Sensor fault identification based on time-lagged PCA in dynamic processes

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.

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 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.

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.

State reconstruction approach is very useful for sensor fault isolation, reconstruction of faulty measurement and the determination of the number of components retained in the principal components analysis (PCA) model. An extension of this approach based on a Nonlinear PCA (NLPCA) model is described in this paper. The NLPCA model is obtained using five layer neural network. A simulation example is given to show the performances of the proposed approach.

In this paper, a fault detection and isolation (FDI) methodology based on an immune system (IS) inspired mechanism known as the dendritic cell algorithm (DCA) is developed and implemented. Our proposed DCA-based FDI methodology is then applied to a well-known wind turbine test model. The proposed DCA-based scheme performs both detection as well as isolation of sensor faults given dual sensor redundancy, unlike other works in the literature that only address the fault detection problem and rely on analytical redundancy approach for accomplishing the fault isolation task. A nonparametric statistical comparison test is also performed to compare the performance of the DCA-based FDI scheme with another IS-based scheme known as the negative selection algorithm. Through extensive simulation case study scenarios the capabilities and performance of our proposed methodologies have been fully demonstrated and justified.

Robust sensor fault detection and isolation of gas turbine engines subjected to time-varying parameter uncertainties

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

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.

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.

This paper proposes a novel principal component analysis (PCA)-based sensor faultisolation and detection method for smart structures: detectability and isolability of eachsensor fault are analyzed using a PCA-based numeric residual generator and theprobability of errors in each sensor is also determined using the Bayesian probabilisticanalysis of these residuals. To demonstrate the performance of the proposed PCA-basedsensor fault isolation and detection methodology, a seismically excited three-story buildingstructure equipped with a magnetorheological damper that is operated by a semi-activenonlinear fuzzy control system is investigated. It is shown that the proposed PCA-basedsensor fault diagnosis approach is effective in identifying sensor faults of smartstructures for hazard mitigation of large structures as a model-free methodology.

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

A multi-engine cluster of a launcher operates in extreme conditions, making the tasks of detection and isolation complex, with only a precise group of faults with small magnitudes worth being detected and isolated. To address this, a model-based Fault Detection and Isolation (FDI) system is developed for a multi-engine cluster using Structural Analysis (SA). A model of a multi-engine cluster composed of three engines is established, where different sensor and actuator faults are considered. After analyzing the model using SA, more than 16,000 residual generator candidates are obtained. An algorithm is then proposed to find subsets of residuals with minimal cardinality capable of isolating all faults. Each subset generates a unique fault signature for each fault, enabling fault isolation. The selection of the best subsets is based on the new definition of the Subset Sensitivity Index (SSI), according to which the subsets composed of the most sensitive residuals have the highest SSI values. The residual sensitivity is calculated using a new method based on the signal-to-noise ratio (SNR), where a new definition of the Residual Sensitivity Index (RSI) is given. Furthermore, a new algorithm is presented for fault isolation. Suppose a theoretical fault signature is not observed. In that case, the algorithm uses the knowledge of the residual sensitivity (RSI) to define which residual did not behave as expected and predicts which fault is active in the system. Monte Carlo simulations are conducted under ten different faults to analyze the performance of the proposed FDI system, including actuators and sensor faults.