Fault Isolation Approach Research Articles

Fault Isolation in MIMO Systems based on Active Decoupling

A decoupling approach for fault isolation in MIMO systems is presented in this letter. The fault isolation approach is based on a closed-loop concept, where the feedback controller is an integrated part of the set-up. The YJBK-parameterization (after Youla, Jabr, Bongiorno, and Kucera) for controllers is introduced. This allows the feedback controller to be modified by changing the YJBK matrix transfer function without changing the nominal feedback controller. Modification of the feedback controller via the YJBK transfer matrix causes the output residual response to be changed in the case of parametric faults and unchanged in the fault-free case. This facilitates fault detection. In the isolation case, the controller is modified in a way such that the residual response from the closed-loop system is independent of the modification for one specific parametric fault and else it depends on the parametric faults. This allows for any specific fault and eventually for all faults to be isolated.

A New Diagnosis Approach of Hybrid Systems through Observers and Hybrid Automata

This paper gives a new approach of fault detection and isolation of hybrid dynamical systems based on hybrid observers and hybrid automata; a methodology for the design of dynamical observers has been proposed. The hybrid observer composed of two blocks: a location observer that identifies the current mode and a continuous observer that detects faults. Although this approach is interesting, it is still unable to detect instantly the change of the continuous state; as a result and in case of a fault, the system can't identify correctly the defected mode. In this paper, a new version is proposed to improve this approach and reach good diagnosis results.

Parity space-based fault isolation using minimum error minimax probability machine

In this paper, a new parity space-based fault isolation (FI) approach is proposed for linear discrete time-invariant systems. The main contribution lies in the integrated design of FI system and fault isolability properties without any assumptions on the probability distribution of unknown inputs. First, a fault isolability evaluation index is defined as the worst-case probability of correct FI in different faulty scenarios, and a new objective function is proposed for the integrated design of parity space-based residual generator, evaluation logic and fault isolability properties. Based on this, the FI problem is formulated as a bank of optimal multiclass classification problems. Next, using the technique of minimum error minimax probability machine (MEMPM), a group of feasible parity space matrices for residual generation are obtained. Furthermore, the FI method by MEMPM is presented with a desirable trade-off between the fault isolability properties in different faulty scenarios. To show the effectiveness of the proposed method, a satellite attitude control system subject to six faults is considered.

Characterisation of interval-observer fault detection and isolation properties using the set-invariance approach

The aim of this paper is to provide a robust Fault Detection and Isolation (FDI) approach that combines the set-invariance approach with a zonotopic interval observer. The effect of the uncertainty is taken into account considering zonotopic-set representations in both the transient and steady states. The set-invariance approach is used to characterize the fault detectability and isolability properties in the steady-state operation of the system. In particular, the Minimum Detectable Fault (MDF) and the Minimum Isolable Fault (MIF) are characterized for several type of faults in separate formulations utilizing the integration of classical sensitivity analysis and set-invariance approaches. Finally, a simulation example based on a two-tanks system is employed to both illustrate and discuss the effectiveness of the proposed approach.

A novel feature-based probability of detection assessment and fusion approach for reliability evaluation of vibration-based diagnosis systems

Structural health monitoring systems are based on suitable sensor techniques allowing online and offline supervision of technical systems. The quantification of sensors/measurement devices is a key issue for qualifying their effectiveness and efficiency and therefore to ensure safe operations. Probability of detection serves as a performance measure for quantifying the reliability of conventional nondestructive testing procedures taking into account statistical variability of sensor-based measurements. For vibration-based supervision approaches and fault detection and isolation methods, the probability of detection approach cannot be applied similarly. This results mainly from the complexity of the dynamical behavior of systems monitored in relation to faults, sensors position (observability), and the related feature extraction or monitoring task. In this contribution, probability of detection evaluation of vibration-based fault detection of elastic mechanical structures to be monitored is developed. Beside a principal discussion of the problem serving as introduction, an example using different sensor types in combination with mechanical modifications of an elastic beam is presented. The a90/95-criteria representing probability of 90% detection at a confidence level of 95% is examined to the measurements and related outcomes. Based on the analysis of a suitably chosen feature (like eigenfrequency or band power) and dependent on the mechanical modes considered, the efficiency and deficiency of the different combinations are shown. Based on the proposed approach, a new insight into the usefulness of different sensor type and fault position combination becomes possible. To improve the detection quality, suitable assumptions in combination with sensor/information fusion are applied to feature-based analysis as detection task using vibration measurements. In addition, based on an experimental evaluation, it can be concluded that a suitable fault-feature probability of detection analysis can be successfully implemented as a new reliable measure for vibration-based fault detection and isolation approaches. Furthermore, decision fusion as the combination of different measurements will allow the improvement of results. Dependent on noise analysis, a trade-off between flaw size detection and probability of falsely characterizing a fault with a90/95 reliability level can be attained.

Review of the Quantitative Resilience Methods in Water Distribution Networks

Water distribution networks (WDNs) are critical contributors to the social welfare, economic growth, and public health in cities. Under the uncertainties that are introduced owing to climate change, urban development, aging components, and interdependent infrastructure, the WDN performance must be evaluated using continuously innovative methods and data acquisition. Quantitative resilience assessments provide useful information for WDN operators and planners, enabling support systems that can withstand disasters, recover quickly from outages, and adapt to uncertain environments. This study reviews contemporary approaches for quantifying the resilience of WDNs. 1508 journal articles published from 1950 to 2018 are identified under systematic review guidelines. 137 references that focus on the quantitative resilience methods of WDN are classified as surrogate measures, simulation methods, network theory approaches, and fault detection and isolation approaches. This study identifies the resilience capability of the WDNs and describes the related terms of absorptive, restorative, and adaptive capabilities. It also discusses the metrics, research progresses, and limitations associated with each method. Finally, this study indicates the challenges associated with the quantification of WDNs that should be overcome for achieving improved resilience assessments in the future.

Structured Joint Sparse Principal Component Analysis for Fault Detection and Isolation

In order to improve the performance of fault isolation and diagnosis of principal component analysis (PCA) based methods, this article proposes a novel fault detection and isolation approach using the structured joint sparse PCA (SJSPCA). The objective function involves two regularization terms: the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$l_{2,1}$</tex-math></inline-formula> norm and the graph Laplacian. By imposing the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$l_{2,1}$</tex-math></inline-formula> norm, SJSPCA is able to achieve row-wise sparsity, and introducing the graph Laplacian term can incorporate structured variable correlation information. The row-sparsity property of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$l_{2,1}$</tex-math></inline-formula> norm ensures that the score indices associated with normal variables approaching zero and the graph Laplacian constraint helps the isolation of correlated faulty variables. Once a fault is detected, a two-stage fault-isolation strategy is considered and a score index is calculated for each variable. It is proved that the proposed two-stage strategy is capable of isolating faulty variables. The improved fault-isolation performance of SJSPCA is illustrated by a simulation example and a gas flow fault observed in an industrial blast furnace iron-making process.

Simultaneous Fault Detection and Isolation Based on Transfer Semi-Nonnegative Matrix Factorization

This paper proposes a simultaneous fault detection and isolation approach based on a novel transfer semi-nonnegative matrix factorization (TSNMF) algorithm. Different from the existing nonnegative matrix factorization (NMF) algorithm, TSNMF takes advantages of a few labeled samples and geometry structures of sample spaces to improve performance. Labeled samples refer to a type of sample whose memberships are already known. On the contrary, unlabeled samples are a type of samples whose memberships are unknown. Theoretically, we will demonstrate how labeled samples and geometry structures of sample spaces can improve fault detection and isolation performance. More importantly, the proposed simultaneous fault detection and isolation approach can achieve the fault detection and isolation purpose without use of monitoring statistics, which means it is easier to be implemented than the existing approaches. In comparison with the existing fault detection and isolation methods, the proposed detection and isolatio...

Incipient sensor fault isolation based on augmented Mahalanobis distance

Incipient sensor fault diagnosis is important to an efficient and optimal operating condition for modern industrial systems. Recently, a new fault detection index called augmented Mahalanobis distance (AMD) has been proposed in our previous work for incipient fault detection. Following detection, fault isolation is also quite desired so as to investigate root causes of the occurred fault. In the present work, the AMD statistic is first revisited and a geometric illustration of AMD is provided, which intuitively shows its superiority for incipient fault detection. Then, with available fault direction information, an incipient sensor fault isolation approach is proposed. Its fault isolability condition is analyzed theoretically and compared with that of the conventional method. For complex sensor faults whose fault direction information is unknown, a corresponding fault isolation strategy is also briefly discussed. Case studies on a high-speed train air brake system and the continuous stirred tank reactor (CSTR) process are carried out, which demonstrate the effectiveness of the AMD based fault detection and isolation methods, in comparison with conventional approaches.

Fault Detection and Isolation for Complex Thermal Management Systems

This paper presents a fault detection and isolation (FDI) approach for actuator faults of complex thermal management systems. In the case of safety critical systems, early fault diagnosis not only improves system reliability, but can also help prevent complete system failure (i.e., aircraft system). In this work, a robust unknown input observer (UIO)-based actuator FDI approach is applied on an example aircraft fluid thermal management system (FTMS). Robustness is achieved by decoupling the effect of unknown inputs modeled as additive disturbances (i.e., modeling errors, linearization errors, parameter variations, or model order reduction errors) from the residuals generated from a bank of UIOs. Robustness is central to avoid false alarms without reducing residual sensitivity to actual faults in the system. System dynamics are modeled using a graph-based approach. A structure preserving aggregation-based model-order reduction technique is used to reduce the complexity of the dynamic model. A reduced-order linearized state space model is then used in a bank of UIOs to generate a set of structured robust (in the sense of disturbance decoupling) residuals. Simulation and experimental results show successful (i.e., no false alarms) actuator FDI in the presence of unknown inputs.

Plug-and-Play Fault Detection and Isolation for Large-Scale Nonlinear Systems With Stochastic Uncertainties

This paper proposes a novel scalable model-based fault detection and isolation approach for the monitoring of nonlinear large-scale systems, consisting of a network of interconnected subsystems. The fault diagnosis architecture is designed to automatically manage the possible plug-in of novel subsystems and unplugging of existing ones. The reconfiguration procedure involves only local operations and communication with neighboring subsystems, thus, yielding a distributed and scalable architecture. In particular, the proposed fault diagnosis methodology allows the unplugging of faulty subsystems in order to possibly avoid the propagation of faults in the interconnected large-scale system. Measurement and process uncertainties are characterized in a probabilistic way leading to the computation, at each time-step, of stochastic time-varying detection thresholds with guaranteed false-alarms probability levels. To achieve this goal, we develop a distributed state estimation scheme, using a consensus-like approach for the estimation of variables shared among more than one subsystem; the time-varying consensus weights are designed to allow plug-in and unplugging operations and to minimize the variance of the uncertainty of the fault diagnosis thresholds. Convergence results of the distributed estimation scheme are provided. A novel fault isolation method is then proposed, based on a generalized observer scheme and providing guaranteed error probabilities of the fault exclusion task. Detectability and isolability conditions are provided. Simulation results on a power network model comprising 15 generation areas show the effectiveness of the proposed methodology.

An Improved Residual Chi-Square Test Fault Isolation Approach in Four-Gyro SINS

An Improved Residual Chi-Square Test Fault Isolation Approach in Four-Gyro SINS

A Community-Based Fault Isolation Approach for Effective Simultaneous Localization of Faults

During program testing, software programs may be discovered to contain multiple faults. Multiple faults in a program may reduce the effectiveness of the existing fault localization techniques due to the complex relationship between faults and failures in the presence of multiple faults. In an ideal case, faults are isolated into fault-focused clusters, each targeting a single fault for developers to localize them simultaneously in parallel. However, the relationship between faults and failures is not easily identified and depends solely on the accuracy of clustering, such as existing clustering algorithms are not able to isolate failed tests to their causative faults effectively which hinder localization effectiveness. This paper proposes a new approach that makes use of a divisive network community clustering algorithm to isolate faults into separate fault-focused communities that target a single fault each. A community weighting and a selection mechanism that aids in prioritizing highly important fault-focused communities to the available developers to debug the faults simultaneously in parallel is also proposed. The approach is evaluated on eight subject programs ranging from medium-sized to large-sized programs (tcas, replace, gzip, sed, flex, grep, make, and ant). Overall, 540 multiple-fault versions of these programs were generated with 2-5 faulty versions. The experimental results have demonstrated that the proposed approach performs significantly better in terms of localization effectiveness in comparison with two other parallel debugging approaches for locating multiple faults in parallel.

A Gas Path Fault Contribution Matrix for Marine Gas Turbine Diagnosis Based on a Multiple Model Fault Detection and Isolation Approach

To ensure reliable and efficient operation of gas turbines, multiple model (MM) approaches have been extensively studied for online fault detection and isolation (FDI). However, current MM-FDI approaches are difficult to directly apply to gas path FDI, which is one of the common faults in gas turbines and is understood to mainly be due to the high complexity and computation in updating hypothetical gas path faults for online applications. In this paper, a fault contribution matrix (FCM) based MM-FDI approach is proposed to implement gas path FDI over a wide operating range. As the FCM is realized via an additive term of the healthy model set, the hypothetical models for various gas path faults can be easily established and updated online. In addition, a gap metric analysis method for operating points selection is also proposed, which yields the healthy model set from the equal intervals linearized models to approximate the nonlinearity of the gas turbine over a wide range of operating conditions with specified accuracy and computational efficiency. Simulation case studies conducted on a two-shaft marine gas turbine demonstrated the proposed approach is capable of adaptively updating hypothetical model sets to accurately differentiate both single and multiple faults of various gas path faults.

Exploiting Bayesian networks for fault isolation: A diagnostic case study of diesel fuel injection system

Fault isolation is known to be a challenging problem in machinery troubleshooting. It is not only because the isolation of multiple faults contains considerable number of uncertainties due to the strong correlation and coupling between different faults, but often massive prior knowledge is needed as well. This paper presents a Bayesian network-based approach for fault isolation in the presence of the uncertainties. Various faults and symptoms are parameterized using state variables, or the so-called nodes in Bayesian networks (BNs). Probabilistically causality between a fault and a symptom and its quantization are described respectively by a directed edge and conditional probability. To reduce the qualitative and quantitative knowledge needed, particular considerations are given to the simplification of Bayesian networks structures and conditional probability expressions using rough sets and noisy-OR/MAX model, respectively. By adopting the simplified approach, symptoms under multiple-fault are decoupled into the ones under every single fault, while the quantity of the conditional probabilities is simplified into the linear form of the faults quantity. Prior knowledge needed in Bayesian network-based diagnostic model is reduced significantly, which decreases the complexity in establishing and applying this diagnosis model. The computational efficiency is improved accordingly in the simplified BN model, after eliminating the redundant symptoms. The fault isolation methodology is illustrated through an example of diesel engine fuel injection system to verify the developed model.

A Voltage-Based Open-Circuit Fault Detection and Isolation Approach for Modular Multilevel Converters With Model-Predictive Control

Fault detection and isolation (FDI) is currently considered a crucial way to increase the reliability of modular multilevel converters (MMCs), which consist of a large number of power electronics submodules (SMs). This paper proposes a fast FDI approach to identifying single open-circuit faults of insulated-gate bipolar transistors in SMs for MMCs with model-predictive control (MPC). The fault detection approach is simply implemented by checking the voltage errors between the measured arm voltages and the estimated ones in the former control cycle. The fault isolation is achieved by checking the switching state directly. The proposed FDI scheme is straightforward, and no additional transducer or measurement is required. Compared with the phase-shifted pulse-width-modulation-based scheme, the MPC has a known and unchanged switching state in a sampling period, which can be utilized for fast location of open-circuit faults. Experimental results show that an open-circuit fault in the MMC can be accurately detected and located in several sampling periods.

Structural Fault Detection and Isolation in Hybrid Systems

This paper develops a structural diagnosis approach for fault detection and isolation in hybrid systems. Hybrid systems are characterized by continuous behaviors that are interspersed with discrete mode changes in the system, making the analysis of behaviors quite complex. In this paper, we address the mode detection problem in hybrid systems as the first step in diagnoser design. The proposed method uses analytic redundancy methods to detect the operating mode of the system even in the presence of system faults. We define hybrid minimal structurally overdetermined (HMSO) sets for hybrid systems. For residual generation, we develop the HMSO selection problem, formulated as a binary integer linear programming optimization problem to minimize the number of selected HMSOs and reduce online computational costs of the diagnosis algorithm. The proposed structural approach does not require preenumeration of all possible modes in the diagnoser design step. Therefore, our approach is feasible for hybrid systems with a large number of switching elements, implying that the system can have a large number of operating modes. The case study demonstrates the effectiveness of our approach. We discuss the results of our case study, and present directions for future work. Note to Practitioners —Developing feasible approaches for online monitoring, fault detection, and fault isolation of complex hybrid and embedded systems, such as automobiles, aircraft, power plants, and manufacturing processes, is essential in securing their safe, reliable, and efficient operation. Frequent changes in the operational modes of these systems because of operator actions, such as changing gears in an automobile, or environmental changes, such as driving on a wet or icy road make the fault detection and isolation task in these systems challenging. It is important to detect and isolate faults in all the operating modes, and at the same time, not mistake a mode change as a fault in the system. In this paper, we propose an approach that exploits the equation structure of hybrid systems behavior to combine mode detection and diagnosis in nonlinear hybrid systems. The proposed algorithm is scalable and efficient. We demonstrate its effectiveness using a case study of a reverse osmosis subsystem in an advances life support system for long duration manned space missions. Important challenges that can affect the success of our approach include the need for sufficiently detailed hybrid models that capture nominal and faulty behavior, and a sufficient number of sensors to make simultaneous mode detection and fault isolation possible.

Online fault detection and isolation of an AIR quality monitoring network based on machine learning and metaheuristic methods

The dynamic process monitoring is discussed in this paper. Kernel principal component analysis (KPCA) is a nonlinear monitoring method that cannot be applied for dynamic systems. Reduced online KPCA (OR-KPCA)is used for fault detection of dynamic processes, which is developed to built a dictionary according to the process status and then, it update the KPCA model and uses it for process monitoring. Also the Tabu search metaheuristic algorithm is used in order to determine the optimal parameter of the kernel function. In this paper, new approaches for online fault isolation, which is a challenging problem in nonlinear PCA, are formulated. An extension of partial PCA and the elimination sensor identification (ESI) to the case of nonlinear systems are presented in a feature space. The partial OR-KPCA and the elimination sensor identification (ESI-KPCA) are generated based on the OR-KPCA method and they consist of developing a set of sub-models. The sub-models are selected according to a pre-designed fault-to-residual structure matrix and by eliminating sequentially one variable from the set of the variables. The proposed fault isolation methods are applied for monitoring an air quality monitoring network. The simulation results show that the proposed fault isolation methods are effective for KPCA.

Data‐driven approach for fault detection and isolation in nonlinear system

SummarySystem diagnosis has been of a great interest for all aspects of industrial processes more precisely to gain in quality. It is based essentially on the analysis of the links between the variables of a system and more precisely on the changes of the relations between these variables, which testify to the presence of faults or anomalies. For that purpose, data modeling is the process of finding a mathematical expression that provides a good fit between given finite sample values of the independent variables and the associated values of the dependent variables of the process.The aim of this paper is to detect and, above all, localize faults affecting a system with nonlinear behavior, when its model is not known a priori. An important part of the presentation is dedicated to the construction of fault indicators capable of locating faults, ie, recognizing the input or output of a system affected by a fault. The first part of this paper is devoted to how to predict the output of a nonlinear behavior system. The second part proposes a way for the detection and isolation of measurement faults based on the proposed prediction model. The relevance of the proposed technique, for modeling and system diagnosis, is illustrated on a simulated example in the context of SIMO and MIMO systems.

A mechanistic fault detection and isolation approach using Kalman filter to improve the security of cyber physical systems

This paper seeks to understand and identify ways in which fault detection and isolation (FDI) methods can be utilized to enhance the cyber-security of cyber-physical systems (CPS). In this work, we have used state estimation to validate relations between process variables, termed invariants, thereby detect the onset of attacks. Multiple attack scenarios were studied and the proposed state estimation method was found to excel in detecting attacks conducted on sensors within the system; provided that not all sensors are compromised during the attack. However, the proposed methods for subsequent isolation and correction faced limitations due to delays or lack of information to pinpoint the attacked component. We demonstrated our proposed approach on a well-instrumented pilot scale water treatment plant equipped with controllers.