Novell无盘工作站远程启动的故障诊断

无盘工作站是Ｎｏｖｅｌｌ网络应用过程中常常使用的设备。然而在实际应用中，常出现无盘站连不上网、找不到网上的服务器、启动时死机等故障。本文通过对无盘站启动原理的分析来探讨这些故障的原因及解决办法。一、无盘站远程启动的工作原理Ｎｏｖｅｌｌ无盘工作站的远程启动过程可以概括为四个步骤：启动；下载远程启动映像文件；重启动；连接上网。依据启动程序工作原理的不同，又可以将远程启动方式分成两种：一种称为“ＲｅｍｏｔｅＢｏｏｔ”（远程启动），另一种称为“ＲｅｍｏｔｅＰｒｏｇｒａｍＬｏａｄ（ＲＰＬ）”（远程程序装载）。这两种启动方法有着显著的差别，因此在安装无盘工作站及故障诊断时是需要加以认真区别的。１．

高压电磁式互感器故障特征与判据

随着高压电磁式互感器可靠性研究的发展与深化．互感器故障现象与故障机理显得越来越复杂和不确定．现用系统论的观点和方法概括了互感器故障的特征，总结了现有故障判据，进而提出了故障的模糊矩阵判据，为故障诊断的计算机化和实时化提供了基础．

Nonlinear online process monitoring and fault diagnosis of condenser based on kernel PCA plus FDA

A novel online process monitoring and fault diagnosis method of condenser based on kernel principle component analysis （KPCA） and Fisher discriminant analysis （FDA） is presented. The basic idea of this method is： First map data from the original space into high-dimensional feature space via nonlinear kernel function and then extract optimal feature vector and discriminant vector in feature space and calculate the Euclidean distance between feature vectors to perform process monitoring. Similar degree between the present discriminant vector and optimal discriminant vector of fault in historical dataset is used for diagnosis. The proposed method can effectively capture the nonlinear relationship among process variables. Simulating results of the turbo generator＇s fault data set prove that the proposed method is effective.

Fault isolation of air handling unit based on neural network

Aiming at various faults in an air conditioning system,the fault characteristics are analyzed.The influence of the faults on the energy consumption and thermal comfort of the system are also discussed.The simulation results show that the measurement faults of the supply air temperature can lead to the increase in energy consumption.According to the fault characteristics,a data-driven method based on a neural network is presented to detect and diagnose the faults of air handling units.First,the historical data are selected to train the neural network so that it can recognize and predict the operation of the system.Then,the faults can be diagnosed by calculating the relative errors denoting the difference between the measuring values and the prediction outputs.Finally,the fault diagnosis strategy using the neural network is validated by using a simulator based on the TRNSYS platform.The results show that the neural network can diagnose different faults of the temperature,the flow rate and the pressure sensors in the air conditioning system.

Development of Management Systems for Electric Vehicle Battery Series

A kind of management system for electric vehicle (EV) battery series was developed. The system can predict residual capacity for EV battery series and mileages. The system can determine if it is necessary for the battery series to be charged. The system can determine which battery is necessary to be updated for the reason of damage or aging. The system can display the total voltage of battery series, extreme voltage and temperature of every battery in the series. The system can display the accumulative discharge for every battery in the series. The system can alarm when both total or extreme voltage is at low level, or temperature of a battery in the series is at high level. The system provided with a microprocessor as key part can collect and record signal of charging and discharging current, total voltage, extreme voltage and temperature for every battery. The mathematical model of residual capacity for EV lead acid batteries was discussed in details. The system operates well in the laboratory and meets the requirement.

Development of Fault Diagnosis System for Spacecraft Based on Fault Tree and G2

Some ideas in the development of fault diagnosis system for spacecraft are introduced. Firstly, the architecture of spacecraft fault diagnosis is proposed hierarchically with four diagnosis frames, i.e., system level, subsystem level, component level and element level. Secondly, a hierarchical diagnosis model is expressed with four layers, i.e., sensors layer, function layer, behavior layer and structure layer. These layers are used to work together to accomplish the fault alarm, diagnosis and localization. Thirdly, a fault-tree-oriented hybrid knowledge representation based on frame and generalized rule and its relevant reasoning strategy is put forward. Finally, a diagnosis case for spacecraft power system is exemplified combining the above with a powerful expert system development tool G2.

Insulation fault diagnosis based on group grey relational grade analysis method for power transformers

Utilising dissolved gases analysis, a new insulation fault diagnosis methodfor power transformers is proposed. This method is based on the group grey relational grade analysismethod. First, according to the fault type and grey reference sequence structure, some typicalfault samples are divided into several sets of grey reference sequences. These sets are structuredas one grey reference sequence group. Secondly, according to a new calculation method of the greyrelational coefficient, the individual relational coefficient and grade are computed. Then accordingto the given calculation method for the group grey relation grade, the group grey relational gradeis computed and the group grey relational grade matrix is structured. Finally, according to therelational sequence, the insulation fault is identified for power transformers. The results of alarge quantity of instant analyses show that the proposed method has higher diagnosis accuracy andreliability than the three-ratio method and the traditional grey relational method. It has goodclassified diagnosis ability and reliability.

Application of Maximum Probability Approach to the Fault Diagnosis of a Servo System

In an actual control system, it is often difficult to find out where the faults are if only based on the outside fault phenomena, acquired frequently from a fault system. So the fault diagnosis by outside fault phenomena is considered. Based on the theory of fuzzy recognition and fault diagnosis, this method only depends on experience and statistical data to set up fuzzy query relationship between the outside phenomena (fault characters) and the fault sources (fault patterns). From this relationship the most probable fault sources can be obtained, to attain the goal of quick diagnosis. Based on the above approach, the standard fuzzy relationship matrix is stored in the computer as a system database. And experiment data are given to show the fault diagnosis results. The important parameters can be on line sampled and analyzed, and when faults occur, faults can be found, the alarm is given and the controller output is regulated.

A Kind of Fuzzy Causal Diagnosis Method

Aim To improve the causal diagnosis method presented by Bandekar and propose a new method of finding the root fault order according to the fault possibility by means of numerical calculation. Methods Based on the causal graph, by utilization of fuzzified threshold value and fuzzy discrimination matrix, a kind of fuzzy causal diagnosis method was given and the fault possibility of each elements in the root fault candidate set (RFCS) was obtained. Results and Conclusion The order of each element in the RFCS can be obtained by the fault possibility, which makes the location of fault much easier. The diagnosis speed of this method is quite high, and by means of the fuzzified threshold value and fuzzy discrimination matrix, the result is more robust to noises and bad parameter's choice.

INTELLIGENT FUSION FOR AEROENGINE WEAR FAULT DIAGNOSIS

Four common oil analysis techniques, including the ferrography analysis （FA）, the spectrometric oil analysis （SOA）, the particle count analysis （PCA）, and the oil quality testing （OQT）, are used to implement the military aeroengine wear fault diagnosis during the test drive process. To improve the precision and the reliability of the diagnosis, the aeroengine wear fault fusion diagnosis method based on the neural networks （NN） and the Dempster-Shafter （D-S） evidence theory is proposed. Firstly, according to the standard value of the wear limit, original data are pre-processed into Boolean values. Secondly, sub-NNs are established to perform the single diagnosis, and their training samples are dependent on experiences from experts. After each sub-NN is trained, diagnosis results are obtained. Thirdly, the diagnosis results of each sub-NN are considered as the basic probability allocation value to faults. The improved D-S evidence theory is applied to the fusion diagnosis, and the final fusion results are obtained. Finally, the method is verified by a diagnosis example.

SIMULATION INVESTIGATION OF AEROENGINE FAULT DIAGNOSIS USING NEURAL NETWORKS

Traditional scheduled maintenance systems are costly, labor intensive, and typically provide noncomprehensive detection and diagnosis of engine faults. The engine monitoring system (EMS) on modern aircrafts has the potential to provide maintenance personnel with valuable information for detecting and diagnosing engine faults. In this paper, an RBF neural network approach is applied to aeroengine gas path fault diagnosis. It can detect multiple faults and quantify the amount of deterioration of the various engine components as a function of measured parameters. The results obtained demonstrate that the accuracy of diagnosis is consistent with practical requirements. The approach takes advantage of the nonlinear mapping feature of neural networks to capture the appropriate characteristics of an aeroengine. The methodology is generic and applicable to other similar plants having high complexity.

APPLICATION OF MULTI-SENSOR DATA FUSION BASED ON FUZZY NEURAL NETWORK IN ROTA TING MECHANICAL FAILURE DIAGNOSIS

At present, multi-se nsor fusion is widely used in object recognition and classification, since this technique can efficiently improve the accuracy and the ability of fault toleranc e. This paper describes a multi-sensor fusion system, which is model-based and used for rotating mechanical failure diagnosis. In the data fusion process, the fuzzy neural network is selected and used for the data fusion at report level. By comparing the experimental results of fault diagnoses based on fusion data wi th that on original separate data,it is shown that the former is more accurate than the latter.

Application of neural network in heating network leakage fault diagnosis

In order to investigate the leak detection strategy of a heating network,a space-based simulation mathematical model for the heating network under leakage conditions is built by graph theory.The pressure changes of all the nodes in the heating network are obtained from node leak and pipe leak conditions.Then,a leakage diagnosis system based on the back propagation（BP）neural network is established.This diagnosis system can predict the leakage pipe by collecting the pressure change data of the monitoring points,which can preliminary estimate the leak location.The usefulness of this system is proved by an example.The experimental results show that the forecast accuracy by this diagnosis system can reach 100%.

DEBRIS MONITORING AND ANALYZING SYSTEM (DMAS) AND ITS APPLICATION IN AEROENGINE

By inspecting and analyzing the debris, which is the most direct and important information units in the lubricating oil, we can monitor the machine condition to predict its failure. The debris monitoring and analyzing system (DMAS) is developed from the traditional iron spectrum technology, and has such characteristics as ease for debris separating, forecasting machine failure automatically and accurately in time and so on. The fundamental theory, components and its application in aeroengine health monitoring of DMAS are presented.

A REALIZATION OF FUZZY LOGIC BY A NEURAL NETWORK

This paper proposes a Fuzzy Neural Network (FNN) model, which uses a propagation algorithm. A logical operation is defined by a set of weights which are independent of inputs. The realization of the basic And,Or and Negation fuzzy logical operations is shown by the fuzzy neuron. A example in fault diagnosis is put forward and the result witnesses some effectiveness of the new FNN model.

ROTOR FAULT DIAGNOSIS OF BRUSHLESS AC GENERATOR WITH ROTARY RECTIFIER

With a three-phase bridge type rectification, some typical rotor faults of a brushless AC generator with a rotary rectifier is analyzed in this paper by the help of computer digital simulation. It is also proPOsed that the rotor faults, whether exist or not, and the causes of the faults may be determined through the monitoring of the average value of the exciting current of the exciter and its principal harmonics.

Integrated automatic HAZOP analysis and fault diagnosis based on Petri net

Based on systematically analyzing the procedure of hazard and operability (HAZOP) study, the author introduces a method of modeling fault diagnosis with the Petri net with fuzzy colors, in which the fuzzy information can be represented effectively in the process of analysis. The author proposes the architecture of a knowledge base, which integrates HAZOP analysis and fault diagnosis, and provides the conditions for constructing the knowledge-based expert system. The author also presents a method of knowledge representation for on-line HAZOP analysis and on-line fault diagnosis is presented based on the technology of Petri net with fuzzy colors, which establishes a technological fundamental for integrating the automatic HAZOP analysis and fault diagnosis.

Fault Diagnosis of Vehicle Transmission System Based on Rough Set Theory

Rough set theory is used to treat the data of vehicle transmission system faults. The minimum fault feature vector can be obtained by calculating the importance and dependency of each attribute. Real time diagnosis, as a result, can be actualized. Ultimate decision making can be done by analyzing the consistency of decision information. The result shows that rough set theory is useful and possesses its unique merits in this field.

ANN Model and Learning Algorithm in Fault Diagnosis for FMS

The fault diagnosis model for FMS based on multi layer feedforward neural networks was discussed An improved BP algorithm,the tactic of initial value selection based on genetic algorithm and the method of network structure optimization were presented for training this model ANN(artificial neural network)fault diagnosis model for the robot in FMS was made by the new algorithm The result is superior to the rtaditional algorithm

Hologamous integrating strategies for incipient fault diagnosis of transformer

This paper studied an integrative fault diagnostic system on the power transformer. On-line monitor items were grounded current of iron core, internal partial discharge and oil dissolved gas. Diagnostic techniques were simple rule-based judgment, fuzzy logistic reasoning and neural network distinguishing. Considering that much faults information was interactional, intellectualized diagnosis was implemented based on integrating the neural network with the expert system. Hologamous integrating strategies were materialized by information-based integrating monitor devices, shared information database on several levels and fusion diagnosis software along thought patterns. The expert system practiced logic thought by logistic reasoning. The neural network realized image thought by model matching. Creative conclusion was educed by their integrating. The diagnosis example showed that the integrative diagnostic system was reasonable and practical.