Showing papers on "Fault detection and isolation published in 2011"

A Survey of Condition Monitoring and Protection Methods for Medium-Voltage Induction Motors

Abstract: Medium-voltage (MV) induction motors are widely used in the industry and are essential to industrial processes. The breakdown of these MV motors not only leads to high repair expenses but also causes extraordinary financial losses due to unexpected downtime. To provide reliable condition monitoring and protection for MV motors, this paper presents a comprehensive survey of the existing condition monitoring and protection methods in the following five areas: thermal protection and temperature estimation, stator insulation monitoring and fault detection, bearing fault detection, broken rotor bar/end-ring detection, and air gap eccentricity detection. For each category, the related features of MV motors are discussed; the effectiveness of the existing methods are discussed in terms of their robustness, accuracy, and implementation complexity. Recommendations for the future research in these areas are also presented.

Recent Advances in Modeling and Online Detection of Stator Interturn Faults in Electrical Motors

Abstract: Online fault diagnosis plays a crucial role in providing the required fault tolerance to drive systems used in safety-critical applications. Short-circuit faults are among the common faults occurring in electrical machines. This paper presents a review of existing techniques available for online stator interturn fault detection and diagnosis (FDD) in electrical machines. Special attention is given to short-circuit-fault diagnosis in permanent-magnet machines, which are fast replacing traditional machines in a wide variety of applications. Recent techniques that use signals analysis, models, or knowledge-based systems for FDD are reviewed in this paper. Motor current is the most commonly analyzed signal for fault diagnosis. Hence, motor current signature analysis is a topic of elaborate discussion in this paper. Additionally, parametric and finite-element models that were designed to simulate interturn-fault conditions are reviewed.

Fault-Diagnosis Applications: Model-Based Condition Monitoring: Actuators, Drives, Machinery, Plants, Sensors, and Fault-tolerant Systems

Abstract: Supervision, condition-monitoring, fault detection, fault diagnosis and fault management play an increasing role for technical processes and vehicles in order to improve reliability, availability, maintenance and lifetime. For safety-related processes fault-tolerant systems with redundancy are required in order to reach comprehensive system integrity.This book is a sequel of the book Fault-Diagnosis Systems published in 2006, where the basic methods were described. After a short introduction into fault-detection and fault-diagnosis methods the book shows how these methods can be applied for a selection of 20 real technical components and processes as examples, such as:Electrical drives (DC, AC)Electrical actuatorsFluidic actuators (hydraulic, pneumatic)Centrifugal and reciprocating pumpsPipelines (leak detection)Industrial robotsMachine tools (main and feed drive, drilling, milling, grinding)Heat exchangersAlso realized fault-tolerant systems for electrical drives, actuators and sensors are presented.The book describes why and how the various signal-model-based and process-model-based methods were applied and which experimental results could be achieved. In several cases a combination of different methods was most successful.The book is dedicated to graduate students of electrical, mechanical, chemical engineering and computer science and for engineers.

Automated fault detection and diagnostics in a building management system

Abstract: Systems and methods for building automation system management are shown and described. The systems and methods relate to fault detection via abnormal energy monitoring and detection. The systems and methods also relate to control and fault detection methods for chillers. The systems and methods further relate to graphical user interfaces for use with fault detection features of a building automation system.

System and method of mechanical fault detection based on signature detection

Abstract: A mechanical fault detection method and system computes a similarity index to quantify the relationship of a measured spectral signature to a theoretical fault signature. A vibration sensor detects vibrations on machinery with rotating components. The vibration sensor generates a representation of the vibration and provides it to a vibration analyzer. The vibration analyzer is configured to identify peak amplitudes in the generated representation, determine a corresponding frequency for each of the peak amplitudes, and match the determined corresponding frequencies to a theoretical set of frequencies. The analyzer determines a number of matching frequencies and identifies a detection condition when the number of matching frequencies meets a predetermined criterion. The system and method combines vibration amplitude threshold detection with similarity index threshold detection to significantly reduce false fault alarms and false pass errors. The system and method is also used to identify incorrect vibration amplitude thresholds.

Fault Detection and Fault-Tolerant Control Using Sliding Modes

Abstract: Introduction.- Fault Detection and Isolation and Fault-tolerant Control.- First-order Sliding-mode Concepts.- Sliding-mode Observers for Fault Detection.- Cascaded Sliding-mode Observers.- Sensor Fault Detection.- Adaptive Sliding-mode Fault-tolerant Control.- Fault-tolerant Control using Sliding Modes with On-line Control Allocation. Model-reference Sliding-mode FTC.- SIMONA Implementation Results.- Case Study I: GARTEUR AG16, El Al Flight 1862 Bijlmermeer Incident.- Case Study II: Propulsion-controlled Aircraft.

Novel surface wave exciters for power line fault detection and communications

Abstract: A novel conformal surface wave (CSW) exciter is introduced which can excite electromagnetic (EM) surface waves along unshielded power line cables non-intrusively. The CSW exciter is small, cost effective and can be easily placed on a power cable compared to conventional monopole type launchers or horn type launchers. Besides cable fault detection, the potential applications of the proposed exciter include broadband power line (BPL) communication and high frequency power transmission.

Non-intrusive cable fault detection and methods

Abstract: In accordance with certain embodiments of the present disclosure, a cable fault detection device is described. The device includes a conformal monopole structure and a ground plane structure. The ground plane structure is configured to be generally parallel to the cable longitudinal axis.

A Probabilistic Fault Detection Approach: Application to Bearing Fault Detection

Abstract: This paper introduces a method to detect a fault associated with critical components/subsystems of an engineered system. It is required, in this case, to detect the fault condition as early as possible, with specified degree of confidence and a prescribed false alarm rate. Innovative features of the enabling technologies include a Bayesian estimation algorithm called particle filtering, which employs features or condition indicators derived from sensor data in combination with simple models of the system's degrading state to detect a deviation or discrepancy between a baseline (no-fault) distribution and its current counterpart. The scheme requires a fault progression model describing the degrading state of the system in the operation. A generic model based on fatigue analysis is provided and its parameters adaptation is discussed in detail. The scheme provides the probability of abnormal condition and the presence of a fault is confirmed for a given confidence level. The efficacy of the proposed approach is illustrated with data acquired from bearings typically found on aircraft and monitored via a properly instrumented test rig.

A Failure-Detection Strategy for IGBT Based on Gate-Voltage Behavior Applied to a Motor Drive System

Abstract: In this paper, a novel failure-detection technique and its analog circuit for insulated gate bipolar transistors (IGBTs), under open- and short-circuit failures, are proposed. This technique is applied to a three-phase induction-motor (IM) drive system. The detection technique is adapted to detect failures of short-circuit and open-circuit in the IGBT, which is based on gate-signal monitoring. The most important issue of this technique is the reduction of time for fault detection. This is very important in a failure-tolerant IM drive based on the material-redundancy approach or protection systems since the detection must be done before the device is damaged, in approximately less than 10 μs. The experimental test and simulations are presented in order to validate the proposed fault-detection technique, and it is validated, achieving replacement of the damaged element in the most suitable time.

Detection and Isolation of Speed-, DC-Link Voltage-, and Current-Sensor Faults Based on an Adaptive Observer in Induction-Motor Drives

Abstract: A sensor fault detection and isolation unit is considered for induction-motor drives based on an adaptive observer with rotor-resistance estimation. Generally, closed-loop induction-motor drives with voltage-source inverters use a speed or position, a dc-link voltage, and two or three phase-current sensors. In the proposed fault-detection and isolation unit, the estimated phase currents and rotor resistance are sent to a decision-making unit, which identifies the faulty sensor type based on a deterministic rule base. In the case of a current-sensor failure, it also detects the phase with erroneous sensor output. It is shown that, unlike the other proposed model-based fault-tolerant systems, using a bank of observers is not necessary, and only one current observer with rotor-resistance estimation is sufficient for isolation of all sensors' faults. The accuracy of the proposed approach is analytically proved. Furthermore, extensive simulation and experimental tests verify the effectiveness of the proposed method at different operating conditions.

An Observer-Based Diagnosis Scheme for Single and Simultaneous Open-Switch Faults in Induction Motor Drives

Abstract: In this paper, the detection and isolation of open-switch faults in induction motor (IM) drives are addressed from a model-based perspective. Residuals are synthesized by using nonlinear observers followed from a directional characterization. First, it is observed that the IM model can be written in a recurrent decoupled structure by taking the stator currents and mechanical velocity as outputs. In this way, residuals can be insensitive to load torque and operating conditions, and simultaneous faults can be addressed. A pulsewidth modulation three-phase inverter is studied as power actuator for the IM. Hence, in order to isolate faults related to the six switching devices, a directional residual evaluation in the -frame is employed. Hence, three residuals are constructed to isolate the faulty switches. The ideas presented in this paper are validated experimentally in a test bench of 1-hp IM under single and simultaneous faults.

Rolling element bearing fault detection in industrial environments based on a K-means clustering approach

Abstract: A K-means clustering approach is proposed for the automated diagnosis of defective rolling element bearings. Since K-means clustering is an unsupervised learning procedure, the method can be directly implemented to measured vibration data. Thus, the need for training the method with data measured on the specific machine under defective bearing conditions is eliminated. This fact consists the major advantage of the method, especially in industrial environments. Critical to the success of the method is the feature set used, which consists of a set of appropriately selected frequency-domain parameters, extracted both from the raw signal, as well as from the signal envelope, as a result of the engineering expertise, gained from the understanding of the physical behavior of defective rolling element bearings. Other advantages of the method are its ease of programming, simplicity and robustness. In order to overcome the sensitivity of the method to the choice of the initial cluster centers, the initial centers are selected using features extracted from simulated signals, resulting from a well established model for the dynamic behavior of defective rolling element bearings. Then, the method is implemented as a two-stage procedure. At the first step, the method decides whether a bearing fault exists or not. At the second step, the type of the defect (e.g. inner or outer race) is identified. The effectiveness of the method is tested in one literature established laboratory test case and in three different industrial test cases. Each test case includes successive measurements from bearings under different types of defects. In all cases, the method presents a 100% classification success. Contrarily, a K-means clustering approach, which is based on typical statistical time domain based features, presents an unstable classification behavior.

Modelling and Control for Intelligent Industrial Systems: Adaptive Algorithms in Robotics and Industrial Engineering

Abstract: Industrial robots in contact-free operation.- Industrial robots in compliance tasks.- Mobile robots and autonomous vehicles.- Adaptive control methods for industrial systems .-Robust control methods for industrial systems.- Filtering and estimation methods for industrial systems.- Sensor fusion-based control for industrial systems.- Fault detection and isolation for industrial systems.- Application of fault diagnosis to industrial systems.- Optimization methods for motion planning of multi-robot systems.- Optimization methods for target tracking by multi-robot systems.- Optimization methods for industrial automation.- Machine learning methods for industrial systems control.- Machine learning methods for industrial systems fault diagnosis.- Applications of machine vision to industrial systems.

A Dependency Graph Approach for Fault Detection and Localization Towards Secure Smart Grid

Abstract: Fault diagnosis in power grids is known to be challenging, due to the massive scale and spatial coupling therein. In this study, we explore multiscale network inference for fault detection and localization. Specifically, we model the phasor angles across the buses as a Markov random field (MRF), where the conditional correlation coefficients of the MRF are quantified in terms of the physical parameters of power systems. Based on the MRF model, we then study decentralized network inference for fault diagnosis, through change detection and localization in the conditional correlation matrix of the MRF. Particularly, based on the hierarchical topology of practical power systems, we devise a multiscale network inference algorithm that carries out fault detection and localization in a decentralized manner. Simulation results are used to demonstrate the effectiveness of the proposed approach.

Generalized Reconstruction-Based Contributions for Output-Relevant Fault Diagnosis With Application to the Tennessee Eastman Process

Abstract: Multivariate statistical process monitoring technologies, including principal component analysis (PCA) and partial least squares (PLS), have been successfully applied in many industrial processes. However, in practice, many PCA alarms do not lead to quality deterioration due to process control and recycle loops in process flowsheets, which hinders the reliability of PCA-based monitoring methods. Therefore, one is more interested to monitor the variations related to quality data, and detect the faults which affect quality data. Recently, a total projection to latent structures (T-PLS) model has been reported to detect output-relevant faults. In this paper, a generalized reconstruction based contribution (RBC) method with T-PLS model is proposed to diagnose the fault type for output-relevant faults. Furthermore, the geometrical property of generalized RBC is analyzed. A detailed case study on the Tennessee Eastman process is presented to demonstrate the use of the proposed method without or with prior knowledge.

Simple Online Fault Detecting Scheme for Short-Circuited Turn in a PMSM Through Current Harmonic Monitoring

Abstract: To detect a stator winding fault caused by a short-circuited turn in a permanent magnet synchronous motor, a simple online fault detecting scheme is presented. The proposed scheme is based on the monitoring of the second-order harmonic components in the q-axis current through harmonic analysis. To verify the effectiveness of the proposed scheme, a test motor which allows an interturn short in the stator winding has been built, and the entire control system has been implemented using the DSP TMS320F28335.

Fault Detection Filter Design for Markovian Jump Singular Systems With Intermittent Measurements

Abstract: This paper addresses the problem of fault detection filter design for discrete-time Markovian jump singular systems with intermittent measurements. The measurement transmission from the plant to the fault detection filter is assumed to be imperfect and a stochastic variable is utilized to model the phenomenon of data missing. Our attention is focused on the design of a fault detection filter such that the residual system is stochastically Markovian jump admissible and satisfies some expected performances. A new necessary and sufficient condition for a class of discrete-time Markovian jump singular systems to be stochastically Markovian jump admissible is proposed in the form of strict linear matrix inequalities. Sufficient conditions are established for the existence of the fault detection filter. Finally, a numerical example is provided to demonstrate the usefulness and applicability of the developed theoretical results.