Sensor Fault Isolation Research Articles

A Self-Healing Induction Motor Drive With Model Free Sensor Tampering and Sensor Fault Detection, Isolation, and Compensation

This paper introduces new schemes for detection, isolation, and compensation of speed and current sensor (CS) faults in field oriented induction motor (IM) drives. These schemes do not use any kind of machine model and motor parameters. This is a prominent feature of the proposed approaches, which only rely on main concepts of field-oriented control (FOC)-based IM drives. Moreover, the proposed fault detection and isolation (FDI) schemes can take partial sensor failures into account. These kinds of faults that might be caused by mechanical or electrical failures, or intentional sensor tampering, are difficult to be dealt with since embedded speed and current control loops react swiftly, and inherently try to camouflage partial sensor failures. CS faults result in imbalance current flow in the motor stator phases, and speed sensor fault repudiates the orthogonally alignment of field and torque current components in field oriented IM drives. These features are cornerstones of introduced FDI methodology. Furthermore, model and parameter free compensators are proposed to recover normal operation of the machine before fault incident. After in-depth analysis, extensive simulation and experimental tests validate the proposed schemes.

Enhanced Sensor Monitoring to Maintain Optimal Aircraft Handling in Case of Faults

The design and validation of an advanced fault detection and diagnosis system to monitor the state-of-practice triplex redundant angle of attack measurement is presented. The proposed system fully solves the angle of attack sensor fault isolation problem. It thereby enhances the availability of the full control system functionalities in the case of multiple angle of attack sensor faults and helps to maintain optimal aircraft handling characteristics. The proposed fault detection and diagnosis system relies on advanced model-based and signal-based fault detection techniques. The used null-space-based method for the model-based fault detection filter design is extended to increase the filter robustness to model uncertainties. The developed approaches are applied and validated in realistic flight scenarios using a high-fidelity benchmark model of a large commercial transport aircraft. The reported results indicate that the use of the presented fault detection methods can help to maintain optimal aircraft handling in case of faults.

Distributed Clustering-based Sensor Fault Diagnosis for HVAC Systems

The paper presents a distributed Sensor Fault Diagnosis architecture for Industrial Wireless Sensor Networks monitoring HVAC systems, by exploiting a recently proposed distributed clustering method. The approach allows the detection and isolation of multiple sensor faults and considers the possible presence of modeling uncertainties and disturbances. Detectability and isolability conditions are provided. Simulation results show the effectiveness of the proposed method for an HVAC system.

Sensor Fault Detection and Isolation Based on Artificial Neural Networks and Fuzzy Logic Applicated on Induction Motor for Electrical Vehicle

<p>Recently, research has picked up a fervent pace in the area of fault diagnosis of electrical vehicle. Like failures of a position sensor, a voltage sensor, and current sensors. Three-phase induction motors are the “workhorses” of industry and are the most widely used electrical machines. This paper presents a scheme for Fault Detection and Isolation (FDI). The proposed approach is a sensor-based technique using the mains current measurement. Current sensors are widespread in power converters control and in electrical drives. Thus, to ensure continuous operation with reconfiguration control, a fast sensor fault detection and isolation is required. In this paper, a new and fast faulty current sensor detection and isolation is presented. It is derived from intelligent techniques. The main interest of field programmable gate array is the extremely fast computation capabilities. That allows a fast residual generation when a sensor fault occurs. Using of Xilinx System Generator in Matlab / Simulink allows the real-time simulation and implemented on a field programmable gate array chip without any VHSIC Hardware Description Language coding. The sensor fault detection and isolation algorithm was implemented targeting a Virtex5. Simulation results are given to demonstrate the efficiency of this FDI approach.</p>

Robust Fault Detection, Isolation, and Accommodation of Current Sensors in Grid Side Converters

The integration of modern renewable energy sources is enabled by the grid side converter (GSC) based on power electronic technology. The operation of the GSC is properly regulated by the GSC controller according to sensor measurements (i.e., of the grid voltage, of the line currents, and of the voltage at the dc-link). However, in case of current sensor faults, the operation of the GSC and of the entire renewable system can be critically affected and catastrophic failures may occur if the sensor fault is not accommodated on time. This paper proposes a model-based Fault Detection, Isolation, and Accommodation (FDIA) scheme, which enables the GSC to overcome faults that may occur on the associated current sensors. The sensor fault detection and isolation scheme has been designed based on analytical redundancy relations, while the accommodation of the faults is based on an adaptive estimation scheme. The proposed FDIA scheme has been applied on a modern GSC and the effectiveness of the scheme has been tested under several multiple current sensor faults and under several grid conditions. Furthermore, the FDIA scheme enables the GSC to operate properly (without causing failures or damages to its components) under current sensor faults and thus, the reliability of the GSC is enhanced.

Data driven sensor and actuator fault detection and isolation in wind turbine using classifier fusion

Abstract Renewable energy sources like wind energy are widely available without any limitation. Reliability of wind turbine is crucial in extracting the maximum amount of energy from the wind. Early fault detection, isolation and successful controller reconfiguration can considerably increase the performance in faulty conditions and prevent failures in the system. Along the same vein, fault diagnosis of wind turbine systems has received much attention in recent years. Fault detection methods based on time and frequency domain signal analysis without explicit mathematical model are state-of-the-art in complex processes. This paper investigates data-driven fault detection and isolation (FDI) design based on fusion of several classifiers for a wind turbine benchmark -second challenge. The proposed method is robust against different operational conditions and measurement errors. In fact, we develop a new data-driven FDI scheme, via analytical redundancy. Multi-Layer Perceptron (MLP), Radial Basis Function (RBF), Decision Tree, and K-Nearest Neighbor (KNN) classifiers are implemented in parallel, and fused together. Feature extraction from measurement signals enriches the information about wind turbine condition and improves decision making of proposed FDI scheme. Simulation results and Monte Carlo sensitivity analysis show the effectiveness of the proposed method.

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.

Enforcement of cost-effective energy conservation on single-fed asynchronous machine using a novel switching strategy

Electric motors consume around 30 to 80% of total energy in industries. Part load operation of which leads to lower efficiency. To improve efficiency and conserve energy under such partial load operating conditions, a novel switching strategy is proposed. Wherein, the stator and rotor windings are alternatively energized (switched) in accordance with the varying load. Adopting this technique with different ratings machine (2.2–150 kW) for linear and non-linear loads, exhibits energy saving of 5–15% and reactive power reduction in the range of 15–70%, over conventional method. Besides energy conservation, the proposed strategy involves less capital cost as well. Sensor fault detection & isolation, stability and overload protection are the additional features addressed in the controller. Techno economic analysis with existing contactor based and power electronic switching strategies are also carried out. In apart, the impact of changes in switchover point on energy savings and payback period is also analyzed.

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.

Improved sensor fault detection, isolation, and mitigation using multiple observers approach

ABSTRACTTraditional fault detection and isolation (FDI) methods analyze a residual signal to detect and isolate sensor faults. The residual signal is the difference between the sensor measurements and the estimated outputs of the system based on an observer. The traditional residual-based FDI methods, however, have some limitations. First, they require that the observer has reached its steady state. In addition, residual-based methods may not detect some sensor faults, such as faults on critical sensors that result in an unobservable system. Furthermore, the system may be in jeopardy if actions required for mitigating the impact of the faulty sensors are not taken before the faulty sensors are identified. The contribution of this paper is to propose three new methods to address these limitations. Faults that occur during the observers' transient state can be detected by analysing the convergence rate of the estimation error. Open-loop observers, which do not rely on sensor information, are used to detect faults on critical sensors. By switching among different observers, we can potentially mitigate the impact of the faulty sensor during the FDI process. These three methods are systematically integrated with a previously developed residual-based method to provide an improved FDI and mitigation capability framework. The overall approach is validated mathematically, and the effectiveness of the overall approach is demonstrated through simulation on a five-state suspension system.

Sensor fault detection and isolation over wireless sensor network based on hardware redundancy

In order to diagnose sensor faults with small magnitude in wireless sensor networks, distinguishability measures are defined to indicate the performance for fault detection and isolation (FDI) at each node. A systematic method is then proposed to determine the information to be exchanged between nodes to achieve FDI specifications while limiting the computation complexity and communication cost.

Sensor Fault Diagnosis for Aero Engine Based on Online Sequential Extreme Learning Machine with Memory Principle

The on-board sensor fault detection and isolation (FDI) system is essential to guarantee the reliability and safety of an aero engine. In this paper, a novel online sequential extreme learning machine with memory principle (MOS-ELM) is proposed for detecting, isolating, and reconstructing the fault sensor signal of aero engines. In many practical online applications, the sequentially coming data chunk usually possesses a characteristic of timeliness, and the overdue training data may mislead the subsequent learning process. The proposed MOS-ELM can improve the training process by introducing the concept of memory principle into the online sequential extreme learning machine (OS-ELM) to tackle the timeliness of the data chunk. Simulations on some time series problems and some benchmark databases show that MOS-ELM performs better in generalization performance, stability, and prediction accuracy than OS-ELM. The experiment results of the MOS-ELM-based sensor fault diagnosis system also verify the excellent generalization performance of MOS-ELM and indicate the effectiveness and feasibility of the developed diagnosis system.

Multiple incipient sensor faults diagnosis with application to high-speed railway traction devices

This paper deals with the problem of incipient fault diagnosis for a class of Lipschitz nonlinear systems with sensor biases and explores further results of total measurable fault information residual (ToMFIR). Firstly, state and output transformations are introduced to transform the original system into two subsystems. The first subsystem is subject to system disturbances and free from sensor faults, while the second subsystem contains sensor faults but without any system disturbances. Sensor faults in the second subsystem are then formed as actuator faults by using a pseudo-actuator based approach. Since the effects of system disturbances on the residual are completely decoupled, multiple incipient sensor faults can be detected by constructing ToMFIR, and the fault detectability condition is then derived for discriminating the detectable incipient sensor faults. Further, a sliding-mode observers (SMOs) based fault isolation scheme is designed to guarantee accurate isolation of multiple sensor faults. Finally, simulation results conducted on a CRH2 high-speed railway traction device are given to demonstrate the effectiveness of the proposed approach.

Sensors fault diagnosis and fault tolerant control for grid connected PV system

This paper discusses a Sensors Fault Detection and Isolation (FDI) and Fault Tolerant Control (FTC) of three phase inverter for PV system application. All sensors used in the inverter control strategy are addressed in this work. The developed FDI algorithm focuses on residuals generation; it is defined by the different between measurement and reconstituted signal. So, estimators block are investigated, such as, two Models References Adaptive Systems (MRAS) are developed to reconstitute the information of three phase line currents and dc link voltage sensors. The reconstituted information is injected to the corresponding FDI algorithm. Grid voltage sensors FDI is presented in this contribution trough the use of scaled virtual flux estimator, based on Second Order Generalized Integrator and Frequency Looked Loop. When a fault detected, FTC is ensured by replacing the erroneous measurement by its corresponding estimator. Theoretical study and simulations results are presented to validate the effectiveness of the proposed method.

Sensor Fault Detection, Isolation, and Estimation in Lithium-Ion Batteries

In battery management systems (BMSs), real-time diagnosis of sensor faults is critical for ensuring the safety and reliability of the battery. For example, a current sensor fault leads to erroneous estimates of state of charge and other parameters, which in turn affects the control actions in the BMS. A temperature sensor fault may lead to ineffective thermal management. In this brief, a model-based diagnostic scheme is presented that uses sliding mode observers designed based on the electrical and thermal dynamics of the battery. It is analytically shown how the extraction of the equivalent output error injection signals on the sliding manifolds enables the detection, the isolation, as well as the estimation of the temperature, voltage, and current sensor faults. This brief includes simulation and experimental studies to demonstrate and evaluate the effectiveness of the proposed scheme. Discussions are also included on the effects of uncertainty and on threshold design.

Quadrotor Sensor Fault Diagnosis with Experimental Results

This paper presents a fault detection, isolation, and estimation scheme for sensor bias faults in accelerometer and gyroscope measurements of quadrotor unmanned air vehicles (UAVs). Based on sliding-mode observer techniques, a robust estimation of the quadrotor roll and pitch angles is obtained by using only accelerometer measurements. Then, a diagnostic scheme is developed for detecting, isolating, and estimating sensor bias faults in the gyroscope and accelerometer measurements. Structured residuals are generated, allowing the detection and isolation of multiple simultaneous sensor faults under consideration. After the faults are detected and isolated, two nonlinear estimators are employed to provide an estimate of the unknown fault magnitude. The stability and estimation performance properties of the nonlinear estimators are established. The sensor fault diagnosis algorithm is implemented and evaluated through experimental results using a real-time indoor quadrotor test environment.

Tuning of controller for an aircraft flight control system based on particle swarm optimization

Purpose This study aims to present a method for the conceptual design and simulation of an aircraft flight control system. Design/methodology/approach The design methodology is based on particle swarm optimization (PSO). PSO can be used to improve the performance of conventional controllers. The aim of the present study is threefold. First, it attempts to detect and isolate faults in an aircraft model. Second, it is to design a proportional (P) controller, a proportional derivative (PD) controller, a proportional-integral (PI) controller and a fuzzy controller for an aircraft model. Third, it is to design a PD controller for an aircraft using a PSO algorithm. Findings Conventional controllers, an intelligent controller and a PD controller-based PSO were investigated for flight control. It was seen that the P controller, the PI controller and the PD controller-based PSO caused overshoot. These overshoots were 18.5, 87.7 and 2.6 per cent, respectively. Overshoot was not seen using the PD controller or fuzzy controller. Steady state errors were almost zero for all controllers. The PD controller had the best settling time. The fuzzy controller was second best. The PD controller-based PSO was the third best, but the result was close to the others. Originality/value This study shows the implementation of the present algorithm for a specified space mission and also for study regarding variation of performance parameters. This study shows fault detection and isolation procedures and also controller gain choice for a flight control system. A comparison between conventional controllers and PD-based PSO controllers is presented. In this study, sensor fault detection and isolation are carried out, and, also, root locus, time domain analysis and Routh–Hurwitz methods are used to find the conventional controller gains which differ from other studies. A fuzzy controller is created by the trial and error method. Integral of squared time multiplied by squared error is used as a performance function type in PSO.

Nonlinear sensor fault diagnosis using mixture of probabilistic PCA models

This paper presents a methodology for sensor fault diagnosis in nonlinear systems using a Mixture of Probabilistic Principal Component Analysis (MPPCA) models. This methodology separates the measurement space into several locally linear regions, each of which is associated with a Probabilistic PCA (PPCA) model. Using the transformation associated with each PPCA model, a parity relation scheme is used to construct a residual vector. Bayesian analysis of the residuals forms the basis for detection and isolation of sensor faults across the entire range of operation of the system. The resulting method is demonstrated in its application to sensor fault diagnosis of a fully instrumented HVAC system. The results show accurate detection of sensor faults under the assumption that a single sensor is faulty.

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.

Model-based sensor fault detection and isolation method for a vehicle dynamics control system

Conventional vehicle electronic stability control requires one steering-wheel angle sensor, one lateral acceleration sensor and one yaw rate sensor to obtain a good control performance. The control system stops working when a sensor fault is detected, which means that the vehicle runs in an unprotected state. Thus, various sensor fault diagnosis algorithms have been designed to detect and isolate the faulty sensor, but these algorithms also can be used for fault-tolerant control to preserve the safety of the vehicle. However, determining which of the different sensors is faulty is very difficult as the conventional residual comparison algorithm can only find the existence of a sensor fault but cannot locate the faulty sensor, and very few research studies have focused on this problem. In this paper, an ingenious sensor fault diagnosis algorithm is proposed. The sensor fault is detected, located and isolated by cross-checking with three different yaw rate estimates. The steering-wheel angle observer and the lateral acceleration observer are designed to provide corresponding estimated sensor signals which are employed to estimate the different yaw rates by using an extended Kalman filter. A novel decision-making process is carefully designed to locate the faulty sensor based on the different yaw rate residuals. Electronic stability control is not interrupted as the faulty sensor signal is reconfigured by the estimated signal. Experimental tests on a real car show that the proposed algorithm is efficient for detecting the sensor fault and identifying which sensor is faulty. Simulations show that the vehicle stability control strategy based on the proposed sensor fault-tolerant control algorithm has a better performance than the traditional control strategy does.