Sensor fault detection and isolation for smart irrigation wireless sensor network based on parity space

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

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 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.

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.

In this paper the problem of fault diagnosis in an aircraft jet engine is investigated by using an intelligent-based methodology. The proposed fault detection and isolation (FDI) scheme is based on the multiple model approach and utilizes autoassociative neural networks (AANNs). This methodology consists of a bank of AANNs and provides a novel integrated solution to the problem of both sensor and component fault detection and isolation even though possibly both engine and sensor faults may occur concurrently. Moreover, the proposed algorithm can be used for sensor data validation and correction as the first step for health monitoring of jet engines. We have also presented a comparison between our proposed approach and another commonly used neural network scheme known as dynamic neural networks to demonstrate the advantages and capabilities of our approach. Various simulations are carried out to demonstrate the performance capabilities of our proposed fault detection and isolation 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.

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.

To improve the reliability of wind turbines, various condition monitoring systems (CMSs) have been developed and most of them transmit data using wired communication channels. Recently, wireless sensor networks (WSNs) have been used to transmit data in wind turbine CMS due to the low cost and easy deployment feature of WSNs. However, since wind turbines are installed in harsh environments, the sensors and sensor nodes used in the WSN-based wind turbine CMSs are easily subject to faults, leading to corruption of the signals used for condition monitoring, which decreases the reliability of the CMS. This paper proposes a three-stage method for the detection and isolation of three most common sensor faults, i.e., SHORT fault, CONSTANT fault, and NOISE fault, in WSN-based wind turbine CMS. The proposed sensor fault detection and isolation greatly increases the accuracy and reliability of wind turbine CMSs. Data collected from wind turbines in the field are used to validate the effectiveness of the proposed method.

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

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

This paper presents the design and analysis of a methodology for detecting and isolating multiple sensor faults in large-scale interconnected nonlinear systems. The backbone of the proposed decentralized methodology is the design of a local sensor fault diagnosis agent dedicated to each interconnected subsystem, without the need to communicate with neighboring agents. Each local sensor fault diagnosis agent is responsible for detecting and isolating multiple faults in the local set of sensors. The local sensor fault diagnosis agent consists of a bank of modules that monitor smaller groups of sensors in the corresponding local sensor set. The detection of faults in each of the sensor groups is conducted using robust analytical redundancy relations, formulated by structured residuals and adaptive thresholds. The multiple sensor fault isolation in each local sensor fault diagnosis agent is realized by aggregating the decisions of the modules and applying a diagnostic reasoning-based decision logic. The performance of the proposed diagnostic scheme is analyzed with respect to sensor fault detectability and multiple sensor fault isolability. A simulation example of two interconnected robot manipulators is used to illustrate the application of the multiple sensor fault detection and isolation methodology.

Model-based fault detection and isolation (FDI) for an overactuated mechatronic vehicle is presented. The linear single-track model is extend to reflect the layout of the overactuated vehicle as well as its longitudinal dynamics, and sensor and actuator faults are added into the model. The DLR Fault Detection Toolbox, which makes use of rational nullspace bases computation to design residual generators, is used for the systematic design of structured residuals. A minimum set of residuals is selected according to their robustness and ability to isolate faults. The fault detection and isolation system is validated in a simulation in connection with a double track model using the parameters of the ROboMObil prototype vehicle.

In this paper, a sensor fault detection and isolation scheme for nonlinear systems is considered. A nonlinear diffeomorphism is introduced to explore the system structure and a simple filter is presented to ‘transform’ the sensor fault into a pseudo‐actuator fault scenario. A sliding mode observer is designed to reconstruct the sensor fault precisely if the system does not experience any uncertainty, and to estimate the sensor fault when uncertainty exists. The reconstruction and estimation signals are based only on available information and thus can be implemented online. Finally, a mass–spring system is used to illustrate the approach. Copyright © 2007 John Wiley & Sons, Ltd.

Fault detection and isolation for multi-type sensors in nuclear power plants via a knowledge-guided spatial–temporal model

The Orbital Cygnus vehicle is being developed to perform autonomous rendezvous with International Space Station (ISS) and to provide cargo services to the faculty. Safety through fault management has been a primary consideration in the design of the trajectories, GN&C algorithms, and software. This paper describes the approach used to design and validate a fault management system required to meet the ISS visiting vehicle safety requirements. The nominal mission trajectory and abort maneuvers were designed using linear covariance analysis and were validated to provide a combination of passive and active collision avoidance and requirement satisfaction through semi-analytical methods and extensive simulation. Hardware faults, such as IMU, LIDAR, and GPS sensor faults, are managed using a highly reliable backup propagation system and Fault Detection and Isolation (FDI) algorithms. These algorithms ensure that key sensor systems are fault tolerant and that faulty measurement sources are detected and identified before they significantly corrupt the navigation system. Critically, the FDI system ensures that sufficient measurements will be available to execute an abort maneuver safely at any time. The IMU, LIDAR and GPS FDI algorithms employ standard parity methods to detect sensor measurement errors and a Maximum Likelihood Estimation (MLE) approach for fault identification when sufficient measurement redundancy exists. In the case of GPS FDI, parity space algorithms are utilized for GPS receiver redundancy as well as measurement redundancy within a particular GPS receiver. This allows independent detection and identification of receiver faults and measurement faults arising from GPS satellite faults or a degraded multipath environment. Fault thresholds for FDI were validated using Monte Carlo analysis in a high fidelity 6DOF simulation.