火力发电厂锅炉故障监测及预警系统设计

文章主要设计用于提高火电厂安全和生产效率的高效火电厂锅炉故障监测预警系统。锅炉设备状态的实时监测和故障预警是通过数据采集与传输、数据预处理和特征提取、故障诊断算法设计、预警策略和系统集成等步骤实现的。采用先进的数据分析技术和机器学习算法,可对潜在的故障模式和异常状态进行快速、准确的识别,并为避免设备损坏或停机带来的损失,及时向运维人员发出预警信号,提醒其采取适当措施。 。

基于无人机LiDAR的高压输电线路树障监测与分析

基于无人机LiDAR点云数据,利用LiDAR360软件对研究区域的点云数据进行点云平滑、重采样和去噪等预处理,随后采用克里金空间插值算法生成数字高程模型(DEM)和数字地表模型(DSM)并进行了精度检验,进而利用ArcGIS软件对DSM和DEM进行相减得到了冠层高度模型(Canopy Height Model,CHM),最后利用以焦点统计为主的一系列空间分析方法对CHM进行了单木树高和单木位置的提取。然后对研究区域内的高压输电线路沿线树木进行了排查,主要排查内容为树线之间的水平距离和垂直距离,将排查结果与高压输电线路树障隐患等级和高压输电线路安全距离标准进行对比,得到了相应的安全评估结果与树障位置。

一般消费级无人机的树障监测精度验证

无人机技术的发展使传统树障监测的方法变得更加数字化和便捷化。为了探究一般消费级无人机测量树高的精度问题,本文以桂林理工大学雁山校区为研究区,把无人机获取的高分辨率影像经处理后分别对比了基于数字冠层高度模型CHM和基于点云信息这两种方法提取树高的精度。试验结果表明:基于点云提取的树高平均绝对误差为12.4 cm,精度总体上优于基于数字冠层高度模型CHM的提取精度;基于CHM提取的树高小于实测高度;另外,圆形树冠的树高提取精度优于锥形树冠。两种方法测量值与真实值的相关性分别为0.97和0.98,证明了使用无人机进行树障监测的可行性。

NEW ALGORITHM FOR RECEIVER AUTONOMOUS INTEGRITY MONITORING

A new integrity metric for navigation systems is proposed based on the measurement domain. Proba-hilistic optimization design offers tools for fault detection by considering the required navigation performance （RNP） parameter and the uncertainty noise. The choice of the proper performance parameter provided the single-valued mapping with the missed detection probability estimates the probability of failure. The desirable characteristics of the residual sensitivity matrix are exploited to increase the efficiency for identifying erroneous observations. The algorithm can be used to support the performance specification and the efficient calculation of the integrity monitoring process. The simulation for non-precision approach （NPA） validates both the viability and the effectiveness of the proposed algorithm.

Industrial LAN for Vibration Monitoring and Fault Diagnosis of Turbo-Generator

The unique of using industrial LAN based on field bus to construct the system of vibration monitoring and fault diagnosis is introduced. The LAN topology, client/server architecture, database and designing of application software for vibration monitoring and fault diagnosis are involved. How to apply industrial LAN to the vibration monitoring and fault diagnosis of turbo generator is discussed, and a scheme of how to construct the industrial LAN for vibration monitoring and fault diagnosis of turbo generator is presented.

Fault Diagnosis of Mechanical Equipments Through Spectrometric oil Analysis for Worn Off Metallic Elements

In the fault prediction of mechanical equipments through spectromectric oil analysis for worn off debris, a method for the determination of the limiting value of wear is proposed and discussed. In order to diagnose the impending failure and to predict the fault modes and locate the fault spots, a comprehensive approach is studied and outlined on the basis of methods of discriminative analysis and fuzzy logic. A fault diagnosis expert system OAFDS developed by the authors for the nonitoring of working conditions of the ND5 locomotive diesel engine Nd5 is briefly introduced.

Fault diagnosis and analysis of main sea water pump based on vibration monitoring in offshore oil field

The main sea water pump is the key equipment for the floating production storage and offloading （FPSO）. Affected by some factors such as hull deformation, sea water corrosion, rigid base and pipeline stress, the vibration value of main sea water pump in the horizontal direction is abnormally high and malfunctions usually happen. Therefore, it is essential to make fault diagnosis of main sea water pump, By conventional off-line monitoring and vibration amplitude spectrum analysis, the fault cycle is found and the alarm value and stop value of equipment are set, which is helpful to equipment maintenance and accident prevention.

Implementation of remote monitoring system for prediction of tool wear and failure using ART2

Remote monitoring of tools for prediction of tool wear in cutting processes was considered, and a method of implementation of a remote-monitoring system previously developed was proposed. Sensor signals were received and tool wear was predicted in the local system using an ART2 algorithm, while the monitoring result was transferred to the remote system via intemet. The monitoring system was installed at an on-site machine tool for monitoring three kinds of tools cutting titanium alloys, and the tool wear was evaluated on the basis of vigilances, similarities between vibration signals received and the normal patterns previously trained. A number of experiments were carried out to evaluate the performance of the proposed system, and the results show that the wears of finishing-cut tools are successfully detected when the moving average vigilance becomes lower than the critical vigilance, thus the appropriate tool replacement time is notified before the breakage.

Online Supervision of Penicillin Cultivations Based on Rolling MPCA

To reduce the variations of the production process in penicillin cultivations, a rolling multivariate statis-tical approach based on multiway principle component analysis (MPCA) is developed and used for fault diagnosis of penicillin cultivations. Using the moving data windows technique, the static MPCA is extended for use in dy-namic process performance monitoring. The control chart is set up using the historical data collected from the past successful batches, thereby resulting in simplification of monitoring charts, easy tracking of the progress in each batch run, and monitoring the occurrence of the observable upsets. Data from the commercial-scale penicillin fer-mentation process are used to develop the rolling model. Using this method, faults are detected in real time and the corresponding measurements of these faults are directly made through inspection of a few simple plots (t-chart, SPE-chart, and T2-chart). Thus, the present methodology allows the process operator to actively monitor the data from several cultivations simultaneously.

Monitoring method of gear teeth failure of hydraulic gear pump based on improved VMD and DBN-DNN of electrical signal

Abundant system operation state information is included in the electrical signal of the hydraulic system motor.How to accurately extract and classify the operation information of electrical signal is the key to realize the condition monitoring of hydraulic system.The early fault characteristics of hydraulic gear pump hidden in the motor current signal are weak and difficult to extract by traditional time-frequency analysis.Based on the correlation coefficient and artificial bee colony algorithm(ABC),the parameter optimization of variational mode decomposition(VMD)is realized in this paper.At the same time,the principle of maximum signal correlation coefficient and kurtosis value is adopted to determine the effective intrinsic mode function(IMF).Moreover,the permutation entropy(PE)and root mean square(RMS)of the effective IMF components are input into the deep belief network(DBN-DNN)as high-dimensional feature vectors.The operation state of gear pump is monitored.The results show that the weak characteristics of current signal of gear pump fault are accurately and stably extracted by this method.The running state of gear pump is monitored and the accuracy of gear fault diagnosis is improved.

Fault diagnosis of chemical processes based on partitioning PCA and variable reasoning strategy

Fault detection and identification are challenging tasks in chemical processes, the aim of which is to decide out of control samples and find fault sensors timely and effectively. This paper develops a partitioning principal component analysis(PPCA) method for process monitoring. A variable reasoning strategy is proposed and applied to recognize multiple fault variables. Compared with traditional process monitoring methods, the PPCA strategy not only reflects the local behavior of process variation in each model(each direction of principal components),but also improves the monitoring performance through the combination of local monitoring results. Then, a variable reasoning strategy is introduced to locate fault variables. Unlike the contribution plot, this method locates normal and fault variables effectively, and gives initiatory judgment for ambiguous variables. Finally, the effectiveness of the proposed process monitoring and fault variable identification schemes is verified through a numerical example and TE chemical process.

CONDITION MONITORING AND FAULT DIAGNOSIS FOR TENSION UNBALANCE OF ROPES IN MULTI-ROPE FRICTION WINDER

This paper analyzes the reasons of the tension unbalance of the ropes in multi-rope fric-tion winder, introduces the method of an on-line monitoring rope tensions with a testing device de-veloped by authors, and proposes the criteria of the fault diagnosis and the method of adjustment for the tension unbalance of the ropes, which is important to the theoretical study on the tension unbalance of the ropes and the maintenance of multi-rope winder.

Design of intelligent transformer monitoring system based on GSM

This paper proposes a method for transformer fault detection system based on GSM network, through the current transformer, voltage sensor information, the known fault information attribute conditions, and the feedback weighted coefficient through the adaptive feed forward, that can obtain high fault information attributes, thus filtering interference factors and timely get fault information properties. It solves the traditional transformer can only judge general fault and fault real-time alarm by GSM network, the system also can measure the transformer oil temperature and oil temperature alarm in super high temperature, to detect the power transformer operation parameters, and the fault running state （over-current, over-voltage, under-voltage, warning）. The actual test system show that, diagnosis ability of fault signal separation ability and small signal identification increases 17%. also meet the requirements of real-time.

Design and Realization of Distribution Network Intelligent Monitoring based on GSM Network

the power network fault detection system can only analyze all kinds of fault signal in stationary sequence： the internal grid and external disturbance presents degeneration, tiny, signal intensity changes randomly fluctuate, that will cause the system to detect the fault isolation ability of confusion and small fault signal is not strong; The article propose a method of power network fault detection for based on GSM, Through the underlying sensing equipment acquisition abnormal information of current, voltage power in the network and GSM networking scheme can filter the interference factors in extraction of fault information from and attribute value. The embedded gateway take STM32 chip as the core to monitoring data processing, to achieve a unified data management and user remote access, realizing method of system software are given, to construct the monitor management information platform. The actual test system show that, identification diagnosis ability of fault signal separation ability and small signal increases 17%, also meet the requirements.

Importance of Monitoring of Emotional Stress and Physiological Functions of Humans during the Night Sleep

The diagnosis of disease and prescription of therapy are mainly realized through the control of physiological functions in humans during wakefulness. However, little attention is paid to evaluate the functional state of a patient in sleep. The efficiency of prescribed medicines is also determined in wakefulness. The effects of these drugs during sleep are usually out-of-sight of a physician. Human psychoemotional state during wakefulness is closely related to sleep. Various serious disturbances in physiological functions appear during the night sleep. They include cardiovascular disorders （hypertensive crisis, myocardial infarction, arrhythmias, stroke, etc.）, which may cause sudden death. A large body of evidence shows that stress is followed by sleep disorders. They serve as a factor, which contributes to the development of stress. It should be noted that sleep is the major anti-stress factor. The vicious circle is formed, which can be revealed by a complex control of human physiological functions during wakefulness and sleep. We developed a new methodology and constructed an information-and-equipment device for monitoring of vital physiological functions of humans during sleep in a real everyday life. This approach holds much promise for the diagnosis of disease and evaluation of therapeutic efficacy.

Dynamic Fault Model for On-Line Evaluation of Yarn End Breakage during Ring Spinning

In this paper, a dynamic fault model is proposed to predict yarn end breakage in the spinning procedure through investigation of fault characteristics. In view of the principle that uniformity bad in raw material causes iustable yarn formation, the investigation focuses on the fault characteristic existing in the dynamic tension. Analyzing the dynamic spinning system, the phenomenon of over random shock in a spinning triangle is discovered to be the main physical event prior to yarn end breakage. The fault characteristic is further confirmed by dynamic tests and signal processing, and can be used to make an approach to predicting yarn end breakage. A relative energy feature is defined for evaluating the tendency of yarn end breakage, and its effectiveness is verified by on.line monitoring tests in the laboratory. The research results show that the proposed dynamic fault model has not only an advantage in indicating the presence of fault characteristics, but also great potentials in quantitating fault in online spinning monitoring.

Application of Neural Networks for Monitoring Mechanical Defects of Rotating Machines

Good monitoring of the deterioration in rotating machinery can result in reduced maintenance costs by minimizing the loss of production due to the number of machine breakdown and decreasing in the number of spare parts. In the present paper, a prognostic method based on recurrent neural networks is applied to forecast the rate of machine deterioration. Promising results have been obtained through the application of this method to the prediction of vibration based fault trends of an auxiliary gearbox of a power generation plant. This method evaluates also the seriousness of damage caused by faults.

Condition Assessment of Oil-Paper Transformer Insulating System

Damages of power transformers are caused partly by construction setting but also by operation （i. e., due to operational and degradation parameters）. A long-term operation and exceeding of the limit values of transformer abbreviate the lifetime of this device very dramatically. A condition of insulating system oilpaper is principal diagnostic indicator that has its essential influence to failure-free operation of power transformers. The paper deals with a description of the experience with developed monitoring system, which is focused mainly on a control of oil filling of experimental transformer. Relative humidity, amount of dissolved gases and increased temperature are observed, as they have decisive influence on insulating system of transformer. Monitoring of these characteristics is completed with analysis of oil samples by infrared spectroscopy in mode of Attenuated Total Reflection technique.

Failure Monitoring of E-Glass/Vinylester Composites Using Fiber Grating Acoustic Sensor

This paper reports an application of an optical fiber sensor in a continuous and in situ failure testing of an E-glass/vinylester top hat stiffener （THS）. The sensor head was constructed from a compact phase-shifted fiber Bragg grating （PS-FBG）. The narrow transmission channel of the PS-FBG is highly sensitive to small perturbation, hence suitable to be used in acoustic emission （AE） assessment technique. The progressive failure of THS was tested under transverse loading to experimentally simulate the actual loading in practice. Our experimental tests have demonstrated, in good agreement with the commercial piezoelectric sensors, that the important failures information of the THS was successfully recorded by the simple intensity-type PS-FBG sensor.

Fiber Laser Sensor Based on a Phase-Shifted Chirped Grating for Acoustic Sensing of Partial Discharges

Acoustic emission monitoring is often used in the diagnosis of electrical and mechanical incipient faults in the high voltage apparatus. Partial discharges are a major source of insulation failure in electric power transformers, and the differentiation from other sources of acoustic emission is of the utmost importance. This paper reports the development of a new sensor concept - a fiber laser sensor based on a phase-shifted chirped fiber grating - for the acoustic emission detection of incipient faults in oil-filled power transformers. These sensors can be placed in the inner surface of the transformer tank wall, not affecting the insulation integrity of the structure and improving fault detection and location. The performance of the sensing head is characterized and compared for different surrounding media： air, water, and oil. The results obtained indicate the feasibility of this sensing approach for the industrial development of practical solutions.