Application of Radial Basis Function Network in Sensor Failure Detection

Aim To detect sensor failure in control system using a single sensor signal. Methods A neural predictor was designed based on a radial basis function network(RBFN), and the neural predictor learned the sensor signal on line with a hybrid algorithm composed of n means clustering and Kalman filter and then gave the estimation of the sensor signal at the next step. If the difference between the estimation and the actural values of the sensor signal exceeded a threshold, the sensor could be declared to have a failure. The choice of the failure detection threshold depends on the noise variance and the possible prediction error of neural predictor. Results and Conclusion\ The computer simulation results show the proposed method can detect sensor failure correctly for a gyro in an automotive engine.

Application of Wavelets Transform to Fault Detection in Rotorcraft UAV Sensor Failure

This paper describes a novel wavelet-based approach to the detection of abrupt fault of Rotorcrafi Unmanned Aerial Vehicle （RUAV） sensor system. By use of wavelet transforms that accurately localize the characteristics of a signal both in the time and frequency domains, the occurring instants of abnormal status of a sensor in the output signal can be identified by the multi-scale representation of the signal. Once the instants are detected, the distribution differences of the signal energy on all decomposed wavelet scales of the signal before and after the instants are used to claim and classify the sensor faults.

An Efficient Adaptive Failure Detection Mechanism for Cloud Platform Based on Volterra Series

Failure detection module is one of important components in fault-tolerant distributed systems,especially cloud platform.However,to achieve fast and accurate detection of failure becomes more and more difficult especially when network and other resources' status keep changing.This study presented an efficient adaptive failure detection mechanism based on volterra series,which can use a small amount of data for predicting.The mechanism uses a volterra filter for time series prediction and a decision tree for decision making.Major contributions are applying volterra filter in cloud failure prediction,and introducing a user factor for different QoS requirements in different modules and levels of IaaS.Detailed implementation is proposed,and an evaluation is performed in Beijing and Guangzhou experiment environment.

SENSOR FAILURE DETECTION AND SIGNAL RECOVERY BASED ON BP NEURAL NETWORK

A study is given on the application of BP neural network (BPNN) in sensorfailure detection in control systems, and on the networ architecture desgn, the redun-dancy,the quickness and the insensitivity to sensor noise of the BPNN based sensor detec-tion methed. Besules, an exploration is made into tbe factors accounting for the quality ofsignal recovery for failed sensor using BPNN. The results reveal clearly that BPNN can besuccessfully used in sensor failure detection and data recovery.

Automatic System for Failure Detection in Hydro-Power Generators

This paper presents an automatic system for failure detection in hydro-power generators. The main idea of this system is to detect failure using current and voltage signals acquired without any type of internal interference in the generator operation. The detected failures could be mechanical or electrical origins, such as: problems in bearings, unwanted vibrations, partial discharges, misalignment, unbalancing, among others. It is possible because the generator acts as a transducer for mechanical problems, and they appear in current and voltage signals. This automatic system based on electric signature analysis has been installed in Itapebi Power Plant generators since 2012. Some results are presented in this paper.

Predicting heartbeat arrival time for failure detection over internet using auto-regressive exogenous model

Predicting heartbeat message arrival time is crucial for the quality of failure detection service over intemet. However, intemet dynamic characteristics make it very difficult to understand message behavior and accurately predict heartbeat arrival time. To solve this problem, a novel black-box model is proposed to predict the next heartbeat arrival time. Heartbeat arrival time is modeled as auto-regressive process, heartbeat sending time is modeled as exogenous variable, the model＇ s coefficients are estimated based on the sliding window of observations and this result is used to predict the next heartbeat arrival time. Simulation shows that this adaptive auto-regressive exogenous （ARX） model can accurately capture heartbeat arrival dynamics and minimize prediction error in different network environments.

Prediction of Link Failure in MANET-IoT Using Fuzzy Linear Regression

A Mobile Ad-hoc NETwork(MANET)contains numerous mobile nodes,and it forms a structure-less network associated with wireless links.But,the node movement is the key feature of MANETs;hence,the quick action of the nodes guides a link failure.This link failure creates more data packet drops that can cause a long time delay.As a result,measuring accurate link failure time is the key factor in the MANET.This paper presents a Fuzzy Linear Regression Method to measure Link Failure(FLRLF)and provide an optimal route in the MANET-Internet of Things(IoT).This work aims to predict link failure and improve routing efficiency in MANET.The Fuzzy Linear Regression Method(FLRM)measures the long lifespan link based on the link failure.The mobile node group is built by the Received Signal Strength(RSS).The Hill Climbing(HC)method selects the Group Leader(GL)based on node mobility,node degree and node energy.Additionally,it uses a Data Gathering node forward the infor-mation from GL to the sink node through multiple GL.The GL is identified by linking lifespan and energy using the Particle Swarm Optimization(PSO)algo-rithm.The simulation results demonstrate that the FLRLF approach increases the GL lifespan and minimizes the link failure time in the MANET.

The characteristics of liver injury induced by Amanita and clinical value of α-amanitin detection

Background:Amanita poisoning as a foodborne disease has raised concerning mortality issues.Reducing the interval between mushroom ingestion and medical intervention could greatly influence the outcomes of Amanita poisoning patients,while treatment is highly dependent on a confirmed diagnosis.To this end,we developed an early detection-guided intervention strategy by optimizing diagnostic process with performingα-amanitin detection,and further explored whether this strategy influenced the progression of Amanita poisoning.Methods:This study was a retrospective analysis of 25 Amanita poisoning patients.Thirteen patients in the detection group were diagnosed mainly based onα-amanitin detection,and 12 patients were diagnosed essentially on the basis of mushroom consumption history,typical clinical patterns and mushroom identification(conventional group).Amanita poisoning patients received uniform therapy,in which plasmapheresis was executed once confirming the diagnosis of Amanita poisoning.We compared the demographic baseline,clinical and laboratory data,treatment and outcomes between the two groups,and further explored the predictive value ofα-amanitin concentration in serum.Results:Liver injury induced by Amanita appeared worst at the fourth day and alanine aminotransferase(ALT)rose higher than aspartate aminotransferase(AST).The mortality rate was 7.7%(1/13)in the detection group and 50.0%(6/12)in the conventional group(P=0.030),since patients in the detection group arrived hospital much earlier and received plasmapheresis at the early stage of disease.The early detection-guided intervention helped alleviate liver impairment caused by Amanita and decreased the peak AST as well as ALT.However,the predictive value ofα-amanitin concentration in serum was still considered limited.Conclusions:In the management of mushroom poisoning,consideration should be given to the rapid detection ofα-amanitin in suspected Amanita poisoning patients and the immediate initiation of medical treatment upon a positive toxin screening result.

INFRARED THERMAL IMAGE STUDY ON THE FOREWARNING OF COAL AND SANDSTONE FAILURE UNDER LOAD

In the experimental study, AGE-782 thermal instrument was used to detect the infrared radiation variation of coal and sandstone (wave-length range 3.6～5.5 μm was used). It's discovered that coal and sandstone failure under load have three kinds of infrared thermal features as well as infrared forewarning messages. That are: (1) temperature rises gradually but drops before failure ; (2) temperature rises gradually but quickly rises before failure; (3) first rises,then drops and lower temperature emerges before failure. The further researches and the prospect of micro-wave remote sensing detection .on ground pressure is also discussed.

Design of Heading Fault-Tolerant System for Underwater Vehicles Based on Double-Criterion Fault Detection Method

This paper proposes a heading fault tolerance scheme for operation-level underwater robots subject to external interference.The scheme is based on a double-criterion fault detection method using a redundant structure of a dual electronic compass.First,two subexpansion Kalman filters are set up to fuse data with an inertial attitude measurement system.Then,fault detection can effectively identify the fault sensor and fault source.Finally,a fault-tolerant algorithm is used to isolate and alarm the faulty sensor.The program can effectively detect the constant magnetic field interference,change the magnetic field interference and small transient magnetic field interference,and conduct fault tolerance control in time to ensure the heading accuracy of the system.Test verification shows that the system is practical and effective.

换热器用无缝钛管失效分析

该文通过采用宏观检测、显微组织分析、扫描电镜分析、能谱分析等方法对流动介质为氯化物溶液的换热器用无缝钛管进行失效分析,发现流动介质为氯化物溶液的换热器中无缝钛管主要是通过以下2种方式发生腐蚀失效的,一种是Fe+离子存在于钛管材表面会在钛管材表面形成点腐蚀,通过点腐蚀的方式使钛管发生腐蚀失效;另一种是当换热器的管板和管材形成一定缝隙时会发生间隙腐蚀,间隙腐蚀发生时钛管会伴随有吸氢反应和自催化作用,最终导致管材产生腐蚀孔洞而失效。同时笔者建议在制作内部流动氯化物溶液的换热器时不易将不锈钢管板和钛管组合使用,并且管板与管材之间的间隙大于0.5 mm以免在使用过程中发生点腐蚀和间隙腐蚀导致换热器泄漏。

基于结构振动响应的长型浮置板轨道隔振器失效检测方法

目前钢弹簧浮置板轨道的隔振器失效判别主要依赖于人工巡检或视觉成像系统,但此类方法效率低、检测滞后且成本高昂。本文提出了一种自动化的检测方法,该方法利用残差学习思想和卷积神经网络基本理论构建数据分类器,并通过列车动荷载作用下的结构动力响应对现役某常见的长型浮置板轨道进行隔振器失效的检测识别。首先,基于车-轨垂向耦合动力学建立考虑隔振器不同服役状态的地铁车辆-浮置板轨道垂向耦合动力学仿真分析模型,进而生成多种运营工况下的数据集,用于网络的训练和性能测试;其次,进一步研究了传感器布置方式对网络检测性能的影响,以决定适宜的传感器布置方案;最后,在实际地铁线路中开展试验,对所提出的方法进行了验证。结果表明:通过对传感器布置位置进行优化,深度残差网络模型的检测准确性显著提升;合理设置传感器的数量也可以提高本文方法的检测性能;此外,通过适当选取传感器布置方案,本文提出的基于深度残差网络的隔振器失效检测方法在仿真数据中实现了98.99%的准确度,并在试验数据中实现了96.33%的准确度。该方法具有较高的可行性和应用潜力,可为地铁浮置板隔振器智能运维提供参考,并有望在未来用于隔振器失效的自动化检测。

高速切换场景下的双门限判决算法研究

传统的越区切换算法无法应对高速列车提速带来的通信质量需求增长,本文提出一种基于双门限判决的位置触发切换算法,以提升越区切换时的通信质量。选择大小双门限值,能够有效减少信号强度波动带来的影响。在接收信号强度波动较小时采用双门限中的较小值,波动较大时则采用较大值,针对每次测量结果实时进行连续双判决。仿真结果表明:相较于传统切换算法,双门限判决算法能够使得切换发生位置更靠近基站重叠区域的中点,进而降低切换掉话率和切换失败率。同时双判决减小了平均切换次数,使通信体验得到了极大提升。

基于DGLPP-SVDD算法的化工过程故障检测

为解决传统全局局部保留投影算法(GLPP)不能充分利用已有故障数据进行特征提取的缺点,提出了判别全局局部保留投影算法(DGLPP)。在数据降维处理后,为应对高斯和非高斯混合分布的过程数据特性,通过支持向量数据描述算法(SVDD)构建故障检测统计量。将两种算法相结合提出基于DGLPP-SVDD的故障检测方法。将DGLPP-SVDD算法应用于TE过程仿真,并与GLPP算法对比,结果表明:DGLPP-SVDD算法具有更短的故障检测滞后时间和更高的故障检测率。

异性纤维清除机的改进与应用

针对目前国内外异性纤维清除机清除效率较低的问题,通过自主研发核心软硬件、采用先进高可靠性元器件和技术措施对异性纤维清除机进行技术改进;详细阐述改进后异性纤维清除机的工作原理、技术参数、安装要点、喷阀参数设置、维护及故障排除措施等,并对改进关键件、改后实际检出率及性价比进行分析。指出:改进后的异性纤维清除机,解决了乱喷误喷、稳定性差、故障率高等问题,识别并检出异性纤维的能力稳定,检出率提高,各单项及综合检出率最高可达88%。

基于大数据的工业设备智能检测与远程运维系统研究

设备的性能及运行的可靠性是企业提高生产效率、降低运营成本的关键。基于某汽车整车厂2017-2019年度的维修统计数据,通过对设备劣化分析、故障类型分类、故障预测性解决方案以及AI技术在故障检测中的应用等内容,深入研究了设备故障机理及运维技术,并提出了基于大数据的设备智能检测与远程运维系统的设计方案。通过远程实时监控、智能检测设备的运行状态、通过大数据分析,发现设备运行潜在的安全风险,预测并预防可能的故障,以提高设备的可靠性、降低运维成本并提升运维效率,为企业的可持续发展提供有力保障。

基于深度学习的机器人电气故障检测与诊断研究

机器人以其高效、高强度的作业方式,被广泛地应用于制造业及其相关领域。但当机器人出现故障时,通常会导致生产线停滞,浪费大量的人力、物力,甚至危及工作人员的生命安全。传统的故障诊断方法耗时长,诊断效率低,且故障辨识精度不高。文章基于深度学习相关理论,通过分析机器人机械臂各个关节及执行器的振动特性,建立了一种适用于工业机器人的故障诊断模型,并以ABBirb120机器人为例进行电气故障检测与诊断的准确率分析。结果表明,在迭代次数达到900以上时,其故障识别准确率趋于99.4%。

血清CK-MB、IL-6、LDH水平联合检测在慢性心力衰竭诊断中的效能

目的:分析血清肌酸激酶同工酶(CK-MB)、白细胞介素-6(IL-6)、乳酸脱氢酶(LDH)水平联合检测在慢性心力衰竭(CHF)诊断中的效能。方法:选取2021年1月至2023年1月该院收治的83例CHF患者进行横断面研究,设为研究组,依据是否发生主要心血管不良事件将其分为预后不良者18例与预后良好者65例,另选取同期于该院体检的83名健康志愿者作为对照组。比较两组、不同心功能分级及不同预后CHF患者血清CK-MB、IL-6、LDH水平,采用Pearson相关性分析血清CK-MB、IL-6、LDH水平与CHF患者心功能分级的相关性,绘制受试者工作特征(ROC)曲线分析治疗后1个月血清CK-MB、IL-6、LDH水平单项及联合检测诊断CHF患者的效能。结果:研究组血清CK-MB、IL-6、LDH水平均高于对照组,差异有统计学意义(P<0.05);不同心功能分级CHF患者血清CK-MB、IL-6、LDH水平比较,Ⅱ级<Ⅲ级<Ⅳ级,差异均有统计学意义(P<0.05);经Pearson相关性分析结果显示,血清CK-MB、IL-6、LDH水平与CHF患者心功能分级均呈正相关(r>0,P<0.05);治疗后1个月,两组血清CK-MB、IL-6、LDH水平均低于治疗前,但预后不良者高于预后良好者,差异有统计学意义(P<0.05);经ROC曲线分析结果显示,血清CK-MB、IL-6、LDH水平单项及联合检测诊断CHF患者预后不良的曲线下面积分别为0.788、0.769、0.760、0.906,且联合检测诊断CHF患者预后不良的效能高于三者单项检测。结论:血清CK-MB、IL-6、LDH水平联合检测诊断CHF的效能高于三者单项检测。

油气田非金属管道失效预测及防控技术研究进展

油气田金属管道腐蚀穿孔风险日趋严重,管道泄漏事故时有发生,玻璃钢管、钢骨架增强聚乙烯复合管、柔性复合管等非金属管道以其良好的耐蚀性和适用性在油气田开发生产系统中逐渐受到青睐。然而,由于管道在长期服役过程中遭受内外压载荷、介质腐蚀等老化作用,随之而来的诸如基体开裂、管体脆断、纤维/基体界面脱黏、层间分离等各种失效问题亟待解决。基于此现状,文章综述了油气田常用非金属管道特点、应用、失效原因,以及非金属管道探测定位、无损检测技术、风险评估和寿命预测方法,针对非金属管道损伤失效的预防在管道制造、施工、运行、应用、维修及关键技术等方面提出了相关建议,并对非金属管道失效预防技术攻关方面进行展望,为非金属油气管道失效预测方法及其防控技术的相关研究提供有效支撑。

多维高斯贝叶斯算法造纸机械设备故障自动检测优化设计

信息技术进步发展背景下,各行业已经开始使用机械化设备进行生产,进而提升生产质量。虽然造纸设备能大幅度的提升整体生产的效率,但由于其整体的复杂性,在生产运动过程中会出现故障。基于此,设计多维高斯贝叶斯算法造纸机械设备故障信息诊断系统;利用完整的框架结构设计客户机/服务器模式,联合服务器与信息查询模块,实现造纸机械设备故障信息诊断系统的体系结构搭建;在此基础上,计算故障信息的松弛度数值,通过判定迭代门限的方式,实现对多维系数信号的处理,完成多为高斯贝叶斯算法造纸机械设备故障自动检测功能。最后结合造纸机械设备故障情况提出同步维修、分步维修、易维修设计等方法,为造纸机械设备的正常平稳运行提供了技术保障。