Reconstructing Sensor Faults Research Articles

Sensor Fault Reconstruction Using Robustly Adaptive Unknown-Input Observers.

Sensors are a key component in industrial automation systems. A fault or malfunction in sensors may degrade control system performance. An engineering system model is usually disturbed by input uncertainties, which brings a challenge for monitoring, diagnosis, and control. In this study, a novel estimation technique, called adaptive unknown-input observer, is proposed to simultaneously reconstruct sensor faults as well as system states. Specifically, the unknown input observer is used to decouple partial disturbances, the un-decoupled disturbances are attenuated by the optimization using linear matrix inequalities, and the adaptive technique is explored to track sensor faults. As a result, a robust reconstruction of the sensor fault as well as system states is then achieved. Furthermore, the proposed robustly adaptive fault reconstruction technique is extended to Lipschitz nonlinear systems subjected to sensor faults and unknown input uncertainties. Finally, the effectiveness of the algorithms is demonstrated using an aircraft system model and robotic arm and comparison studies.

Fault-tolerant control for vehicle with vertical and lateral dynamics

As the possibility of sensor faults in the vehicle chassis system is higher and influences the vehicle stability, this paper deals with active fault-tolerant control for vehicle with vertical and lateral dynamics. It focuses on the combined control of active suspension system and electronic stability control with sensor faults based on the interaction between vehicle with vertical and lateral dynamics. A 9-degree-of-freedom vehicle integrated model is adopted for accurate control. The aim of the controller is to improve riding comfort when the vehicle is driving straight and improve lateral stability when the vehicle is steering in the presence of external disturbances and sensor faults. First, an H∞-based method is introduced to reconstruct the sensor fault signals, and meanwhile, the method can also observe the unmeasured signals. Based on the reconstruction faults and observed signals, a gain scheduling controller is utilized to guarantee the performance of the integrated model under different driving conditions, and the steering input is chosen as the scheduling parameter. Three different conditions, step steering input, single lane change input, and sensor faults, are considered. The main contributions of this study are as follows: (1) an H∞-based observer was designed for sensor fault estimation of the vertical and lateral integrated model and (2) a gain scheduling controller was designed to improve the performance of the integrated system. Simulations results indicated that the active fault-tolerant controller can reconstruct sensor faults and observe the unmeasured states exactly, and the linear parameter varying framework–based gain scheduling controller ensures the system performance adaptively.

Sensor fault estimation using LPV sliding mode observers with erroneous scheduling parameters

This paper proposes a linear parameter-varying sliding mode observer for the purpose of simultaneously estimating the system states and reconstructing sensor faults. Furthermore, some of the measured scheduling parameters are also assumed to be unreliable, and the corresponding values used in the observer are adapted to maintain the performance level of the observer. The adaptive algorithm is driven by the ‘equivalent output error injection’ signal associated with the reduced-order sliding motion. Sufficient conditions are given to ensure asymptotic stability of the state estimation error system, ensuring both the state estimation errors and the estimation errors associated with the scheduling parameters converge to zero. The efficacy of the scheme has been evaluated based upon an industrial high-fidelity aircraft benchmark scenario involving a simultaneous total loss of airspeed and angle of attack measurements.

Active FTC for waterjets propelled ships based on Adaptive Estimator and Sliding Techniques

Abstract: This paper focuses on the design and validation of an active Fault Tolerant Control (FTC) approach dealing with sensor and actuator faults for a ship propelled by two water jets. The proposed FTC relies on an adaptive estimation to reconstruct sensor faults and a second order sliding mode observer based on an adaptive super twisting algorithm to reconstruct the actuator faults. A nonlinear model of the considered ship, issued by a previous work, is used to prove the effectiveness of the proposed FTC in the path following maneuvers, speed regulation, and tracking course. The inputs of this model are the command orders to apply to the water jets including the two engines, the steering and reversing units for each jet. Details of the design process of the faults reconstruction and reconfiguration are given. The stability of the closed-loop system and uniform boundedness of the tracking error are proved based on Lyapunov theory. Simulations with many faults scenario are carried out. Results show that the proposed controller can successfully accommodate faults associated with sensors and actuators.

Sensor fault identification and reconstruction of indoor air quality (IAQ) data using a multivariate non-Gaussian model in underground building space

Sensors have been widely used to monitor indoor air quality (IAQ) in underground subway stations of metro systems. These IAQ sensors suffer reliability problems due to the hostile environment in which the sensors have to function. The sensor fault provides incorrect information to ventilation systems installed in the metro stations, thus, it influences energy consumption and passengers’ comfort in the metro stations. In this paper, a new sensor validation method based on independent component analysis (ICA) was proposed to detect, identify and reconstruct sensor faults in a metro system. The ICA method extracts essential information from non-Gaussian distributed IAQ data. To detect and identify the sensor faults, squared prediction error (I-SPE) and sensor validity index (I-SVI) of the ICA model are proposed, respectively. Then, an iterative reconstruction algorithm was developed to the identified faulty sensor to bring the fault back to normal. Two types of sensor faults (bias and drifting faults) that were introduced to a particulate matter (PM) sensor in the metro system of Seoul, Korea are tested. The results of this study showed that the ICA-based method with accurate fault validity can detect, identify and reconstruct the sensor faults more precisely than the principal component analysis (PCA)-based method.

Reconstruction of sensor faults for a class of uncertain nonlinear systems using adaptive sliding mode observers

The present paper proposes a new scheme for estimating sensor faults for a class of uncertain nonlinear systems using adaptive sliding mode observers (SMOs). Initially, a state and output transformation is introduced to transform the original system into two subsystems such that the first subsystem (subsystem-1) has system uncertainties but is free from sensor faults and the second subsystem (subsystem-2) has sensor faults but without any uncertainties. The sensor faults in subsystem-2 are then transformed to actuator faults using integral observer based approach. Two SMOs are designed such that the effects of system uncertainties in subsystem-1 are completely eliminated and the sensor faults presenting in subsystem-2 can be reconstructed using the equivalent output error injection term. The Lipschitz constant is assumed to be unknown in the work and this problem can be solved by integrating adaptation laws to the gain of SMOs. The sufficient condition of the stability of the proposed scheme has been derived and expressed as Linear Matrix Inequalities (LMIs). The effectiveness of the proposed scheme in reconstructing sensor faults is illustrated considering an example of a single-link robotic arm with revolute elastic joint. The simulation results demonstrate that the proposed scheme can reconstruct sensor faults even in the presence of large system uncertainties. Incipient sensor faults can also be accurately estimated using the proposed scheme.

Sensor fault reconstruction and observability for unknown inputs, with an application to wastewater treatment plants

In this article, we propose a general methodology for identifying and reconstructing sensor faults on dynamical processes. This methodology is issued from the general identification theory developed in the previous papers (Busvelle, E., and Gauthier, J.-P. (2003), ‘On Determining Unknown Functions in Differential Systems, with an Application to Biological Reactor’, ESAIM: Control, Optimisation and Calculus of Variations, 9, 509–553; Busvelle, E., and Gauthier, J.-P. (2004), ‘New Results on Identifiability of Nonlinear Systems’, in 2nd Symposium on Systems, Structure and Control, Oaxaca, Mexico; Busvelle, E., and Gauthier, J.-P. (2005), ‘Observation and Identification Tools for Non Linear Systems. Application to a Fluid Catalytic Cracker’, International Journal of Control, 78, 208–234): in fact, this identification theory also provides a general framework for the problem of ‘observability with unknown inputs’. Indeed, many problems of fault detection can be formulated as such observability problems, the (eventually additive) faults being just considered as unknown inputs. Our application to ‘sensor fault detection’ for wastewater treatment plants (WWTP) constitutes an ideal academic context to apply the theory: first, in this 3-5 case (3 sensors, 5 states), the theory applies generically and, second, any system is naturally under the ‘observability canonical form’ required to apply the basic high-gain observer from Gauthier and Kupka (Gauthier, J.-P., and Kupka, I. (1994), ‘Observability and Observers for Nonlinear Systems’, SIAM Journal on Control, 32, 975–994). A simulation study on the Bleesbrük WWTP is proposed to show the effectiveness of this approach.

Sensor fault diagnosis for nonlinear processes with parametric uncertainties

This paper addresses the problem of detecting, discriminating, and reconstructing sensor faults for nonlinear systems with known model structure but uncertainty in the parameters of the process. The convenience of the proposed technique lies in the fact that historical operational data and/or a priori fault information is not required to achieve accurate fault reconstruction except for fixed, short intervals. The overall fault diagnosis algorithm is composed of a series of nonlinear estimators, which estimates parameter and a fault isolation and identification filter. Parameter estimation and fault reconstruction cannot be performed accurately since faults and parametric uncertainty interact with each other. Therefore, these two tasks are performed at different time scales, where the fault diagnosis takes place at a more frequent rate than the parameter estimation. It is shown that the fault can be reconstructed under some realistic assumptions and the performance of the proposed methodology is evaluated on a simulated chemical process exhibiting nonlinear dynamic behavior.

Sliding mode observers for detection and reconstruction of sensor faults

This paper proposes two methods for detecting and reconstructing sensor faults using sliding mode observers. In both methods, fictitious systems are introduced in which the original sensor fault appears as an actuator fault. The original sensor faults are then reconstructed using a ‘secondary’ sliding mode observer. For both methods, there are certain conditions which must be satisfied for successful fault detection and reconstruction. The methods are demonstrated using a chemical process example.