A distributed architecture for sensor fault detection and isolation using adaptive approximation

ABSTRACTQuick and timely fault detection is of great importance in control systems reliability. Undetected faulty sensors could result in irreparable damages. Although fault detection and isolation (FDI) methods in control systems have received much attention in the last decade, these techniques have not been applied for some classes of nonlinear systems yet. This paper deals with the issues of sensor fault detection and isolation for a class of Lipschitz uncertain nonlinear system. By introducing a coordinate transformation matrix for states and output, the original system is first divided into two subsystems. The first subsystem is affected by uncertainty and disturbance. The second subsystem just has sensor faults. The nonlinear term is separated to linear and pure nonlinear parts. For fault detection, two sliding mode observers (SMO) are designed for the two subsystems. The stability condition is obtained based on the Lyapunov approach. The necessary matrices and parameters are obtained by solving the linear matrix inequality (LMI) problem. Furthermore, two sliding mode observers are designed for fault isolation. Finally, the effectiveness of the proposed approach is illustrated by simulation examples.

Detection, isolation and identification of multiple actuator and sensor faults in nonlinear dynamic systems: Application to a waste water treatment process

This paper covers model-based fault detection and isolation for linear and nonlinear distributed parameter systems (DPS). The first part mainly deals with actuator, sensor and state fault detection and isolation for a class of DPS represented by a set of coupled linear partial differential equations (PDE). A filter based observer is designed based on the linear PDE representation using which a detection residual is generated. A fault is detected when the magnitude of the detection residual exceeds a detection threshold. Upon detection, several isolation estimators are designed using filters whose output residuals are compared with predefined isolation thresholds. A fault on a linear DPS is declared to be of certain type if the corresponding isolation estimator output residual is below its isolation threshold while the other fault isolation estimator output residual is above its threshold. Next, the fault location is determined when a state fault is identified. The second part of this paper focuses on fault detection and isolation of nonlinear DPS by using a Luenberger type observer. Here fault isolation framework is introduced to isolate actuator, sensor and state faults with isolability condition by using additional boundary measurements and without filters. Finally, the effectiveness of the proposed fault detection and isolation schemes for both linear and nonlinear DPS are demonstrated through simulation.

In this paper, an online sensor fault detection and isolation (FDI) scheme is proposed based on an emerging Artificial Immune System (AIS) algorithm, namely Dendritic Cell Algorithm (DCA). Our proposed methodology is utilized in a distributed manner in order to perform sensor FDI in complex systems. The proposed methodology is then applied to a wind turbine benchmark model in order to demonstrate its capabilities. 2016 IEEE.

A fault detection and isolation (FDI) scheme is proposed for a class of nonlinear systems with unknown faults, system uncertainties and external disturbances. The main feature is that fault isolation and control reconfiguration are integrated via a supervisory switching strategy. By constructing a set of monitoring functions (MFs) to supervise some variables generated in the backstepping procedure, the candidate controllers are switched to detect the occurrence of fault and identify the specific fault type. It is shown that the tracking error can be preserved within a prescribed performance regardless of system faults. Case study of a manipulator system subject to different faults is carried out to validate the effectiveness of this FDI approach.

Sensor fault detection and isolation of an industrial gas turbine using partial adaptive KPCA

On the application of interval PCA to process monitoring: A robust strategy for sensor FDI with new efficient control statistics

This paper presents a unified fault isolation scheme for process faults and sensor faults in a class of nonlinear uncertain systems. The proposed fault diagnosis architecture consists of a fault detection estimator and a bank of isolation estimators, each corresponding to a particular fault type. Based on the class of nonlinear systems and fault types under consideration, adaptive thresholds are derived for the isolation estimators, and fault isolability conditions are rigorously investigated, characterizing the class of process faults and sensor faults that are isolable by the proposed scheme.

This paper addresses the Fault Detection and Isolation (FDI) of the problem of multiple non-concurrent faults on actuator and sensor of quadrotors, which is described by a nonlinear model. To the best of authors' knowledge, this paper provides, for the first time, a solution to the FDI problem for quadrotors affected by both actuator and sensor faults. The literature proposes FDI schemes dedicated only to actuators or only to sensors, indeed. The Non Linear Geometric Approach is based on the mathematical model of the system and allows to determine detection filters sensitive to a selected set of fault and structurally decoupled from the other faults (out of the selected set) thus providing the base for the fault isolation. Several simulation trials have been performed and the analysis of the results proves that the developed diagnostic system represents an effective solution to the problem of multiple actuator and sensor fault detection and isolation for quadrotors.

Sensor fault detection and isolation via high-gain observers: Application to a double-pipe heat exchanger

This chapter addresses the early detection and isolation of sensor faults in a systematic and unified way and illustrates the approach on wind turbine simulation data. Three problems are successively considered: individual signal monitoring, fault detection and isolation (FDI) in redundant sensors, and FDI based on analytical redundancy. In all three cases, a specific approach to generate fault indicators, also called residuals, is presented and combined with an online statistical change detection/isolation algorithm. The considered case studies consist of wind turbine generator speed monitoring, as well as FDI in the stator current and voltages of a wind-driven doubly fed induction generator. For the latter problem, the fact that the three-phase signals are balanced allows one to determine a simple signal model from which a multiobserver scheme is designed for residual generation.

This paper considers model-based fault detection of large-scale (possibly rank-deficient) dynamic systems. Assuming only global (and not local) observability over a sensor network, we introduce a single time-scale networked estimator/observer. Sensors take local outputs/measurements of system states with partial observability and share their information (including estimation and/or output) over a communication network, and gain distributed observability. We define the conditions on the network structure ensuring distributed observability and stabilising the error dynamics. However, system outputs are prone to faults and uncertainties, which affect the state estimation of all sensors as a consequence of communicating (possibly) faulty data. From the cyber-physical-systems (CPS) perspective, such faults add bias to the data transferred from the physical layer (dynamic system) to the cyber layer (sensor network). In this work, we propose a localised fault detection and isolation (FDI) mechanism at sensors to secure distributed estimation. This protocol enables every sensor to locally identify the possible fault at the sensor measurement, and, via local detection and isolation, to prevent the spread of biased/faulty information over the network. This distributed isolation and localisation of fault follows from our partial observability assumption instead of full observability at every sensor. Then, other sensors can estimate/track the system by using observationally-equivalent output information to recover for possible loss of observability. In particular, we study rank-deficient systems as they are known to demand more information-sharing, and thus, are more vulnerable to the spread of possible faults over the network. One challenge is the detection of faults in the presence of system/output noise without making (simplifying and unrealistic) upper-bound assumptions on the noise support. We resolve this by adopting probabilistic threshold designs on the residuals. Further, we show that additive faults at rank-deficiency-related outputs affect the residuals at all sensors, a consequence that mandates more constraints on the (distributed) FDI strategy. We address this problem by constrained LMI design of the feedback gain matrix. Finally, we design q-redundant distributed estimators, resilient to isolation/removal of up to q number of faulty sensors, and further, we consider thresholding residual history over a sliding time-window, known as the stateful FDI.

Sensor fault detection and isolation filter for polytopic LPV systems: A winding machine application

An approach for model-based fault detection and isolation (FDI) of sensor and process faults for nonlinear processes is presented. The process is decomposed into several sub-processes and for each process a nonlinear model is identified. This model bank consisting of fuzzy models (Takagi-Sugeno type) is used to generate several different estimates for process outputs and states. Comparing these estimates with the actual measured ones leads to residuals which indicate the state of the system and provide information about the source of possible faults. The two ways to implement a model, as a parallel or as a series-parallel model lead to different FDI results. Hence, this different sensitivity is also investigated in this contribution. The practical applicability is illustrated on an industrial scale thermal plant. Here, seven different process faults and eight different sensor faults can be detected.

This paper presents a model based fault detection and isolation (FDI) of internal sensors for a powered wheelchair. Three fault modes of five internal sensors (two wheel-encoders, two steering-potentiometers and one yaw-rate gyro) are handled: hard fault, noise fault and scale fault modes. On the hard fault the sensor output is stuck at a constant value. The noise fault causes the sensor output with large noise. The scale fault changes the sensor parameter from the fault-free. The hard and noise faults are diagnosed based on their mode probability estimated with the variable structure interacting multi-model (VSIMM) estimator. The scale fault is diagnosed based on the wheelchair velocity estimated with a laser scan matching. For fault-tolerant operation of the wheelchair even in the presence of sensor faults, output estimates of faulty sensors are exploited to control the wheelchair. Experimental results validate the effectiveness of our FDI method.