Fault Detection And Isolation System Research Articles

Design and Implementation of a Fuzzy Classifier for FDI Applied to Industrial Machinery.

In the present work, the design and the implementation of a Fault Detection and Isolation (FDI) system for an industrial machinery is proposed. The case study is represented by a multishaft centrifugal compressor used for the syngas manufacturing. The system has been conceived for the monitoring of the faults which may damage the multishaft centrifugal compressor: instrument single and multiple faults have been considered as well as process faults like fouling of the compressor stages and break of the thrust bearing. A new approach that combines Principal Component Analysis (PCA), Cluster Analysis and Pattern Recognition is developed. A novel procedure based on the statistical test ANOVA (ANalysis Of VAriance) is applied to determine the most suitable number of Principal Components (PCs). A key design issue of the proposed fault isolation scheme is the data Cluster Analysis performed to solve the practical issue of the complexity growth experienced when analyzing process faults, which typically involve many variables. In addition, an automatic online Pattern Recognition procedure for finding the most probable faults is proposed. Clustering procedure and Pattern Recognition are implemented within a Fuzzy Faults Classifier module. Experimental results on real plant data illustrate the validity of the approach. The main benefits produced by the FDI system concern the improvement of the maintenance operations, the enhancement of the reliability and availability of the compressor, the increase in the plant safety while achieving reduction in plant functioning costs.

Sensor FDI System Based on Discrete‐TimeFractional‐Order Observers

The design of sensor fault detection and isolation (FDI) systems has been studied widely by several authors of different fields of science (engineering, mathematics, physics) to ensure the continuous operation of systems or processes. This research presents the design and evaluation of a fault detection and isolation (FDI) system based on discrete fractional‐orderhigh‐gain observers (DFOHGO) for the outlet temperature sensors of a heat exchanger. The formulation of the discrete fractional‐order observers is based on the Grünwald–Letnikov fractional‐order derivative. The FDI system is based on a bank of two DFOHGOs to carry out a residual evaluation. The obtained results showed that the DFOHGOs provide accurate estimations of the temperatures. This allows switching the faulty sensor signal to the temperature estimations provided by the DFOHGOs when a fault occurs.

Actuator fault reconstruction using FDI system based on sliding mode observers

Interplanetary space missions require spacecraft autonomy in order to fulfill the mission objective. The fault detection and isolation (FDI) system increases the level of autonomy and can ensure the safety of the spacecraft by detecting and isolating potential faults before they become critical. The proposed FDI system is based on an innovative bank of SMOs (sliding mode observers), designed for different fault scenarios cases. The FDI system design aims to detect and isolate actuators and measurement units’ faults used by the satellite control system and considers the nonlinear model of the satellite dynamics. This approach gives the possibility of fault reconstruction based on the information provided by an equivalent injection signal, allowing to reconstruct external perturbances and faults. The SMO chattering phenomenon is avoided by using the pseudo-sliding function, being a linear approximation of the signum function, which gives the possibility of using the equivalent injection signal for fault reconstruction purposes. The proposed fault reconstruction methodology is illustrated by a case study for a 6U Cubesat.

Fault detection and isolation of gas turbine: Hierarchical classification and confidence rate computation

In this paper, a Fault Detection and Isolation (FDI) system based on an ensemble-based hierarchical classifier is devised to detect and isolate twelve typical turbine faulty scenarios and a healthy scenario. The hierarchical classifier is designed based on eight studied classifiers' best-practice confusion patterns, ranging from classical to state-of-the-art methods. The proposed hierarchical classifier discriminates five faulty classes with almost perfect accuracy while increasing the accuracy of healthy scenario discrimination by 10%, which is particularly important since it significantly reduces the false alarm rate for any form of fault. The overall accuracy of classification is improved by 2%, as well. Along with the classifier, an appropriate notion of confidence rate helps evaluate the classifier's decisions, especially when unlabeled data are involved. This motivation leads to devising a confidence rate concept that suits the devised classifier well. The obtained results show that the introduced confidence rate changes monotonically with classification accuracy. In the case of the high confidence rate of unlabeled data, this property is utilized to label the data with high accuracy. The proposed confidence rate is particularly effective in decision-making in the labeling of challenging mixed-up scenarios. Obtained results show that the classifier's decisions are reliable, with a confidence rate greater than 0.7. The developed FDI system is examined with simulated and real data, where obtained results verify the satisfactory performance of the system.

An Acoustic Fault Detection and Isolation System for Multirotor UAV

With the rising popularity of unmanned aerial vehicles (UAVs) and increasing variety of their applications, the task of providing reliable and robust control systems becomes significant. An active fault-tolerant control (FTC) scheme requires an effective fault detection and isolation (FDI) algorithm to provide information about the fault’s occurrence and its location. This work aims to present a prototype of a diagnostic system intended to recognize and identify broken blades of rotary wing UAVs. The solution is based on an analysis of acoustic emission recorded with an onboard microphone array paired with a lightweight yet powerful single-board computer. The standalone hardware of the FDI system was utilized to collect a wide and publicly available dataset of recordings in real-world experiments. The detection algorithm itself is a data-driven approach that makes use of an artificial neural network to classify characteristic features of acoustic signals. Fault signature is based on Mel Frequency Spectrum Coefficients. Furthermore, in the paper an extensive evaluation of the model’s parameters was performed. As a result, a highly accurate fault classifier was developed. The best models allow not only a detection of fault occurrence, but thanks to multichannel data provided with a microphone array, the location of the impaired rotor is reported, as well.

Fault Detection and Isolation for UAVs using Neural Ordinary Differential Equations

In recent years, the increasing complexity and diversity of data-based fault detection and isolation (FDI) methods usually require high computational efforts in the pre-processing stage, large amounts of data, and, most of the time, some feature extraction to obtain relevant information for the data-based algorithms. This paper proposes using the Neural Ordinary Differential Equations (NODE) framework to represent the dynamics of the studied plant and later employ such representation in FDI system design. Such an approach enables loss optimization to be performed jointly in the plant dynamics and external inputs without previous use of complex pre-processing and is useful for working with nonlinear systems. The approach is first validated using a simulated Unmanned Aerial Vehicle (UAV) and later applied to a data-set that contains actuators and sensors faults. Ultimately, the proposed approach is compared with other usual machine learning techniques, showing better performance metrics.

Hypergraph Model for Wireless Sensor Networks Supervision Design

Security is an important condition to ensure the proper functioning of wireless sensor networks (WSNs). The prevention-based security approaches as cryptographic mechanisms must be enriched by a reactive schemes such as intrusion detection systems (IDSs). But, for safety and reliability reasons, moreover to IDSs, WSN supervision mechanisms are also required. Two activities are concerned: fault detection and isolation (FDI) and fault-tolerant control (FTC). All FDI systems consist of comparing the real behaviour of system with the nominal behaviour given by a model: the difference leads to a fault indicator named residual. Graphical modelling is an important step in FDI realisation. The innovative interest of this paper is the use of hypergraph formalism to model the interconnection between different Components WSN network (CNs). In our proposed approach, the hypergraph model is built from WSNs missions, such that each hyperedge is associated with a particular mission. Based on the structural properties of this model, an online WSN supervision strategy can be defined. Moreover, a fault signature matrix (FSM), as a logic decision, is used to provide the list of faults, which can be detected and isolated.

A Geometric Approach to Fault Detection and Isolation in a Grid-Connected Inverter

We present a nonlinear geometric approach to fault detection and isolation (FDI) in a grid-connected inverter system. Open-switch faults in inverter power transistors together with faults in grid voltages and source current sensors are detected and isolated in the presence of disturbance. The detection process is based on dynamic model of an inverter, including a dc-bus capacitor and an output <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$RL$ </tex-math></inline-formula> filter. The proposed FDI system does not rely on balanced or zero-sum conditions in the grid point and is functional under nonsinusoidal voltages and currents.

Fuzzy FDI System for Flyback Converters

In this article, the design of a fault detection and isolation (FDI) system for a flyback converter, based on a fuzzy unknown input observer, is presented. The proposed FDI system aims to detect the saturation in the inductor core as a fault, considering the current increment can cause an avalanche effect provoking a failure in the system. A Takagi–Sugeno model of the flyback converter is used. The observer's convergence and the stability of the fault diagnosis system are guaranteed by Lyapunov's theory and the estimation error used to determine the necessary LMIs. A comparison of different FDI methods applied in fuzzy systems is performed in order to select the most robust system, capable of estimating the current in the system even in the presence of external disturbances. The system validation is performed in both, simulation and online implementation, using experimental data.

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.

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 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.

Fault Diagnosis in Sensors of Boiler Following Control of a Thermal Power Plant

In this paper a system of fault detection and isolation (FDI) in sensors of boiler following control of a thermal power plant is presented. The approach is based on a polytopic quasi-Linear Parameter Variant (q-LPV) model derived from a simplified nonlinear model for the boiler-turbine unit representing the dynamic behavior of the pressure in the first stage of the turbine, drum pressure, superheated steam pressure and electrical power. The nonlinear model is parameterized with real data operation of a thermal power plant, in a wide range of loads (low, medium and high). For residual generation a Luenberger-like observer based on the polytopic q-LPV model is designed. The performance of the FDI system is evaluated adequately by using a simulator in Simulink based on the nonlinear model. The main contribution of this work is to demonstrate that our proposed approach is feasible to be applied in a real thermal power plant.

Implementation of a fault tolerant system for the internal combustion engine’s MAF sensor

In this work, we present the design of a fault detection and isolation (FDI) system for the mass air flow (MAF) sensor of an internal combustion (IC) engine based on adaptive observers, which in turn are based on a couple of ordinary differential equations which express the dynamics of the intake temperature and the pressure. Specifically, the observers were designed to estimate the MAF value that enters into the system in order to generate analytical redundancy, i.e. to have two signals of the same variable, one provided by the observers and the other one provided by the sensor. The FDI system allows the uninterrupted operation of the IC engine, after a total failure of the MAF sensor, by switching the faulty signal to the healthy value given by the observers. This healthy value is injected to the engine control unit (ECU). Experimental results show the effectiveness of the proposed system.

Bolster spring fault detection strategy for heavy haul wagons

ABSTRACTAn on-board health monitoring system is proposed for heavy haul wagons in this paper including a signal-based fault detection and isolation (FDI) method and an on-line fault diagnose strategy. Such a system, to be feasible on freight wagons, must be sufficiently cheap and robust, hence the design assumes the constraint of using only two accelerometers mounted on the front left and right rear part of each carbody in a heavy haul train. This paper focuses on the detection of bolster spring fault conditions. The problem is made more complex by the modes of failure which might be expected in bolster spring nests. Types of spring failure are firstly identified and discussed covering situations of broken (shortening springs) and softening (individual spring loss from a nest or cross-section loss through corrosion). The effects of these faults and their detectability were investigated using simulations on straight and curved track and using a fully detailed model of a typical 40 t axle-load heavy haul wagon. The simulation results were then examined and compared using cross-correlation analysis and an FDI system was proposed. The FDI system introduced five possible fault indicators. Initial results indicated that it was possible to detect changes in bolster stiffness of ±25%. An on-line fault diagnoses strategy is proposed for bolster spring faults which only requires data from wagon monitoring during travel around sharp curves to detect and the occurrence of confirm faults. The functionality envisaged needs only a ‘once per trip’ monitoring site, such as a sharper curve, and is aimed at condition monitoring rather than the provision of alarms or comprehensive monitoring of all events.

Two-Level Fault Detection and Isolation Algorithm for Vehicle Platoon

To deal with the fault of the vehicle platoon, we have established a fault detection and isolation (FDI) system with two-level fault diagnosis architecture. For simplicity, we divide the FDI architecture into two kinds: system failure and component element failure. To detect these faults, we set up the FDI mathematical model of the fleet based on the vehicular spacing, and the sensor FDI model of a certain vehicle. Meanwhile, we construct the state space model of the fleet, and design the residual generator using the space geometry method for system failure. To design the residual generation model of the fleet for component element failure, we strengthen the structure analysis of both the fleet and a certain vehicle. What’s more, to elucidate the factors that cause the change of vehicle distance, the virtual force analysis is introduced. Using the adaptive threshold method, it can enhance both the sensitivity of the FDI system to the residual and the robustness to the disturbance. To promote the vehicle itself and the fleet’s information perception ability, all vehicles (Autonomous Mobile Robots) are equipped with infrared distance measuring sensors, odometers, a pair of incremental optical encoders, and so on. The experimental results show that the proposed method is reliable and efficient for FDI of fleet.

Model-Based Fault Diagnosis System Verification Using Reachability Analysis

In model-based fault detection and isolation (FDI) systems, fault indicating signals (FISs) such as residuals and fault estimates are corrupted by various noises, uncertainties and variations. It becomes challenging to verify whether an FDI system still works or not in real life applications. It is also challenging to select a threshold so that false alarm rate and missed detection rate are kept low depending on real operation conditions. This paper proposes solutions to the aforementioned problems by quantitatively analyzing the effect of uncertainties on FIS. The problems are formulated into reachability analysis problem for uncertain systems. The reachable sets of FIS are calculated under normal and selected faulty cases, respectively. From these reachable sets, the effectiveness of an FDI system can be qualitatively verified under described uncertainties. A dedicated threshold can be further chosen to be robust to all possible described uncertainties. As a by-product, the minimum detectable fault can also be quantitatively determined by checking the intersection of the computed reachable sets. The proposed approach is demonstrated by evaluating an FDI algorithm of a motor in the presence of parameter uncertainties, unknown load, and sensor noises, where a fault estimation-based approach is adopted to diagnose amplifier, velocity, and current sensor faults.

Fault detection and isolation system for boiler-turbine unit of a thermal power plant

In this study, a sensor fault detection and isolation (FDI) system is presented for a boiler-turbine unit of a thermal power plant in Mexico. The FDI system is based on a Luenberger-like observer for residual generation. An adaptive threshold for residual evaluation is considered to avoid false alarms. The observer is based on a quasi-linear parameter variant (quasi-LPV) model of the boiler-turbine unit parameterized with real data of the plant in a wide range of operations, namely, at low, medium, and high loads. The quasi-LPV model adequately represents the dynamics of more critical variables including first stage turbine pressure, superheated steam pressure, drum pressure, and electric power. The performance of the FDI system is evaluated in a practical scenario by using real data from the thermoelectric plant. The main contribution of this study involves proposing a reliable fault diagnosis system to detect sensor faults in a wide operational range of the process based on the quasi-LPV framework.

Aircraft engine sensor fault diagnostics using an on-line OBEM update method.

This paper proposed a method to update the on-line health reference baseline of the On-Board Engine Model (OBEM) to maintain the effectiveness of an in-flight aircraft sensor Fault Detection and Isolation (FDI) system, in which a Hybrid Kalman Filter (HKF) was incorporated. Generated from a rapid in-flight engine degradation, a large health condition mismatch between the engine and the OBEM can corrupt the performance of the FDI. Therefore, it is necessary to update the OBEM online when a rapid degradation occurs, but the FDI system will lose estimation accuracy if the estimation and update are running simultaneously. To solve this problem, the health reference baseline for a nonlinear OBEM was updated using the proposed channel controller method. Simulations based on the turbojet engine Linear-Parameter Varying (LPV) model demonstrated the effectiveness of the proposed FDI system in the presence of substantial degradation, and the channel controller can ensure that the update process finishes without interference from a single sensor fault.

Actuator Fault Tolerant Control Based on a MIMO-MPC: Application in a Double-Pipe Heat Exchanger

In this work, an experimental fault tolerant control (FTC) implementation is presented. The FTC is based on a multi-input multi-output (MIMO) model predictive control (MPC). The aim of the FTC is to keep on operating a double-pipe counter-current heat exchanger even if the main actuator of the heat exchanger is stuck open. To develop the FTC, an adaptive observer was implemented in order to design a fault detection and isolation (FDI) system. In the FDI system, the cold and hot water flow rate estimations by the adaptive observer are compared to the control signals provided by the MPC. The results of the implementation of the FTC using a MIMO model predictive control were compared to the results obtained in a previous work which was developed using model-following control.