Fault detection and isolation for complex system

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.

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.

substantially from the prescribed or expected characteristics. To accommodate failures, the baseline inner-loop control is assisted by a fault detection and isolation algorithm tasked with monitoring and reporting control surface health. Similarly, the baseline outer-loop guidance is augmented with an adaptive neural network forming an adaptive outer-loop guidance system. These adaptive subsystems have been formulated for, and applied to, a transport aircraft model in a nonlinear, high-fidelity aircraft simulation environment to demonstrate the concept. Included results demonstrate that the fault detection and isolation and adaptive outer-loop functionality consistently improve tracking performance during both piloted maneuvers and autonomous waypoint path following. In some cases, these retrofit subsystems enable tasks to be completed that were not previously feasible. Adaptive enhancement of the robustness of existing non-adaptive systems has been an area of significant interest for many years. Although application in the aircraft community is of great benefit for maximizing performance and safety for unexpected changes in the dynamics due to flight control failures, damage, and adverse environmental conditions, certification of these technologies for safety-critical flight systems is challenging. The approach described here is developed under the constraint of maintaining a retrofit architecture thereby allowing the baseline, previously certified, software to remain. The augmented modules serve to assist systems already in place. This paper discusses the development and application of independent and modular adaptive subsystems that are retrofitted to existing guidance and control architectures. The baseline inner-loop control is assisted by a fault detection and isolation (FDI) algorithm tasked with monitoring and reporting control surface health. Similarly, the baseline outer-loop (BOL) guidance is augmented with an adaptive neural network collectively forming an adaptive outer-loop (AOL) guidance system. In general, an adequate inner-loop feedback control law is necessary to faithfully track guidance commands, and various inner-loop control design techniques can provide the requisite performance. Even in the case of degraded inner-loop tracking capability, the outer-loop guidance system may still meet its objectives by inflating inner-loop commands to compensate for inner-loop tracking errors. Nonetheless, faithful innerloop tracking is desirable to reduce the burden on other subsystems, and this is especially the case for piloted

The Matrix converters (MCs) are widely used in a large number of applications such as Aircraft, Submarines, and AC drives. In these applications, many chances of occurrence of a critical fault that leads to a halt of the system, and due to the failure of power electric switches, the MC operations can be compromised and there is a fear of burning the components caused by the whole system shutdown. Therefore, a Fault-Tolerant Control System (FTCS) is extremely necessary for the continued operations of the Three-Phase Matrix Converter (TPMC) to improve the reliability and productivity of the system under faulty conditions. In this paper, an advanced FTCS is proposed based on detection, and dual hardware redundancy. Two types of faults are injected into the system to observe the performance of the proposed system: internal open circuit faults on switches and external short circuit faults at the load side. A Fault Detection and Isolation (FDI) unit is used to detect and isolate the faulty switches using Artificial Neural Networks (ANN), and dual hardware redundancy in the switches has been proposed for fault tolerance. In case of external fault, a load-side fault detector is implemented using ANN. The simulation results in MATLAB/Simulink environment show the accurate and stable working of TPMC under faulty conditions, and hardware-in-the-loop is implemented with STM32-Nucleo-F103RB board to verify open circuit fault results. The proposed work dual redundant FTC with FDI unit offers an excellent solution for the continued performance of TPMC which enhances the reliability of the system.

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.

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.

Modeling and fault diagnosis design for HVAC systems using recurrent neural networks

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.

<div class="htmlview paragraph">The stochastic Fault Detection and Isolation (FDI) algorithm, known as the statistical local approach, is applied in a model-based framework to the diagnosis of component faults in the air-intake system of an automotive engine. The FDI scheme is first presented as a general methodology that permits the detection of faults in complex nonlinear systems without the need for building inverse models or numerous observers. Although sensor and actuator faults can be detected by this FDI methodology, component faults are generally more difficult to diagnose. Hence, this paper focuses on the detection and isolation of component faults for which the local approach is especially suitable. The challenge is to provide robust on-board diagnostics regardless of the inherent nonlinearities in a system and the random noise present. In particular, the local approach is shown to simplify and reduce the complicated FDI problem to the standard problem of detecting changes in the mean value of a Gaussian vector with a constant covariance matrix. In doing so, the least square score is implemented as our primary residual, and the statistical properties of the so-called improved residuals are used to detect changes in the parameters of a parametric model. Applying the local approach, in both offline and online configurations, to a mean-value model of a spark-ignition engine, component faults in the air-intake system, namely small abrupt changes in the volumetric efficiency coefficient <i>η<sub>o</sub></i> and the throttle discharge coefficient <i>C<sub>d</sub></i>, have been successfully detected and isolated. The FDI methodology shows robustness to modeling errors and noise, proving the fact that a perfect model is not required to monitor a given system. Since unknown plant parameters are usually determined by identification techniques, which never yield perfect results, modeling errors are indeed expected in practice. Consequently, the local approach sheds promises for robust on-board diagnostics in the face of the overall nonlinear powertrain problem.</div>

Dedicated Observer Scheme (DOS) was used for fault detection and isolation which adopted EMB (electro-mechanical braking) system. Base on the mechanical model of EMB, two independent Kalman filters were designed for the fault detection of EMB sensors. Computer simulation showed that DOS has made a good use at fault isolation and detection for EMB sensors, and it also had high real-time property.

A fault detection and isolation (FDI) system is presented that can detect and isolate nuclear power plant (NPP) faults occurring in interacting systems. The proposed methodology combines two tools, observer-based residual generation and neural network pattern matching, into a powerful, hybrid diagnostic system. A computer-based model of a commercial boiling water reactor (BWR) is used as the reference plant.Two FDI methods are implemented on each of two BWR systems, and their performance characteristics are compared. One method uses conventional neural network techniques that use parameter values for input, and a second, hybrid methodology uses system models to create residuals for input to a neural network. Both FDI systems show good generalization abilities, but only the hybrid system decouples system interactions. Although implementation is impractical for all NPP systems, this hybrid technique is most useful in specific applications where operators have difficulty diagnosing faults in strongly interacting systems.

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.

This paper describes a new method for detecting sensor faults using sliding mode observers. In general, required capabilities of FDI(Fault Detection and Isolation) filters are sensitivity of occurring faults and robustness against disturbances. A FDI filter that proposed in this paper can estimate faults even if disturbances exist. Moreover, we discretize continuous-time FDI filters into discrete-time FDI filters using difference equations and apply the FDI filter to a vibration experimental system. An example demonstrates the effectiveness of the proposed method in comparison with conventional methods.

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 combined data‐driven and observer‐design methodology for fault detection and isolation (FDI) in hybrid process systems with switching operating modes is proposed. The main contribution is to construct a unified framework for FDI by integrating Gaussian mixture models (GMM), subspace model identification (SMI), and results from unknown input observer (UIO) theory. Initially, a GMM is built to identify and describe the multimodality of hybrid systems using the recorded input/output process data. A state‐space model is then obtained for each specific operating mode based on SMI if the system matrices are unknown. An UIO is designed to estimate the system states robustly, based on which the fault detection is laid out through a multivariate analysis of the residuals. Finally, by designing a set of unknown input matrices for specific fault scenarios, fault isolation is performed through the disturbance‐decoupling principle from the UIO theory. A significant benefit of the developed framework is to overcome some of the limitations associated with individual model‐based and data‐based approaches in dealing with the problem of FDI in hybrid systems. Finally, the validity and effectiveness of the proposed monitoring framework are demonstrated using a numerical example, a simulated continuous stirred tank heater process, and the Tennessee Eastman benchmark process. © 2014 American Institute of Chemical Engineers AIChE J, 60: 2805–2814, 2014