Fault Detection And Isolation System Research Articles

Sensor fault detection and isolation system for a condensation process

This article presents the design of a sensor Fault Detection and Isolation (FDI) system for a condensation process based on a nonlinear model. The condenser is modeled by dynamic and thermodynamic equations. For this work, the dynamic equations are described by three pairs of differential equations which represent the energy balance between the fluids. The thermodynamic equations consist in algebraic heat transfer equations and empirical equations, that allow for the estimation of heat transfer coefficients. The FDI system consists of a bank of two nonlinear high-gain observers, in order to detect, estimate and to isolate the fault in any of both outlet temperature sensors. The main contributions of this work were the experimental validation of the condenser nonlinear model and the FDI system.

Pipeline leakage detection and isolation: An integrated approach of statistical and wavelet feature extraction with multi-layer perceptron neural network (MLPNN)

Leakage diagnosis of hydrocarbon pipelines can prevent environmental and financial losses. This work proposes a novel method that not only detects the occurrence of a leakage fault, but also suggests its location and severity. The OLGA software is employed to provide the pipeline inlet pressure and outlet flow rates as the training data for the Fault Detection and Isolation (FDI) system. The FDI system is comprised of a Multi-Layer Perceptron Neural Network (MLPNN) classifier with various feature extraction methods including the statistical techniques, wavelet transform, and a fusion of both methods. Once different leakage scenarios are considered and the preprocessing methods are done, the proposed FDI system is applied to a 20-km pipeline in southern Iran (Goldkari-Binak pipeline) and a promising severity and location detectability (a correct classification rate of 92%) and a low False Alarm Rate (FAR) were achieved.

Sensors and actuator fault tolerant control applied in a double pipe heat exchanger

Abstract In this work, we present the design of a Fault-Tolerant Control (FTC) system, applied in the outlet temperature sensors and in the control valve (actuator) of a concentric double pipe counter-current flow heat exchanger. The FTC consists in a sensors Fault Detection and Isolation (FDI) system and an actuator FDI system. The sensors FDI system is based on analytical redundancy, in such a way that a bank of modified Kalman filters is developed in order to estimate the two outlet temperatures of the heat exchanger. To develop the modified Kalman filters a multi-linear models approach is used. So that, if a sensor fault is detected by the FDI system the measured temperature signal is replaced by the temperature estimation provided by the modified Kalman filter. Moreover, to detect an actuator fault a comparison between the control valve behaviors (the control valve voltage is used to estimate the water flow rate) and a predefined flow rate for each linear model is carried out. In order to keep the continuous operation of the heat exchanger even in fault presence a model-following control law is introduced, such that, when an actuator fault occurs, the FDI system detect the fault and immediately the model-following control makes the fault accommodation in order to compensate the actuator fault. The proposed scheme is presented with experimental data on-line. The successful tests are presented and discussed.

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.

Experimental validation of an actuator fault tolerant control system using virtual sensor: Application in a double pipe heat exchanger

This work presents an actuator fault tolerant control (FTC) system applied in a counter-current double pipe heat exchanger. The heat exchanger has two input variables which are, cold and hot water flow rates. The main actuator manipulates the cold water flow rate in order to control the hot water temperature. The goal of this work is to keep in continuous operation the counter-current double pipe heat exchanger, even if the main actuator is stuck-open. In case of failure on the main actuator, it is proposed to control the process with the second input variable (by manipulating the hot water flow rate). To achieve our aim, we designed an FTC system based in a fault detection and isolation (FDI) system and a Model Following Control (MFC). To develop the FDI system an adaptive nonlinear observer to estimate the flow rates in the heat exchanger for both streams (hot and cold) was implemented. The FDI is performed by the comparison between the flow rate estimation (by the adaptive nonlinear observer) and the flow rate provided by the control law. The nonlinear flow rate estimation is needed because the real plant does not have digital flowmeters to measure the flow rates online. The FDI system allows to detect and isolate the fault actuator then, the control signal and the failure accommodation is realized by the MFC. The results showed that it is possible to keep the heat exchanger in continuous operation even if the main actuator is stuck-open.

Data driven fault detection and isolation: a wind turbine scenario

Uno de los mayores inconvenientes presentes en la generación de energía eólica son los altos costos de mantenimiento asociados a fallas mecánicas. Este problema se hace más evidente en las turbinas de viento de escala industrial, en donde incrementos en el tamaño y la capacidad nominal traen consigo problemas adicionales asociados a vibraciones estructurales y efectos aeroelásticos en las hojas. Debido al incremento en la capacidad de operación, es imprescindible detectar de manera eficiente fallas y degradaciones en el sistema, garantizando la integridad, su fiabilidad y reduciendo los costos de operación. Este artículo presenta un sistema para la detección y aislamiento de fallas (FDI), basado en técnicas "Guiadas por los datos " (Data driven, abreviado DD). La arquitectura propuesta es una estrategia de varios niveles en donde: (i) el primer nivel detecta la ocurrencia de una falla (detección), mientras que (ii) el segundo identifica su origen (aislamiento). Se estudiaron y compararon cuatro técnicas de clasificación para cada uno de los niveles (Máquinas de Vectores de Soporte, Redes Neuronales Artificiales, K vecinos cercanos y Mezcla de Gaussianas). La mejor estrategia en cada nivel fue seleccionada para construir el sistema FDI. El rendimiento del sistema propuesto se evalúa en un modelo de referencia de una turbina eólica de escala comercial.

Design of a Fault Detection and Isolation System for Intelligent Vehicle Navigation System

This paper deals with the design of a fault detection and isolation (FDI) system for an intelligent vehicle, a vehicle equipped with advanced driver assistance system (ADAS). The ADASs are outfitted with sensors for acquiring various information about the vehicle and its surroundings. Since these sensors are sensitive to faults, an efficient FDI system should be developed. The designed FDI system is comprised of three parts: a detection part, a decision part, and a fault management part. The detection part applies a generalized observer scheme (GOS). In the GOS, there is bank of extended Kalman filters (EKFs), each excited by all except one sensor measurement. The residual generated from the measurement update of each EKF is therefore sensitive to all sensor faults but one. This way, the fault sensitivity pattern of the residual makes it possible to detect a fault and locate the faulty sensor. The designed FDI system has been implemented and tested off-line with actual experiment data. Good results have been obtained with diagnosing individual sensor faults and outputting fault-free vehicle states.

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.

Application of Factorization and Gap Metric Techniques to Fault Detection and Isolation Part I: A factorization technique based FDI framework

The motivation and the objective of this work are to initiate a systematic application of the well-established 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 linear time-invariant systems with model uncertainties and multiplicative faults. We address FDI issues both in the open- and closed-loop configurations. The paper consists of two parts. In the first part, the coprime factorization technique aided system modelling and FDI schemes are addressed, which also build the basis for our work in the second part.

Simulation of Sensor Fault Diagnosis for Wind Turbine Generators DFIG and PMSM Using Kalman Filter

The fault detection and isolation of generators used in wind turbines gathering interest as to maximize the reliability and avail of distributed energy systems with recent unmatched growth in construction of offshore wind farms. In particular it is interested in performing fault detection and isolation (FDI) of incipient faults affecting the measurements of the three-phase signals (currents) in a controlled DFIG and PMSG. Although different authors have dealt with FDI for sensors in induction machines and in DFIGs, most of them rely on the machine model with constant parameters. However, the parameter uncertainties due to changes in the operating conditions will produce degradation in the performance of such FDI systems. The robust techniques to detect faults are exist but there is a need of extra sensor. This paper proposed a systematic methodology for the design of sensor FDI systems with the following characteristics: i) capable of detecting and isolating incipient additive (bias) faults, ii) robust against changes in the references/disturbances affecting the controlled DFIG and PMSG as well as modeling/parametric uncertainties, iii) residual generation system based on a multi-observer strategy to enhance the isolation process, The designed sensor FDI systems have been validated using measured voltages, as well as simulated data from a controlled DFIG. First the state space models of DFIG and PMSM explained followed by kalman filter introduction and current sensor fault detection using a bank of kalman filter named dedicated Observer Scheme and generalized Observer scheme to detect simultaneous and multiple faults was theorized and simulated using MATLAB simulation tool .The simulation results were summarized with and without Sensor fault.

Combined Signal and Model-Based Sensor Fault Diagnosis for a Doubly Fed Induction Generator

The problem of multiplicative and/or additive fault detection and isolation (FDI) in the current sensors of a doubly fed induction generator (DFIG) is considered in the presence of model uncertainty. A residual generator based on the DFIG model is proposed using the structure of the classical generalized observer scheme. However, each observer in this scheme is replaced by a robust H_/H∞ fault detection filter followed by a Kalman-like observer. The latter further attenuates the effect of the modeling uncertainties on the residuals. It exploits the specific pattern induced by the balanced three-phase nature of all the electric signals. It turns out that the FDI problem then amounts to detecting an abrupt change in the mean of the residual vector in the additive fault case, or the appearance of sine waves superimposed on a white noise vector in the multiplicative fault case. A decision algorithm made of a combination of generalized likelihood ratio algorithms allows us to detect and isolate the additive and multiplicative sensor faults. The complete FDI system is tested through simulations on a controlled DFIG.

Fault detection and isolation of a dual spool gas turbine engine using dynamic neural networks and multiple model approach

In this paper, a fault detection and isolation (FDI) scheme for an aircraft jet engine is developed. The proposed FDI system is based on the multiple model approach and utilizes dynamic neural networks (DNNs) to accomplish this goal. Towards this end, multiple DNNs are constructed to learn the nonlinear dynamics of the aircraft jet engine. Each DNN corresponds to a specific operating mode of the healthy engine or the faulty condition of the jet engine. Using residuals obtained by comparing each network output with the measured jet engine output and by invoking a properly selected threshold for each network, reliable criteria are established for detecting and isolating faults in the jet engine components. The fault diagnosis task consists of determining the time as well as the location of a fault occurrence subject to presence of unmodeled dynamics, disturbances, and measurement noise. Simulation results presented demonstrate and illustrate the effectiveness of our proposed dynamic neural network-based FDI strategy.

Application of a Fault Detection and Isolation System on a Rotary Machine

The paper illustrates the design and the implementation of a Fault Detection and Isolation (FDI) system to a rotary machine like a multishaft centrifugal compressor. A model-free approach, that is, the Principal Component Analysis (PCA), has been employed to solve the fault detection issue. For the fault isolation purpose structured residuals have been adopted while an adaptive threshold has been designed in order to detect and to isolate the faults. To prove the goodness of the proposed FDI system, historical data of a nitrogen centrifugal compressor employed in a refinery plant are considered. Tests results show that detection and isolation of single as well as multiple faults are successfully achieved.

Robust fault detection and isolation in stochastic systems

This article outlines the formulation of a robust fault detection and isolation (FDI) scheme that can precisely detect and isolate simultaneous actuator and sensor faults for uncertain linear stochastic systems. The given robust fault detection scheme based on the discontinuous robust observer approach would be able to distinguish between model uncertainties and actuator failures and therefore eliminate the problem of false alarms. Since the proposed approach involves estimating sensor faults, it can also be used for sensor fault identification and the reconstruction of true outputs from faulty sensor outputs. Simulation results presented here validate the effectiveness of the proposed robust FDI system.

Sampling rate optimization for fault diagnosis of distributed networked control systems

In this paper, the problem of Fault Detection and Isolation (FDI) is addressed in a particular setting. Autonomous Control Systems (ACS) sharing a single communication channel (Wireless network for example) is considered. The objective is to optimize the sampling rate for the exchange of information between the ACS and the FDI system in order to improve the efficient of fault diagnosis algorithm. In this setting, reducing the number of times that the FDI system is activated implies an optimization of transmission rate in order to reduce energy consumption.

Data-Driven Design of Model-based Fault Diagnosis Systems

In this paper, recent development of data-driven design of fault detection and isolation (FDI) systems is presented. The major attention and focus are on the design schemes for observer-based FDI systems.

Sensor fault detection and isolation via high-gain observers: Application to a double-pipe heat exchanger

This paper deals with fault detection and isolation (FDI) in sensors applied to a concentric-pipe counter-flow heat exchanger. The proposed FDI is based on the analytical redundancy implementing nonlinear high-gain observers which are used to generate residuals when a sensor fault is presented (as software sensors). By evaluating the generated residual, it is possible to switch between the sensor and the observer when a failure is detected. Experiments in a heat exchanger pilot validate the effectiveness of the approach. The FDI technique is easy to implement allowing the industries to have an excellent alternative tool to keep their heat transfer process under supervision. The main contribution of this work is based on a dynamic model with heat transfer coefficients which depend on temperature and flow used to estimate the output temperatures of a heat exchanger. This model provides a satisfactory approximation of the states of the heat exchanger in order to allow its implementation in a FDI system used to perform supervision tasks.

Sensor fault detection and isolation in doubly-fed induction generators accounting for parameter variations

A fault detection and isolation (FDI) system for monitoring rotor current sensors in a doubly-fed induction generator (DFIG) for wind turbine applications is presented. The FDI system is designed so that the effect of parameter variations (resistances and inductances) is minimized. The residual generation is based on the generalized observer scheme (GOS) including parameter estimation. A decision system made of a combination of vector CUSUM (Cumulative sum) algorithms is used to process the residual vector and to achieve detection and isolation of incipient (small magnitude) faults. The approach is validated using signals obtained from a simulated vector-controlled DFIG.

Subspace method aided data-driven design of fault detection and isolation systems

This paper deals with data-driven design of fault detection and isolation (FDI) systems. The basic idea is to identify a primary form of residual generators, instead of the process model, directly from test data and, based on it, to design advanced FDI systems. The proposed method can be applied for the parity space and observer based detection and isolation of sensor and actuator faults as well as the construction of soft-sensors. The application of the proposed method is illustrated by a simulation study on the Tennessee Eastman process.

Design of an auto-associative neural network by using design of experiments approach

Advanced monitoring systems enable integration of data-driven algorithms for various tasks, for e.g., control, decision support, fault detection and isolation (FDI), etc. Due to improvement of monitoring systems, statistical or other computational methods can be implemented to real industrial systems. Algorithms which rely on process history data sets are promising for real-time operation especially for online process monitoring tasks, e.g., FDI. However, a reliable FDI system should be robust to uncertainties and small process deviations, thus, false alarms can be avoided. To achieve this, a good model for comparison between process and model is needed and for easier FDI implementation, the model has to be derived directly from process history data. In such cases, model-based FDI approaches are not very practical. In this paper a nonlinear statistical multivariate method (nonlinear principal component analysis) was used for modeling, and realized with auto-associative artificial neural network (AANN). A Taguchi design of experiments (DoE) technique was used and compared with a classic approach, where according to the analysis best AANN model structure was chosen for nonlinear model. Parameters that are important for neural network’s performance have been included into a joint orthogonal array to consider interactions between noise and control process variables. Results are compared to AANN design recommendations by other authors, where obtained nonlinear model was designed for reliable fault detection of very small faults under closed-loop conditions. By using Taguchi DoE robust design on AANN, an improved and reliable FDI scheme was achieved even in case of small faults introduced to the system. The accuracy and performance of AANN and FDI scheme were tested by experiments carried out on a real laboratory hydraulic system, to validate the proposed design for industrial cases.