Proposed Fault Detection And Isolation Method Research Articles

A Comprehensive Fault Detection and Isolation Method for DC Microgrids Using Reduced-Order Unknown Input Observers

Fault diagnosis is of critical importance to the safety of power electronic devices in DC microgrids. To detect and isolate different component faults in DC microgrids, this paper introduces a comprehensive protection scheme using reduced-order unknown input observers (ROUIOs). As opposed to conventional protection strategies, the proposed method provides a centralized fault detection and isolation (FDI) solution for DC microgrids that covers multiple faults occurring in different components in a unified process. Moreover, it reduces the complexity of observer model and relaxes the requirements of measurement signals compared with existing observer-based FDI methods for DC microgrids. To this end, the state-space model of a multi-terminal DC microgrid with different faults is first established. On this basis, a bank of ROUIOs are designed with the aim of classifying different component faults in the system. At last, the performance of the proposed FDI method is verified through numerical simulations with MATLAB/Simulink and hardware tests. Test results show that the proposed method can accurately detect and isolate different component faults in DC microgrids in a short response time of 1 ms.

Stochastic Event-Triggered Fault Detection and Isolation Based on Kalman Filter.

In this article, the robust fault detection and isolation (FDI) Kalman filter is extended based on stochastic event-triggered schedulers for discrete linear systems consisting of deterministic/stochastic unknown inputs with nonzero mean and colored measurement noise. In the proposed FDI method, first, a subspace of the main system that significantly attenuates disturbance effects is proposed. After that, the fusion method is proposed for dealing with the colored measurement-noise problem in designing the Kalman filter and preventing from leading to measurement noise with zero mean and zero covariance. The stochastic event-triggered schedulers are considered as Gaussian functions. Therefore, the Gaussian property of innovation sequence is preserved, and consequently, the recursive equation of the Kalman filter need not be extended based on approximation techniques. Finally, design parameters are chosen based on the convex optimization problem such that the lowest communication rate between the sensor node and FDI filter, and also the best FDI performance are achieved. The proposed FDI method is evaluated to detect and isolate stator interturn short circuit and broken rotor bars faults with unbalanced voltage as disturbance in three-phase induction motors.

A New Fault Diagnosis Approach for Heavy-Duty Gas Turbines

Effective fault detection, estimation, and isolation are essential for the safety and reliability of gas turbines. In this article, a hybrid fault detection and isolation (FDI) approach is presented for condition monitoring of heavy-duty gas turbines. First, nonlinear dynamical models are constructed using an orthonormal basis function and an adaptive neuro-fuzzy inference system through experimental data. Following that, a fuzzy inference system is employed to estimate the fault severity. For this aim, fuzzy rules are extracted from the fault patterns by defining appropriate features. Later, various faults are isolated using an ensemble decision tree classifier. The proposed nonlinear modeling compensates for disturbances and uncertainty in the system and leads to adaptive thresholds for fault detection, which reduces the false alarm rate. Moreover, the proposed FDI method brings high accuracy in fault estimation by properly modeling a bounded uncertainty using the adaptive threshold. Experimental data are applied to validate the gas turbine model. Test results indicate that the proposed hybrid FDI method via adaptive threshold overwhelms the other FDI methods, where the misclassified data are 5.6%.

Fault Detection and Isolation for a Class of Nonlinear Systems Based on Gerschgorin Theorem and Optimization Approach

This article is concerned with the fault detection and isolation (FDI) problem for a class of nonlinear systems described by the T–S fuzzy models. Based on the concept of minimum unobservability subspace and geometric property of factor space, a set of FDI filters where each residual is only affected by one fault and completely decoupled from other faults is designed. Furthermore, in the decoupling space, the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$H_{\infty }/H_{-}$ </tex-math></inline-formula> performance indexes are provided to enhance the sensitivity of residual to faults and robustness to disturbances. In particular, to solve the nonconvex filter design problem caused by introducing the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$H_{-}$ </tex-math></inline-formula> index, the Gerschgorin theorem is first used to linearize the corresponding filter design conditions in the outer region of a ball. Then, the FDI filter design problem is converted into a convex optimization one, which is solved via the linear matrix inequality (LMI) control Toolbox, and the advantages and effectiveness of the proposed FDI method are verified through two simulation examples.

Enhanced chiller faults detection and isolation method based on independent component analysis and k-nearest neighbors classifier

The fault detection and isolation (FDI) of centrifugal chiller system is essential for improving energy efficiency and reducing energy consumption. Considering the non-Gaussianity of chiller measurement, this paper proposes an effective non-Gaussian chiller FDI method based on independent component analysis (ICA) and k-nearest neighbor (KNN) classifier. First of all, an enhanced fault detection method is developed by combining exponentially weighted moving average and ICA (EWICA). The exponentially weighted moving average is employed to develop a dynamic threshold scheme, which reduces the false alarm rate (FAR) and improves the fault detection rate (FDR) of chiller faults. Then, based on the KNN with cosine similarity metric, a novel fault isolation method using the direction of the residual vector is presented to isolate the chiller faults. To further improve the isolation performance, the number of neighbors (k) is optimized by 10-fold cross validation. Finally, the proposed FDI method is validated by using a data set from the ASHRAE Research Project 1043 (RP-1043). The FDI performance of the proposed method is comprehensively analyzed and compared with six state-of-the-art methods. Both of detection and isolation results suggest that the EWICA-based method delivers superior performance for chiller FDI.

Data-driven design of fault detection and isolation method for distributed homogeneous systems

Driven by the increasing needs in large-scale industrial processes for maintaining stable operation using process data, a data-driven fault detection and isolation (FDI) method for distributed homogeneous systems is proposed in this paper. To this end, the original distributed homogeneous system is first decomposed into several separate subsystems using invertible transformations of the state, input, and output variables. On this basis, the fault detection method according to the one-step identification of the stable kernel representation (SKR) is developed for the transformed subsystems. It is followed by the fault estimation for the transformed subsystems along with implementation on fault isolation for the original distributed system. Experiments comparing the proposed FDI method and the centralized FD method are performed using a numerical example. In this specific example, the proposed FDI method has a computational complexity of 2.32×107, while the centralized FD method has a computational complexity of 9.31×107. This shows the effectiveness and advantages of the proposed method.

Parameter estimation based-FDI method enhancement with mixed particle filter

Mobile robots have a crucial role in many domains. Currently, service robots can be found in our homes, transportation robots in warehouses, logistic robots in factories and even on Mars, where rovers are exploring its surface. These intelligent machines are becoming increasingly autonomous, as they have to achieve longer and longer missions. Thus, to maintain autonomy and to guarantee material security, Fault Detection and Isolation (FDI) is a mandatory task. A fault causes a mission to fail or, worse, it may damage materials and operators if the robot goes out of control. In this paper, we propose to address the FDI process. First, we present two modified versions of two known methods based on parameter estimation. Then, a mixed version of Particle Filter (PF) which combines a standard PF and a Gaussian PF to generate a better performing FDI method is proposed. These methods are studied in the case of a wheel block fault. Simulation and real robot experimentation results prove the added value of our proposed FDI method over the other two approaches.

Real-Time Hierarchical Neural Network Based Fault Detection and Isolation for High-Speed Railway System Under Hybrid AC/DC Grid

Reliable and comfortable high-speed railway (HSR) has skyrocketed in popularity as a transportation medium for traveling around the world. High-voltage direct current (HVDC) electrification system has been introduced to the HSR gradually. However, the coexistence of AC and DC systems will last for a long time because AC railway systems are still in the dominant position. A detailed HSR traction system transient model operating under the hybrid AC/DC grid was established in PSCAD/EMTDC. We proposed a real-time fault detection and isolation (FDI) method for the simulated model using neural network (NN). Hierarchical structure of the monitoring system has been employed. Low-level sub-monitors supervised the conditions of their local regions and the top-level monitor collected all the feedback from sub-monitors making the final evaluation of the entire HSR system based on a voting strategy. Both off-line and real-time experiments were conducted to validate the effectiveness of the proposed method. In the experiments, the sub-monitors were designed based on Gated Recurrent Unit (GRU) algorithm and implemented on the Xilinx VCU128 FPGA board. For the off-line experiment, the sub-monitors used the training and testing dataset both from PSCAD/EMTDC to construct the architecture of their individual GRU networks and to verify how great the networks can be. For the real-time task, the sub-monitors interfaced with a real-time HSR system emulator running on the Xilinx VCU118 FPGA board to test the performance in the real-time application. The results proved that our proposed FDI method has the capability of real-time detection and can achieve better accuracy within reasonable time and resource consumption than other NN-based methods. Moreover, the method was capable of standing against noises from measured signals to some extent.

Enhanced Control Algorithm in PMSMs with Machine Current Sensor FDI and Current Management for Hardware Protection

This paper proposes an enhanced control algorithm in permanent magnet synchronous machines (PMSMs) with machine current sensor fault detection and isolation (FDI) and current management for hardware protection. Different from existing work on machine current sensor FDI, the proposed method addresses the sensor FDI problem under co-existence of PMSM current sensor and non-sensor imbalance faults, and thereby help prevent the controller performance degradation caused by a faulty machine current sensor signal. Meanwhile, hardware protection is achieved through the proposed algorithm which has a unique feature of providing online overcurrent regulation and source power management at the same time to enhance protection of power inverter and power source. Benchmark comparison of the proposed sensor FDI method to state-of-the-art techniques is carried out to validate the good performance of the proposed FDI method in terms of fault detection time, false detection rate and missed detection rate. Sensitivity analysis of the proposed current management algorithm with overcurrent regulation and source power management is performed under changing rotor flux linkage and machine stator d-axis self-inductance, as an example to demonstrate the robustness of the proposed algorithm for hardware protection against system parameter variations. The effectiveness of the proposed algorithm is confirmed by simulation results in MATLAB for a PMSM.

Adaptive System Identification and Severity Index-Based Fault Diagnosis in Motors

In this paper, a model-based fault detection and isolation (FDI) method is presented using an adaptive system identification approach. The proposed FDI method consists of three essential steps: adaptive modeling and residual generation, fault detection using adaptive hybrid threshold, and fault identification using fault severity indices. The primary task is based on current signal modeling using an input–output identification method. The modeled signal is utilized for residual generation and a dynamic and hybrid thresholding method is used for residual analysis and fault detection. Moreover, the concept of fault severity indices is incorporated for the identification of fault type and severity level. In this study, the proposed method is experimentally investigated using an induction motor testbed. Fault detection and identification is performed for broken rotor bar as well as inner race and outer race bearing faults. Experimental results are included to demonstrate the feasibility of the proposed method for fault detection and isolation. The results demonstrate robust fault detection and accurate fault isolation. The proposed fault diagnosis method provides an efficient flexible solution for improving system reliability and safety.

Combining set-theoretic UIO and invariant sets for optimal guaranteed robust fault detection and isolation

This paper proposes an optimal guaranteed robust fault detection and isolation (FDI) method combing the set-theoretic unknown input observer (SUIO) and the robust positively invariant (RPI) sets for linear time-invariant (LTI) systems. The optimality of the proposed FDI method is achieved under a two-layer framework. The first layer allows to design a single optimal FDI-oriented SUIO with RPI set separation-based guaranteed FDI conditions to reduce the FDI conservatism of single observer. The second layer consists in designing an optimal observer configuration for a bank of optimal SUIOs to reduce the FDI conservatism of all the SUIOs together. Finally, the effectiveness of the proposed guaranteed robust FDI method is illustrated by using a four-tank system.

Model-Based Fault Tolerant Control for the Thermal Management of PEMFC Systems

Proton exchange membrane fuel cells (PEMFCs) are considered as one of the most promising power sources for transportation in the near future. Proper and robust thermal management is a key issue in fuel cell applications. This paper mainly studies the sensor fault detection and isolation (FDI) and fault tolerant control (FTC) for the thermal management of PEMFC systems. The thermal model of fuel cell is established and analyzed by structure analysis, and the residual generator of sensor faults are designed by Dulmage–Mendelsohn decomposition to rearrange the biadjacency matrix of the thermal model. A sliding-mode-based active FTC (AFTC) strategy is proposed for the thermal management of fuel cell systems. The effectivenesses of the proposed FDI method and AFTC strategy are verified on a fuel cell test bench. The experimental results show that the temperature of the PEMFC stack can be maintained at the reference value with high accuracy even when sensor fails.

Machine Current Sensor FDI Strategy in PMSMs

This work proposes a machine current sensor fault detection and isolation (FDI) strategy in permanent magnet synchronous machines (PMSMs) resilient to multiple faults. The fault detection is performed by comparing the measured and estimated DC link currents. The fault isolation is achieved according to machine phase signal estimation and the corresponding residual examination. Single sensor fault, multiple sensor faults and non-sensor fault are covered by the proposed FDI method. The proposed sensor FDI method is not influenced by machine imbalance, feasible for FDI of both single and multiple machine current sensor faults, and capable of distinguishing between machine current sensor fault and non-sensor fault. The proposed method is validated with simulation studies in MATLAB, and the studies demonstrate that the proposed machine current sensor FDI method, different from existing methods, has a unique feature of being able to handle multiple sensor faults with the consideration of non-sensor fault disturbance.

New Method of Faults Diagnostic based on Neuro-Dynamic Sliding Mode for Flat Nonlinear Systems

This paper addresses the problem of simultaneous actuator, process and sensor Fault Detection and Isolation (FDI) for nonlinear system having flatness properties with the presence of disturbances and which are operating in closed-loop. In particular, the nonlinear system is corrupted with additive actuator, process or sensor faults with simultaneous occurrence. In this case, the residual signals might be sensitive to all of these faults that can appear in the system. The proposed FDI method is based on both input and parameter estimators that are designed in parallel. With the flatness property of such system, the design of these two estimators requires information on the measured outputs and their successive derivatives. To estimate these last one, a new scheme of the 2nd-order dynamic sliding mode differentiator is proposed. Residuals are next defined as the difference between the estimated and expected behavior. In order to isolate the faults, dynamic neural networks technique is employed. Besides, comparative study between this new differentiator and the well-known 2nd-order Levant’s differentiator is provided to show the pros and cons of the proposed FDI method. This latter is validated by the simulation results and is carried out on a three tank system.

Stochastic Hybrid Estimator Based Fault Detection and Isolation for Wind Energy Conversion Systems with Unknown Fault Inputs

In recent years, the wind energy conversion system (WECS) has been becoming the vital system to acquire wind energy. However, the high failure rate of WECSs leads to expensive costs for the maintenance of WECSs. Therefore, how to detect and isolate the faults of WECSs with stochastic dynamics is the pressing issue in the literature. This paper proposes a novel comprehensive fault detection and isolation (FDI) method for WECSs. First, a stochastic model predictive control (SMPC) controller is studied to construct the closed-loop system of the WECS. This controller is based on the Markov-jump linear model, which could precisely establish the stochastic dynamics of the WECS. Meanwhile, the SMPC controller has satisfied control performance for the WECS. Second, based on the closed-loop system with SMPC, the stochastic hybrid estimator (SHE) is designed to estimate the continuous and discrete states of the WECS. Compared with the existing estimators for WECSs, the proposed estimator is more suitable for WECSs since it considers both the continuous and discrete states of WECSs. In addition, the proposed estimator is robust to the fault input. Finally, with the proposed estimator, the comprehensive FDI method is given to detect and isolate the actuators’ faults of the WECS. Both the system status and the actuators’ faults can be detected by the FDI method and it can effectively quantify the actuators’ fault by the fault residuals. The simulation results suggest that the SHE could effectively estimate the hybrid states of the WECS, and the proposed FDI method gives satisfied fault detection performance for the actuators of the WECS.

Decentralized fault detection and isolation using bond graph and PCA methods

This paper proposes a new method for fault detection and isolation of a large-scale system by the bond graph (BG) model and the principal component analysis (PCA) technique in a decentralized architecture. The proposed method is entitled the BG-PCA technique. The main objective is to address the problem of monitoring large-scale system components by fault detection and isolation (FDI) methods. In the modeling framework, the BG model is presented by exploiting its structural and causal properties and the diagnostic bond graph (DBG). In measurement noise and perturbation conditions, the probability density function takes place to generate a clear decision procedure to detect the operating mode. In our approach, we firstly generate the structured residues. Furthermore, with a decentralized architecture, we can locate in what subsystem a fault is first detected. Finally, for isolation, the generation of the structured residues method from PCA is exploited to ensure localization. To validate the suggested BG-PCA algorithm, simulations are computed on a three-tank system to show the efficiency of the proposed FDI method, and satisfactory results are found.

A signal-based fault detection and classification method for heavy haul wagons

ABSTRACTThis paper proposes a signal-based fault detection and isolation (FDI) system for heavy haul wagons considering the special requirements of low cost and robustness. The sensor network of the proposed system consists of just two accelerometers mounted on the front left and rear right of the carbody. Seven fault indicators (FIs) are proposed based on the cross-correlation analyses of the sensor-collected acceleration signals. Bolster spring fault conditions are focused on in this paper, including two different levels (small faults and moderate faults) and two locations (faults in the left and right bolster springs of the first bogie). A fully detailed dynamic model of a typical 40t axle load heavy haul wagon is developed to evaluate the deterioration of dynamic behaviour under proposed fault conditions and demonstrate the detectability of the proposed FDI method. Even though the fault conditions considered in this paper did not deteriorate the wagon dynamic behaviour dramatically, the proposed FIs show great sensitivity to the bolster spring faults. The most effective and efficient FIs are chosen for fault detection and classification. Analysis results indicate that it is possible to detect changes in bolster stiffness of ±25% and identify the fault location.

Robustness Analysis and Experimental Validation of a Fault Detection and Isolation Method for the Modular Multilevel Converter

This paper presents a fault detection and isolation (FDI) method for open-circuit faults of power semiconductor devices in a modular multilevel converter (MMC). The proposed FDI method is simple with only one sliding-mode observer (SMO) equation and requires no additional transducers. The method is based on an SMO for the circulating current in an MMC. An open-circuit fault of power semiconductor device is detected when the observed circulating current diverges from the measured one. A fault is located by employing an assumption-verification process. To improve the robustness of the proposed FDI method, a new technique based on the observer injection term is introduced to estimate the value of the uncertainties and disturbances; this estimated value can be used to compensate the uncertainties and disturbances. As a result, the proposed FDI scheme can detect and locate an open-circuit fault in a power semiconductor device while ignoring parameter uncertainties, measurement error, and other bounded disturbances. The FDI scheme has been implemented in a field-programmable gate array using fixed-point arithmetic and tested on a single-phase MMC prototype. Experimental results under different load conditions show that an open-circuit faulty power semiconductor device in an MMC can be detected and located in less than 50 ms.

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

In this paper, a novel robust sensor fault detection and isolation (FDI) strategy using the multiple model-based (MM) approach is proposed that remains robust with respect to both time-varying parameter uncertainties and process and measurement noise in all the channels. The scheme is composed of robust Kalman filters (RKF) that are constructed for multiple piecewise linear (PWL) models that are constructed at various operating points of an uncertain nonlinear system. The parameter uncertainty is modeled by using a time-varying norm bounded admissible structure that affects all the PWL state space matrices. The robust Kalman filter gain matrices are designed by solving two algebraic Riccati equations (AREs) that are expressed as two linear matrix inequality (LMI) feasibility conditions. The proposed multiple RKF-based FDI scheme is simulated for a single spool gas turbine engine to diagnose various sensor faults despite the presence of parameter uncertainties, process and measurement noise. Our comparative studies confirm the superiority of our proposed FDI method when compared to the methods that are available in the literature.

Detecting and isolating abrupt changes in linear switching systems

In this paper, a novel fault detection and isolation (FDI) method for switching linear systems is developed. All input and output signals are assumed to be corrupted with measurement noises. In the proposed method, a ‘lifted’ linear model named as stochastic hybrid decoupling polynomial (SHDP) is introduced. The SHDP model governs the dynamics of the switching linear system with all different modes, and is independent of the switching sequence. The error-in-variable (EIV) representation of SHDP is derived, and is used for the fault residual generation and isolation following the well-adopted local approach. The proposed FDI method can detect and isolate the fault-induced abrupt changes in switching models’ parameters without estimating the switching modes. Furthermore, in this paper, the analytical expressions of the gradient vector and Hessian matrix are obtained based on the EIV SHDP formulation, so that they can be used to implement the online fault detection scheme. The performance of the proposed method is then illustrated by simulation examples.