压力容器声发射检测技术的应用

本文通过阐述声发射检测的基本原理,总结了声发射检测的特点,介绍了国内外声发射检测技术的发展历程和现状,并概述了声发射检测技术在压力容器制造、定期维修和检验中的应用进展。     

刍议压力容器检测技术

目前对压力容器的检测方法有很多种，无损检测的主要常用技术有射线、超声、磁粉和渗透，还有新技术如有声发射、磁记忆等。本文着重探讨了磁粉检测技术在压力容器检测中的应用的情况，涉及无线检测技术、磁粉检测和磁轭地、交叉法磁轭法、触头法、线圈法等检测方法，并对检测时的注意事项进行了相关阐述。     

压力容器检测技术的分析

目前，压力容器在化工、机械、冶金等行业都有了广泛的应用，而压力容器在使用过程中很容易受到外界条件的影响而发生锈蚀、开裂等缺陷，为了使得压力容器的使用安全，需要对其进行定期检测。无损检测技术已经应用于我国压力容器行业，这在压力容器的检测中处于非常重要的地位，目前，常用的无损检测方法有：超声波检测、渗透检测、磁粉检测、声发射检测以及射线检测技术，每一种检测方法都有其优点以及适用范围，本文主要介绍了几种常用的无损检测技术以及如何选择合适的无损检测技术，以保证压力容器的安全性。     

浅谈压力容器无损检测技术的应用

随着科技的不断进步及对安全的日益重视，安全检查的手段也不断完善。对压力容器安全的检测手段就是无损检测。我国现代工业的不断发展，对产品质量、结构安全、使用可靠性等提出了新的要求，因为无损检测技术具备不破坏试件与检测灵敏度高等优势，因而其应用越来越广泛应用并对无损检测的进一步发展和展望。     

压力容器无损检测技术探讨

压力容器检验的目的就是防止压力容器发生失效事故。目前对压力容器的检测方法有多种,主要无损检测的常用技术有射线、超声、磁粉和渗透及新技术有声发射、磁记忆等。文章着重探讨了磁粉检测技术在压力容器检测中的应用的情况,涉及无线检测技术、磁粉检测和磁轭地、交叉法磁轭法、触头法、线圈法等检测方法,并对检测时的注意事项进行了相关阐述。     

压力容器无损检测技术的原理及应用

介绍当前压力容器制造和使用过程中所采用的无损检测技术,包括射线、超声、磁粉,渗透等常规技术和声发射、磁记忆等新技术,并论述他们的工作原理、优缺点和应用范围.     

浅析压力容器无损检测技术与应用

随着我国科学技术的不断发展，我国工业技术也得到了飞速的发展。在生产工作中我们不定时的对压力容器进行检测和检查，以保证生产的安全性。我们对压力容器检测主是为了防止压力容器故障发生事故而失效，避免不必的浪费和保证敏感度。因此，本文对无损检测技术进行定义、选择无损技术的求和无损检测技术的应用原理进行探讨。     

压力容器无损检测技术的探讨

介绍了无损检测的几种方法,并进一步说明了这几种检测方法的使用范围及优缺点,着重分析了射线检测法与超声波检测法之间的关系及这两种方法使用时应注意的事项.     

浅谈压力容器无损检测技术

本文根据压力容器制造和使用的不同阶段,对采用的无损检测技术分别加以介绍,阐述了对压力容器原材料、压力容器制造和使用过程中采用的无损检测技术,并说明了采用这些检测技术的目的,通过采用各种无损检测技术进行检验,可以有效防止安全事故的发生。     

关于压力容器无损检测工作技术的分析

本文介绍了压力容器的工作原理以及应用特点,重点针对压力容器的无损检测技术分析,分别从从超声、射线、磁粉、渗透、涡流、声发射、红外线、漏磁和磁记忆检测方面进行论述。希望能给相关工作者带来一定的参考价值。     

声发射测试系统的发展

综述了声发射测试系统的发展历程及现状。展望了其发展前景，给出了两种分类依据，重点介绍了几种目前先进的声发射仪．     

在用制冷压力容器试压工质及其应用技术的研究

压力试验是在用制冷装置压力容器安全监察和检验工作的重点之一,本文在介绍常用制冷剂的热力特性的基础上,分析和探讨了采用替代制冷剂后系统运行参数的变化、压力试验介质的基本选择、容器压力参数变化和不同制冷剂的压力试验以及安全装置的设定等.     

声发射测量系统的研制

介绍一种干涉式光纤声发射传感器,其分辨率高达１０＾－１０ｍ量级,频率范围为１００ｋＨｚ至１．４ＭＨｚ,响应时间小于１μｓ,具有很高的测量稳定性,且可用于状态监测和故障诊断。     

声发射监听划痕法评价膜层结合情况的有效性

本文介绍声发射技术在判断划痕临界载荷以推定膜层与基材结合情况的研究结果。实验的试样是玻璃基材真空蒸镀铝膜、软钢上电镀镍层以及不同硬度的钢样上离子镀超硬氮化钛膜层。在脆性硬质基材上无论镀软膜或硬膜,划痕试验中一旦监听到声发射信号即表明基材与膜层结合失效,此时的划痕载荷即为临界载荷。塑性基材镀软膜层直到划痕到基材也无声发射信号出现。超硬膜层与钢基材的划痕试验中,声发射信号出现时载荷往往低于临界载荷。两者的差值决定于超硬膜的厚度和膜层与基材的硬度差。     

The Use of Acoustic Emission for Estimation of Bearing Defect Size

Vibration monitoring of rolling element bearings is possibly the most established diagnostic technique for rotating machinery. The application of Acoustic Emission (AE) for bearing diagnosis is gaining ground as a complementary diagnostic tool, however, limitations in the successful application of the AE technique have been partly due to the difficulty in processing, interpreting and classifying the acquired data. Furthermore, the extent of bearing damage has eluded the AE diagnostician. The investigation reported in this paper was centered on the application of the Acoustic Emission technique for identifying the size of a defect on a radially loaded bearing. An experimental test-rig was designed such that defects of varying sizes could be seeded onto the outer race of a test bearing. It was concluded that AE can provide an indication of the defect size, thus allowing the user to monitor the rate of degradation on the bearing. This article was originally published as “The Use of Acoustic Emission for Estimation of Bearing Defect Size” in Essential Technologies for Successful Prognostics, Proceedings of the 59th Meeting of the Society for Machinery Failure Prevention Technology, Society for Machinery Failure Prevention Technology, 2005, pp. 583–591, and is reprinted with permission.     

复合材料损伤失效的声发射检测研究进展

综述了复合材料损伤失效的声发射检测研究进展,从损伤源定位及损伤模式的识别与分类两方面进行了介绍。在损伤源定位方面模态声发射相比模式识别更有效,在损伤模式的分类方面模式识别技术更加有效,且人工神经网络及小波神经网络在复合材料声发射方面的研究较多。另外,介绍了模糊模式识别技术用于声发射信号分类及聚类的研究情况,根据复合材料声发射信号复杂重叠性的特点,模糊理论结合模式识别技术可以进一步实现复合材料声发射信号更有效的分析。     

Limitation of Acoustic Emission for Identifying Seeded Defects in Gearboxes

Acoustic Emissions (AE) is gaining ground as a Nonestructive Technique (NDT) for health diagnosis on rotating machinery. Vast opportunities exist for development of the AE technique on various forms of rotating machinery, including gearboxes. This paper reviews recent developments in application of AE to gear defect diagnosis. Furthermore, experimental results are presented that examine and explore the effectiveness of AE for gear defect diagnosis. It is concluded that application of AE to artificially seeded gear defect detection is fraught with difficulties, particularly for fault identification. In addition, the viability of the AE technique for gear defect detection by making observations from nonrotating components of a machine is called into question. Nevertheless, guidance is offered on applying the technique for monitoring the natural wear of gears.     

Neural Network Burst Pressure Prediction in Composite Overwrapped Pressure Vessels Using Mathematically Modeled Acoustic Emission Failure Mechanism Data

The purpose of this research was to predict burst pressures in composite overwrapped pressure vessels (COPVs) by using mathematically modeled acoustic emission (AE) data. Both backpropagation neural network (BPNN) and multiple linear regression (MLR) analyses were performed on various subsets of the low proof pressure AE data to predict burst pressures and to determine if the two methods were comparable. AE data were collected during hydrostatic burst testing on the 15-inch diameter COPVs. Once collected, the AE data were filtered to eliminate noise then classified into AE failure mechanism data using a MATLAB Kohonen self-organizing map (SOM). The matrix cracking only amplitude distribution data were mathematically modeled using bounded Johnson distributions with the four Johnson distribution parameters – ?, λ, γ, and η – employed as inputs to make both the BPNN and MLR predictions. The burst pressure predictions generated using a MATLAB BPNN resulted in a worst case error of 1.997% as compared to ?1.666% for the MLR analysis, suggesting comparability. However, the MLR analysis required the data from all nine COPVs to get approximately the same results as the BPNN training on just five COPVs; plus, MLR analyses are intolerant to noise, whereas BPNNs are not.     

Acoustic Emission Study of Fatigue Cracks in Materials Used for AVLB

The Armored Vehicle Launch Bridge (AVLB) is subjected to cyclic loading during launching as well as during tank crossings. The cyclic loading causes cracks to initiate in critical bridge components, and then to propagate. Unless these cracks are detected and repaired before they rapidly grow to reach their critical stage of propagation, the failure of bridge components can occur. Three AVLB components, the splice doubler angle, the splice plate, and the bottom chord, are susceptible to fatigue damage. In the present study, laboratory fatigue tests on the materials used for the components, aluminum 2014-T6, aluminum 7050-T765, and ASTM A36 steel, were conducted using the acoustic emission (AE) fatigue crack monitoring technique. A total of fourteen compact-tension specimens were prepared in this study: six aluminum 2014-T6, four aluminum 7050-T76511, and four ASTM A36 steel specimens. The characteristics of AE signals associated with the stress intensity factor, K, were obtained to understand AE behavior corresponding to the fatigue crack growth in the materials. Several AE parameters, such as AE counts, energy, and hits, have been shown to be useful tools for detecting cracks, providing early warnings, and preventing failure of the AVLB structures. A major jump in AEcount rate as well as AE hit rate occurred when K_max reached a value of about 30~MPam (27 ksiin.) for aluminum 2014-T6 specimens and about 50 MPam (46 ksiin.) for aluminum 7050-T76511 specimens. Also, AE source location techniques were able to successfully locate the path ofcrack propagation.     

制冷装置中压力容器的耐压试验

在制冷压力容器定期耐压试验中，针对不同制冷剂和冷冻润滑油制定了不同的耐压试验工艺，通过现场使用，证明所采用的试验方法是可行的，解决了国内制冷装置中压力容器的耐压试验难题。