Showing papers on "Fault detection and isolation published in 2008"

Fault Detection in Induction Machines Using Power Spectral Density in Wavelet Decomposition

Abstract: Motor-current-signature analysis has been successfully used in induction machines for fault diagnosis. The method, however, does not always achieve good results when the speed or the load torque is not constant, because this causes variations on the motor-slip and fast Fourier transform problems appear due to a nonstationary signal. This paper proposes a new method for motor fault detection, which analyzes the spectrogram based on a short-time Fourier transform and a further combination of wavelet and power-spectral-density (PSD) techniques, which consume a smaller amount of processing power. The proposed algorithms have been applied to detect broken rotor bars as well as shorted turns. Besides, a merit factor based on PSD is introduced as a novel approach for condition monitoring, and a further implementation of the algorithm is proposed. Theoretical development and experimental results are provided to support the research.

Models for Bearing Damage Detection in Induction Motors Using Stator Current Monitoring

Abstract: This paper describes a new analytical model for the influence of rolling-element bearing faults on induction motor stator current. Bearing problems are one major cause for drive failures. Their detection is possible by vibration monitoring of characteristic bearing frequencies. As it is possible to detect other machine faults by monitoring the stator current, a great interest exists in applying the same method for bearing fault detection. After a presentation of the existing fault model, a new detailed approach is proposed. It is based on the following two effects of a bearing fault: 1. the introduction of a particular radial rotor movement and 2. load torque variations caused by the bearing fault. The theoretical study results in new expressions for the stator current frequency content. Experimental tests with artificial and realistic bearing damage were conducted by measuring vibration, torque, and stator current. The obtained results by spectral analysis of the measured quantities validate the proposed theoretical approach.

A Fuzzy-Based Approach for the Diagnosis of Fault Modes in a Voltage-Fed PWM Inverter Induction Motor Drive

Abstract: This paper investigates the use of fuzzy logic for fault detection and diagnosis in a pulsewidth modulation voltage source inverter (PWM-VSI) induction motor drive. The proposed fuzzy technique requires the measurement of the output inverter currents to detect intermittent loss of firing pulses in the inverter power switches. For diagnosis purposes, a localization domain made with seven patterns is built with the stator Concordia current vector. One is dedicated to the healthy domain and the six others to each inverter power switch. The fuzzy bases of the proposed technique are extracted from the current analysis of the fault modes in the PWM-VSI. Experimental results on a 1.5-kW induction motor drive are presented to demonstrate the effectiveness of the proposed fuzzy approach.

A General Approach for the Transient Detection of Slip-Dependent Fault Components Based on the Discrete Wavelet Transform

Abstract: In this paper, a general methodology based on the application of discrete wavelet transform (DWT) to the diagnosis of the cage motor condition using transient stator currents is exposed. The approach is based on the identification of characteristic patterns introduced by fault components in the wavelet signals obtained from the DWT of transient stator currents. These patterns enable a reliable detection of the corresponding fault as well as a clear interpretation of the physical phenomenon taking place in the machine. The proposed approach is applied to the detection of rotor asymmetries in two alternative ways, i.e., by using the startup current and by using the current during plugging stopping. Mixed eccentricities are also detected by means of the transient-based methodology. This paper shows how the evolution of other non-fault-related components such as the principal slot harmonic (PSH) can be extracted with the proposed technique. A compilation of experimental cases regarding the application of the methodology to the previous cases is presented. Guidelines for the easy application of the methodology by any user are also provided under a didactic perspective.

Diagnosis of Bearing Faults of Induction Machines by Vibration or Current Signals: A Critical Comparison

Abstract: Early diagnosis of faults in induction machines is an extensively investigated field, for cost and maintenance savings. Mechanical imbalances and bearing faults account for a large majority of faults in a machine, especially for small-medium size machines. Therefore their diagnosis is an intensively investigated field or research. Recently many research activities were focused on the diagnosis of bearing faults by current signal. Stator current components are generated at predictable frequencies related to the electrical supply and mechanical frequencies of bearing faults. However their detection is not always reliable, since the amplitude of fault signatures in the current signal is very low. This paper compares the bearing fault detection capability obtained with vibration and current signals. To this aim a testbed is realized that allows to test vibration and current signal on a machine with healthy or faulty bearings. Signal processing techniques for both cases are reviewed and compared in order to show which procedure is best suited to the different type of bearing faults. The paper contribution is the use of a simple and effective signal processing technique for both current and vibration signals, and a theoretical analysis of the physical link between faults and current components including torque ripple effects. As expected because of the different nature of vibration and current, bearing fault diagnosis is effective only for those fault whose mechanical frequency rate is quite low. Experiments are reported that confirm the proposed approach.

The Status of DC Micro-Grid Protection

Abstract: AC microgrids are a convenient approach to integrating distributed energy systems with utility power systems. On the other hand, DC micro-grids can lead to more efficient integration of distributed generation. They are the preferred topology for present shipboard, aircraft and automotive power systems and hold promise for future environmentally friendly office buildings, homes, rural areas and industrial power parks. However, standards, guidelines, practical experience and cost effective implementations for DC system protection are well behind practices in AC system protection. This paper presents a comprehensive overview of the body of research on protection of DC micro-grids, presented with a goal of identifying and advancing the field. The paper presents a discussion of the current status of dc micro-grid protection, including the use of electro-mechanical circuit breakers, solid state circuit breakers, protective system design, ground fault location and fault isolation.

FPGA-Based Real-Time Power Converter Failure Diagnosis for Wind Energy Conversion Systems

Abstract: This paper discusses the design, implementation, experimental validation, and performances of a field-programmable gate array (FPGA)-based real-time power converter failure diagnosis for three-leg fault tolerant converter topologies used in wind energy conversion systems (WECSs). The developed approach minimizes the time interval between the fault occurrence and its diagnosis. We demonstrated the possibility to detect a faulty switch in less than 10 mus by using a diagnosis simultaneously based on a ldquotime criterionrdquo and a ldquovoltage criterion.rdquo To attain such a short detection time, an FPGA fully digital implementation is used. The performances of the proposed FPGA-based fault detection method are evaluated for a new fault tolerant back-to-back converter topology suited for WECS with doubly fed induction generator (DFIG). We examine the failure diagnosis method and the response of the WECS when one of the power switches of the fault tolerant back-to-back converter is faulty. The experimental failure diagnosis implementation based on ldquoFPGA in the looprdquo hardware prototyping verifies the performances of the fault tolerant WECS with DFIG.

Model-based Process Supervision: A Bond Graph Approach

Abstract: Model-based fault detection and isolation requires a mathematical model of the system behaviour. Modelling is important and can be difficult because of the complexity of the monitored system and its control architecture. The authors use bond-graph modelling, a unified multi-energy domain modelling method, to build dynamic models of process engineering systems by composing hierarchically arranged sub-models of various commonly encountered process engineering devices. The structural and causal properties of bond-graph models are exploited for supervisory systems design. The structural properties of a system, necessary for process control, are elegantly derived from bond-graph models by following the simple algorithms presented here. Additionally, structural analysis of the model augmented with available instrumentation indicates directly whether it is possible to detect and/or isolate faults in some specific sub-space of the process. Such analysis aids in the design and resource optimization of new supervision platforms. Static and dynamic constraints, which link the time evolution of the known variables under normal operation, are evaluated in real time to determine faults in the system. Various decision or post-processing steps integral to the supervisory environment are discussed in this monograph; they are required to extract meaningful data from process state knowledge because of unavoidable process uncertainties. Process state knowledge has been further used to take active and passive fault accommodation measures. Several applications to academic and small-scale-industrial processes are interwoven throughout. Finally, an application concerning development of a supervision platform for an industrial plant is presented with experimental validation. Model-based Process Supervision provides control engineers and workers in industrial and academic research establishments interested in process engineering with a means to build up a practical and functional supervisory control environment and to use sophisticated models to get the best use out of their process data.

An Efficient Algorithm for Finding Minimal Overconstrained Subsystems for Model-Based Diagnosis

Abstract: In model-based diagnosis, diagnostic system construction is based on a model of the technical system to be diagnosed. To handle large differential algebraic imemodels and to achieve fault isolation, a common strategy is to pick out small overconstrained parts of the model and to test these separately against measured signals. In this paper, a new algorithm for computing all minimal overconstrained subsystems in a model is proposed. For complexity comparison, previous algorithms are recalled. It is shown that the time complexity under certain conditions is much better for the new algorithm. This is illustrated using a truck engine model.

On-Line Monitoring of Mechanical Faults in Variable-Speed Induction Motor Drives Using the Wigner Distribution

Abstract: This paper deals with the detection of mechanical load faults in induction motors during speed transients. The detection strategy is based on stator current analysis. Mechanical load faults generally lead to load torque oscillations at specific frequencies related to the mechanical rotor speed. The torque oscillations produce a characteristic sinusoidal phase modulation of the stator current. Speed transients result in time-varying supply frequencies that prevent the use of classical, Fourier transform-based spectral estimation. This paper proposes the use of a time-frequency distribution, the Wigner Distribution, for stator current analysis. Fault indicators are extracted from the distribution for on-line condition monitoring. The proposed methods are implemented on a low-cost digital signal processor. Experimental results in a steady-state and during transients with load torque oscillations and load imbalance are presented.

Sensor Placement for Fault Diagnosis

Abstract: An algorithm is developed for computing which sensors to add to meet a diagnosis requirement specification concerning fault detectability and fault isolability. The method is based only on the structural information in a model, which means that possibly large and nonlinear differential-algebraic models can be handled in an efficient manner. The approach is exemplified on a model of an industrial valve where the benefits and properties of the method are clearly shown.

Robust fault detection with missing measurements

Abstract: This paper investigates the problem of robust fault detection for uncertain systems with missing measurements. The parameter uncertainty is assumed to be of polytopic type, and the measurement missing phenomenon, which appears typically in a network environment, is modelled by a stochastic variable satisfying the Bernoulli random binary distribution. The focus is on the design of a robust fault detection filter, or a residual generation system, which is stochastically stable and satisfies a prescribed disturbance attenuation level. This problem is solved in the parameter-dependent framework, which is much less conservative than the quadratic approach. Both full-order and reduced-order designs are considered, and formulated via linear matrix inequality (LMI) based convex optimization problems, which can be efficiently solved via standard numerical software. A continuous-stirred tank reactor (CSTR) system is utilized to illustrate the design procedures.

Fault Detection and Fault-Tolerant Control of a Civil Aircraft Using a Sliding-Mode-Based Scheme

Abstract: This paper presents a sliding-mode approach for fault-tolerant control of a civil aircraft, where both actuator and sensor faults are considered. For actuator faults, a controller is designed around a state-feedback sliding-mode scheme where the gain of the nonlinear unit vector term is allowed to adaptively increase at the onset of a fault. Unexpected deviation of the switching variables from their nominal condition triggers the adaptation mechanism. The controller proposed here is relatively simple and yet is shown to work across the entire "up and away" flight envelope. For sensor faults, the application of a robust method for fault reconstruction using a sliding-mode observer is considered. The novelty lies in the application of the sensor fault reconstruction scheme to correct the corrupted measured signals before they are used by the controller, and therefore the controller does not need to be reconfigured.

An Effective Neural Approach for the Automatic Location of Stator Interturn Faults in Induction Motor

Abstract: This paper presents a neural approach to detect and locate automatically an interturn short-circuit fault in the stator windings of the induction machine. The fault detection and location are achieved by a feedforward multilayer-perceptron neural network (NN) trained by back propagation. The location process is based on monitoring the three-phase shifts between the line current and the phase voltage of the machine. The required data for training and testing the NN are experimentally generated from a three-phase induction motor with different interturn short-circuit faults. Simulation, as well as experimental, results are presented in this paper to demonstrate the effectiveness of the used method.

Combined Wavelet-SVM Technique for Fault Zone Detection in a Series Compensated Transmission Line

Abstract: This paper presents a combined wavelet-support vector machine (SVM) technique for fault zone identification in a series compensated transmission line. The proposed method uses the samples of three line currents for one cycle duration to accomplish this task. Initially, the features of the line currents are extracted by first level decomposition of the current samples using discrete wavelet transform (DWT). Subsequently, the extracted features are applied as inputs to a SVM for determining the fault zone (whether the fault is before or after the series capacitor, as observed from the relay point). The feasibility of the proposed algorithm has been tested on a 300-km, 400-kV series compensated transmission line for all the ten types of faults through detailed digital simulation using PSCAD/EMTDC. Upon testing on more than 25000 fault cases with varying fault resistance, fault inception angle, prefault power transfer level, percentage compensation level, and source impedances, the performance of the developed method has been found to be quite promising.

Method and system for providing customer controlled notifications in a managed network services system

Abstract: An approach for supporting automated fault isolation and recovery is provided. A notification configuration option is transmitted to a browser interface utilized by a user associated with a customer network that is monitored by a service provider, wherein the user selects the notification configuration option to input notification information. The notification information is received, via the browser interface, from the customer. A notification message is received from a platform configured to create a workflow event in response to an alarm indicative of a fault within the customer network, wherein isolation and recovery of the fault is performed according to the workflow event, the notification message including information about the customer network during the fault isolation and recovery process. The notification message is transmitted in accordance with the stored notification information.

Bearing Fault Detection Via Stator Current Noise Cancellation and Statistical Control

Abstract: This paper proposes a new approach to detect in situ bearing faults via stator current monitoring. For in situ bearing faults, the characteristic bearing fault frequencies may not exist, particularly at an early stage. In addition, the bearing fault signatures are usually subtle compared to the dominant components in the sampled stator current. Therefore, in this paper, a noise cancellation technique is used to suppress those dominant components that are not related to a potential bearing fault. The remaining components, i.e., the noise-cancelled stator current, are then closely related to the health condition of the bearing. Furthermore, it is observed that under the presence of a bearing fault, the noise-cancelled stator current displays a significant amount of degrees of uncontrolled variation in its magnitude. The uncontrolled variation is detected by observing the samples falling outside the three-sigma limits on Shewhart's control charts. Therefore, it is possible to detect in situ bearing faults by detecting the variation in magnitude of the noise-cancelled stator current, as verified by online experiments performed in this paper.

Artificial Neural Networks for the Modelling and Fault Diagnosis of Technical Processes

Abstract: Modelling Issue in Fault Diagnosis.- Locally Recurrent Neural Networks.- Approximation Abilities of Locally Recurrent Networks.- Stability and Stabilization of Locally Recurrent Networks.- Optimum Experimental Design for Locally Recurrent Networks.- Decision Making in Fault Detection.- Industrial Applications.- Concluding Remarks and Further Research Directions.

Fault Tolerant Circuit Topology and Control Method for Input-Series and Output-Parallel Modular DC-DC Converters

Abstract: This paper presents a modular, fault tolerant dc-dc converter topology that utilizes common duty ratio control to ensure equal sharing of input voltage and output current in input-series output-parallel configuration. The input-series connection allows the use of low voltage MOSFET's optimized for very low RDS,ON resulting in lower conduction losses. The common-duty-ratio scheme does not require a dedicated control loop for input voltage or output current sharing. The fault tolerant protection and control scheme accommodates failure of one or more modules, and ensures input voltage and load current sharing among the remaining healthy modules. The design of a new sensing scheme for detection of fault is presented. The analysis of the topology and the underlying principles are presented. The dependence of peak current from the source and in the protection switch in case of failure of a single converter has been analyzed and the various design tradeoff issues are discussed. The theoretical predictions are validated with simulation and experimental results. The proposed method is simple and gives good dynamic response to changes in input, load, and during fault. This topology is especially suited for space applications where a high level of fault tolerance can be achieved through designed redundancy.

Artificial neural network approach for fault detection in rotary system

Abstract: The detection and diagnosis of faults in technical systems are of great practical significance and paramount importance for the safe operation of the plant. An early detection of faults may help to avoid product deterioration, performance degradation, major damage to the machinery itself and damage to human health or even loss of lives. The centrifugal pumping rotary system is considered for this research. This paper presents the development of artificial neural network-based model for the fault detection of centrifugal pumping system. The fault detection model is developed by using two different artificial neural network approaches, namely feed forward network with back propagation algorithm and binary adaptive resonance network (ART1). The training and testing data required are developed for the neural network model that were generated at different operating conditions, including fault condition of the system by real-time simulation through experimental model. The performance of the developed back propagation and ART1 model were tested for a total of seven categories of faults in the centrifugal pumping system. The results are compared and the conclusions are presented.

High Frequency Resolution Techniques for Rotor Fault Detection of Induction Machines

Abstract: Motor current signature analysis (MCSA) is the reference method for the diagnosis of medium-large machines in industrial applications. However, MCSA is still an open research topic, as some signatures may be created by different phenomena, wherein it may become sensitive to load and inertia variations, and with respect to an oscillating load torque, although suitable data normalization can be applied. Recently, the topic of diagnostic techniques for drives and low to medium size machines is becoming attractive, as the procedure can be embedded in the drive at no additional thanks to a dedicated firmware, provided that a suitable computational cost is available. In this paper, statistical time-domain techniques are used to track grid frequency and machine slip. In this way, either a lower computational cost or a higher accuracy than traditional discrete Fourier transform techniques can be obtained. Then, the knowledge of both grid frequency and machine slip is used to tune the parameters of the zoom fast Fourier transform algorithm that either increases the frequency resolution, keeping constant the computational cost, or reduces the computational cost, keeping constant the frequency resolution. The proposed technique is validated for rotor faults.

Design of a steady-state detector for fault detection and diagnosis of a residential air conditioner

Abstract: This paper presents a general methodology for developing a steady-state detector for a vapor compression system based on a moving window and using standard deviations of seven measurements selected as features. The feature thresholds and optimized moving window size were based upon steady-state no-fault tests and startup transient tests. The study showed that evaporator superheat and condenser subcooling were sufficient for determining the onset of steady-state during the startup transient. However, they misidentified steady-state during indoor temperature change tests where evaporator saturation temperature and air temperature change across the evaporator were needed for proper steady-state identification. Hence, the paper recommends including all fault detection and diagnosis (FDD) features in the steady-state detector to ensure the robustness of the detector because different features may play key roles with different transients.

A New Fault Detection Scheme for Networked Control Systems Subject to Uncertain Time-Varying Delay

Abstract: Fault detection of networked control systems (NCS) subject to uncertain time-varying delay is studied in this paper. For the convenience of residual generator design, influence caused by network-induced delay is first transformed into time-varying polytopic uncertainty, which greatly facilitates further manipulation. Then design of the optimal residual generator is formulated as a model matching problem, i.e., to design a residual generator best matching the optimal residual generator of NCS in the delay free case. This procedure is assisted by parameter-dependent Lyapunov function matrix based bounded real lemma, which has been proved to be much better than single constant Lyapunov function matrix based results. This approach not only can be used in conditions that the variation part of the delay is less than one sampling period, but also can be applied to scenarios where the variation part of the delay is larger than one sampling period. Simulation results are also given to illustrate effectiveness of the proposed method.

Robust sliding mode observer-based actuator fault detection and isolation for a class of nonlinear systems

Abstract: In this article, an actuator fault detection and isolation scheme for a class of nonlinear systems with uncertainty is considered. The uncertainty is allowed to have a nonlinear bound which is a general function of the state variables. A sliding mode observer is first established based on a constrained Lyapunov equation. Then, the equivalent output error injection is employed to reconstruct the fault signal using the characteristics of the sliding mode observer and the structure of the uncertainty. The reconstructed signal can approximate the system fault signal to any accuracy even in the presence of a class of uncertainty. Finally, a simulation study on a nonlinear aircraft system is presented to show the effectiveness of the scheme.

A comparative study on classification of features by SVM and PSVM extracted using Morlet wavelet for fault diagnosis of spur bevel gear box

Abstract: The condition of an inaccessible gear in an operating machine can be monitored using the vibration signal of the machine measured at some convenient location and further processed to unravel the significance of these signals. This paper deals with the effectiveness of wavelet-based features for fault diagnosis using support vector machines (SVM) and proximal support vector machines (PSVM). The statistical feature vectors from Morlet wavelet coefficients are classified using J48 algorithm and the predominant features were fed as input for training and testing SVM and PSVM and their relative efficiency in classifying the faults in the bevel gear box was compared.