Model-based Fault Detection And Isolation Research Articles

Robust Detection of Minute Faults in Uncertain Systems Using Energy Activity

Fault detection is one of the key steps in Fault Detection and Isolation (FDI) and, therefore, critical for subsequent prognosis or implementation of Fault Tolerant Control (FTC). It is, therefore, advisable to utilize detection algorithms which are quick and can detect the smallest faults. Model-based detection methods satisfy both these criteria and should be preferred. However, a big limitation for model-based methods is that they require the accurate value of the component parameters, which is difficult to obtain in real situations. This limits the accuracy of model-based methods. This paper proposes a new method for fault detection using Energy Activity (EA) which can detect minute levels of fault in systems with high component uncertainty. Different forms of EA are developed for use as an FDI metric. The proposed forms are simulated using a two-tank system under various types of faults. The results are compared with each other and with the traditional model-based FDI method using Analytical Redundancy Relations (ARRs). The simulations are performed considering model uncertainties to check the inherent performance of the methods. From initial simulations, it is established that the integral form of EA is most suited for fault detection. The integral for if EA is then tested using a real two-tank system considering both the model and measurement uncertainties.

Fault-Tolerant Control of a Three-Phase Permanent Magnet Synchronous Motor for Lightweight UAV Propellers via Central Point Drive

This paper deals with the development and the performance characterization of a novel Fault-Tolerant Control (FTC) aiming to the diagnosis and accommodation of electrical faults in a three-phase Permanent Magnet Synchronous Motor (PMSM) employed for the propulsion of a modern lightweight fixed-wing UAV. To implement the fault-tolerant capabilities, a four-leg inverter is used to drive the reference PMSM, so that a system reconfiguration can be applied in case of a motor phase fault or an inverter fault, by enabling the control of the central point of the three-phase connection. A crucial design point is to develop Fault-Detection and Isolation (FDI) algorithms capable of minimizing the system failure transients, which are typically characterized by high-amplitude high-frequency torque ripples. The proposed FTC is composed of two sections: in the first, a deterministic model-based FDI algorithm is used, based the evaluation of the current phasor trajectory in the Clarke’s plane; in the second, a novel technique for fault accommodation is implemented by applying a reference frame transformation to post-fault commands. The FTC effectiveness is assessed via detailed nonlinear simulation (including sensors errors, digital signal processing, mechanical transmission compliance, propeller loads and electrical faults model), by characterizing the FDI latency and the post-fault system performances when open circuit faults are injected. Compared with reports in the literature, the proposed FTC demonstrates relevant potentialities: the FDI section of the algorithm provides the smallest ratio between latency and monitoring samples per electrical period, while the accommodation section succeeds in both eliminating post-fault torque ripples and maintaining the mechanical power output with negligible efficiency degradation.

Model-Based FDI Applied to a Piezoelectric Active Vibration Suppression System for Smart Flexible Spacecraft

Fault Detection and Isolation (FDI) techniques have captured extensive interest and attention in modern autonomous systems; in particular, they are of foremost importance in space applications, due to their scientific relevance, cost and current inability of doing on-orbit maintenance of space systems. In this scenario, FDI strategies are required to counteract possible failure events that, if not properly handled, can reduce system performance or compromise the realization of the mission objectives. In this paper, a model-based FDI strategy is implemented onboard a satellite equipped with a very large mesh reflector on which a distributed network of smart actuators/sensors is mounted to actively counteract undesired elastic vibrations. In particular, the detection and isolation of a possible piezo-actuator failure occurring in the Active Vibration Control (AVC) system of the antenna is addressed by a bank of Unknown Input Observers (UIOs). The design of the proposed UIOs is derived by solving a Linear Matrix Inequality (LMI) problem, which provides the conditions for their existence, and it is based on the linearized 3D state-space model of the controlled spacecraft, under the assumption that all the uncertainties, exogenous disturbances and measurement noises are neglected. Furthermore, pole assignment in the sense of D-stability is integrated in the standard formulation of the UIO to guarantee an adequate transient behaviour of the observers. Finally, an extensive Monte Carlo simulation campaign is conducted to assess the effectiveness of the proposed FDI architecture and its robustness against modelling uncertainties and measurement noise.

Fault Detection and Isolation for a Cooling System of Fuel Cell via Model-based Analysis

The development of fuel cell electric vehicles in recent years has led to increased interest in the use of fuel cells as sources of renewable energy. To achieve successful commercialization of fuel cell vehicles, it will be necessary to guarantee the safety, reliability, and lifetime of fuel cell systems by predictive fault detection and isolation (FDI). In this study, the parity equation, an observer, and a Kalman filter are employed together to compare the characteristics of FDI, focusing on the sensors of the thermal management system. Residuals corresponding to the difference between temperature outputs of linear models under driving cycles and nonlinear temperature outputs are used to isolate faults. Then, assessment of three model-based sensor FDI schemes is used to isolate sensor faults using the Cumulative Sum Control Chart (CUSUM) method. Generated residuals are evaluated by CUSUM to detect the presence of a sensor fault. As a result, isolated sensor faults are assessed.

Teaching Model-based Fault Detection and Isolation using a Virtual Laboratory Environment

Abstract Fault detection and isolation (FDI) systems play a key role to provide efficiency, reliability and safety in today’s industrial processes. The teaching of FDI systems is facilitated if it is carried out not only with theoretical lectures but also with practical experiences. This paper proposes a virtual laboratory environment (VLE) to carry out online practical experiences with FDI systems for a benchmark process. Thanks to this VLE, students can set up faults in sensors, actuators or in the process itself, program model-based FDI algorithms and test FDI system performance. The use of this environment is illustrated by testing the performance of FDI systems for the quadruple-tank process (4TP) under different fault scenarios. Finally, the procedure of using this proposal for practical experience with two model-based FDI design methods is shown.

Sensor Fault Detection and Isolation for Degrading Lithium-Ion Batteries in Electric Vehicles Using Parameter Estimation with Recursive Least Squares

With the increase in usage of electric vehicles (EVs), the demand for Lithium-ion (Li-ion) batteries is also on the rise. The battery management system (BMS) plays an important role in ensuring the safe and reliable operation of the battery in EVs. Sensor faults in the BMS can have significant negative effects on the system, hence it is important to diagnose these faults in real-time. Existing sensor fault detection and isolation (FDI) methods have not considered battery degradation. Degradation can affect the long-term performance of the battery and cause false fault detection. This paper presents a model-based sensor FDI scheme for a Li-ion cell undergoing degradation. The proposed scheme uses the recursive least squares (RLS) method to estimate the equivalent circuit model (ECM) parameters in real time. The estimated ECM parameters are put through weighted moving average (WMA) filters, and then cumulative sum control charts (CUSUM) are implemented to detect any significant deviation between unfiltered and filtered data, which would indicate a fault. The current and voltage faults are isolated based on the responsiveness of the parameters when each fault occurs. The proposed FDI scheme is then validated through conducting a series of experiments and simulations.

Active Fault Identification by Optimization of Test Designs

Model-based fault detection and isolation (FDI) methods are used to determine faults by examining the deviation of sensed information from anticipated system trajectories. In this paper, a comprehensive model-based FDI framework is proposed to improve fault identifiability and reduce false alarms during maintenance testing. In this framework, robust maintenance tests are designed and conducted, followed by false alarm analysis. The optimal tests designed improve the identifiability of faults by manipulating system inputs to maximize information with respect to faults, in the form of sensitivities of the system outputs. Each test design is evaluated a posteriori using the system model to explore whether false alarms are plausible, given system uncertainty and measurement noise. The proposed framework is applied on two case studies that compare the identifiability of faults at nominal and optimal system test conditions. The first case study focuses on a plate fin heat exchanger with various levels of particulate fouling at steady-state and transient conditions. The second case study deals with the same type of fault but in an aircraft environmental control system with multiple sources of uncertainty.

Switch fault diagnosis for boost DC–DC converters in photovoltaic MPPT systems by using high‐gain observers

Open- and short-circuit faults (OSCFs) in boost dc–dc converters for photovoltaic (PV) maximum power point trackers (MPPTs) imply an inefficiency after fault is triggered, which affect the security and profitability of PV projects. Hence, fault detection and isolation (FDI) techniques have become an important issue for PV technology. In this study, a model-based FDI technique is proposed to boost dc–dc converters in PV MPPT systems. As is well-known, major issues of model-based FDI techniques have always been parametric uncertainty and no-modelled dynamics. This study focuses on how to mitigate these shortcomings by applying a high-gain observer (HGO) as a residual generator. A striking feature of HGO's is that exponential stability is still guaranteed for bounded disturbances (or faults). As demonstrated in this study, under an integral control action in the closed-loop control system, OSCFs are characterised for ever-growing signals, enabling the suggested FDI scheme. Also, the FDI proposal is decoupled from PV current (irradiance changes) and load variations, thereby avoiding false alarms. Moreover, the output-injection gain and thresholds are selected such that the fault diagnosis is achieved in eight switching cycles, enabling a fast and reliable diagnosis. Experimental results are illustrated to validate the FDI scheme proposed in this study.

Active fault diagnosis for uncertain systems using optimal test designs and detection through classification

Fault detection and isolation (FDI) is becoming increasingly difficult due to the complexity and uncertainty of modern systems. For industrial systems with explicit models available, model-based active FDI tests can improve the capability for fault diagnosis. These tests should be determined and evaluated prior to implementation to minimize on-site computational costs. In this paper, a methodology is presented for the design optimization and assessment of tests for active fault diagnosis. The objective is to maximize the information from system outputs with respect to faults while minimizing the correlation between faults and uncertainty. After a test is designed, it is deployed with a k-nearest neighbor algorithm combined with principal component analysis.Two case studies are used to verify the proposed methodology, a three-tank system and a diesel engine.

Model‐based strategy for open‐circuit faults diagnosis inn‐level CHB multilevel converters

Here, the authors study open-circuit faults (OCFs) in the power switches of multilevel converters with a model-based perspective. In specific, the authors address single-phase cascaded H-bridge (CHB) converters with n -levels in its output voltage (CHB- n L), which are designed as shunt active power filters. In this task, the OCFs are modelled by fault profiles with an additive structure in each subsystem of the CHB- n L converter. These additive fault profiles have constant and oscillatory components, where the constant term sign indicates the pair of damaged switches in the H-bridge. Hence, a sliding-mode integral observer is proposed to estimate the constant terms of the fault profiles. The complexity of the observer depends on the number of H-bridges in the CHB- n L converter. As a result, the proposed fault detection and isolation (FDI) scheme relies on the estimated constant terms of the additive fault profiles to achieve a robust and fast diagnosis stage. The proposed model-based FDI scheme is validated experimentally under single and multiple fault scenarios, and model uncertainty. During the evaluation, the derived methodology only requires less than one cycle of the fundamental frequency to isolate the faults, and show robustness to load changes and parametric uncertainty.

Computational cost of an exergy-based fault detection scheme implemented in a commercial process simulator

Abstract Energy-based fault detection and isolation (FDI) schemes show promise in terms of applicability to petrochemical industries and the broader process industries. Model-based FDI schemes offer pronounced advantages over more easily implemented data-driven FDI techniques. However, the implementation of model-based FDI schemes require the development of analytic models, and as such, widespread adoption is lacking. Although process simulators derive analytic models internally, comprehensive exergy-based parameters are not calculated automatically. In this work Aspen Hysys is extended by means of user variables to allow the automatic calculation of physical and chemical exergy for each process stream. Initial results indicate that additional computational overhead of approximately 20% is incurred and that the calculation technique scales well with model size. This calculation method allows the development of a truly hybrid model- and data-based approach to FDI within the process industries.

Model-based FDI for Agile Spacecraft with Multiple Actuators Working Simultaneously

Current and future space missions require agile and reliable spacecraft capable of trailing and keeping the required attitude. Most of the agile spacecraft missions are near-Earth based but some are placed far away from Earth and its influence. One example of such missions is the Athena mission, which requires the spacecraft to perform fast and large-angle attitude slew manoeuvres. Such manoeuvres often imply simultaneous use of multiple actuators such as thrusters and reaction wheels (RWs). A fault in any of these actuators might lead to partial or full damage of sensitive spacecraft instruments. In this research project, a novel model-based Fault Detection and Isolation (FDI) strategy is proposed, which is able to detect and isolate various actuator faults, such as stuck-open/closed thruster, thruster leakage, loss of effectiveness of all thrusters, and change of RW friction torque due to change of Coulomb and/or viscosity factor. Moreover, the proposed FDI strategy is also able to detect and isolate faults affecting the RWs tachometer. The design of the FDI algorithm is based on a multiplicative extended Kalman filter, a generalised likelihood ratio thresholding of the residual signals, and a logic algorithm which unequivocally link the faults to the symptoms. The performance and robustness of the proposed FDI strategy are evaluated using Monte Carlo simulations and carefully defined FDI performance indices. In addition, the influence of faults’ magnitudes, times of fault occurrence, and uncertainties’ magnitudes on the FDI system performance are evaluated. Preliminary results suggest promising performance in terms of detection/isolation times, miss-detection/isolation rates, and false alarm rates. Also, uncertainties on the spacecraft inertia seem to have a negative impact on the FDI performance. In order to fully understand the research project presented here, graduate-level knowledge on rigid body dynamics and kinematics, control theory, and filters applied to estimation might be required. If any of these areas are not known by the reader, it is recommended to read some of the associated literature referenced in the bibliography.

Optimal Combination of Fault Detection and Isolation Methods of Integrated Navigation Algorithm for UAV

Performing endurance flight for long-term missions, unmanned aerial vehicles (UAV) are safety–critical systems that requires high reliability. To achieve long flight time and high reliability altogether, S/W-based fault detection and isolation (FDI) methods are applied rather than H/W redundancy due to limitation of loading weight. This study focused on model-based FDI techniques against faults, difficult to sense via signal-based FDI methods. The existing observer-based and parameter identification-based methods have difficulties separating the faulty sensor from correlated fault sources or sensors that output the same type of measurements. In this study, the fault detection parameter was designed to detect a faulty sensor from correlated fault sources. The combination of the proposed method and the existing residual-based methods was also designed to separate the faulty sensor from other independent auxiliary sensors with same measurement variables. In addition, the optimal combination of the CUSUM test and technique using the fault detection parameter was designed for UAVs. The algorithm was installed on INS and verified via vehicle mounted test and captive flight test.

Active Fault Diagnosis on a Hydraulic Pitch System Based on Frequency-Domain Identification

The blade pitch system is a critical subsystem of variable-speed variable-pitch wind turbines that is characterized by a high failure rate. This paper addresses the fault detection and isolation (FDI) of a blade pitch system with hydraulic actuators. Focus is placed on incipient multiplicative faults, namely hydraulic oil contamination with water and air, bearing damage resulting in increased friction, and drop of the supply pressure of the hydraulic pump. An active model-based FDI approach is considered, where changes in the operating conditions (i.e., mean wind speed and turbulence intensity) are accounted through the identification of a linear parameter-varying model for the pitch actuators. Frequency-domain estimators are used to identify continuous-time models in a user-defined frequency band, which facilitates the design of the FDI algorithm. Besides, robustness with respect to noise in measurements and stochastic nonlinear distortions is ensured by estimating confidence bounds on the parameters used for FDI. The approach is thoroughly validated on a wind turbine simulator based on the FAST software that includes a detailed physical model of the hydraulic pitch system. This paper presents the design methodology and validation results for the proposed FDI approach. We show that an appropriate design of the excitation signal used for active fault detection allows an early fault diagnosis (except for oil contamination with water) while ensuring a short experiment duration and an acceptable impact on the wind turbine operation.

Fault-tolerant model predictive control of a direct methanol-fuel cell system with actuator faults

This paper investigates fault tolerant model predictive control (MPC) of a direct methanol fuel cell (DMFC) system with several faults in the methanol feeding pump. An active FTMPC strategy with a hierarchal structural design is developed. The focus here is on fault detection and isolation (FDI) and the implementation of fault-tolerant strategies within the control algorithm. To this end, a model-based FDI scheme with virtual sensors is first developed by means of the real-time diagnosis of fault occurrence during operation. Thereby, several faults in the methanol pump are characterized and the information integrated into the MPC algorithm in each fault case. Strategies are presented to reconfigure the active fault-tolerant MPC to keep the DMFC system stable in case of a feeding failure. Moreover, economic, stability and lifetime characteristics are also integrated into the active fault-tolerant MPC. The proposed FDI and FTMPC scheme is tested experimentally in a DMFC test rig with a 5-cell DMFC stack to demonstrate the effectiveness and robustness of the designed approach. Several fault scenarios with the FTMPC are shown. Particularly in the case of fuel cells, fault tolerance is necessary to meet the goals of long-lasting system stability and efficiency.

Euresis-filter: a robust multiple-model FDI technique for small satellites' thruster faults

Fault detection and isolation (FDI) techniques are properties of critical importance for any aerospace missions where autonomy and fault identification (malfunction diagnosis) must be present all the time. Current work provides an extended presentation of the Euresis-filter, a multiple model-based FDI technique suitable for small satellites' thruster faults. The main scope of the proposed multiple model-based FDI function is to maintain the spacecraft (S/C) operational and safe (healthy status) as required by the mission, including the analysis, development and design of the Euresis-filter to the LPF model and Monte Carlo simulation results related to thruster faults. Results reveal that the Euresis-filter can serve as an advanced FDI and fault tolerance control (FTC) technique in terms of reliability, safety and mission success when compared to classical FDI/FDIR approaches.

Euphoria-filter: a robust H<SUB align="right">2/H<SUB align="right">∞ technique amenable to the Lisa-Pathfinder thruster faults

The main purpose of the fault detection and isolation (FDI) function is to maintain the spacecraft (S/C) or any other unnamed aerial vehicle (UAVs) operational and safe (healthy) as required by the mission. Thus, robust FDI is the key property for all aerospace missions and applications, wherever autonomy is of paramount importance. Current work provides a focused insight and lessons learned of the euphoria-filter, a robust H2/H∞ model-based FDI technique, with specific application to the Lisa-Pathfinder thruster failures.

Modeling, diagnosis and estimation of actuator faults in vehicle suspensions

This paper deals with the modeling, diagnosis and estimation of faults in automotive Semi-Active (SA) dampers, particularly oil leakages in the actuator. An experimental multiplicative fault model is proposed and statistically validated with an index error of 15% for damper leakage. The fault model is used as design basis for two Fault Detection and Isolation (FDI) frameworks. The Frequency-based Fault Estimator (FFE) is based on the effect of the damper fault in the frequency domain and the Robust Parity Space (RPS) consists in a residual generator sensitive to the fault in the time domain. The model-based FDI systems were experimentally validated in a 1:5 scaled vehicle, fully instrumented and equipped with SA dampers. The experimental results show that, while both approaches represent suitable options for commercial applications, the RPS estimator has the fastest detection time and proportionality to the fault level. In addition, the RPS approach has better robustness to vehicle mass uncertainties. On the other hand, the FFE presents lower sensitivity to road profile and semi-active damper input variations. Additionally, this estimator requires a lower number of sensors and has a lower computational overhead.

Model-Based Fault Detection and Isolation Scheme for a Rudder Servo System

This paper offers a model-based fault detection and isolation (FDI) scheme for a rudder servo system (RSS) that manages ship navigation. Inherent nonlinearities and unknown external disturbances present great challenges in applying FDI technologies to RSS in practice. This paper presents the derivation of the state equations and commonly encountered fault types of RSS from physical laws, and the design of five nonlinear unknown input observers (NUIOs) capable of eliminating the influences of unknown disturbance while detecting and isolating faults. In the NUIOs, no boundary assumption is made on the disturbance, and their parameter matrices are obtained using a linear-matrix-inequality method. Along with an algorithm that programs the logic rules for FDI, the model-based FDI scheme has been implemented on an actual RSS test rig for validating the RSS model and experimentally evaluating it in a real-world environment; both actuator and sensor faults are considered. Based on experimental tests, an adaptive threshold method is introduced to improve decision making and hence effectively eliminate false alarms. Experimental results show that the model-based scheme is efficient and can be used for online FDI.

Application of Factorization and Gap Metric Techniques to Fault Detection and Isolation Part II: Gap metric technique aided FDI performance analysis

The motivation and the objective of this work are to initiate a systematic application of the system factorization and gap metric techniques to fault detection and isolation (FDI) study. This promises an alternative and, in some cases, more efficient way to deal with analysis and design of model-based FDI systems. Our focus is on FDI for uncertain systems with multiplicative faults. We address FDI issues both in the open- and closed-loop configurations. The paper consists of two parts. After the coprime factorization technique aided system modelling and FDI schemes have been addressed in the first part, the second part is dedicated to the introduction of a so-called K-gap and, based on it, the definition of FDI performance indicators as well as their application to FDI performance analysis.