Comparison of Economic Life of Tractors in Iran Using Condition Monitoring （CM） and Breakdown Maintenance （BM） Methods

Repair and maintenance costs are the most important factors affecting decision making about substituting agricultural machineries. This decision is made based on the economic life （time） of machineries. In this research, condition monitoring of MF285 and MF399 tractors was performed using engine oil analysis to find the optimum life time of tractor substitution in comparison with the breakdown maintenance method in Iran. All recorded information about fixed and variable costs were selected as data base and analyzed. Data were divided （classified） based on period of annual working time. Using power regression analysis led to find mathematical models for the optimum time life definition. Cumulative working time （X） was selected as independent and cumulative costs based on definite percent of initial price （Y） was considered as dependent variable and a power law equation was found to express the costs of both MF399 and MF285 tractors as a function of working time. Results showed that in CM method, average of economic life was 13 and 11 years for MF399 and MF285, respectively. It was also found that in BM method, economic life wasl0 and 8.5 years for MF399 and MF285, respectively.

Integrated Condition Monitoring of Large Captive Power Plants and Aluminum Smelters

Condition monitoring is implementation of the advanced diagnostic techniques to reduce downtime and to increase the efficiency and reliability.The research is for determining the usage of advanced techniques like Vibration analysis,Oil analysis and Thermography to diagnose ensuing problems of the Plant and Machinery at an early stage and plan to take corrective and preventive actions to eliminate the forthcoming breakdown and enhancing the reliability of the system.Nowadays,the most of the industries have adopted the condition monitoring techniques as a part of support system to the basic maintenance strategies.Major condition monitoring technique they follow is Vibration Spectrum Analysis,which can detect faults at a very early stage.However implementation of other techniques like Oil analysis or Ferrography,Thermography etc.can further enhance the data interpretation as they would detect the source of abnormality at much early stage thus providing us with a longer lead time to plan and take the corrective actions.In Large Captive Power Plants and Aluminium Smelters,Integrated Condition Monitoring techniques play an important role as stoppage of primary system and its auxiliaries(boiler,steam turbine,generator,coal and ash handling plants etc.)results into the stoppage of the entire plant,which in turn leads to loss of productivity.From economical and operational point of view,it is desirable to ensure optimum level of system availability.

A combined risk-based and condition monitoring approach:developing a dynamic model for the case of marine engine lubrication

This paper focuses on the creation of a dynamic probabilistic model which simulates deterioration trends of a marine engine lubricationsystem. The approach is based on risk and the implementation is achieved through a dynamic Bayesian network (dBN).Risk can be useful for decision making, while dBNs are a powerful tool for risk modelling and prediction models. The model takesinto account deterioration of engine components, oil degradation and the off-line condition monitoring technique of oil analysis, inthe context of predictive maintenance. The paper aims to efficiently predict probability evolution for main engine lubrication failureand to decide upon the most beneficial schemes from a variety of lubrication oil analysis interval schemes by introducing monetarycosts and producing the risk model. Real data and respective analysis, along with expert elicitation, are utilized for achieving modelquantification, while themodel is materialized through a code in the Matlab environment. Results from the probabilistic model showa realistic simulation for the system and indicate the obvious, that with more frequent oil analyses and respective maintenance orrepairs, the probability of failure drops significantly. However, the results from the risk model highlight that the costs can redefinescheme suggestions, as they can correspond to low probabilities of failure but also to higher costs. A two-month interval scheme issuggested, in contrast to the most preferred practice among shipping companies of a three-month interval. The developed model isin general identified as a failure prediction tool focusing on marine engine lubrication failure.

Fractional Envelope Analysis for Rolling Element Bearing Weak Fault Feature Extraction

The bearing weak fault feature extraction is crucial to mechanical fault diagnosis and machine condition monitoring. Envelope analysis based on Hilbert transform has been widely used in bearing fault feature extraction. A generalization of the Hilbert transform, the fractional Hilbert transform is defined in the frequency domain, it is based upon the modification of spatial filter with a fractional parameter, and it can be used to construct a new kind of fractional analytic signal. By performing spectrum analysis on the fractional envelope signal, the fractional envelope spectrum can be obtained. When weak faults occur in a bearing, some of the characteristic frequencies will clearly appear in the fractional envelope spectrum. These characteristic frequencies can be used for bearing weak fault feature extraction. The effectiveness of the proposed method is verified through simulation signal and experiment data. © 2017 Chinese Association of Automation.

基于改进信息熵的直接刀具状态监测设备部署

在不拆刀情况下,基于机器视觉的在线刀具状态监测系统可完成刀具磨损测量和状态评估,但与在线捕获刀具图像质量息息相关的系统部署参数选择却鲜有研究.为解决上述问题,本文构建基于改进信息熵的多项式回归模型以实现刀具状态监测系统的最优部署.首先,使用自适应阈值方法去除捕获刀具图像中背景要素干扰,并通过信息熵指标评估图像中刀具磨损区域的成像质量;然后,构建相机工作距离、曝光时间与所提出评价指标之间的多项式回归模型以描述部署参数与提出评价指标的映射关系;最后,应用最小二乘法求取多项式模型系数获得最优部署参数.在确保自变量的因子水平涵盖最优部署参数情况下设计正交实验,实验结果表明:提出的评价指标与工作距离、曝光时间等部署参数之间均存在主效应关系,符合光学成像系统的变化规律;与支持向量机、决策树和K近邻(K-nearest neighbor,KNN)算法等非线性回归预测模型相比,三次多项式回归模型预测误差最小,其平均绝对误差、均方误差、均方根误差分别为0.022631,0.00068,0.026069;在多项式回归模型求解的最优部署参数下,所捕获的刀具图像的测量精度达到96.76%,提高0.74%,满足刀具状态监测的精度要求.

基于大数据技术的热轧辊道电机监测模型的设计与应用研究

本文旨在研究和应用大数据技术设计热轧辊道电机监测模型,以提高热轧辊道电机的运行效率和可靠性。通过借助大数据处理平台和相关算法提取有用的特征和模式,以及对热轧辊道电机数据的分析,并使用数据挖掘和机器学习算法来设计预测模型,设计了基于大数据技术的热轧辊道电机监测模型,为热轧板带企业提供了一个可靠的监测和预测工具。

基于FPGA与卷积神经网络分析的机床状态监测系统

提出基于FPGA与神经网络分析的状态监测系统,对机床主轴健康状态进行监控,在主轴状态异常时诊断其故障,为实现设备状态监控提供思路。以滚动轴承故障数据集为轴承原始信号输入,基于FPGA进行算力资源分配,建立Python深度学习模型。对建立的学习模型用Verilog语言进行RTL级描述,并进行仿真、综合。最后在FPGA开发板上进行仿真验证,共采用测试样本数据43066个,随机抽取样本数据进行测试,计算得到系统平均准确率为91.0%,相对准确率达94.5%,验证了所提方法和系统的有效性。

农业机械智能维护系统设计与实现

随着农业机械设备的日益复杂化和集成化,农业机械设备维护和管理是保证农业机械稳定、安全和可靠运行的关键。传统的维护方式主要依赖于定期检查和经验判断,存在维护周期不合理、故障预测不准确、维修滞后等问题,容易导致设备突发故障,影响农业生产进程。该文提出了一种基于机器学习和大数据分析的智能维护系统,通过安装在农业机械上的传感器,实时采集设备运行数据,包括温度、振动、压力、电流等多种参数。数据通过无线通信网络传输至云端平台,进行数据存储和处理,并结合利用机器学习算法和大数据分析技术,对采集到的数据进行分析和处理,实现设备状态的实时监测、故障预测和健康评估。

新时代电力设备检修现状及管理措施

在电力行业的快速发展和技术革新浪潮中,电力设备检修作为保障电网稳定运行的关键环节,面临着前所未有的挑战与机遇。随着新材料、新技术的应用,以及数字化、智能化水平的提升,电力设备的维护管理模式正经历着深刻的变革。在这样的背景下,电力设备的检修工作必须紧跟时代步伐,不断创新和优化检修策略,以适应新技术的发展需求。因此,聚焦于新时代下电力设备检修的现状及其管理措施,分析了当前电力设备维护面临的主要问题,并从多个方面探讨了电力设备检修的优化策略,旨在为电力行业的设备维护和管理提供实用的参考和指导。通过这一研究,期望能够助力电力行业在新时代下实现更高效、更安全、更可持续的发展。

Smart and collaborative industrial IoT: A federated learning and data space approach

Industry 4.0 has become a reality by fusing the Industrial Internet of Things(IIoT)and Artificial Intelligence(AI),providing huge opportunities in the way manufacturing companies operate.However,the adoption of this paradigm shift,particularly in the field of smart factories and production,is still in its infancy,suffering from various issues,such as the lack of high-quality data,data with high-class imbalance,or poor diversity leading to inaccurate AI models.However,data is severely fragmented across different silos owned by several parties for a range of reasons,such as compliance and legal concerns,preventing discovery and insight-driven IIoT innovation.Notably,valuable and even vital information often remains unutilized as the rise and adoption of AI and IoT in parallel with the concerns and challenges associated with privacy and security.This adversely influences interand intra-organization collaborative use of IIoT data.To tackle these challenges,this article leverages emerging multi-party technologies,privacy-enhancing techniques(e.g.,Federated Learning),and AI approaches to present a holistic,decentralized architecture to form a foundation and cradle for a cross-company collaboration platform and a federated data space to tackle the creeping fragmented data landscape.Moreover,to evaluate the efficiency of the proposed reference model,a collaborative predictive diagnostics and maintenance case study is mapped to an edge-enabled IIoT architecture.Experimental results show the potential advantages of using the proposed approach for multi-party applications accelerating sovereign data sharing through Findable,Accessible,Interoperable,and Reusable(FAIR)principles.

水处理机日常状态监测在保障血液透析运行安全中的作用

对水处理机日常数据进行监测和记录,分析其运行状态,探讨水处理机日常状态监测在保障血液透析运行安全中的作用。收集医院医学装备科近5年水处理机34700余条运行数据,通过分析其日常参数的数据变化、获取其日常状态信息,积累了识别、诊断、及时排查与处置其常见故障的经验,同时关注其各系统部件的老化、机器磨损等客观因素带来的安全隐患,保证透析用水的合格与安全,在保证水处理机稳定运行中发挥了重要作用。

基于预防维修的多元化维修管理体系

针对点检受控管理模型、故障分析模型、可靠性管理模型和状态监测管理模型进行研究,建立了以点检和故障分析为核心的设备故障预警体系;基于状态维修、定期维修、可靠性维修等维修理论,设计了以预防维修为主导的多元化维修管理体系.应用实例表明,该研究成果可有效应用于企业设备资产管理系统中设备运行、维护、维修管理模块的业务设计.

基于相关主成分分析和极限学习机的风电机组主轴承状态监测研究

提出了一种基于相关系数法和主成分分析法以及极限学习机相结合的主轴承状态监测方法。该方法利用相关系数法选取数据采集与监视控制系统中与主轴承温度相关的变量作为初始输入变量;再用主成分分析法对所选变量进行降维处理,以消除变量之间的相关性和冗余性;进一步利用极限学习机建立主轴承正常运行时的温度模型并用其进行温度预测;最后利用滑动窗和核密度估计方法对残差进行分析,并基于实测的数据进行主轴承故障模拟。结果表明,该方法可有效地实现主轴承潜在故障的预测。

状态检修技术及其辅助分析系统的应用

状态检修是近年来输变电企业普遍关注的技术。文中提出了一种基于模糊数学和专家系统的状态检修分析模型,它集成了各种信息和分析手段,弥补了一般电力企业在线监测和故障诊断手段缺乏的问题。文章同时阐述了绍兴电力局开发并已应用的状态检修计算机辅助分析系统的设计思路和主要模块。

设备噪声监测中主分量的特征表示

研究了设备噪声监测中主分量特征表示的提取及应用。在设备噪声时域和频域统计模式特征基础上,通过主分量分析探讨了设备噪声模式的主分量特征表示方法,引入相关度的概念分析了主分量特征表示对设备状态的表征能力,提出了选择有效维数的主分量特征表示进行设备噪声监测的方案。通过在EQ6100型发动机上预先模拟四种连杆轴承磨损故障,测取噪声信号,实例分析显示了低维主分量特征表示可以有效表征设备状态。实验最后对测试集样本进行状态监测得到了100%的准确率,表明了主分量特征表示用于设备噪声监测的有效性。

面向优化检修的真空断路器状态监测与诊断

结合大型电厂开展设备优化检修的需求,对真空断路器状态监测与诊断系统进行了研究。通过对真空断路器进行故障模式、影响及危害分析(FMECA),确定了系统所需开展的监测项目以及采取的监测方式;提出了一种多层次、集成分布式体系结构;详细阐述了系统的硬件结构和软件功能。实际应用表明,系统能够为状态监测、诊断分析提供先进的工具,有利于提高检修质量、减少维护成本,对逐步实现状态检修提供了积极的作用。

设备状态智能诊断模型的自更新机制及其UML建模

为解决在设备状态监测应用中建立智能诊断模型经常面临历史样本数据空间有限的问题，研究了智能诊断模型的自更新机制，并采用统一建模语言对其进行了分析建模。在此基础上，给出了该机制的实施架构。该机制的基本思想是用实际设备状态监测过程中的监测数据来更新智能诊断模型。在此机制作用下，通过在设备状态监测过程中跟踪设备状态的变化，一个基于有限的设备状态样本空间训练的智能诊断模型能够在模型失效的情况下，通过学习新设备状态下的监测数据不断提升其诊断能力。该机制的可行性和有效性通过实例应用得到了验证。

水电机组空蚀在线监测系统的研究

介绍了水电机组空蚀在线监测的原理和实现方法 ,提出了具体的空蚀在线监测系统结构设计方案 ,并在八盘峡水电厂4号机组上进行了现场试验 ,得出了空蚀强度随负荷变化的特性曲线 ,为水电厂开展状态检修进行了有益的探索。

油液分析技术及其在现代造纸机械故障诊断中的应用

介绍了国内外油液分析技术研究与应用进展,对比分析了几种油液分析方法,进一步提出将油液分析技术应用到现代造纸机械日常在线维护、状态监测与故障诊断中的必要性、潜在价值、基本思路和前景,以促进我国造纸工业加快适应和提高应用现代化装备的技术水平。

基于粒子群优化LS-SVM的车刀磨损量识别技术研究

刀具的磨损状态直接影响产品加工质量、成本和效率,对刀具磨损量的实时监测识别具有重要意义。针对刀具磨损状态先验样本少和常规神经网络识别模型收敛速度慢、易陷入局部极小值等问题,提出了基于最小二乘支持向量机(LS-SVM)的刀具磨损识别方法,并针对支持向量机的惩罚因子和核参数对模型识别精度影响较大的问题,提出一种根据个体适应度来调整惯性权重的自适应粒子群算法进行自动参数寻优。以车削加工为研究对象,采集加工过程中的切削力信号,应用小波包分析技术提取反映刀具磨损状态的特征信息作为识别模型的输入,然后利用训练好的自适应粒子群算法优化后的LS-SVM识别模型进行刀具磨损量识别。实验结果表明,该自适应粒子群优化算法比标准粒子群优化算法参数寻优能力更强;粒子群优化LS-SVM模型能高效地实现刀具磨损量识别,与BP神经网络相比具有更高的精度,且所需样本数较少,训练速度更快。