Fault detection,location and classification of a transmission line

Neural Computing and Applications - Transient stability is very important in power system. Large disturbances like fault in a transmission line are a concern which needs to be disconnected as...     

Fault detection and isolation in transient states using principal component analysis

This paper proposes the use of principal component analysis (PCA) for process monitoring and fault detection and isolation in processes with several operation modes and long transient states and start-ups. The principal aspects of the PCA approach and the necessary transformations for dealing with this type of processes are presented. In this paper a classical PCA model is used for each steady state of the process and a modification of a batch PCA approach is applied to the transient states of the continuous process. So, in this last case, the PCA model is performed over a three way matrix arranged with the values of the measured variables of several past transitions with a nominal behaviour. This approach presents some problems, such as the unfolding, alignment and imputation. The methods proposed to deal with these problems are explained in detail and compared in order to design a fault detection and isolation method. Two examples are considered to perform the tasks explained. In both cases good results are obtained.     

基于颜色和几何关系的人脸检测方法

针对复杂背景下的彩色图像提出了一种基于颜色和几何关系的人脸检测方法.选择YCbCr色彩空间进行肤色分割,在候选人脸区域的灰度图像中进行图像复杂度的计算以确定眼睛区域的大致位置,并利用嘴巴的红色特性在候选人脸区域的YIQ色彩空间中确定嘴巴的大致位置,最后利用眼睛、嘴巴的几何关系精确定位人脸.实验结果表明,该方法速度快、检测率高,与其它算法相比有很好的检测效果.     

Fault location using digraph and inverse direction search with application

A fault location method for large-scale plants is described. Fault location is executed using the following procedure: first, a digraph which indicates the failure propagation network of a plant is drawn using nodes corresponding to the devices of the plant or failure modes of the devices. Arrows which correspond to the direction of failure propagation between adjacent nodes are drawn in. Second, some nodes are chosen as candidates of the failure origin by back-tracing, using the arrows, starting from the nodes which indicate abnormal states such as rapidly rising pressure. Third, the candidates are screened by using failure propagation probabilities between adjacent nodes, failure propagation time between adjacent nodes, and back-tracing, starting from the nodes which indicate normal states. Finally, the failure propagation probabilities and the failure rates of the devices are used to evaluate the priority ranking among the screened candidates. This method is applied to pump and evaporation plants.     

基于可传递置信模型的模拟电路融合诊断方法研究

本文提出了一种有效的基于可转移置信模型的模拟电路故障融合诊断方法,解决模拟电路诊断中故障特征信息缺乏和决策融合中异质信息不相容等问题。该法首先采用所提出的电路温度故障信息的提取规则,将获得的温度故障信息输入隶属度函数进行故障预识别,再结合基于可测点电压神经网络故障预识别结果,利用可传递置信模型进行电路融合诊断。通过具体电路实验仿真,表明了该方法的有效性,提高了模拟电路融合诊断准确率。     

Fault detection using robust multivariate control chart

We introduce a new multivariate statistical process control chart for fault detection using robust statistics and principal component analysis. The proposed approach consists of two main steps. In the first step, a robust covariance matrix is determined using the minimum covariance determinant algorithm. In the second step, an eigen-analysis of the robust correlation matrix is performed to derive the robust control limits of the proposed multivariate chart. Our experimental results illustrate the much better fault detection performance of the proposed method in comparison with existing statistical monitoring and process controlling charts.     

Fault detection and isolation using viability theory and interval observers

This paper proposes the use of interval observers and viability theory in fault detection and isolation (FDI). Viability theory develops mathematical and algorithmic methods for investigating the viability constraints characterisation of dynamic evolutions of complex systems under uncertainty. These methods can be used for checking the consistency between observed and predicted behaviour by using simple sets that approximate the exact set of possible behaviour (in the parameter or state space). In this paper, FDI is based on checking for an inconsistency between the measured and predicted behaviours using viability theory concepts and sets. Finally, an example is provided in order to show the usefulness of the proposed approach.     

WSN中基于端到端的贪婪故障定位算法

为了维持无线传感器网络的正常运行,所有的故障链路需要被精确定位。将该问题转换为基于端到端的数据引导,以减少主动监测次数为目的的最优监测序列的问题。提出了通过拓扑拆分得到故障子图,并通过子图的概率集进一步计算节省主动探测次数的基于节点监测多条链路的启发式贪婪算法NTHG(node testing using heuristic greedy)。仿真结果表明仅需要监测小部分的节点,就可以定位网络中所有的故障链路。与该问题最新的解决算法LTHG(link testing using heristic greedy)相比,新算法需要更少的监测次数和平均CPU耗时,从而很好地降低了网络能耗,缩短了故障定位耗时。     

基于概率模型的遗传规划在故障诊断中的应用

采用在遗传规划中使用概率模型的新方法采解决一系列故障诊断问题。故障诊断可被看为是一个多级分类问题。遗传规划在解决复杂问题上有很大的优势,而这种优势在故障诊断中仍然显著。而且,使用概率模型作为适应函数能提高诊断的精确性,最后用这种方法解决机电设备的故障诊断。结果显示,使用基于概率模型的遗传规划解决机电设备的故障诊断比人工神经网络优越。     

Fault analysis for networks with concurrent error detection

The authors propose an approach for fault analysis and simulation of networks designed to have concurrent detection properties. The analysis characterizes all faults that may affect a device and determines the coverage, extracting test vectors and other parameters for evaluating device quality     

Fast detection of impact location using kernel extreme learning machine

Damage location detection has direct relationship with the field of aerospace structure as the detection system can inspect any exterior damage that may affect the operations of the equipment. In the literature, several kinds of learning algorithms have been applied in this field to construct the detection system and some of them gave good results. However, most learning algorithms are time-consuming due to their computational complexity so that the real-time requirement in many practical applications cannot be fulfilled. Kernel extreme learning machine (kernel ELM) is a learning algorithm, which has good prediction performance while maintaining extremely fast learning speed. Kernel ELM is originally applied to this research to predict the location of impact event on a clamped aluminum plate that simulates the shell of aerospace structures. The results were compared with several previous work, including support vector machine (SVM), and conventional back-propagation neural networks (BPNN). The comparison result reveals the effectiveness of kernel ELM for impact detection, showing that kernel ELM has comparable accuracy to SVM but much faster speed on current application than SVM and BPNN.     

Fault detection in continuous processes using multivariate statistical methods

The approach to process monitoring known as multivariate statistical process control (MSPC) has developed as a distinct technology, closely related to the field of fault detection and isolation. A body of technical research and industrial applications indicate a unique applicability to complex large-scale processes, but has paid relatively little attention to generic live process issues. In this paper, the impact of various classes of generic abnormality in the operation of continuous process plants on MSPC monitoring is investigated. It is shown how the effectiveness of the MSPC approach may be understood in terms of model and signal-based fault detection methods, and how the multivariate tools may be configured to maximize their effectiveness. A brief review of MSPC for the process industries is also presented, indicating the current state of the art.     

Fault detection for service mobile robots using model-based method

Detection of faults is a topic of high importance because it increases robot dependability, a requirement for the wide acceptance of service robots in domestic environments. This work takes a model-based approach for detecting and identifying actuator faults on differential-drive mobile robots in an indoor environment. An error-bound is calculated between the estimated and measured robot states which is constantly adapted based on the current state and input signals. A fault is detected when the estimation error is outside this bound. The model parameters are learned by the robot using an adaptive law, after the robot deployment in the target environment. Model uncertainties have an important impact on the fault detection performance, and are dealt with by considering the uncertainty bounds in the bound calculations. This ensures no false alarms occur when the uncertainty remains bounded during normal operation. Furthermore an extension to the method is proposed that addresses the problem of detecting small faults. The method is experimentally validated on a iRobot Roomba autonomous robot.     

Fault detection for discrete-time LPV systems using interval observers

This paper is concerned with the fault detection (FD) problem for discrete-time linear parameter-varying systems subject to bounded disturbances. A parameter-dependent FD interval observer is designed based on parameter-dependent Lyapunov and slack matrices. The design method is presented by translating the parameter-dependent linear matrix inequalities (LMIs) into finite ones. In contrast to the existing results based on parameter-independent and diagonal Lyapunov matrices, the derived disturbance attenuation, fault sensitivity and nonnegative conditions lead to less conservative LMI characterisations. Furthermore, without the need to design the residual evaluation functions and thresholds, the residual intervals generated by the interval observers are used directly for FD decision. Finally, simulation results are presented for showing the effectiveness and superiority of the proposed method.     

Fault detection on robot manipulators using artificial neural networks

Nowadays, gas welding applications on vehicle’s parts with robot manipulators have increased in automobile industry. Therefore, the speed of end-effectors of robot manipulator is affected on each joint during the welding process with complex trajectory. For that reason, it is necessary to analyze the noise and vibration of robot’s joints for predicting faults. This paper presents an experimental investigation on a robot manipulator, using neural network for analyzing the vibration condition on joints. Firstly, robot manipulator’s joints are tested with prescribed of trajectory end-effectors for the different joints speeds. Furthermore, noise and vibration of each joint are measured. And then, the related parameters are tested with neural network predictor to predict servicing period. In order to find robust and adaptive neural network structure, two types of neural predictors are employed in this investigation. The results of two approaches improved that an RBNN type can be employed to predict the vibrations on industrial robots.     

Alternative search techniques for face detection using location estimation and binary features

The sliding window approach is the most widely used technique to detect an object from an image. In the past few years, classifiers have been improved in many ways to increase the scanning speed. Apart from the classifier design (such as the cascade), the scanning speed also depends on a number of different factors (such as the grid spacing, and the scale at which the image is searched). When the scanning grid spacing is larger than the tolerance of the trained classifier it suffers from low detections. In this paper, we present a technique to reduce the number of missed detections when fewer subwindows are processed in the sliding window approach for face detection. This is achieved by using a small patch to predict the location of the face within a local search area. We use simple binary features and a decision tree for location estimation as it proved to be efficient for our application. We also show that by using a simple interest point detector based on quantized gradient orientation, as the front-end to the proposed location estimation technique, we can further improve the performance. Experimental evaluation on several face databases show better detection rate and speed with our proposed approach when fewer number of subwindows are processed compared to the standard scanning technique.     

Fault detection and diagnosis for delay-range-dependent stochastic systems using output PDFs

International Journal of Control, Automation and Systems - This paper investigates a new fault detection and diagnosis(FDD) scheme for delay-range-dependent stochastic systems. Compared with...     

Selective detection of HCHO gas using mixed oxides of ZnO/ZnSnO3

Mixed oxides of ZnO/ZnSnO₃ doped with Au element were prepared by a hydrothermal process. The crystal structure, composition and ceramic microstructure of the powders obtained were characterized by X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), transmission electron microscopy (TEM) and scanning electron microscopy (SEM). The results show that the product is the mixture of ZnO/ZnSnO₃; its particle size is about 500 nm with good dispersivity in shape. The sensitivity, selectivity, response and recovery properties of the ZnO/ZnSnO₃-based sensors were investigated by mixing a target gas in air. It is found that the sensors have remarkable sensitivity to HCHO vapor and satisfactory selectivity to other gases.     

Fault detection and prognosis of assembly locating systems using piezoelectric transducers

Fixture faults have been identified as a principal root cause of defective products in assembly lines; however, there exists a lack of fast and accurate monitoring tools to detect fixture fault damage. Locating fixture damage causes a decrease in product quality and production throughput due to the extensive work required to detect and diagnosis a faulty fixture. In this paper, a unique algorithm is proposed for fixture fault monitoring based on the use of autoregressive models and previously developed piezoelectric impedance fixture sensors. The monitoring method allows for the detection of changes within a system without the need for healthy references. The new method also has the capability to quantify deterioration with respect to a calibrated value. Deterioration prognosis can then be facilitated for structural integrity predictions and maintenance purposes based on the quantified deterioration and forecasting algorithms. The proposed robust methodology is proven to be effective on an experimental setup for monitoring damage in locating fixtures. Fixture wear and failure are successfully detected by the methodology, and fixture structural integrity prognosis is initiated.