Showing papers on "Fault detection and isolation published in 2013"

From Model, Signal to Knowledge: A Data-Driven Perspective of Fault Detection and Diagnosis

Abstract: This review paper is to give a full picture of fault detection and diagnosis (FDD) in complex systems from the perspective of data processing. As a matter of fact, an FDD system is a data-processing system on the basis of information redundancy, in which the data and human's understanding of the data are two fundamental elements. Human's understanding may be an explicit input-output model representing the relationship among the system's variables. It may also be represented as knowledge implicitly (e.g., the connection weights of a neural network). Therefore, FDD is done through some kind of modeling, signal processing, and intelligence computation. In this paper, a variety of FDD techniques are reviewed within the unified data-processing framework to give a full picture of FDD and achieve a new level of understanding. According to the types of data and how the data are processed, the FDD methods are classified into three categories: model-based online data-driven methods, signal-based methods, and knowledge-based history data-driven methods. An outlook to the possible evolution of FDD in industrial automation, including the hybrid FDD and the emerging networked FDD, are also presented to reveal the future development direction in this field.

Fault-Tolerant Control of Wind Turbines: A Benchmark Model

Abstract: This paper presents a test benchmark model for the evaluation of fault detection and accommodation schemes. This benchmark model deals with the wind turbine on a system level, and it includes sensor, actuator, and system faults, namely faults in the pitch system, the drive train, the generator, and the converter system. Since it is a system-level model, converter and pitch system models are simplified because these are controlled by internal controllers working at higher frequencies than the system model. The model represents a three-bladed pitch-controlled variable-speed wind turbine with a nominal power of 4.8 MW. The fault detection and isolation (FDI) problem was addressed by several teams, and five of the solutions are compared in the second part of this paper. This comparison relies on additional test data in which the faults occur in different operating conditions than in the test data used for the FDI design.

Fault Detection for Modular Multilevel Converters Based on Sliding Mode Observer

Abstract: This letter presents a fault detection method for modular multilevel converters which is capable of locating a faulty semiconductor switching device in the circuit. The proposed fault detection method is based on a sliding mode observer (SMO) and a switching model of a half-bridge, the approach taken is to conjecture the location of fault, modify the SMO accordingly and then compare the observed and measured states to verify, or otherwise, the assumption. This technique requires no additional measurement elements and can easily be implemented in a DSP or microcontroller. The operation and robustness of the fault detection technique are confirmed by simulation results for the fault condition of a semiconductor switching device appearing as an open circuit.

Time Synchronization Attack in Smart Grid: Impact and Analysis

Abstract: Many operations in power grids, such as fault detection and event location estimation, depend on precise timing information. In this paper, a novel Time Synchronization Attack (TSA) is proposed to attack the timing information in smart grid. Since many applications in smart grid utilize synchronous measurements and most of the measurement devices are equipped with global positioning system (GPS) for precise timing, it is highly probable to attack the measurement system by spoofing the GPS. The effectiveness of TSA is demonstrated for three applications of phasor measurement unit (PMU) in smart grid, namely transmission line fault detection, voltage stability monitoring and event locationing. The validity of TSA is demonstrated by numerical simulations.

A Sensor Fault Detection and Isolation Method in Interior Permanent-Magnet Synchronous Motor Drives Based on an Extended Kalman Filter

Abstract: Interior permanent-magnet synchronous motors (IPMSMs) become attractive candidates in modern hybrid electric vehicles and industrial applications. Usually, to obtain good control performance, the electric drives of this kind of motor require one position, one dc link, and at least two current sensors. Failure of any of these sensors might lead to degraded system performance or even instability. As such, sensor fault resilient control becomes a very important issue in modern drive systems. This paper proposes a novel sensor fault detection and isolation algorithm based on an extended Kalman filter. It is robust to system random noise and efficient in real-time implementation. Moreover, the proposed algorithm is compact and can detect and isolate all the sensor faults for IPMSM drives. Thorough theoretical analysis is provided, and the effectiveness of the proposed approach is proven by extensive experimental results.

Fault Detection and Isolation in Low-Voltage DC-Bus Microgrid System

Abstract: A fault detection and isolation scheme for low-voltage dc-bus microgrid systems is presented in this paper. Unlike traditional ac distribution systems, protection has been challenging for dc systems. The goals of the proposed scheme are to detect the fault in the bus between devices and to isolate the faulted section so that the system keeps operating without disabling the entire system. To achieve these goals, a loop-type dc-bus-based microgrid system, which has a segment controller between connected components, is proposed. The segment controller consists of master and slave controllers that monitor currents and control the segment separation, which include solid-state bidirectional switches and snubber circuits. The proposed system can detect faults on the bus regardless of fault current amplitude or the power supply's feeding capacity. The proposed concepts have been verified by OrCAD/PSpice simulations and experiments on hardware test bed.

A Sensor Fault Detection and Isolation Method in Interior Permanent-Magnet Synchronous Motor Drives Based on an Extended Kalman Filter

Abstract: Interior permanent-magnet synchronous motors (IPMSMs) become attractive candidates in modern hybrid electric vehicles and industrial applications. Usually, to obtain good control performance, the electric drives of this kind of motor require one position, one dc link, and at least two current sensors. Failure of any of these sensors might lead to degraded system performance or even instability. As such, sensor fault resilient control becomes a very important issue in modern drive systems. This paper proposes a novel sensor fault detection and isolation algorithm based on an extended Kalman filter. It is robust to system random noise and efficient in real-time implementation. Moreover, the proposed algorithm is compact and can detect and isolate all the sensor faults for IPMSM drives. Thorough theoretical analysis is provided, and the effectiveness of the proposed approach is proven by extensive experimental results.

Detection and Diagnosis of Faults in Induction Motor Using an Improved Artificial Ant Clustering Technique

Abstract: The presence of electrical and mechanical faults in the induction motors (IMs) can be detected by analysis of the stator current spectrum. However, when an IM is fed by a frequency converter, the spectral analysis of stator current signal becomes difficult. For this reason, the monitoring must depend on multiple signatures in order to reduce the effect of harmonic disturbance on the motor-phase current. The aim of this paper is the description of a new approach for fault detection and diagnosis of IMs using signal-based method. It is based on signal processing and an unsupervised classification technique called the artificial ant clustering. The proposed approach is tested on a squirrel-cage IM of 5.5 kW in order to detect broken rotor bars and bearing failure at different load levels. The experimental results prove the efficiency of our approach compared with supervised classification methods in condition monitoring of electrical machines.

Quality-relevant and process-relevant fault monitoring with concurrent projection to latent structures

Abstract: This paper proposes a new concurrent projection to latent structures is proposed in this paper for the monitoring of output-relevant faults that affect the quality and input-relevant process faults. The input and output data spaces are concurrently projected to five subspaces, a joint input-output subspace that captures covariations between input and output, an output-principal subspace, an output-residual subspace, an input-principal subspace, and an input-residual subspace. Fault detection indices are developed based on these subspaces for various fault detection alarms. The proposed monitoring method offers complete monitoring of faults that happen in the predictable output subspace and the unpredictable output residual subspace, as well as faults that affect the input spaces only. Numerical simulation examples and the Tennessee Eastman challenge problem are used to illustrate the effectiveness of the proposed method. © 2012 American Institute of Chemical Engineers AIChE J, 59: 496–504, 2013

An MRAS-Based Diagnosis of Open-Circuit Fault in PWM Voltage-Source Inverters for PM Synchronous Motor Drive Systems

Abstract: In this paper, a simple and low-cost open-circuit fault detection and identification method for a pulse-width modulated (PWM) voltage-source inverter (VSI) employing a permanent magnet synchronous motor is proposed. An open-circuit fault of a power switch in the PWM VSI changes the corresponding terminal voltage and introduces the voltage distortions to each phase voltage. The proposed open-circuit fault diagnosis method employs the model reference adaptive system techniques and requires no additional sensors or electrical devices to detect the fault condition and identify the faulty switch. The proposed method has the features of fast diagnosis time, simple structure, and being easily inserted to the existing control algorithms as a subroutine without major modifications. The simulations and experiments are carried out and the results show the effectiveness of the proposed method.

A New Approach to Fault Diagnostics for Permanent Magnet Synchronous Machines Using Electromagnetic Signature Analysis

Abstract: This paper proposes a novel approach to health monitoring and multifault detection in permanent magnet synchronous machines using direct flux measurement with search coils. Unlike other spectrum-based fault detection schemes, only the fundamental frequency component of the measured voltage is utilized for fault detection. Therefore, the performance of the proposed scheme is not affected by nonstationary speed or harmonics introduced by the power supply. In addition, location of interturn short circuits and direction of static eccentricity can be detected, which have never been done by any other scheme. In spite of the invasive nature of the technique, it is very suitable for mission-critical applications and emerging applications such as off-shore wind turbines, hybrid vehicle technology, and military applications, where early detection of faults is of paramount importance. 2-D simulations using finite element analysis have been presented to validate the proposed method under different operating conditions. Experimental introduction of stator interturn short circuit, demagnetization, and static eccentricity has been discussed, and the proposed scheme is experimentally implemented to examine its effectiveness.

Development of advanced FDD and FTC techniques with application to an unmanned quadrotor helicopter testbed

Abstract: As the first part, this paper presents an overview on the existing works on fault detection and diagnosis (FDD) and fault-tolerant control (FTC) for unmanned rotorcraft systems. Considered faults include actuator and sensor faults for single and multi-rotor systems. As the second part, several FDD and FTC techniques developed recently at the Networked Autonomous Vehicles Lab of Concordia University are detailed along with experimental application to a unique and newly developed quadrotor helicopter testbed.

Design and Application of Surface Wave Sensors for Nonintrusive Power Line Fault Detection

Abstract: This paper describes a novel conformal surface wave (CSW) launcher that can excite electromagnetic surface waves along unshielded power line cables nonintrusively This CSW launcher can detect open circuit faults on power cables Unlike conventional horn-type launchers, this CSW launcher is small, lightweight, and cost effective, and can be placed easily on a power cable For a nonintrusive open fault detection, the error is <; 5% when the cable length is <; 10 m, which is comparable with other direct-connect fault-finding techniques For a cable length of 1514 m, 76% error is noted Besides cable fault detection, the potential applications of the proposed launcher include broadband power line communication and high-frequency power transmission

Achieving scalable model-based testing through test case diversity

Abstract: The increase in size and complexity of modern software systems requires scalable, systematic, and automated testing approaches. Model-based testing (MBT), as a systematic and automated test case generation technique, is being successfully applied to verify industrial-scale systems and is supported by commercial tools. However, scalability is still an open issue for large systems, as in practice there are limits to the amount of testing that can be performed in industrial contexts. Even with standard coverage criteria, the resulting test suites generated by MBT techniques can be very large and expensive to execute, especially for system level testing on real deployment platforms and network facilities. Therefore, a scalable MBT technique should be flexible regarding the size of the generated test suites and should be easily accommodated to fit resource and time constraints. Our approach is to select a subset of the generated test suite in such a way that it can be realistically executed and analyzed within the time and resource constraints, while preserving the fault revealing power of the original test suite to a maximum extent. In this article, to address this problem, we introduce a family of similarity-based test case selection techniques for test suites generated from state machines. We evaluate 320 different similarity-based selection techniques and then compare the effectiveness of the best similarity-based selection technique with other common selection techniques in the literature. The results based on two industrial case studies, in the domain of embedded systems, show significant benefits and a large improvement in performance when using a similarity-based approach. We complement these analyses with further studies on the scalability of the technique and the effects of failure rate on its effectiveness. We also propose a method to identify optimal tradeoffs between the number of test cases to run and fault detection.

Open- and Short-Circuit Switch Fault Diagnosis for Nonisolated DC–DC Converters Using Field Programmable Gate Array

Abstract: Fault detection (FD) in power electronic converters is necessary in embedded and safety critical applications to prevent further damage. Fast FD is a mandatory step in order to make a suitable response to a fault in one of the semiconductor devices. The aim of this study is to present a fast yet robust method for fault diagnosis in nonisolated dc–dc converters. FD is based on time and current criteria which observe the slope of the inductor current over the time. It is realized by using a hybrid structure via coordinated operation of two FD subsystems that work in parallel. No additional sensors, which increase system cost and reduce reliability, are required for this detection method. For validation, computer simulations are first carried out. The proposed detection scheme is validated on a boost converter. Effects of input disturbances and the closed-loop control are also considered. In the experimental setup, a field programmable gate array digital target is used for the implementation of the proposed method, to perform a very fast switch FD. Results show that, with the presented method, FD is robust and can be done in a few microseconds.

Fuzzy Logic System-Based Adaptive Fault-Tolerant Control for Near-Space Vehicle Attitude Dynamics With Actuator Faults

Abstract: This paper addresses the problem of fault-tolerant control (FTC) for near-space vehicle (NSV) attitude dynamics with actuator faults, which is described by a Takagi-Sugeno (T-S) fuzzy model. First, a general actuator fault model that integrated varying bias and gain faults, which are assumed to be dependent on the system state, is proposed. Then, sliding mode observers (SMOs) are designed to provide a bank of residuals for fault detection and isolation. Based on Lyapunov stability theory, a novel fault diagnostic algorithm is proposed, which removes the classical assumption that the time derivative of the output error should be known. Further, for the two cases where the state is available or not, two accommodation schemes are proposed to compensate for the effect of the faults. These schemes do not need the condition that the bounds of the time derivative of the faults should be known. In addition, a sufficient condition for the existence of SMOs is derived according to Lyapunov stability theory. Finally, simulation results of NSV are presented to demonstrate the efficiency of the proposed FTC approach.

State Observer-Based Sensor Fault Detection and Isolation, and Fault Tolerant Control of a Single-Phase PWM Rectifier for Electric Railway Traction

Abstract: This paper presents an easy and a robust sensor fault detection and isolation (FDI) and fault tolerant control (FTC) of a single phase PWM rectifier for electrical railway traction application. Catenary current sensor and dc link voltage sensor failures are considered. The FDI method is based on observers and residual generation. The different FDI algorithm steps allow a good detection and isolation of the sensor fault and identify the faulty sensor. The reconfiguration strategy consists of two steps with open loop control and closed loop control working. Simulation results are presented to illustrate the good performance of the FTC procedure. Experimental results are also presented to show the effectiveness of the proposed FDI and FTC algorithms and good performances of the rectifier after the reconfiguration.

High-Resolution Parameter Estimation Method to Identify Broken Rotor Bar Faults in Induction Motors

Abstract: The classical multiple signal classification (MUSIC) method has been widely used in induction machine fault detection and diagnosis. This method can extract meaningful frequencies but cannot give accurate amplitude information of fault harmonics. In this paper, we propose a new frequency analysis of stator current to estimate fault-sensitive frequencies and their amplitudes for broken rotor bars (BRBs). The proposed method employs a frequency estimator, an amplitude estimator, and a fault decision module. The frequency estimator is implemented by a zoom technique and a high-resolution analysis technique known as the estimation of signal parameters via rotational invariance techniques, which can extract frequencies accurately. For the amplitude estimator, a least squares estimator is derived to obtain amplitudes of fault harmonics, without frequency leakage. In the fault decision module, the fault diagnosis index from the amplitude estimator is used depending on the load conditions of the induction motors. The fault index and corresponding threshold are optimized by using the false alarm and detection probabilities. Experimental results obtained from induction motors show that the proposed diagnosis algorithm is capable of detecting BRB faults with an accuracy that is superior to the zoom-based MUSIC algorithm.

Outlier detection rules for fault detection in solar photovoltaic arrays

Abstract: Solar photovoltaic (PV) arrays are unique power sources that may have uncleared fault current when utilizing conventional overcurrent protection devices. To monitor the PV operation and detect these unnoticed faults, outlier detection rules have been proposed for fault detection based on instantaneous PV string current. This paper discusses three rules in detail: 3-Sigma rule, Hampel identifier, and Boxplot rule. Unlike other methods, the proposed methods do not require weather measurement or efforts in model training. Our experimental results show that Hampel identifier and Boxplot rule may be recommended for PV fault detection. Furthermore, the proposed models become more reliable as the number of PV measurements increases. The developed methods may be integrated with PV monitoring system for real-time operation.

Least-Squares Fault Detection and Diagnosis for Networked Sensing Systems Using A Direct State Estimation Approach

Abstract: In this paper, the problems of fault detection, isolation, and estimation are considered for a class of discrete time-varying networked sensing systems with incomplete measurements. A unified measurement model is utilized to simultaneously characterize both the phenomena of multiple communication delays and data missing. A least-squares filter that minimizes the estimation variance is first designed for the addressed time-varying networked sensing systems, and then a novel residual matching (RM) approach is developed to isolate and estimate the fault once it is detected. The RM strategy is implemented via a series of Kalman filters, where each filter is designed to estimate the augmented signal composed of the system state and a specific fault signal. The design scheme for each filter is proposed in a recursive way. The main idea for the fault detection and estimation is that the Kalman filter with least residual value is regarded as corresponding to the right fault signal, and its estimation is utilized to represent the actual occurred fault. The effectiveness of our proposed method is demonstrated via simulation experiments on a real Internet-based three-tank system.

Test Case Prioritization for Continuous Regression Testing: An Industrial Case Study

Abstract: Regression testing in continuous integration environment is bounded by tight time constraints. To satisfy time constraints and achieve testing goals, test cases must be efficiently ordered in execution. Prioritization techniques are commonly used to order test cases to reflect their importance according to one or more criteria. Reduced time to test or high fault detection rate are such important criteria. In this paper, we present a case study of a test prioritization approach ROCKET (Prioritization for Continuous Regression Testing) to improve the efficiency of continuous regression testing of industrial video conferencing software. ROCKET orders test cases based on historical failure data, test execution time and domain-specific heuristics. It uses a weighted function to compute test priority. The weights are higher if tests uncover regression faults in recent iterations of software testing and reduce time to detection of faults. The results of the study show that the test cases prioritized using ROCKET (1) provide faster fault detection, and (2) increase regression fault detection rate, revealing 30% more faults for 20% of the test suite executed, comparing to manually prioritized test cases.

Inter-Turn Fault Detection in PM Synchronous Machines by Physics-Based Back Electromotive Force Estimation

Abstract: In this paper, the inter-turn short circuit fault detection in permanent magnet synchronous machines (PMSM) using an open-loop physics-based back electromotive force (EMF) estimator is presented. The back EMF estimator is designed based upon a current mode tracking scheme. The thermal and saturation aspects of the machine are considered in the design of the estimator. The design procedure and stability criteria of the estimator are presented in detail. The fault detection is carried out based on the difference between the estimated back EMF and a reference back EMF. A 0.8 (kW) PMSM is studied experimentally as well as numerically under different inter-turn fault and operational contingencies. The numerical modeling is accomplished by a finite-element-based model coupled with the thermal network and polluted with inter-turn fault. The acceptable agreement between the simulated and experimental result validates the modeling process. The back EMF estimator fault detection system is led to discriminative inter-turn fault signatures in a fraction of second for wide speed range even in the presence of harmonic loads and dynamic eccentricities.