to incorporate fault tolerance to avoid performance deterioration and harmful circumstances. Robust sensor fault detection and isolation (FDI) as well as fault-tolerant control (FTC) for a robotic system are discussed in this work. The goal of this research is to create an FDI method that uses a super-twisting third-order sliding mode (STW-TOSM) observer to estimate residual signals. A suggested system based on a higher-order sliding mode (HOSM) observer/controller approaches is used to accomplish this. In comparison to an active FTC technique, the test results demonstrate high level of performance. Finally, simulation results illustrate the efficacy of the presented techniques.</p>

Unknown input observer-based fault diagnosis of speed sensors in dual clutch transmission

Speed sensors in the dual clutch transmission (DCT) play an essential role in designing the vehicle launching and gear shifting strategy. The speed information can also be used to monitor whether the DCT system operates normally. The vehicle performance will degrade rapidly once the speed sensor occurs fault, which may lead to poor driving experience. With increasing working hours and poor working environment, the speed sensors are prone to failure. In order to monitor the sensor failure, this article proposes a robust speed sensor fault diagnosis algorithm based on an unknown input observer (UIO). First, the dynamic model of a DCT powertrain is constructed which captures the internal and external disturbance in the system. Based on the dynamic model, a sensor fault detection algorithm is presented by designing an UIO. Second, a set of UIOs is developed to identify which sensor occurs fault. Finally, a sensor fault estimation method using the UIO is proposed. Simulation results reveal that the speed sensor faults can be detected, isolated and estimated, which may further be used for fault-tolerant control of a DCT system.

Aircraft robust data-driven multiple sensor fault diagnosis based on optimality criteria

A general robust data-driven scheme for the Fault Detection, Isolation and Estimation of multiple sensor faults is proposed and validated using multi-flight data records. Robustness to modelling uncertainty and noise is achieved through an optimized data-driven design of the three blocks that constitute the scheme. First, a robust Fault Detection (FD) filter given by the linear combination of previously identified Analytical Redundancy Relationships (AARs) is derived as the solution of a multi-objective optimization where the minimum fault sensitivity is maximized while the standard deviation (STD) of the filtered error, in nominal condition, is minimized. Then, a Fault Pre-Isolation (FPI) block is introduced to select a restricted number of sensors containing with high likelihood the subset of the faulty sensors. In this phase, robustness is achieved through the data-driven design of a redundant number of Multiple-ARRs and a voting logic. Finally, the robust Fault Isolation (FI) is achieved relying on the design of a large collection of additional AARs whose fault signatures are specifically designed to optimize, at the same time, noise immunity while maximizing the decoupling of the (pre-isolated) fault directions. A procedure based on fault amplitude reconstruction is proposed to isolate the set of faulty sensors sequentially. The proposed scheme has been applied to the design of a multiple Fault Diagnosis scheme for a set of 8 sensors of a semi-autonomous aircraft basing on multi-flight data. Validation results are compared with state-of-the-art multiple Fault Diagnosis schemes.

Dependable Sensor Fault Reconstruction in Air-Path System of Heavy-Duty Diesel Engines.

This paper addresses the problem of robust sensor faults detection and isolation in the air-path system of heavy-duty diesel engines, which has not been completely considered in the literature. Calibration or the total failure of a sensor can cause sensor faults. In the worst-case scenario, the engines can be totally damaged by the sensor faults. For this purpose, a second-order sliding mode observer is proposed to reconstruct the sensor faults in the presence of unknown external disturbances. To this aim, the concept of the equivalent output error injection method and the linear matrix inequality (LMI) tool are utilized to minimize the effects of uncertainties and disturbances on the reconstructed fault signals. The simulation results verify the performance and robustness of the proposed method. By reconstructing the sensor faults, the whole system can be prevented from failing before the corrupted sensor measurements are used by the controller.

Quadratic Support Vector Machine and K-Nearest Neighbor Based Robust Sensor Fault Detection and Isolation

Fault detection plays a serious role in high-cost and safety-critical processes. There are two main drivers for continuous improvement in the area of early detection of process faults safety and reliability of technical plants. Detect fault in Geophone string sensors (SG-10) are very important in oil exploration to avoid loss economy. Methods are developed to enable earlier detection of process faults than the traditional limit and trend checking based on a single process variable and the development of these methods is a key matter. Classification methods will be used for pattern recognition and as such is appropriate for fault detection. In supervised training input-output pairs, both for normal and fault conditions, are presented to the network. The models were trained on the free fault and fault sensors. Then the Quadratic Support Vector Machine (QSVM) and k-Nearest Neighbor (KNN) as the classifiers are used. The test results for measuring the performance of 1232 sample classifiers from data show that the accuracy of fault-free sensor recognition is 97.4 % and 100% consecutively for these classifiers.

Simple and Robust Current Sensor Fault Detection and Compensation Method for 3-Phase Inverters

The present paper proposes a genuine, simple to implement, robust and reliable method for current sensors fault detection and compensation used for 3-phase inverters. Its functionality is based on an algorithm programed into a Field Programmable Gate Array (FPGA) as general controller. Usually, 3-phase inverters are controlled using field oriented control (FOC) which is generally triggered to sample the measured currents on the established switching frequency. As the latter is much smaller than the base clock of the FPGA, this allows hundreds of free time samples to perform other calculations in-between two FOC samplings. In this paper, a method that uses these free and unused time samples is presented. This method performs calculations for fault detection and compensation ensuring that at each new FOC sampling, this will receive the correct current data to reach continuous operation despite faults. The interleaving principle of the FOC with the fault detection method, as it will be proven in the paper, ensures high reliability of the complete controller diminishing the possibility of undesired or faulted readings to disturb the FOC’s calculations. The fault detection philosophy is based on continuous comparison of the instantaneous measured currents (sensor’s response) against reference values. The experimental results presented in the paper prove reliable operational performances of the method in both steady state and transient conditions. The added value of the paper consists in its genuine approach to handle the fault detection and compensation in-between two PWM ticks, ensuring that no faulted measurements can reach the control unit. This added value is based on functionality divided on several functions triggered by an internal generated clock synchronizing and handshaking their operations towards one goal: fault detection and compensation.

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.

Finite-time sensor fault diagnosis observer design for attitude control systems of satellite based on sampled-data descriptor systems

In this paper, a finite-time robust sensor fault diagnosis observer with non-singular structure is proposed for Attitude Control Systems of satellite based on Lipschitz non-linear sampled-data descriptor systems with state time-varying delay. Firstly, an Attitude Control Systems model based on sampled-data descriptor system with time-varying delay is proposed and transformed into a discrete-time non-singular one. Then, a sensor fault diagnosis observer is designed upon on the state estimation error and the measurement residual. The finite-time stability and robustness of the augmented error dynamic model is analyzed, the H infinity performance index in finite-time is satisfied. As the confining matrices of the observer parameters do not meet the Linear Matrix Inequality, a cone complementary linearization iteration algorithm is proposed to solve this problem. Residual evaluation function and the threshold are introduced for judging if the faults occur. At the end, simulation examples are given to illustrate the effectiveness of this method.

Simultaneous Robust Control and Sensor Fault Detection for a Ducted Coaxial-Rotor UAV

This paper addresses the aerodynamic modeling, observer-based state-feedback robust control and sensor fault detection for a laboratory ducted coaxial-rotor UAV (DCUAV). First, by introducing the main model elements of this novel unmanned vehicle, the detailed nonlinear mathematical model of the hovering flight UAV is presented. Second, through introducing a weighting matrix and a new form of change-of-variables, a new method is proposed by designing two different systems simultaneously as detector and controller. An observer-based controller is proposed to achieve the control objective and finite-frequency sensor fault detection objective simultaneously. The observer-based controller design method is derived from a new formulation of linear matrix inequality (LMI), which can achieve the prescribed H ∞ performance, H - performance and the stability of the closed-loop system. By constructing a new matrix decomposition form, the simultaneous design of detector parameters and controller parameters is solved. Finally, simulations are conducted for the hover flight with disturbances and sensor faults, the results show the satisfactory control performance and fault detection performance.

Towards Robust Simultaneous Actuator and Sensor Fault Estimation for a Class of Nonlinear Systems: Design and Comparison

This paper aims at providing two competitive solutions to the problem of estimating state as well as sensor and actuator faults. The development starts with a common system description, which is transformed into a nonlinear descriptor system-like form. The appealing property of such an approach is that the sensor fault is eliminated from the output equation by suitably extending the state vector. Subsequently, a novel fault estimation scheme is proposed and its error dynamics are carefully described. Having such a unified framework, two competitive design procedures are proposed. One is based on the celebrated $\mathcal {H}_{\infty }$ while the other employs the quadratic boundedness strategy. With the estimators, the paper provides a unified framework for assessing the uncertainty of the resulting estimates in the form of uncertainty intervals. Thus, both solutions can be compared from the theoretical perspective. The final part of the paper contains a numerical simulation example concerning a twin-rotor system that clearly shows the comparison and performance of the above approaches. Subsequently, an experimental study concerning a multi-tank system using real data is reported.

A quadratic boundedness approach to a neural network-based simultaneous estimation of actuator and sensor faults

The paper is devoted to the problem of a neural network-based robust simultaneous actuator and sensor faults estimator design for the purpose of the fault diagnosis of nonlinear systems. In particular, the methodology of designing a neural network-based fault estimator is developed. The main novelty of the approach is associated with possibly simultaneous sensor and actuator faults under imprecise measurements. For this purpose, a linear parameter-varying description of a recurrent neural network is exploited. The proposed approach guaranties a predefined disturbance attenuation level and convergence of the estimator. In particular, it uses the quadratic boundedness approach to provide uncertainty intervals of the achieved estimates. The final part of the paper presents an illustrative example concerning the application of the proposed approach to the multitank system fault diagnosis.

Robust Multiple Sensor Fault–Tolerant Control For Dynamic Non–Linear Systems: Application To The Aerodynamical Twin–Rotor System

Abstract The paper deals with the problem of designing sensor-fault tolerant control for a class of non-linear systems. The scheme is composed of a robust state and fault estimator as well as a controller. The estimator aims at recovering the real system state irrespective of sensor faults. Subsequently, the fault-free state is used for control purposes. Also, the robust sensor fault estimator is developed in a such a way that a level of disturbances attenuation can be reached pertaining to the fault estimation error. Fault-tolerant control is designed using similar criteria. Moreover, a separation principle is proposed, which makes it possible to design the fault estimator and control separately. The final part of the paper is devoted to the comprehensive experimental study related to the application of the proposed approach to a non-linear twin-rotor system, which clearly exhibits the performance of the new strategy.

Sensor fault and state estimation for uncertain fuzzy descriptor systems: An LMI approach

This paper deals with the problem of robust sensor fault diagnosis of Takagi–Sugeno fuzzy uncertain descriptor systems affected by bounded external disturbance with unmeasurable premise variables. This problem is solved using a descriptor approach to easily convert the stability conditions into linear matrix inequalities). By augmenting the sensor fault into a state vector, a fuzzy descriptor observer is constructed to simultaneously estimate the state and sensor faults and attenuate the effect of both modelling uncertainties and external disturbance on the estimation error. The faults affecting the system behaviour are considered as an auxiliary state variable. Based on the Lyapunov theory and [Formula: see text] technique, two different approaches are proposed to study the convergence of the state estimation error and the stability conditions are given in terms of linear matrix inequalities. Finally, an application to a model of rolling disk is given to show the applicability of the proposed approaches.