工程机械故障诊断技术研究综述及展望

对工程机械的常见故障、当前研究的诊断方法和技术进行综述,详细介绍了5种故障诊断方法的特点及研究现状,并选择一项实例展示了故障诊断技术在工程机械中的应用,最后分析了工程机械故障诊断技术的未来挑战和发展趋势。

认知视野下的工程故障

工程故障因其较大的危害性而为人们所深恶痛绝。本文将工程故障分为原发性及继发性两类,从认识论的角度分析了原发性工程故障的必然性及继发性工程故障难防性的学理机制。本文认为,人类认识能力的后觉性及人类活动结果的后种系生成性,是工程故障发生的随机性及某种程度上不可预见性的认知根源。工程故障不但不可能完全避免,而且是工程活动的必要环节,是工程知识的唯一来源,有其积极意义。基于上述,本文结论部分提出了关于工程活动的一些建议。

工程机械故障检测技术及维修措施分析

工程建设与规划工作在开展的时候,机械故障的发生在很大程度上限制了工程的正常建设,不仅会直接对工程建设进度造成影响,也会造成不小的经济损失。也正是因此,工程机械故障检测技术及维修措施近年来也成为工程界内较为关注的问题,而本文也是就此展开了实践研究,希望借此来有效提升工程机械故障检测及维修效果,促进工程机械设备正常运行。

工程机械故障诊断与修理技术的比较研究

通过对工程机械故障诊断与修理技术的总结和分析,我们可以得出以下结论。了解工程机械故障的常见表现和评估方法对故障的诊断和修理至关重要。工程师需要熟悉不同类型故障的特点,如噪音、振动、外观变化等,并结合故障评估表和检验规范等工具,准确评估故障的严重程度。工程机械故障诊断技术包括观察法、试验法和故障诊断仪等多种方法。观察法通过观察工程机械的外观、声音、振动等来判断故障的原因和位置;试验法则是通过对工程机械进行试验和测量,来确定故障的类型和程度;而故障诊断仪则是一种高级的故障诊断技术,通过采集和分析工程机械的数据,实现对故障的精确诊断。工程机械的故障修理技术包括常规修理方法和创新修理技术。常规修理方法主要包括更换零部件、修复等,可以恢复工程机械的正常运行;而创新修理技术如无损修复、智能修理等,则可以提高修理效果和节约成本。不同修理技术有各自的优缺点,需要根据具体情况选择合适的技术。工程机械故障诊断与修理技术的发展为工程机械的维修和保养提供了重要的支持和指导。未来,随着大数据分析、机器学习和人工智能等技术的不断发展,工程机械故障诊断与修理技术将变得更加智能化、自动化,提高故障诊断的准确性和修理的效率。然而,也需要克服一些技术挑战,如数据采集与处理、算法优化等。工程机械故障诊断与修理技术的研究将为相关领域的技术研究和实践提供重要的参考和指导。

PROFIBUS应用工程中故障问题的研究与实践

利用故障控制工程的概念,研讨了PROFIBUS应用工程中实际所遇到的工程故障问题,并从系统规划设计、工程设计、安装、产品质量、现场环境、使用维护等各个环节,提出了减少PROFIBUS应用工程中故障的具体措施以及排除PROFIBUS应用工程中故障的方法。

工程机械故障检测技术及维修措施

近年来,我国已经逐渐进入了机械化时代,工程制造水平飞速提升,机械的构造和性能也更加复杂。受到各种因素影响,工程机械装置出现故障停机问题的次数也在持续增加,严重影响了实际生产效率。要想解决这一问题,就需要做好工程机械故障检测工作,保证工程机械故障检测技术的实际应用水平,做好维修策略的规划。基于此,文章分析了机械故障类型及成因,探究了技术操作要点,以求提升我国的工程机械故障检测技术及维修水平,延长工程机械使用寿命,进一步提高我国工程机械行业的生产水平,为我国的发展作出一定贡献。

故障树诊断法在工程机械故障排查中的应用探析

工程机械的使用,大大提升了作业效率。在使用期间,需定期检查,以避免由于机械自身故障延误工期。而在故障排除方面,采取故障树诊断法较为流行。为此,文章针对故障树诊断法在工程机械故障排查中的应用的相关问题进行了探究,发现故障树诊断法虽为一种崭新的方法,但是其在诊断和排除工程机械故障中的成效较为突出,可以明显缩短故障排查时间,节省了工程机械故障排查的成本,是以往故障排查方法所不能企及的。文章对故障树诊断法在工程机械故障排查中的应用进行了介绍,并对该方法的发展进行了展望,期待可以更好地为故障树诊断法的广泛、深入应用奠定基础,继而助力工程顺利完成。

故障树诊断法在工程机械故障排查中的应用分析

以CPC30型柴油动力叉车为研究对象,基于故障树诊断方法,对叉车最常见的3类故障,即底盘离合器故障、制动系统故障以及转向故障建立了故障树,结合工程实际案例分析,证明了该方法的可行性与合理性。

工程机械故障检测技术及维修措施

随着建设事业发展,工程量和工程质量都在不断提升,一些新的工程机械也开始被应用,进一步提高了施工效率。由于各种工 程机械的结构比较复杂,且技术性能较强,在长期使用中会由于自身老化、操作不当等因素而加大故障突发性,因此对工程机械进行故障 检测是相当必要的,要对发现的问题进行维修处置,完善其使用性能,确保施工质量安全,使工程机械能够发挥其积极作用。

浅谈低压采集工程管理注意事项及常见故障排除方法

近年来,在低压用电信息采集工程建设中,现场工程管理、档案信息管理、系统流程优化、常见故障排除等工程建设过程中的细节,直接影响到低压采集工程的应用效果。本文主要针对工程建设中注意的关键点、档案信息的管理、系统流程优化、常见故障排除等内容进行分析和介绍,以供交流和参考。

隧道施工安全风险评估与防控措施研究

隧道施工是一项复杂而危险的工程活动,其安全风险评估与防控至关重要。针对隧道建设过程中的潜在安全隐患,进行了细致地审查与剖析,进而推出了一系列切实可行的安全防护对策。针对各类潜在的安全隐患,制定了针对性的防范措施,涉及强化建设工地的作业管制、提升作业人员的防护觉悟、改进施工作业的流程技术。最后,经过亲临现场调查研究和具体案例的深入剖析,证实了所建议的安全措施既有效又可行,这为隧道工程的安全管理提供了宝贵的决策支持。

FLIGHT CONFLICT FO RECASTING BASED ON CHAOTIC TIME SERIES

In order to grasp the evolution of flight conflict amount accurately and to forecast the amount, chaos in flight conflicts is studied. Firstly, a fault tree of flight conflicts is established based on the man-machine-environ- ment system engineering theory. The chaotic characteristics of flight conflict are analyzed from the qualitative point of view. Secondly, an improved chaotic algorithm for the largest Lyapunov exponent is proposed based on the small-data method and the wavelet de-noising theory. Chaos in flight conflict time series is identified by the improved chaotic algorithm from the quantitative point of view. Finally, a case study by the chaos forecasting al- gorithm is performed and the results are evaluated by the gray error checking ： Correlative value of posterior error is 0. 220 9〈0. 35, and micro-error probability is 0. 985 3〉0.95. Such results show the chaos forecasting algo- rithm is effective, thus it is feasible to analyze and forecast flight conflict by chaotic theory.

由“失败学”研究所想到的

只要关注国际动态的朋友便会发现,自诺查丹玛斯“1999人类大劫难”预言的真相被昭揭后,全世界公众已在联合国21世纪“国际减灾战略”的旗帜指导下不断加深对人为灾害及事故规律的认识。20世纪末,一此触目惊心的人为灾难虽已成为史实,但其惨痛的教训是应永远铭记的。

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.

Multi Boost with ENN-based ensemble fault diagnosis method and its application in complicated chemical process

Fault diagnosis plays an important role in complicated industrial process.It is a challenging task to detect,identify and locate faults quickly and accurately for large-scale process system.To solve the problem,a novel Multi Boost-based integrated ENN(extension neural network) fault diagnosis method is proposed.Fault data of complicated chemical process have some difficult-to-handle characteristics,such as high-dimension,non-linear and non-Gaussian distribution,so we use margin discriminant projection(MDP) algorithm to reduce dimensions and extract main features.Then,the affinity propagation(AP) clustering method is used to select core data and boundary data as training samples to reduce memory consumption and shorten learning time.Afterwards,an integrated ENN classifier based on Multi Boost strategy is constructed to identify fault types.The artificial data sets are tested to verify the effectiveness of the proposed method and make a detailed sensitivity analysis for the key parameters.Finally,a real industrial system—Tennessee Eastman(TE) process is employed to evaluate the performance of the proposed method.And the results show that the proposed method is efficient and capable to diagnose various types of faults in complicated chemical process.

Machine learning based online fault prognostics for nonstationary industrial process via degradation feature extraction and temporal smoothness analysis

Fault degradation prognostic, which estimates the time before a failure occurs and process breakdowns, has been recognized as a key component in maintenance strategies nowadays. Fault degradation processes are, in general,slowly varying and can be modeled by autoregressive models. However, industrial processes always show typical nonstationary nature, which may bring two challenges: how to capture fault degradation information and how to model nonstationary processes. To address the critical issues, a novel fault degradation modeling and online fault prognostic strategy is developed in this paper. First, a fault degradation-oriented slow feature analysis(FDSFA) algorithm is proposed to extract fault degradation directions along which candidate fault degradation features are extracted. The trend ability assessment is then applied to select major fault degradation features. Second, a key fault degradation factor(KFDF) is calculated to characterize the fault degradation tendency by combining major fault degradation features and their stability weighting factors. After that, a time-varying regression model with temporal smoothness regularization is established considering nonstationary characteristics. On the basis of updating strategy, an online fault prognostic model is further developed by analyzing and modeling the prediction errors. The performance of the proposed method is illustrated with a real industrial process.

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.

Deformation failure and countermeasures of deep tertiary extremely soft rock roadway in Liuhai coal mine

In view of failure phenomena with nonlinear large deformation including extensive damage,whole section destruction in short time,high rate of repair,most destruction forms occurred in the tertiary roadway of soft rocks engineering in Liuhai mine,according to the methods of geological survey,theoretical analysis,numerical calculation and in-situ test,the composite failure mechanism of molecular expansion,tectonic stress,gravity stress and engineering deviatoric stress,faults and random joint in this area is analyzed deeply,then an coupling support of double-layer-truss is proposed.The research results show that the first wave of deformation energy was released by bolt-mesh-cable fixed into the roof,floor and two sides of the roadway.While the second wave of deformation energy was released through the interface function between double-layer-truss and the surrounding rock.The double-layer-truss that characterized by high strength,good integrity can absorb high deformation energy of surrounding rocks,which led to the uniform distribution of the stress.Engineering practice shows this technology has been successfully applied to control the deformation failure of the tertiary extremely soft rock roadway.

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.

Combined ANN prediction model for failure depth of coal seam floors

Failure depth of coal seam floors is one of the important considerations that must be kept in mind when mining is carried out above a confined aquifer. In order to study the factors that affect the failure depth of coal seam floors such as mining depth, coal seam pitch, mining thickness, workface length and faults, we propose a combined artificial neural networks (ANN) prediction model for failure depth of coal seam floors on the basis of existing engineering data by using genetic algorithms to train the ANN. A practical engineering application at the Taoyuan Coal Mine indicates that this method can effectively determine the network struc- ture and training parameters, with the predicted results agreeing with practical measurements. Therefore, this method can be applied to relevant engineering projects with satisfactory results.