Showing papers on "Fault detection and isolation published in 2016"

Real-Time Motor Fault Detection by 1-D Convolutional Neural Networks

Abstract: Early detection of the motor faults is essential and artificial neural networks are widely used for this purpose. The typical systems usually encapsulate two distinct blocks: feature extraction and classification. Such fixed and hand-crafted features may be a suboptimal choice and require a significant computational cost that will prevent their usage for real-time applications. In this paper, we propose a fast and accurate motor condition monitoring and early fault-detection system using 1-D convolutional neural networks that has an inherent adaptive design to fuse the feature extraction and classification phases of the motor fault detection into a single learning body. The proposed approach is directly applicable to the raw data (signal), and, thus, eliminates the need for a separate feature extraction algorithm resulting in more efficient systems in terms of both speed and hardware. Experimental results obtained using real motor data demonstrate the effectiveness of the proposed method for real-time motor condition monitoring.

Fault Detection Filtering for Nonlinear Switched Stochastic Systems

Abstract: In this note, the fault detection filtering problem is solved for nonlinear switched stochastic system in the T-S fuzzy framework. Our attention is concentrated on the construction of a robust fault detection technique to the nonlinear switched system with Brownian motion. Based on observer-based fault detection fuzzy filter as a residual generator, the proposed fault detection is formulated as a fuzzy filtering problem. By the utilization of the average dwell time technique and the piecewise Lyapunov function technique, the fuzzy-parameter-dependent fault detection filters are designed that guarantee the resulted error system to be mean-square exponential stable with a weighted ${\mathcal H}_{\infty}$ error performance. Then, the corresponding solvability condition for the fault detection fuzzy filter is also established by the linearization procedure technique. Finally, simulation has been presented to show the effectiveness of the proposed fault detection technique.

Sapienz: multi-objective automated testing for Android applications

Abstract: We introduce Sapienz, an approach to Android testing that uses multi-objective search-based testing to automatically explore and optimise test sequences, minimising length, while simultaneously maximising coverage and fault revelation. Sapienz combines random fuzzing, systematic and search-based exploration, exploiting seeding and multi-level instrumentation. Sapienz significantly outperforms (with large effect size) both the state-of-the-art technique Dynodroid and the widely-used tool, Android Monkey, in 7/10 experiments for coverage, 7/10 for fault detection and 10/10 for fault-revealing sequence length. When applied to the top 1,000 Google Play apps, Sapienz found 558 unique, previously unknown crashes. So far we have managed to make contact with the developers of 27 crashing apps. Of these, 14 have confirmed that the crashes are caused by real faults. Of those 14, six already have developer-confirmed fixes.

Fault Detection and Interruption in an Earthed HVDC Grid Using ROCOV and Hybrid DC Breakers

Abstract: Different HVDC grid types and the respective protection options are discussed. An earthed bipole HVDC grid was modeled in PSCAD, and using simulation results, the necessity of di/dt limiting inductors to contain the rise of fault currents within the capacity of current hybrid dc breakers is demonstrated. The impact of different inductor sizes on current rise was studied. A fault detection and localization scheme using the rate of change of voltage measured at the line side of the di/dt limiting reactors is proposed. The protection system was modeled and tested under different fault types and locations. The results show that the proposed method of HVDC grid protection is feasible using the current hybrid dc breaker technology. A systematic procedure for setting the necessary protection threshold values is also demonstrated.

Motor Bearing Fault Detection Using Spectral Kurtosis-Based Feature Extraction Coupled With K -Nearest Neighbor Distance Analysis

Abstract: Bearing faults are the main contributors to the failure of electric motors. Although a number of vibration analysis methods have been developed for the detection of bearing faults, false alarms still result in losses. This paper presents a method that detects bearing faults and monitors the degradation of bearings in electric motors. Based on spectral kurtosis (SK) and cross correlation, the method extracts fault features that represent different faults, and the features are then combined to form a health index using principal component analysis (PCA) and a semisupervised k -nearest neighbor (KNN) distance measure. The method was validated by experiments using a machinery fault simulator and a computer cooling fan motor bearing. The method is able to detect incipient faults and diagnose the locations of faults under masking noise. It also provides a health index that tracks the degradation of faults without missing intermittent faults. Moreover, faulty reference data are not required.

Observer-Based Fault Detection for Nonlinear Systems With Sensor Fault and Limited Communication Capacity

Abstract: In this technical note, a new fault detection design scheme is proposed for interval type-2 (IT2) Takagi-Sugeno (T-S) fuzzy systems with sensor fault based on a novel fuzzy observer. The parameter uncertainties can be captured by the membership functions of the IT2 fuzzy model. The premise variables of the plant are perfectly shared by the fuzzy observer. A stochastic process between the plant and the observer is considered in the system. A fault sensitive performance is established, and then sufficient conditions are obtained for determining the fuzzy observer gains. Finally, simulation results are provided to verify the effectiveness of the presented scheme.

Performance-Driven Distributed PCA Process Monitoring Based on Fault-Relevant Variable Selection and Bayesian Inference

Abstract: Multivariate statistical process monitoring involves dimension reduction and latent feature extraction in large-scale processes and typically incorporates all measured variables. However, involving variables without beneficial information may degrade monitoring performance. This study analyzes the effect of variable selection on principal component analysis (PCA) monitoring performance. Then, it proposes a fault-relevant variable selection and Bayesian inference-based distributed method for efficient fault detection and isolation. First, the optimal subset of variables is identified for each fault using an optimization algorithm. Second, a sub-PCA model is established in each subset. Finally, the monitoring results of all of the subsets are combined through Bayesian inference. The proposed method reduces redundancy and complexity, explores numerous local behaviors, and provides accurate description of faults, thus improving monitoring performance significantly. Case studies on a numerical example, the Tennessee Eastman benchmark process, and an industrial-scale plant demonstrate the efficiency.

Robust Model-Based Fault Diagnosis for PEM Fuel Cell Air-Feed System

Abstract: In this paper, the design of a nonlinear observer-based fault diagnosis approach for polymer electrolyte membrane (PEM) fuel cell air-feed systems is presented, taking into account a fault scenario of sudden air leak in the air supply manifold. Based on a simplified nonlinear model proposed in the literature, a modified super-twisting (ST) sliding mode algorithm is employed to the observer design. The proposed ST observer can estimate not only the system states, but also the fault signal. Then, the residual signal is computed online from comparisons between the oxygen excess ratio obtained from the system model and the observer system, respectively. Equivalent output error injection using the residual signal is able to reconstruct the fault signal, which is critical in both fuel cell control design and fault detection. Finally, the proposed observer-based fault diagnosis approach is implemented on the MATLAB/Simulink environment in order to verify its effectiveness and robustness in the presence of load variation.

A Review on Recent Development of Spacecraft Attitude Fault Tolerant Control System

Abstract: Motivated by several accidents, attitude control of a spacecraft subject to faults/failures has gained considerable attention in a wider range of aerospace engineering and academic communities. This paper is concerned with industrial practices and theoretical approaches for fault tolerant control (FTC) and fault detection and diagnosis (FDD) in spacecraft attitude control system. An overview on recent development of spacecraft attitude FTC system design is presented. The basis of a FTC system is introduced. The existing engineering FTC techniques and theoretical methodologies, including their advantages and disadvantages, are discussed. Moreover, closely associated with the reliability-relevant issues, recent progress in attitude FTC design strategies is reviewed. A brief review of some open problems in the general area of spacecraft attitude control design subject to components faults/failures is further concluded.

A Combined Wavelet and Data-Mining Based Intelligent Protection Scheme for Microgrid

Abstract: This paper presents an intelligent protection scheme for microgrid using combined wavelet transform and decision tree. The process starts at retrieving current signals at the relaying point and preprocessing through wavelet transform to derive effective features such as change in energy, entropy, and standard deviation using wavelet coefficients. Once the features are extracted against faulted and unfaulted situations for each-phase, the data set is built to train the decision tree (DT), which is validated on the unseen data set for fault detection in the microgrid. Further, the fault classification task is carried out by including the wavelet based features derived from sequence components along with the features derived from the current signals. The new data set is used to build the DT for fault detection and classification. Both the DTs are extensively tested on a large data set of 3860 samples and the test results indicate that the proposed relaying scheme can effectively protect the microgrid against faulty situations, including wide variations in operating conditions.

Fault detection, classification and location for transmission lines and distribution systems: a review on the methods

Abstract: A comprehensive review on the methods used for fault detection, classification and location in transmission lines and distribution systems is presented in this study. Though the three topics are highly correlated, the authors try to discuss them separately, so that one may have a more logical and comprehensive understanding of the concepts without getting confused. Great significance is also attached to the feature extraction process, without which the majority of the methods may not be implemented properly. Fault detection techniques are discussed on the basis of feature extraction. After the overall concepts and general ideas are presented, representative works as well as new progress in the techniques are covered and discussed in detail. One may find the content of this study helpful as a detailed literature review or a practical technical guidance.

A review on different pipeline fault detection methods

Abstract: Pipeline faults like leakage and blockage always create problem for engineers. Detection of exact fault quantity and its location is necessary for smooth functioning of a plant or industry and safety of the environment. In this paper brief discussion is made on various pipeline fault detection methods viz. Vibration analysis, Pulse echo methodology, Acoustic techniques, Negative pressure wave based leak detection system, Support Vector Machine (SVM) based pipeline leakage detection, Interferometric fibre sensor based leak detection, Filter Diagonalization Method (FDM), etc. In this paper merit and demerits of all methods are discussed. It is found that these methods have been applied for specific fluids like oil, gas and water, for different layout patterns like straight and zigzag, for various lengths of pipeline like short and long and also depending on various operating conditions. Therefore, a comparison among all methods has been done based on their applicability. Among all fault detection methods, Acoustic reflectometry is found most suitable because of its proficiency to identify blockages and leakage in pipe as small as 1% of its diameter. Moreover this method is economical and applicable for straight, zigzag and long, short length pipes for low, medium and high density fluid.

Fault Diagnosis and Tolerant Control of Single IGBT Open-Circuit Failure in Modular Multilevel Converters

Abstract: The modular multilevel converter (MMC) is distinguished by its modularity that is the use of standardized submodules (SMs). To enhance reliability and avoid unscheduled maintenance, it is desired that an MMC can remain operational without having to shut down despite some of its SMs are failed. Particularly, in this paper, complete fault diagnosis and tolerant control solution, including the fault detection, fault tolerance, fault localization, and fault reconfiguration, have been proposed to ride through the insulated gate bipolar transistor open-circuit failures. The fault detection method detects the fault by means of state observers and the knowledge of fault behaviors of MMC, without using any additional sensors. Then, the MMC is controlled in a newly proposed tolerant mode until the specific faulty SM is located by the fault localization method; thus, no overcurrent problems will happen during this time interval. After that, the located faulty SM will be bypassed while the remaining SMs are reconfigured to provide continuous operation. Throughout the fault periods, it allows the MMC to operate smoothly without obvious waveform distortion and power interruption. Finally, experimental results using a single-phase scaled-down MMC prototype with six SMs per arm show the validity and feasibility of the proposed methods.

Data-Driven Process Monitoring Based on Modified Orthogonal Projections to Latent Structures

Abstract: Quality- or output-related fault detection has attracted much attention in recent years. Several approaches have been developed to solve this issue based on postprocessing schemes. However, further studies find that these methods gradually lose their functions when amplitudes of quality-unrelated faults increase; in addition, they still consume a relatively large amount of calculation load in practice. In this brief, we propose a new structure of preprocessing–modeling–postprocessing, within which modified orthogonal projections to latent structures (MOPLS) method is developed. Compared with the previous approaches, the new method significantly improves the performance of quality-related fault detection. In addition, it reduces the number of required latent variables, thus it has a quite lower computational load than the previous ones. A numerical example and the Tennessee Eastman process are used to verify the effectiveness of the proposed approach.

Nonunit Protection of HVDC Grids With Inductive DC Cable Termination

Abstract: This paper deals with nonunit protection of HVDC grids by proposing a set of parameters that characterizes the open protection zones together with an efficient method to determine the thresholds on these parameters. Selective HVDC grid protection schemes must detect and discriminate faults within the first milliseconds of the fault transient and consequently differ considerably from existing ac protection schemes. Due to the accompanying speed requirement, primary protection is expected to be based on open protection zones as communication delay impedes fast operation. In this paper, the principles of the nonunit protection scheme are developed based on reflection of a traveling wave at an inductive termination. Next, the method to obtain the protection scheme thresholds is elaborated. The method accurately calculates the thresholds for HVDC grids with an arbitrary topology. A sensitivity analysis of these thresholds toward grid and fault parameters demonstrates the applicability of the proposed protection scheme in cable-based HVDC grids with inductive cable termination. The results obtained with the reduced grid model are validated by comparison against simulations using a detailed model implemented in PSCAD.

Event-Triggered Fault Detection Filter Design for a Continuous-Time Networked Control System

Abstract: This paper studies the problem of event-triggered fault detection filter (FDF) and controller coordinated design for a continuous-time networked control system (NCS) with biased sensor faults. By considering sensor-to-FDF network-induced delays and packet dropouts, which do not impose a constraint on the event-triggering mechanism, and proposing the simultaneous network bandwidth utilization ratio and fault occurrence probability-based event-triggering mechanism, a new closed-loop model for the considered NCS is established. Based on the established model, the event-triggered ${H} _{{\infty }}$ performance analysis, and FDF and controller coordinated design are presented. The combined mutually exclusive distribution and Wirtinger-based integral inequality approach is proposed for the first time to deal with integral inequalities for products of vectors. This approach is proved to be less conservative than the existing Wirtinger-based integral inequality approach. The designed FDF and controller can guarantee the sensitivity of the residual signal to faults and the robustness of the NCS to external disturbances. The simulation results verify the effectiveness of the proposed event-triggering mechanism, and the FDF and controller coordinated design.

Open-Circuit Fault Diagnosis and Fault-Tolerant Control for a Grid-Connected NPC Inverter

Abstract: This paper presents an open-circuit fault detection method for a grid-connected neutral-point clamped (NPC) inverter system. Further, a fault-tolerant control method under an open-circuit fault in clamping diodes is proposed. Under the grid-connected condition, it is impossible to identify the location of a faulty switch by the conventional methods which usually use the distortion of outputs because the distortion of the outputs is the same in some fault cases. The proposed fault detection method identifies the location of the faulty switch and the faulty clamping diode of the NPC inverter without any additional hardware or complex calculations. In the case of the clamping diode faults, the NPC inverter can transfer full rated power with sinusoidal currents by the proposed fault-tolerant control. The feasibility of the proposed fault detection and the fault-tolerant control methods for the grid-connected NPC inverter are verified by simulation and experimental results.

Comparative Study of Predictive and Resonant Controllers in Fault-Tolerant Five-Phase Induction Motor Drives

Abstract: One of the most attractive features of multiphase machines is the fault-tolerant capability due to the higher number of phases. Different postfault control strategies based on hysteresis, proportional integral (PI)-resonant, and predictive techniques have been recently proposed. They all proved their capabilities to withstand fault situations and to preserve the fundamental component of the air-gap field, while achieving minimum losses, maximum torque per ampere, and reducing torque vibrations. Nonetheless, due to their recent introduction, no thorough study has yet appeared comparing the performance of these controllers. In this paper, two open-phase fault-tolerant control schemes are experimentally compared in a real five-phase induction machine. The controllers being compared are based on PI-resonant and predictive control techniques, respectively. The experiments include pre- and postfault situations. Obtained results show that both control methods offer nearly the same performance. When compared, predictive control provides faster control response and superior performance at low-speed operation but is found to be less resilient to fault detection delays and to have higher current ripple. Regarding the controller implementation, it is shown that the transition from prefault to postfault operation involves modeling the nonlinear effects observed when an open-phase fault occurs for the predictive controller and proper retuning of the PI trackers for the PI-resonant controller, to ensure postfault operation.

Sensor Fault Detection, Isolation, and Identification Using Multiple-Model-Based Hybrid Kalman Filter for Gas Turbine Engines

Abstract: In this paper, a novel sensor fault detection, isolation, and identification (FDII) strategy is proposed using the multiple-model (MM) approach. The scheme is based on multiple hybrid Kalman filters (MHKFs), which represents an integration of a nonlinear mathematical model of the system with a number of piecewise linear (PWL) models. The proposed fault detection and isolation (FDI) scheme is capable of detecting and isolating sensor faults during the entire operational regime of the system by interpolating the PWL models using a Bayesian approach. Moreover, the proposed MHKF-based FDI scheme is extended to identify the magnitude of a sensor fault using a modified generalized likelihood ratio method that relies on the healthy operational mode of the system. To illustrate the capabilities of our proposed FDII methodology, extensive simulation studies are conducted for a nonlinear gas turbine engine. Various single and concurrent sensor fault scenarios are considered to demonstrate the effectiveness of our proposed online hierarchical MHKF-based FDII scheme under different flight modes. Finally, our proposed hybrid Kalman filter (HKF)-based FDI approach is compared with various filtering methods such as the linear, extended, unscented, and cubature Kalman filters corresponding to both interacting and noninteracting MM-based schemes. Our comparative studies confirm the superiority of our proposed HKF method in terms of promptness of the fault detection, lower false alarm rates, as well as robustness with respect to the engine health parameter degradations.