电容式电压互感器故障分析及预防措施

在全世界发展的进程中,对于电力的需求一直都只增不减。电压互感器作为十分重要的电力测量设备,在不断呈现电力系统运行状态与保护电力系统稳定性的同时,也承担着十分重要的安全责任。尤其是电容式电压互感器,一直都是我国变电站组成结构当中重要的设备之一,其价值不可忽视。主要对电容式电压互感器故障进行深入的探讨和分析,并结合当前故障的各种诱导因素,阐述电容式电压互感器故障预防的相关措施。

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.

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.

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.

DIAGNOSIS OF DAMPING FAULTS IN HELICOPTER ROTOR HUB BASED ON FUSELAGE VIBRATIONS

Damping faults in a helicopter rotor hub are diagnosed by using vibration signals from the fuselage. Faults include the defective lag damper and raspings in its flap and feathering hinges. Experiments on the diagnosis of three faults are carried out on a rotor test rig with the chosen fault each time. Fuselage vibration signals from specified locations are measured and analyzed by the fast Fourier transform in the frequency domain. It is demonstrated that fuselage vibration frequency spectra induced by three faults are different from each other. The probabilistic neural network （PNN） is adopted to detect three faults. Results show that it is feasible to diagnose three faults only using fuselage vibration data.

Wavelet neural network based fault diagnosis in nonlinear analog circuits

The theories of diagnosing nonlinear analog circuits by means of the transient response testing are studled. Wavelet analysis is made to extract the transient response signature of nonlinear circuits and compress the signature dada. The best wavelet function is selected based on the between-category total scatter of signature. The fault dictionary of nonlinear circuits is constructed based on improved back-propagation（BP） neural network. Experimental results demonstrate that the method proposed has high diagnostic sensitivity and fast fault identification and deducibility.

Time between failures model and failure analysis of CNC system

To provide basis for the reliability improvement design of CNC system, the failure data of a type of CNC system in one year are collected under field conditions in workshops. The distribution model parameters of time between failures are estimated by least square method and hypothesis testing is done by d-test method. It is proved that the time between failures of the CNC system follows Weibull distribution and the system has entered into the wear-out failure period. The failure positions and failure causes are analyzed further to indicate the weak subsystems of the CNC system. It can be found that servo unit, electrical system, detecting unit and power supply are principal failure positions and the main failure cause is breakage of components. The corresponding improvement measures are put forward. The paper provides a reference to reliability design and analysis of CNC system for the manufacturer and has great guidance to using and maintaining CNC system for the user.

Risk Management of Key Issues of FPSO

Risk analysis of key systems have become a growing topic late of because of the development of offshore structures. Equipment failures of offioading system and fire accidents were analyzed based on the floating production, storage and offioading （FPSO） features. Fault tree analysis （FTA）, and failure modes and effects analysis （FMEA） methods were examined based on information already researched on modules of relex reliability studio （RRS）. Equipment failures were also analyzed qualitatively by establishing a fault tree and Boolean structure function based on the shortage of failure cases, statistical data, and risk control measures examined. Failure modes of fire accident were classified according to the different areas of fire occurrences during the FMEA process, using risk priority number （RPN） methods to evaluate their severity rank. The qualitative analysis of FTA gave the basic insight of forming the failure modes of FPSO offioading, and the fire FMEA gave the priorities and suggested processes. The research has practical importance for the security analysis problems of FPSO.

Mechanical behavior and failure analysis of brittle sandstone specimens containing combined flaws under uniaxial compression

Based on the axial stress-axial strain curves,the effect of fissure angle on the strength and deformation behavior of sandstone specimens containing combined flaws is analyzed.The mechanical parameters of sandstone specimens containing combined flaws are all lower than that of intact specimen,but the reduction extent is distinctly related to the fissure angle.The results of sandstone specimens containing combined flaws are obtained by the acoustic emission,which can be used to monitor the crack initiation and propagation.The ultimate failure mode and crack coalescence behavior are evaluated for brittle sandstone specimens containing combined flaws.Nine different crack types are identified on the basis of their geometry and crack coalescence mechanism(tensile crack,hole collapse,far-field crack and surface spalling)for combined flaws.The photographic monitoring was also adopted for uniaxial compression test in order to confirm the sequence of crack coalescence in brittle sandstone specimens containing combined flaws,which recorded the real-time crack coalescence process during entire deformation.According to the monitored results,the effect of crack coalescence process on the strength and deformation behavior is investigated based on a detailed analysis for brittle sandstone specimens containing combined flaws by using digital photogrammetry.

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.

Fault Diagnosis in Chemical Process Based on Self-organizing Map Integrated with Fisher Discriminant Analysis

Fault diagnosis and monitoring are very important for complex chemical process. There are numerous methods that have been studied in this field, in which the effective visualization method is still challenging. In order to get a better visualization effect, a novel fault diagnosis method which combines self-organizing map (SOM) with Fisher discriminant analysis (FDA) is proposed. FDA can reduce the dimension of the data in terms of maximizing the separability of the classes. After feature extraction by FDA, SOM can distinguish the different states on the output map clearly and it can also be employed to monitor abnormal states. Tennessee Eastman (TE) process is employed to illustrate the fault diagnosis and monitoring performance of the proposed method. The result shows that the SOM integrated with FDA method is efficient and capable for real-time monitoring and fault diagnosis in complex chemical process.

Integration of Fault Analysis and Interlock Controller Synthesis for Batch Processes

Integration amongst various decision-making processes, such as planning, design, and operation is necessary to dynamic and flexible batch production. To achieve a batch production integration, utilization of common models used for various decision-making processes is an effective approach. From this point of view, a batch system common model as described by a Petri net is proposed. In this article, a fault diagnosis technique for batch processes is presented using information about fault propagation and the possibilities of integration of fault analysis and controller synthesis are discussed on the basis of the Petri net based common models.

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.

A new fracture model for the prediction of longwall caving characteristics

A new numerical model is presented to simulate fracture initiation and propagation in geological structures. This model is based on the recent amalgamation of established failure and fracture mechanics theory, which has been implemented to the finite difference FLAC code as a constitutive FISH userdefined-model. Validation of the model has been studied on the basis of comparing the transitional failure modes in rock. It is shown that the model is capable of accurately simulating fracture distributions over entire brittle to ductile rock phases. The application of the model during longwall retreat simulation highlighted several caving characteristics relevant to varying geological condition. The distribution and behaviour of modelled fractures were both realistic and shown to provide an enhanced post failure analysis to geological structures in FLAC. Moreover, the model introduces new potential insight towards the failure analysis of more complicated problems. This is best suited towards improving safety and efficiency in mines through the prediction of various key fractures and caving characteristics of geological structures.

Reverse Engineering Analysis Based on Differential Fault Analysis Against Secret S- boxes

To evaluate the security of cipher algo- rithrrs with secret operations, we built a new reverse engineering analysis based on Differential Fault Analysis （DFA） to recover the secret S-boxes in Secret Private Network （SPN） and Feistel structures, which are two of the most typical structures in block ciphers. This paper gives the general definitions of these two structures and proposes the reverse engineering analysis of each structure. Furthermore, we evaluate the complexity of the proposed reverse analyses and theoretically prove the effectiveness of the reverse method. For the Twoflsh-like and AES-like algorithrm, the experimental results verify the correctness and efficiency of the reverse analysis. The proposed reverse analysis can efficiently recover the secret S-boxes in the encryp＇don algorithms writh SPN and Feistel structures. It can successfully recover the Twoflsh- like algorithm in 2.3 s with 256 faults and the AES- like algorithm in 0.33 s with 23 faults.

Lifeline system network reliability calculation based on GIS and FTA

Lifelines, such as pipeline, transportation, communication, electric transmission and medical rescue systems, are complicated networks that always distribute spatially over large geological and geographic units. The quantification of their reliability under an earthquake occurrence should be highly regarded, because the performance of these systems during a destructive earthquake is vital in order to estimate direct and indirect economic losses from lifeline failures, and is also related to laying out a rescue plan. The research in this paper aims to develop a new earthquake reliability calculation methodology for lifeline systems. The methodology of the network reliability for lifeline systems is based on fault tree analysis （FTA） and geological information system （GIS）. The interactions existing in a lifeline system ale considered herein. The lifeline systems are idealized as equivalent networks, consisting of nodes and links, and are described by network analysis in GIS. Firstly, the node is divided into two types： simple node and complicated node, where the reliability of the complicated node is calculated by FTA and interaction is regarded as one factor to affect performance of the nodes. The reliability of simple node and link is evaluated by code. Then, the reliability of the entilre network is assessed based on GIS and FTA. Lastly, an illustration is given to show the methodology.

A Novel Systematic Method of Quality Monitoring and Prediction Based on FDA and Kernel Regression

A novel systematic quality monitoring and prediction method based on Fisher discriminant analysis (FDA) and kernel regression is proposed. The FDA method is first used for quality monitoring. If the process is un-der normal condition, then kernel regression is further used for quality prediction and estimation. If faults have oc-curred, the contribution plot in the fault feature direction is used for fault diagnosis. The proposed method can ef-fectively detect the fault and has better ability to predict the response variables than principle component regression (PCR) and partial least squares (PLS). Application results to the industrial fluid catalytic cracking unit (FCCU) show the effectiveness of the proposed method.

Modeling and monitoring of nonlinear multi-mode processes based on similarity measure-KPCA

A new modeling and monitoring approach for multi-mode processes is proposed.The method of similarity measure(SM) and kernel principal component analysis(KPCA) are integrated to construct SM-KPCA monitoring scheme,where SM method serves as the separation of common subspace and specific subspace.Compared with the traditional methods,the main contributions of this work are:1) SM consisted of two measures of distance and angle to accommodate process characters.The different monitoring effect involves putting on the different weight,which would simplify the monitoring model structure and enhance its reliability and robustness.2) The proposed method can be used to find faults by the common space and judge which mode the fault belongs to by the specific subspace.Results of algorithm analysis and fault detection experiments indicate the validity and practicability of the presented method.

Hybrid Support Vector Machines-Based Multi-fault Classification

Support Vector Machines （SVM） is a new general machine-learning tool based on structural risk minimization principle. This characteristic is very signific ant for the fault diagnostics when the number of fault samples is limited. Considering that SVM theory is originally designed for a two-class classification, a hybrid SVM scheme is proposed for multi-fault classification of rotating machinery in our paper. Two SVM strategies, 1-v-1 （one versus one） and 1-v-r （one versus rest）, are respectively adopted at different classification levels. At the parallel classification level, using l-v-1 strategy, the fault features extracted by various signal analysis methods are transferred into the multiple parallel SVM and the local classification results are obtained. At the serial classification level, these local results values are fused by one serial SVM based on 1-v-r strategy. The hybrid SVM scheme introduced in our paper not only generalizes the performance of signal binary SVMs but improves the precision and reliability of the fault classification results. The actually testing results show the availability suitability of this new method.

Reconstruction based approach to sensor fault diagnosis using auto-associative neural networks

Fault diagnostics is an important research area including different techniques.Principal component analysis(PCA)is a linear technique which has been widely used.For nonlinear processes,however,the nonlinear principal component analysis(NLPCA)should be applied.In this work,NLPCA based on auto-associative neural network(AANN)was applied to model a chemical process using historical data.First,the residuals generated by the AANN were used for fault detection and then a reconstruction based approach called enhanced AANN(E-AANN)was presented to isolate and reconstruct the faulty sensor simultaneously.The proposed method was implemented on a continuous stirred tank heater(CSTH)and used to detect and isolate two types of faults(drift and offset)for a sensor.The results show that the proposed method can detect,isolate and reconstruct the occurred fault properly.