程控交换机常见故障判断及处理方法探讨

近年来,科学技术发展迅速,带动了现代技术的迅猛发展,现代化建设离不开通讯,并且通讯被渗透到人们生活的各个领域中。程控交换机作为影响通信畅通度及通信质量的有效影响较大,但是在实际的应用过程中容易出现诸多的故障,对其正常工作造成了较大的影响。工作人员应认真履行自身的工作职责,对程控交换机出现故障的原因进行判断,并提出合理化的故障处理方法。

CAUSALITY DIAGRAM BASED SAFETY ANALYSIS OF MICRO TURBOJET ENGINE

An improved safety analysis based on the causality diagram for the complex system of micro aero-engines is presented.The study is examined by using the causality diagram in analytical failure cases due to rupture or pentration in the receiver of micro turbojet engine casing,and the comparisons are also made with the results from the traditional fault tree analysis.Experimental results show two main advantages：（1）Quantitative analysis which is more reliable for the failure analysis in jet engines can be produced by the causality diagram analysis;（2）Graphical representation of causality diagram is easier to apply in real test cases and more effective for the safety assessment.

On-Line Broken-Bar Fault Diagnosis System of Induction Motor

Induction motor faults including mechanical and electrical faults are reviewed.The fault diagnosis methods are summarized.To analyze the influence of stator current,torque,speed and rotor current on faulted bars,a time-stepping transient finite element(FE)model of induction motor with bars faulted is created in this paper.With wavelet package analysis method and FFT method, the simulation result of finite element is analyzed.Based on the simulation analysis,the on-line fault diagnosis system of induction motor with bars faulted is developed.With the speed of broken bars motor changed from 1 478 r/min to 1 445 r/min,the FFT power spectra and the wavelet package decoupling factors are given.The comparison result shows that the on-line diagnosis system can detect broken-bar fault efficiently.

Combination Method of Principal Component Analysis and Support Vector Machine for On-line Process Monitoring and Fault Diagnosis

On-line monitoring and fault diagnosis of chemical process is extremely important for operation safety and product quality. Principal component analysis (PCA) has been widely used in multivariate statistical process monitoring for its ability to reduce processes dimensions. PCA and other statistical techniques, however, have difficulties in differentiating faults correctly in complex chemical process. Support vector machine (SVM) is a novel approach based on statistical learning theory, which has emerged for feature identification and classification. In this paper, an integrated method is applied for process monitoring and fault diagnosis, which combines PCA for fault feature extraction and multiple SVMs for identification of different fault sources. This approach is verified and illustrated on the Tennessee Eastman benchmark process as a case study. Results show that the proposed PCA-SVMs method has good diagnosis capability and overall diagnosis correctness rate.

Support Vector Machine for mechanical faults classification

Support Vector Machine (SVM) is a machine learning algorithm based on the Statistical Learning Theory (SLT), which can get good classification effects with a few learning samples. SVM represents a new approach to pattern classification and has been shown to be particularly successful in many fields such as image identification and face recognition. It also provides us with a new method to develop intelligent fault diagnosis. This paper presents an SVM based approach for fault diagnosis of rolling bearings. Experimentation with vibration signals of bearing was conducted. The vibration signals acquired from the bearings were directly used in the calculating without the preprocessing of extracting its features. Compared with the Artificial Neural Network (ANN) based method, the SVM based method has desirable advantages. Also a multi-fault SVM classifier based on binary clas- sifier is constructed for gear faults in this paper. Other experiments with gear fault samples showed that the multi-fault SVM classifier has good classification ability and high efficiency in mechanical system. It is suitable for on line diagnosis for mechanical system.

Fuzzy Random Reliability Analysis of Blocky Rock-Mass in Slopes

Slope stability assessment is a geotechnical problem characterized by many sources of uncertainty. In clas- sical reliability analysis, only the randomness of uncertainties is taken into account but the fuzziness of them is ignored. In this paper, a fuzzy probability approach and a fuzzy JC method are presented for the reliability analysis. The two methods have been applied to stability analysis of a certain slope of permanent ship lock in the Three-Gorges Project. The results obtained from these two methods are basically the same. However, compared with the fuzzy probability means, the fuzzy JC method can reflect the real situation better because it uses a fuzzy-based analysis applied to not only limit state equation but also mechanical parameters.

Distributed virtual environment-based safety control for Internet tele-robots

The inherent complexity and uncertainty of multi-operator multi-robot （MOMR） tele-operation system make its safeguard an essential problem. Hazardous factors in the system are analyzed using fault tree analysis（FTA） technology, and three-layer interactive safety architecture with information flow is designed in modules to control the factors according to the holistic control mode. After that, distributed virtual environment （DVE） including the characteristics of virtual guide （VG） technology is discussed to help the operators achieve some tasks through the visibility of control commands, time-delay, movement collision and operators＇ intentions. Finally an experiment is implemented to test the efficiency of safety control architecture by using two robots to place some building blocks in the same workspace.

Mechanical Analysis for Progressive Failure of Debris Landslide

It is of significance to research failure mechanism of debris landslides that are widespread in the Three Gorges Reservoir Area. Based on the statistical analysis of the developmental law and failure mode of debris landslides in the Three Gorges Reservoir, the mode of progressive failure is found. The mechanical model for progressive failure of debris landslides with two slip bands is also established by applying slice method. According to the results of the downslide force between adjacent slices, if the downslide force of lower slice is larger than zero, the slice fails along the major sliding surface, otherwise it is stable. In result, the failure range is obtained. The stress function can be determined through dimensional analysis of failure slice. According to static boundary conditions of the slice, stress state of any point in the slice can be obtained. Then stress state of any point in the secondary slip band can also be established. The failure of the secondary slip band is judged on the basis of Mohr-Coulomb failure criterion. Therefore, a mechanical method is proposed to analyze the progressive failure of debris landslide with two slip bands.

THE MACHINE LEARNING SYSTEM OF TELEPHONE NETWORKS MANAGEMENT

The problem of fault information process in telephone networks manage ment system in AT & T in the US has been solved with stepanwise learning approach.This method makes the information decrease step by step by means of merge and sort, classifies the information to several typical classes and establishes the knowledge base (KB) eventually. If new fault information is inputted, we will call the knowl edge in KB and predict the related faults which will happen.

DPHM: A FAULT DETECTION PROTOCOL BASED ON HEARTBEAT OF MULTIPLE MASTER-NODES

In most of fault detection algorithms of distributed system, fault model is restricted to fault of process, and link failure is simply masked, or modeled by process failure. Both methods can soon use up system resource and potentially reduce the availability of system. A fault Detection Protocol based on Heartbeat of multiple Master-nodes (DPHM) is proposed, which can immediately and accurately detect and locate faulty links by adopting voting and electing mechanism among master-nodes. Thus, DPHM can effectively improve availability of system. In addition, in contrast with other detection protocols, DPHM reduces greatly the detection cost due to the structure of master-nodes.

Stochastic differential equation software reliability growth model with change-point

This paper presents software reliability growth models(SRGMs) with change-point based on the stochastic differential equation(SDE).Although SRGMs based on SDE have been developed in a large scale software system,considering the variation of failure distribution in the existing models during testing time is limited.These SDE SRGMs assume that failures have the same distribution.However,in practice,the fault detection rate can be affected by some factors and may be changed at certain point as time proceeds.With respect to this issue,in this paper,SDE SRGMs with changepoint are proposed to precisely reflect the variations of the failure distribution.A real data set is used to evaluate the new models.The experimental results show that the proposed models have a fairly accurate prediction capability.

Dependability Analysis for Fault-Tolerant Computer Systems Using Dynamic Fault Graphs

Dependability analysis is an important step in designing and analyzing safety computer systems and protection systems.Introducing multi-processor and virtual machine increases the system faults' complexity,diversity and dynamic,in particular for software-induced failures,with an impact on the overall dependability.Moreover,it is very different for safety system to operate successfully at any active phase,since there is a huge difference in failure rate between hardware-induced and softwareinduced failures.To handle these difficulties and achieve accurate dependability evaluation,consistently reflecting the construct it measures,a new formalism derived from dynamic fault graphs(DFG) is developed in this paper.DFG exploits the concept of system event as fault state sequences to represent dynamic behaviors,which allows us to execute probabilistic measures at each timestamp when change occurs.The approach automatically combines the reliability analysis with the system dynamics.In this paper,we describe how to use the proposed methodology drives to the overall system dependability analysis through the phases of modeling,structural discovery and probability analysis,which is also discussed using an example of a virtual computing system.

Wavelets applied to early fault analysis of hoist gear box

Using wavelets, the vibration signal of a certain mine hoist gear box was analyzed. By multiple comparison analysis, the rational wavelet basis function was determined. Fault characteristic frequencies of hoist gear box were identified. The research indicates that the hoist＇s fault information is non-stationary, and non-stationary signal is clearly extracted by using db20 wavelet as basis function. The db20 wavelet is the proper wavelet base for vibration signal analysis of the hoist gear box.

A Transmission Line Fault Classification Approach by Support Vector Machines

This paper presents an approach for shunt faults detection and classification in transmission line using Support Vector Machine （SVM）. The paper compares between using three line post-fault current samples for one-half cycle and one-fourth cycle from the inception of the fault as inputs for SVM. Two SVMs are used, first SVMabc is used for faulty phase detection and second SVMg is used for ground detection. SVMs with polynomial kernel with different degrees are used to obtain the best classification score. The classification test results show that the proposed method is accurate and reliable.

Multi-scale bistable stochastic resonance array: A novel weak signal detection method and application in machine fault diagnosis

Weak signal detection based on stochastic resonance （SR） can hardly succeed when noise intensity exceeds the optimal value of SR. This paper explores a novel parallel bistable SR array mechanism by decomposed multi-scale noises from input signal. A smoother output with lower noise is obtained from the combination of colored noise SR ellect and parallel bistable SR array. The influence of noise intensity and array size on the SR effect and output noise intensity is analyzed through numerical simu- lation. A signal detection method based on the new SR mechanism and normalized scale transform is proposed for the case of heavy background noise. Simulation is conducted to confirm the effectiveness of parameter tuning and amplitude tuning of normalized scale transform on the proposed SR model. The proposed method has three advantages： the input noise intensity of each unit is reduced by wavelet decomposition; the output noise level decreases due to array ensemble average; the SR effect of each unit is optimized by normalized scale transform for high frequency signal. Experiment on bearing inner and outer race fault diagnosis has verified the effectiveness and advantages of the proposed SR model in comparison with traditional SR method and kurlogram.

Comparisons between centrifuge and numerical modeling results for slope toppling failure

This paper presents series studies on the toppling mechanism by centrifuge tests and numerical simulations. Two different discrete element methods, i.e., the continuum-based discrete element method(CDEM) and the discontinuous deformation analysis(DDA), are adopted. The modeling results show that both the methods can accurately capture the failure modes of the centrifuge tests, including three distinct zones and two failure surfaces. Comparisons are made between the physical test and numerical simulation results. The critical inclination angle of the tilting table where the slope models are fixed on can be moderately predicted by the two methods, with different degrees of precision. The error between the test results and the simulated results is within 1% for the slope models without rock-bridges by both CDEM and DDA. However, it is amplified for the staggered-joint models that simulate the rock-bridges. With DDA, the average error is about 5%, and the maximum error is up to 17%. While with CDEM, the errors for the aligned-joint models are ranged from 1% to 6%, and it is from 10% to 29% for the staggered-joint models. The two numerical methods show the capability in simulating toppling failure of blocky rock mass with and without rock-bridges. The model with rock-bridges which provides a certain bending resistance is more stable than the one without any rock-bridge. In addition, the two failure surfaces were observed, which is different from the common understanding that only one failure surface appears.

Fault Tree Analysis of Feeding Control System for Computer Numerical Control Heavy-Duty Horizontal Lathes with Multiple Common Cause Failure Groups

The lathes are basic machine tools for manufacturing cylindrical parts. In recent years, the DLseries computer numerical control(CNC) heavy-duty horizontal lathes(HDHLs) have been widely used in the transportation, energy and aviation industries. High availability of the CNC heavy-duty lathes is demanded to guarantee the efficiency and benefit of these manufacturing industries. As one of the key subsystems of the HDHLs, the feeding control system is studied in this paper on reliability modeling and reliability analysis. The fault tree analysis(FTA) method is used for reliability modelling of the feeding control system. Considering the multiple common cause failure groups(CCFGs) existing in the system, a modified beta factor parametric model is introduced to model the common cause failure(CCF) in system. The reliability of feeding control system is then obtained and the effect of CCF on the reliability of the whole system is studied as well.