工业汽轮机振动故障分析与处理技术研究

工业汽轮机的振动是影响整个机组安全平稳运转的最主要原因,产生振动的因素较多,如转子不平衡、热变形、不均匀、共振、碰撞等,还涉及轴承方面的自激振动,包括油膜振荡、汽流激振等。本文结合某台汽轮机的实际状况,对汽轮机的异常振动进行故障分析,并提出了处理技术方案。     

某660MW超临界机组给水泵汽轮机调节系统故障分析及处理

某660MW超临界机组给水泵汽轮机在试运中,出现挂闸失败、调节油压低、速关阀不能正常开启、跳闸等问题,延误机组启动时间,严重影响机组和电网的安全运行。本文结合给水泵汽轮机调节系统油路流程和工作原理,分析出现挂闸失败、调节油压低等问题的原因,总结出该类型调节系统的故障特征,提出解决思路和处理方法,并通过现场试验,验证了处理方法的正确性,为快速判断和处理该类型调节系统类似故障提供依据;同时结合给水泵汽轮机速关阀结构原理,推导出速关阀中活动部件的力平衡方程,分析了速关阀动作过程对给水泵汽轮机挂闸的影响及其不能正常开启的原因,并从速关组件的结构原理出发,结合速关阀挂闸时的动作过程,推导出给水泵汽轮机挂闸成功与否启动油压力下降速度相差大的原因,为准确分析跳闸提供依据。     

汽轮机调门关闭故障原因分析及处理

汽轮机调门作为汽轮机进汽的调节机构,直接影响汽轮机的调节性能和安全性能。本文从2次汽轮机调门突然关闭的故障现象出发,依据汽轮机调门伺服阀的结构及汽机调门动作的工作原理,通过深入分析汽机阀位指令和阀位眨馈等历史记录,认为造成汽轮机调门突然关闭的主要原因是EH油品质不合格,提出包括更换伺服阀和提高EH油品质等6项解决措施,最终消除汽轮机调门突然关闭的故障,对处理同类型的异常和故障具有较好的参考价值     

汽轮机0.5s快关阀与和利时MACS V系统通信的实现

介绍汽轮机0.5s快关阀与和利时MACS V系统通信的实现。     

云桌面实验室故障分析与处理

快速解决虚拟云桌面系统的故障对于帮助云桌面实验室的稳定运行和保障教学的顺利非常关键。本文通过经验总结法,结合官方文献和网络参考文献,对基于Vmware技术虚拟云桌面实验室管理中遇到的问题进行归纳分析,并提出有效的解决方案。在实际操作中,采用该方案可以快速解决实验室的故障。     

汽轮机内缸有限元分析与网格划分方案优化

为研究不同网格划分方案对汽轮机内缸应力场计算结果的影响,对某汽轮机中压内缸进行建模,选择热固耦合的有限元法采用4种不同的网格划分方案进行计算,得到额定负荷工况下典型应力集中部位的等效应力。根据计算结果,分析网格整体控制和局部细化过程的不同设置参数对整个求解过程的影响。综合考虑求解成本和计算精度,最终确定一种理想的四面体网格划分方案,该方案可在结果准确合理的前提下提高计算效率。     

探讨计算机硬件故障分析与维护处理

随着科学技术不断发展与进步,计算机硬件所需要的技术要求越来越高,因此,我们应该对其出现的故障进行及时分析,并且还要在分析的基础上提高维护的水平。本文将对计算机硬件出现故障的原因进行分析,通过分析来提出具体故障处理方法以及维护手段。     

某核电厂汽轮机中压调节阀故障研究与处理

为了妥善自主处理某核电厂汽轮机中压调节阀故障及查找原因,系统性梳理与分析了同类机组运行经验及当前机组的阀门改进项。采用现场试验的方法,分析故障机组阀门的试验数据,确认了液压油质、比例阀控制为非致因因素;分析在近期大修商运机组的新旧比例阀性能比对试验的数据,进一步确认了新比例阀在该机组上的工作性能弱于旧比例阀。通过分析在调试机组接入比例阀接口装置测量得到的压降数据和尝试调整减压阀定值,可复现与消除故障。结果证明了减压阀定值设定不当是导致阀门故障的原因。该研究为后序机组阀门故障处理提供了借鉴。     

660MW超临界燃煤机组低压旁路阀频繁内漏处理方法探索研究

本文以某公司2×660MW超临界燃煤机组低压旁路阀频繁内漏（德国西门子原装进口产品）为例,经多次解体发现阀座与阀芯的密封面损坏,主要原因是由于杂质、异物等金属硬物卡涩所致,而系统复杂致锅炉侧死角异物难于彻底吹扫干净,一直严重影响机组安全经济运行。通过对低压旁路阀入口加装滤网、阀门研磨处理、减压孔扩孔、更换阀门、旁路阀阀芯改造等几种方案综合对比优缺点,最后确定对阀芯改造并进行可行性分析,实施效果明显,有效防止了阀芯与阀座密封面损伤,彻底解决了低压旁路阀频繁内漏问题,确保了机组运行安全经济性,无疑对具有类似问题的火电机组解决方案有一定的参考意义。     

500kV变电站变电运行中的故障分析与处理研究

变电站是电力系统中重要组成部分,对电力设备的运行与维护起到了至关重要的作用。若变电系统出现了问题,将会对整个电力网络造成影响,因而加强变电站运行中的故障分析十分重要。本文首先阐述了变电站运行中出现故障的原因,结合变电站的实际运行情况,分析了500k V变电站变电运行中故障的处理方法,旨在加强变电站运行故障处理,促使变电系统安全有效运行,提高企业与社会效益。     

Fault diagnosis of turbine based on fuzzy cross entropy of vague sets

The fuzzy cross entropy of vague sets, so-called vague cross entropy, is introduced by analogy with the cross entropy of probability distributions. And then a new method of the fault diagnosis is proposed on the basis of the vague cross entropy and is applied to the fault diagnosis of turbine. The vague cross entropy between a testing sample and the knowledge of system faults is evaluated in the fault diagnosis of the turbine vibration. If the cross-entropy value is small, the testing sample is near to a type of fault knowledge. Then, the type of vibration fault is determined according to the minimum cross-entropy value. The fault-diagnosis example of the turbine demonstrates that the proposed method cannot only diagnose the main fault types of the turbine, it can also detect useful information for future trends and multi-fault analysis.     

数据驱动的燃气涡轮机跳闸预警方法的研究

燃气涡轮机已被广泛运用于现代工业中,其跳闸事件的发生将产生巨大的经济损失,因此,对燃气涡轮机的跳闸事件进行预测有重要的经济意义.然而,燃气涡轮机跳闸的预测研究是一个崭新的领域,研究成果非常有限,且缺乏数据驱动的预测方法和理论研究.从数据的预处理开始,研究了从数据的归一化、特征选择到特征值选择、特征值粒化等系列问题,并从各个角度设计了Elman神经网络的预测模型实验,对实验结果进行对比,得到了一系列建立并改善数据驱动的Elman网络跳闸预警系统的方法和有益经验,以供其他相关研究参考.     

高压电力电缆故障的起因诊断和处理

主要针对高压电力电缆的常见故障,探讨电力电缆故障诊断的一般方法和步骤,并举出实例提出了相关处理意见。     

A novel fault diagnosis technique for wind turbine gearbox

The gearbox is one of the most important parts of a mechanical equipment. The importance of fault diagnosis in rotating machineries for preventing catastrophic accidents and ensuring adequate maintenance has received considerable attention. In this study, a fault diagnosis method based on gearbox vibration signal monitoring is used to differentiate the signal characteristics of different working conditions and improve the accuracy of diagnosis. The time-domain sequence approximate entropy (ApEn) adaptive strategy is used to propose a wind turbine intelligent fault diagnosis algorithm based on a wavelet packet transform (WPT) filter and a cross-validated particle swarm optimized (CPSO) kernel extreme learning machine (KELM). First, the correlation between the parameter requirements of the intelligent diagnosis system and the system complexity analysis is analyzed. Then, the parameters related to the wavelet filter is determined by calculating the ApEn of the time-domain sequence. Finally, a compact wind turbine gearbox test bench is constructed and tested to validate the proposed ApEn-WPT+CPSO-KELM to identify gearbox-related faults for verification. Results show that the proposed ApEn-WPT+CPSO-KELM method can accurately identify four states of the wind turbine gearbox.     

A comprehensive fault diagnosis technique for reversible logic circuits

Fault diagnosis is a complex and challenging problem in reversible logic circuits. The paper proposes a novel fault diagnosis technique for missing control faults in reversible logic circuits. The main focus of this technique is to extract the unique fault signature for each missing control fault in the circuit. The fault signatures are the sequences of test vectors to identify the location of the faults. Based on these fault signatures a unique fault diagnosis tree is built. Our proposed fault diagnosis algorithm is used to traverse the fault diagnosis tree to find the presence and location of the fault. The traversal process is simple and fast. The algorithm executes in linear time and experimental results for benchmark circuits show the reduction of test patterns compared to earlier works.     

Adaptive sliding mode observer for sensor fault diagnosis of an industrial gas turbine

Sensors are one of the crucial components in gas turbines and the failure in sensor measurements can lead to serious problems in maintaining their safety and performance requirements. Our aim in this paper is to develop an adaptive sliding mode observer for sensor fault diagnosis in an industrial gas turbine. The proposed observer has a robustness against gas turbine parameter uncertainties caused by degradations without any priori knowledge about the bounds of faults and parameter uncertainties. The efficiency of the proposed fault diagnosis approach is validated with Matlab/Simulink simulations and the realistic gas turbine data extracted from the PROOSIS software.     

Slow feature analysis for monitoring and diagnosis of control performance

Recently, slow feature analysis (SFA), a novel dimensionality reduction technique, has been adopted for integrated monitoring of operating condition and process dynamics. By isolating temporal behaviors from steady-state information, the SFA-based monitoring scheme enables improved discrimination of nominal operating point changes from real faults. In this study, we demonstrate that the temporal dynamics is an additional indicator of control performance changes, and further exploit its unique efficacy in control performance monitoring. Because of its data-driven nature and ease from first-principle knowledge, the SFA-based monitoring scheme allows an overall assessment of the plant-wide control performance and is compatible with different control strategies. An attractive feature of the SFA-based approach compared to existing ones is that generic process monitoring indices are used, which renders contribution plots naturally applicable to real-time diagnosis of control performance. As a result, potential fault variables as root causes of control performance changes can be identified, including not only controlled variables (CV) but also manipulated variables (MV) and disturbance variables (DV). Simulated and experimental studies demonstrate the effectiveness of the proposed method.     

Fault diagnosis of an industrial gas turbine prototype using a system identification approach

In this work, a model-based procedure exploiting analytical redundancy for the detection and isolation of faults on a gas turbine simulated process is presented. The main point of the paper consists of exploiting an identification scheme in connection with dynamic observer or filter design procedures for diagnostic purposes. Thus, black-box modelling and output estimation approaches to fault diagnosis are in particular advantageous in terms of solution complexity and performance achieved. Moreover, the suggested scheme is especially useful when robust solutions are considered for minimising the effects of modelling errors and noise, while maximising fault sensitivity. In order to experimentally verify the robustness of the solution obtained, the proposed FDI strategy has been applied to the simulation data of a single-shaft industrial gas turbine plant in the presence of measurement and modelling errors. Hence, extensive simulations of the test-bed process and Monte Carlo analysis are the tools for assessing experimentally the capabilities of the developed FDI scheme, when compared also with different data-driven diagnosis methods.     

Distribution recurrence plots and measures: Effective signal analysis tools for fault diagnosis of wind turbine drivetrain system

Extracting sensitive information from vibration signal has become a frequently adopted way in fault diagnosis. However, most previous methods fragmented the relationship between quantification and visualization analysis, which affects the interpretability, accuracy and comprehensiveness of the extracted information. To this end, this paper proposes distribution recurrence plots (DRP) and measures (DRM) to realize the unity of visualization and quantification analysis of the signals. Specifically, DRP is a novel feature graphical representation method following the thought of symbolic dynamics. Derived from DRP, DRM is developed containing four quantifiers for extracting comprehensive fault features that allows a multiclass support vector machine (SVM) to identify the fault types of wind turbine drivetrain system (WTDS). Specially in DRM, pattern entropy is a newly designed quantifier by considering pattern distribution to obtained more accurate quantitative representation of the signals. Using simulated data, DRP and DRM are validated to reveal the intrinsic structural changes for different dynamic systems and robustness to noise. Applications on wind turbine gearbox illustrate that the proposed method has favorable diagnosis performance and stability compared with other competitors. This approach is easy to interpret, is robust to noise, and has a low computational burden, becoming viable for WTDS fault diagnosis.