Filter-Based Fault Detection and Isolation in Distributed Parameter Systems Modeled by Parabolic Partial Differential Equations

A fault detection and isolation scheme is addressed for a class of linear distributed parameter systems (DPS) described by partial differential equations (PDE). In contrast to using ordinary differential equations (ODE) for describing DPS, in this paper, a filter based observer based on the linear PDE representation is proposed with an output measurement. A fault is declared active when the magnitude of the detection residual exceeds a predefined threshold. Upon detection, an actuator and a sensor fault isolation estimators are activated to identify the fault type when their isolation residual is below a predefined threshold and the other is above the threshold. When both actuator and sensor fault isolation estimator residuals are above their isolation thresholds, a state fault is considered to have occurred. Upon isolation, the magnitude of the fault parameter is identified. Finally, the performance of the fault detection and isolation scheme is demonstrated on a heat reactor system which is represented by linear parabolic PDEs.

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

This paper presents a new model-based fault detection and estimation framework for a class of multi-input and multi-output (MIMO) nonlinear distributed parameter systems (DPS) described by partial differential equations (PDE) with actuator and sensor faults. The fault functions cover both abrupt and incipient faults. A Luenberger type observer is used to monitor the health of the DPS as a detection observer on the basis of the nonlinear PDE representation of the system with measured output vector. By taking the difference between measured and estimated outputs from this observer, a residual signal is generated for fault detection. If the detection residual exceeds a predefined threshold, a fault will be claimed to be active. Once an actuator or a sensor fault is detected and the fault type is identified, an appropriate fault parameter update law is developed to learn the fault dynamics online with the help of an additional output measurement. Eventually, the proposed detection and estimation framework is demonstrated on a nonlinear process.

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.

SummaryThis paper proposes an integrated actuator and sensor active fault‐tolerant model predictive control scheme. In this scheme, fault detection is implemented by using a set‐valued observer, fault isolation (FI) is performed by set manipulations, and fault‐tolerant control is carried out through the design of a robust model predictive control law. In this paper, a set‐valued observer is used to passively complete the fault detection task, while FI is actively performed by making use of the constraint‐handling capability of robust model predictive control. The set‐valued observer is chosen to implement fault detection and isolation (FDI) because of its simple mathematical structure that is not affected by the type of faults such as sensor, actuator, and system‐structural faults. This means that only one set‐valued observer is needed to monitor all considered actuator and sensor statuses (health and fault) and to carry out the fault detection and isolation task instead of using a bank of observers (each observer matching a health/fault status). Furthermore, in the proposed scheme, the advantage of robust model predictive control is that it can effectively deal with system constraints, disturbances, and noises and allow to implement an active FI strategy, which can improve FI sensitivity when compared with the passive FI methods. Finally, a case study based on the well‐known two‐tank system is used to illustrate the effectiveness of the proposed fault‐tolerant model predictive control scheme. Copyright © 2016 John Wiley & Sons, Ltd.

The problem of designing and developing a hybrid fault detection and isolation (FDI) scheme for a non-linear system that is subject to large environmental disturbances is investigated. In the proposed FDI scheme, a hybrid architecture is introduced, which is composed of a bank of residual generators and a discrete-event system (DES) fault diagnoser. A novel set of residuals is generated so that a DES fault diagnoser will robustly detect and isolate actuator faults in the system by incorporating an appropriate combination of residuals and their sequential features. Necessary and sufficient conditions for the existence of a set of residuals that are used by the DES fault diagnoser are derived based on the non-linear geometric FDI approach. The proposed hybrid FDI scheme is then applied to actuator fault detection and isolation of an almost-lighter-than-air-vehicle. Simulation results presented demonstrate and validate the performance capabilities of the proposed hybrid FDI scheme.

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.

The brake system is a key system for the safe operation and stopping of trains. As a core component of brake systems, the brake actuation unit (BAU) is essential for slowing down or stopping trains, and faults in the BAU will affect the safety and efficiency of train operation. In order to detect and locate faults in the BAU in time, a fault detection and isolation (FDI) strategy, based on mutual residuals (MRs), principal component analysis (PCA) and improved reconstruction-based contribution plots (IRBCP), was proposed. Firstly, the structural composition and working principle of the BAU were introduced, and its typical failure modes and effects were analyzed. Secondly, considering that the fault detection threshold is not easy to determine due to the variable operating conditions of the BAU, the steady-state fault feature based on MR was extracted. Thirdly, fault detection and isolation were realized based on PCA and the IRBCP algorithm. Finally, by using the fault injection method, case studies on test-rig experiment data of brake systems were conducted; the fault detection rate was 99% and the effectiveness of the proposed strategy was validated by the test data. The proposed strategy shows fast computing ability, and is suitable for systems with dynamic time-varying and nonlinear characteristics.

In this paper, the problem of distributed fault detection and isolation (FDI) for multi-agent systems (MAS) with complex-weights directed communication topology in the presence of actuator faults is studied. It is assumed that each agent only has access to local state information of neighboring agents. The communication network is also assumed to be a weights directed graph (digraph) whose edges are attributed with complex weights. First, a novel representation of the complex-weighted graph is proposed. More specifically, the difficulty with complex coefficient in the problem of FDI for MASs with complex-weights directed communication topology is indeed resolved. Then, using the novel representation, a bank of observers is constructed to compute residual vectors with an aim of allowing the FDI of actuator fault to occur on any agent of the MASs. It is proved that the proposed FDI scheme is capable of FDI in the MASs with complex-weights directed communication topology. Finally, some numerical example results are provided to demonstrate the effectiveness of the proposed scheme.

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.

With the increased availability of condition monitoring data on the one hand and the increased complexity of explicit system physics-based models on the other hand, the application of data-driven approaches for fault detection and isolation has recently grown. While detection accuracy of such approaches is generally very good, their performance on fault isolation often suffers from the fact that fault conditions affect a large portion of the measured signals thereby masking the fault source. To overcome this limitation, we propose a hybrid approach combining physical performance models with deep learning algorithms. Unobserved process variables are inferred with a physics-based performance model to enhance the input space of a data-driven diagnostics model. The resulting increased input space gains representation power enabling more accurate fault detection and isolation. To validate the effectiveness of the proposed method, we generate a condition monitoring dataset of an advanced gas turbine during flight conditions under healthy and four faulty operative conditions based on the Aero-Propulsion System Simulation (C-MAPSS) dynamical model. We evaluate the performance of the proposed hybrid methodology in combination with two different deep learning algorithms: deep feed forward neural networks and Variational Autoencoders, both of which demonstrate a significant improvement when applied within the hybrid fault detection and diagnostics framework. The proposed method is able to outperform pure data-driven solutions, particularly for systems with a high variability of operating conditions. It provides superior results both for fault detection as well as for fault isolation. For the fault isolation task, it overcomes the smearing effect that is commonly observed in pure data-driven approaches and enables a precise isolation of the affected signal. We also demonstrate that deep learning algorithms provide a better performance on the fault detection task compared to the traditional machine learning algorithms.

To monitor the dynamics and non-stationarity inherent in industrial processes, we propose a novel incipient fault detection and isolation scheme grounded in a probabilistic perspective, using the Cauchy–Schwarz (CS) divergence. Our innovation lies in the utilization of marginal CS divergence for incipient fault detection and the conditional CS divergence for fault isolation. This approach neither require prior parametric assumptions about the underlying data distribution nor depend on historical fault data, while simultaneously providing explanatory diagnostics. Beyond this, we develop a change point detection-base diagnosis technique for practical engineering applications. This online process monitoring technique guarantees timely intervention to uphold process stability and safety. We demonstrate the compelling performance, higher detection rate and lower alarm rate, of the CS divergence over prevalent divergence-based approaches, such as Kullback–Leibler divergence, Wasserstein distance and Mahalanobis distance. We also illustrate the explanatory insights offered by conditional CS divergence in fault isolation on synthetic data, benchmarks of continuous stirred-tank reactor process and continuous stirred-tank heater process and even a real-world continuous catalytic reforming process. Code of this CS divergence based fault detection and isolation is available at https://github.com/Feiya-Lv/Incipient-Fault-Detection-and-Isolation-with-Cauchy--Schwarz-Divergence.

DC microgrids are becoming an attractive technology for future power grids. However, the fault detection and isolation (FDI) schemes for dc microgrids are still in the nascent state. This article presents an FDI method for dc microgrids using the regularity information of the second derivative of current (SDOC). As the major contribution of this article, the coherence between the singular feature in the SDOC and the short-circuit fault in dc lines is first proved and applied to FDI. Furthermore, a singularity detection approach using stationary wavelet transform (SWT) is introduced. With this FDI method, not only the fault isolation but also the fault-type classification can be achieved based on only local current measurements. Moreover, compared with other local current-based methods, this method has a distinct advantage in the robustness against the nonfault disturbances. The effectiveness of this FDI method was verified through hardware tests under the real-time (RT) simulation of various fault scenarios in a 5-kV three-terminal dc microgrid model based on dual-active-bridge (DAB) converters. This FDI method can be generalized to dc microgrids with different topologies and converters.

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.

Model-based fault isolability and isolation of persistent faults: Centralized and distributed implementations