Multiple Fault Isolation Research Articles

Fault Isolation and Fault‐Tolerant Control Design for Non‐Gaussian Stochastic Distribution Control Systems With Multiple Sensor Faults

ABSTRACTA fault isolation, estimation and fault‐tolerant control algorithm is proposed for non‐Gaussian stochastic distribution control systems with disturbance and multiple sensor faults. Sensor faults are represented as actuator faults virtually, and an observer is devised to detect the sensor fault occurrence time. Then two subsystems are separated by the expanded system through introducing the coordinate transformation matrices. One subsystem contains only sensor faults and does not contain disturbance and the other contains sensor faults and disturbance, which provides convenience for fault isolation. The faults are estimated respectively by the multiple fault isolation observers with the same number of sensors. A fault‐tolerant control scheme is proposed after getting the fault information to compensated sensor faults and track the desired probability density function. Finally, a MATLAB simulation example is used to verify the feasibility of the algorithm.

Actuator and sensor fault isolation in a class of nonlinear dynamical systems

Fault isolation in dynamical systems is a challenging task due to modeling uncertainty and measurement noise, interactive effects of multiple faults and fault propagation. This paper proposes a unified approach for isolation of multiple actuator or sensor faults in a class of nonlinear uncertain dynamical systems. Actuator and sensor fault isolation are accomplished in two independent modules, that monitor the system and are able to isolate the potential faulty actuator(s) or sensor(s). For the sensor fault isolation (SFI) case, a module is designed which monitors the system and utilizes an adaptive isolation threshold on the output residuals computed via a nonlinear estimation scheme that allows the isolation of single/multiple faulty sensor(s). For the actuator fault isolation (AFI) case, a second module is designed, which utilizes a learning-based scheme for adaptive approximation of faulty actuator(s) and, based on a reasoning decision logic and suitably designed AFI thresholds, the faulty actuator(s) set can be determined. The effectiveness of the proposed fault isolation approach developed in this paper is demonstrated through a simulation example.

Fault detection and identification for rolling mill main drive system based on integrated observer under iterative learning strategy

In this article, the problem of multiple fault detection, isolation and reconfiguration of the rolling mill main drive system containing external disturbances is investigated. Considering the nonlinear frictional damping between the rolls and the rolled parts, a nonlinear mathematical model of the main drive system of the mill is established. A comprehensive fault diagnosis scheme based on observer is addressed for this system subjected to unknown external interference. The proposed scheme is divided into two parts. In the first stage, a set of sliding mode observers is designed for system fault detection, and a fault isolation criterion is proposed based on observer redundancy and generalised residual set theory to reveal the fault source. In the second stage, combined with the iterative learning algorithm, an iterative learning-unknown input observer is constructed to realise the accurate estimation of the fault signal. Unlike the existing fault estimation methods, the iterative learning-unknown input observer designed in this article uses the state estimation error of the previous iteration to estimate the fault signal in the current iteration period. Using [Formula: see text] synthesis to design observers for the system will guarantee fault diagnosis robustness. The Lyapunov theory and linear matrix inequality are introduced to prove the convergence of the proposed observer. The simulation study of a 1780-mm hot strip mill evaluates the proposed scheme. Simulation results demonstrate that the sliding mode observer approach can detect faults in the main drive system and isolate faults accurately. In contrast, the iterative learning-unknown input observer method has the lowest fault reconfiguration error (99.87% smaller than the extended state observer, 99.77% smaller than the unknown input observer) and achieves accurate fault signal tracking.

Multiple fault isolation method for micro thrusters of drag-free systems

This paper proposes a multiple fault isolation framework for micro thrusters of drag-free systems, based on the minimum number of isolation filters. Based on the consideration of fault isolatability, the original system is reasonably divided into two completely isolated subsystems, and then two sets of fault isolation filters are designed based on the principles of duality design for state feedback decoupling, which achieves residual decoupling, and therefore, can detect and isolate multiple faults occurred on the micro thrusters. In particular, this method has low computational burden (a feature of practical importance in reducing the usage of onboard resources) and short isolation time, making it suitable for on-board fast and precise fault isolation. The simulation results demonstrate the efficacy of the proposed method when utilized for a drag-free system with 12 field emission electric propulsion (FEEP) thrusters.

Directed-Graph-Learning-Based Diagnosis of Multiple Faults for High Speed Train With Switched Dynamics.

This article addresses the distributed multiple fault isolation, modeling, and the closed-loop fault estimation under asynchronous switching for high speed train (HST) with switched dynamics, which is composed of traction, coasting, and braking. First, directed-graph-quantum-learning-based multiple-agent system (MAS) classifiers are introduced to characterize the joints effects of multiple faults. Some sufficient conditions are derived under the condition that the multiple fault topology contains a directed spanning tree and cycle edge, and these conditions guarantee that the multiple fault isolation problem can be solved under randomized learning techniques. Then, single-integrator agents are employed to capture the time-varying topology of multiple fault modeling, in which edge agreement and persistence condition are used to guarantee asymptotic consensus. After that, a novel robust fault estimation design along with the switched Lyapunov function and average dwell time is proposed for the possible power actuator faults subject to asynchronous switching and electromagnetic interferences. In addition, switched estimators are designed such that the closed-loop system is asymptotically stable. A multiple fault isolation and estimation case is investigated to validate the application of this methodology.

A Random Forest and Model-Based Hybrid Method of Fault Diagnosis for Satellite Attitude Control Systems

Fault diagnosis is the key technology to guarantee the reliability and safety of satellite attitude control systems. Although model-based methods have achieved good fault diagnosis performance, various factors (such as closed-loop design, model nonlinearity, and external disturbances) in the satellite attitude control systems still bring challenges to fault isolation. Meanwhile, data-driven methods can assist model-based methods for fault diagnosis based on residual signals (generated from model-based methods) and input/output data to relieve the difficulty. Based on the above motivations, a novel model and data dual-driven fault diagnosis approach is proposed in this paper for satellite attitude control systems. Firstly, an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H_i</i> / <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> optimization based fault detection filter is considered as a residual generator, which is designed to be robust against disturbance and sensitive to fault. Then, the occurrence of a fault can be detected based on the residual evaluation. Eventually, a random forest algorithm is developed to achieve fault isolation with system input/output and residual signals. A simulation experiment, including fault detection and fault isolation of single faults and multiple faults, is conducted to show that the proposed approach is effective and better than the other three methods.

Multiple fault detection and isolation using artificial neural networks in sensors of an internal combustion engine

Technology advances rapidly in vehicles with internal combustion engines; therefore, detecting vehicles’ faults becomes more complex, primarily due to the increased usage of sensors and actuators to improve engine performance. Specialized instruments for detecting vehicle faults can sometimes show errors in multiple sensors when there is only one issue in the system, and they are not responsible for isolating faults. Then, it must add superior algorithms for fault detection and isolation (FDI) in the Internal Combustion Engine to these diagnostic instruments. For this reason, this research focuses on designing an individual and multiple fault detection and isolation system based on artificial neural networks for an internal combustion engine. The proposed scheme detects individual and multiple faults in the throttle position sensors (TPS), mass air-flow (MAF), and manifold absolute pressure (MAP). The FDI system uses five artificial neural networks (ANN). Each ANN estimates the value of two sensors using the engine speed and the air–fuel ratio (AFR) signal to generate analytical redundancy. When a sensor fault occurs, the FDI system substitutes the faulty signal with a healthy signal estimate provided by an ANN. Suppose a second fault arises in another sensor. The FDI system can replace the faulty signal with the ANN’s estimated signal, allowing the internal combustion engine to run even with multiple faults.

Development of Input Training Neural Networks for Multiple Sensor Fault Isolation

This paper considers the problem of inhibiting smearing effects for multiple sensor fault isolation. Although the reconstruction-based approach has received considerable attention and achieved excellent results, the investigation into nonlinear systems has been relatively limited. A new reconstruction-based input training neural network aided by the bound and branch method (BAB-RBITNN) is proposed for the nonlinear systems so as to achieve the desired performance of multiple sensor fault isolation. In BAB-RBITNN, a hypothetical faulty direction is naturally integrated into the objective testing function as residual weights for reducing the negative impacts of inaccurate sensor data, taking advantage of the non-static ITNN testing process. The weighted objective function is then considered as the fitness function to obtain the minimum predicted residual by the covariance matrix adaptation evolutionary strategy algorithm. The corresponding optimized fitness is regarded as the reconstruction-based index, which is the critical indicator of fault isolation in the proposed method. Besides, the BAB method is implemented to handle the combinatorial optimization problem instead of a brute search for intelligently pinpointing the real faulty sensors. The efficiency of the proposed method is shown through a validation example and two industrial examples. Comparisons with other methods, including reconstruction-based principal component analysis and reconstruction-based auto-associative neural network techniques, are also presented.

Robust Multiple Fault Isolation Based on Partial-orthogonality Criteria

Robust Multiple Fault Isolation Based on Partial-orthogonality Criteria

Research on Micro-Fault Detection and Multiple-Fault Isolation for Gas Sensor Arrays Based on Serial Principal Component Analysis

Machine learning algorithms play an important role in fault detection and fault diagnosis of gas sensor arrays. Because the gas sensor array will see stability degradation and a shift in output signal amplitude under long-term operation, it is very important to detect the abnormal output signal of the gas sensor array in time and achieve accurate fault location. In order to solve the problem of low detection accuracy of micro-faults in gas sensor arrays, this paper adopts the serial principal component analysis (SPCA) method, which combines the advantages of principal component analysis (PCA) in the linear part and the advantages of kernel principal component analysis (KPCA) in the nonlinear part. The experimental results show that this method is more sensitive to micro-faults and has better fault detection accuracy than the fault detection methods of PCA and KPCA. In addition, in order to solve the current problem of low accuracy of multiple-fault isolation, a SPCA-based reconstruction contribution fault isolation method is proposed in this paper. The experimental results show that this method has higher fault isolation accuracy than the method based on contribution graph.

An integrated methodology for system-level early fault detection and isolation

Fault diagnosis is an indispensable technique to ensure the high-performance operation of a mechanical system during its life-cycle. Existing research works mainly limits in the diagnosis of single component or some certain components of interest. The systematic investigation of fault diagnosis for a full system is rare, making it inefficient to implement the method developed in the practice of a complete engineering system. In this paper, a system-level fault diagnosis methodology is proposed based on fault behaviour analysis, optimal sensor placement and intelligent data analytics for multiple fault detection and isolation in a complex machine system. A dynamical model of a mechanical system is firstly constructed using Bond graph, and the response characteristics of observable parameters under different faults are derived by analysing the functional relationships of system variables. A set partitioning theory-based sensor placement approach is then presented for designing a condition monitoring system with a quantity-optimal set of sensors for a desired performance of fault isolatability. Two multivariate statistic measures, i.e. Hotelling’s T2 and Q are calculated from the system operation parameters as the indices to detect the potential faults. The abnormal parameters are separated by constructing a contribution plot once a fault is reported. The separated abnormal parameters are then input to a Bayesian network model, and the real root cause of the abnormity is isolated by blending the observations with expert knowledge of diagnosis. The diesel engine lubrication system, which involves three common domains: mechanical, hydraulic and thermodynamic processes, is taken as an example to show the implementation and performance of proposed approach. This approach can be used to design a system-level fault diagnosis scheme that including various tasks such as fault behaviour analysis, sensor placement, data collection, data processing, information fusion in order to achieve an accurate fault isolation ultimately.

Aircraft robust data-driven multiple sensor fault diagnosis based on optimality criteria

A general robust data-driven scheme for the Fault Detection, Isolation and Estimation of multiple sensor faults is proposed and validated using multi-flight data records. Robustness to modelling uncertainty and noise is achieved through an optimized data-driven design of the three blocks that constitute the scheme. First, a robust Fault Detection (FD) filter given by the linear combination of previously identified Analytical Redundancy Relationships (AARs) is derived as the solution of a multi-objective optimization where the minimum fault sensitivity is maximized while the standard deviation (STD) of the filtered error, in nominal condition, is minimized. Then, a Fault Pre-Isolation (FPI) block is introduced to select a restricted number of sensors containing with high likelihood the subset of the faulty sensors. In this phase, robustness is achieved through the data-driven design of a redundant number of Multiple-ARRs and a voting logic. Finally, the robust Fault Isolation (FI) is achieved relying on the design of a large collection of additional AARs whose fault signatures are specifically designed to optimize, at the same time, noise immunity while maximizing the decoupling of the (pre-isolated) fault directions. A procedure based on fault amplitude reconstruction is proposed to isolate the set of faulty sensors sequentially. The proposed scheme has been applied to the design of a multiple Fault Diagnosis scheme for a set of 8 sensors of a semi-autonomous aircraft basing on multi-flight data. Validation results are compared with state-of-the-art multiple Fault Diagnosis schemes.

Multiplicative Fault Detection and Isolation in Dynamic Systems Using Data-Driven K-Gap Metric based kNN Algorithm

In this paper, a fault detection and isolation scheme for multiplicative faults in dynamic systems based on data-driven K-Gap metric and k-nearest neighbour (kNN) classification is proposed. To detect multiplicative faults, the standard classification task of kNN is studied from the viewpoint of system analysis. To this end, the data-driven stable kernel representation based on input/output data is presented for feature extraction capturing the dynamic of linear time-invariant (LTI) systems. Data-driven K-Gap metric is used as an alternative tool for distance measure between two kernel subspaces in the kNN algorithm. A simulation example on the three-tank system (DTS200) demonstrates the successful detection and isolation of various multiplicative faults

Assessment of Dynamic Bayesian Models for Gas Turbine Diagnostics, Part 2: Discrimination of Gradual Degradation and Rapid Faults

There are many challenges that an effective diagnostic system must overcome for successful fault diagnosis in gas turbines. Among others, it has to be robust to engine-to-engine variations in the fleet, it has to discriminate between gradual deterioration and abrupt faults, and it has to identify sensor faults correctly and be robust in case of such faults. To combine their benefits and overcome their limitations, two diagnostic methods were integrated in this work to form a multi-layer system. An adaptive performance model was used to track gradual deterioration and detect rapid or abrupt anomalies, while a series of static and dynamic Bayesian networks were integrated to identify component degradation, component abrupt faults, and sensor faults. The proposed approach was tested on synthetic data and field data from a single-shaft gas turbine of 50 MW class. The results showed that the approach could give acceptable accuracy in the isolation and identification of multiple faults, with 99% detection and isolation accuracy and 1% maximum error in the identified fault magnitude. The approach was also proven robust to sensor faults, by replacing the faulty signal with an estimated value that had only 3% error compared to the real measurement.

Data-Driven Fault Diagnosis for Satellite Control Moment Gyro Assembly with Multiple In-Phase Faults

A satellite can only complete its mission successfully when all its subsystems, including the attitude control subsystem, are in healthy condition and work properly. Control moment gyroscope is a type of actuator used in the attitude control subsystems of satellites. Any fault in the control moment gyroscope can cause the satellite mission failure if it is not detected, isolated, and resolved in time. Fault diagnosis provides an opportunity to detect and isolate the occurring faults and, if accompanied by proactive remedial actions, it can avoid failure and improve the satellite reliability. In this paper, an enhanced data-driven fault diagnosis is introduced for fault isolation of multiple in-phase faults of satellite control moment gyroscopes that has not been addressed in the literature before with high accuracy. The proposed method is based on an optimized support vector machine, and the results yield fault predictions with up to 95.6% accuracy. In addition, a sensitivity analysis with regard to noise, missing values, and missing sensors is done. The results show that the proposed model is robust enough to be used in real applications.

Linear time invariant system fault isolation: A static compensation decoupling method

This paper investigates the problem of linear time invariant system multiple faults detection and isolation in a certain frequency interval. A fault detection(FD) scheme which is composed by a conventional Luenberger observer is considered. Using diagonal dominance decoupling method, we design a novel fault isolation compensator in a certain frequency interval to realize fault isolation. Different from conventional design of fault isolation filter, the fault isolation compensator is designed independently. Since the compensator doesn’t change the convergence of the original FD scheme, it has ability to upgrade a stable FD scheme to an fault detection and isolation (FDI) scheme. By the end, numerical examples are given to illustrate the effectiveness of the proposed method.

Improved PCA model for multiple fault detection, isolation and reconstruction of sensors in nuclear power plant

Safety is the most important indicator of a nuclear power plant. The extensive use of sensors could help operators gain more information about nuclear power plants. However, it also increases the risk of sensor failures. Therefore, it is necessary to study the multiple fault detection, isolation and reconstruction (FDIR) of sensors. For the traditional principal component analysis (PCA) model, this paper proposes two improvements. The first improvement is to propose a Corrected Reconstruction Algorithm (CRA) to improve the accuracy of the reconstruction. The traditional PCA reconstruction has lower accuracy when reconstructing multi-sensor faults. The second improvement is a cyclic PCA (CPCA) monitoring model to detect multi-sensor failures. The purpose of this is to improve the PCA model's ability to detect multiple sensor faults. Finally, the data from an actual nuclear power plant is used for modeling and verification. Simulation tests show that the CPCA model could accurately detect the different kinds of sensor faults and successfully reconstruct fault data.

Multiple-Model-Based Diagnosis of Multiple Faults With High-Speed Train Applications Using Second-Level Adaptation

Due to the time-varying characteristics and the interacted nature of multiple faults in the high-speed train (HST), the fault modeling, isolation, and severity estimation cannot be described accurately using a single model, which may result in poor performance of the conventional fault diagnosis methods. This article introduces the idea of multiple models and second-level adaptation techniques to diagnose multiple faults of the HST traction motor. First, a reduced model description for the multiple faults is given. Then, a multiple fault isolation framework is developed to simplify the time-varying fault parameters space segmentation. Based on the decoupled fault set, a fault estimation scheme with second-level adaptation is used to provide a reliable alarm priority for different fault scenarios. A case study is performed to verify the effectiveness of the proposed approach.

Fault detection and isolation in batteries power electronics and chargers

This paper focuses on the residual-based fault detection and isolation (FDI) in batteries power electronics and chargers. Currently, isolation of multiple faults is performed by generating a bank of residuals, one residual signal for each fault. It is shown that, with a combination of different residual evaluation functions, various battery and electronics faults, including battery short/open circuit, sensor biases, input voltage drop, and switches short/open circuit, can be detected and isolated by using the magnitude and slope of a residual signal or its norm that is generated from the battery voltage. In this paper, the constant-current constant-voltage (CCCV) battery chargers based on buck and boost converters are studied. The FDI systems with observer- and historical-based residual generation methods are designed and simulation results are discussed.

Model-Based Diagnosis of Multiple Faults in Hybrid Dynamical Systems With Dynamically Updated Parameters

In this paper, online fault detection and isolation (FDI) of multiple faults for a hybrid dynamical system based on partly decentralized model-based diagnosis architecture is presented. A quantitative model-based diagnosis method for both discrete and parametric faults occurring sequentially is proposed here, in which system global model is partitioned into various subsystems by analyzing the global fault sensitivity signature matrix (GFSSM) and mode change sensitivity signature matrix (MCSSM) of the global system. A local diagnoser is designed for each subsystem resulting in local GFSSM and MCSSM; and supervisory diagnoser is required only when if more than one local diagnoser generate alarm. The proposed method can also be used to solve the problem of sequentially occurring faults detection and isolation for a centralized or distributed architecture-based diagnosis. Residual sensitivity signature with the GFSSM and MCSSM is used to determine a smaller set of possible fault candidates after detection of a fault by the respective local diagnoser. It is shown that use of fault direction information from GFSSM and MCSSM improves the fault isolation process for discrete or parametric fault and also improves the local parameter estimation process. The identified discrete fault or estimated parameters are then used to update the corresponding nominal model which allows for FDI of the next fault. For robust FDI, adaptive residual threshold is used which accounts for the process, measurement uncertainties and mode change, including the parameter estimation uncertainties. Algorithms and codes have been developed in industry preferred MATLAB-LabVIEW interface for real time monitoring of the hybrid system. The developed method is first tested on the four-tank hybrid system model through simulation. Then it is experimentally validated with real experimental data collected from a reduced-scale equivalent hybrid electronic circuit model of a bench mark two-tank system for checking the robustness of the proposed approach. The developed method is shown to produce accurate diagnosis of sequentially occurring faults in hybrid systems and saves valuable CPU time as compared with centralized approach.