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为了提高水电机组运行状态的安全性与稳定性,设计了一种水电机组状态的监测及数据分析系统。设计了水电机组状态监测系统的基本架构,采集水电机组运行状态数据信息,分析数据特征,利用小波变换计算,进行数据的预处理,应用A/D编码变换计算,提取水电机组运行状态的特征信号,采用支持向量机算法,实现水电机组状态的实时辨识与监测。仿真模拟试验结果显示,设计系统对100组特征信号监测完成所用时间的平均值为0.124 s,具有实时性与高效性。监测结果的评价指标平均值分别为0.002 1、0.013 3、0.015 9,均在标准极限1.0范围内,具有精准性。 相似文献
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作为往复式压缩机监测系统的创新,把撞击传感器和振动传感器组合在一起,共同构成机械动力性能故障的监测手段;把VB和MATLAB结合在一起.共同构成振动信号功率谱分析显示程序.本文所涉及的往复压缩机在线监测系统已投入正常运行,并取得良好效果. 相似文献
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为了解决汽轮发电机组的性能检测和故障诊断问题,将独立主元分析引入汽轮机性能监控领域,提出了一种基于独立主元分析(ICA)的电厂机组性能监测与评估新方法。通过ICA算法计算数据的独立主元,进一步计算监控统计量I2,I2和SPE来监测和评估系统的运行。若监控统计量在控制置信限以下,则认为系统运行正常;若统计量超过控制限,则判断为系统有故障或异常发生,运行和维修人员可以根据监测结果及时排查故障发生的原因,消除安全隐患,从而确保机组的安全稳定运行。某电厂机组故障数据仿真研究试验验证了该方法的有效性和合理性。 相似文献
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通过对水利灯泡贯流式机组长期运行存在的故障隐患分析,阐述飞来峡机组空气间隙在线监测系统建立的必要性,介绍目前机组空气间隙在线监测系统的构成、设备选型、系统与监控系统接口等情况,最后通过TN8000软件对机组气隙等状态进行在线监测,对任一时间段的实时采样数据进行积累统计产生图形和分析报告,便于对机组的健康状态进行分析和评估。 相似文献
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针对企业机组状态监测及故障诊断,探索了网络化在线监测与故障诊断的网络系统的构建,从软硬件配置到系统功能的开发,从数据采集到组态设置做了有益的探索,很好解决了企业大型机组安全运行的要求。 相似文献
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提出一种基于LM(Levenberg-Marquardt)算法优化的 BP (Back Propagation)神经网络的多级往复式压缩机压缩机气阀故障诊断方法。以6M25-185/314氢氮气压缩机的 6级压差和6级温差作为网络的输入向量,建立可对往复式压缩机一至六级气阀故障进行在线监测及故障诊断的LM-BP神经网络模型。以100组故障数据作为网络训练样本,30组数据作为网络检测样本进行故障诊断,结果表明,LM-BP神经网络相比于变梯度BP神经网络和RBF神经网络诊断更快速稳定且准确率达到96%以上。利用Matlab软件平台建立的LM-BP 神经网络故障诊断模型,模型简单便于在工程实际中应用。 相似文献
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介绍中控WebField ECS-700系统在加氢精制新氢往复式压缩机气量无级调节系统中的应用.提出气量无级调节系统的控制算法和方案的设计,并分析可变分程点控制方案的应用和实现方式。 相似文献
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往复活塞式压缩机设计软件 总被引:1,自引:0,他引:1
为给往复活塞式压缩机设计人员提供设计指导,开发具有通用性的往复活塞式压缩机设计软件.该软件根据往复活塞式压缩机设计理论和国内设计人员的设计习惯,采用Java语言开发,使用Swing开发用户界面和Hibernate操纵数据库.设计人员能够根据需要选择或者构建压缩机形式,因此该软件具有一定的通用性.演示实例表明,该软件具有友好的用户界面,易于修改和查看结果,可提高设计人员的工作效率. 相似文献
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Indicator diagram plays an important role in the health monitoring and fault diagnosis of reciprocating compressors. Different shapes of indicator diagram indicate different faults of reciprocating compressor. A proper feature extraction and pattern recognition method for indicator diagram is significant for practical uses. In this paper, a novel approach is presented to handle the multi-class indicator diagrams recognition and novelty detection problems. When multi-class faults samples are available, this approach implements multi-class fault recognition; otherwise, the novelty detection is implemented. In this approach, the discrete 2D-Curvelet transform is adopted to extract the representative features of indicator diagram, nonlinear PCA is employed for multi-class recognition to reduce dimensionality, and PCA is used for novelty detection. Finally, multi-class and one-class support vector machines (SVMs) are used as the classifier and novelty detector respectively. Experimental results showed that the performance of the proposed approach is better than the traditional wavelet-based approach. 相似文献
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根据原料气压缩机的工作过程,采用西门子PLC S7-300作为下位机,触摸屏作为人机界面,构成压缩机组控制系统,由PLC对压缩机的压力、温度及其他参数等信号进行采集、控制、监控及联锁保护和报警,实现了自动化.组态软件制作的监控操作界面,操作直观方便.实际运行表明,采用该控制系统提高了生产效率,取得了良好的经济效益. 相似文献
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《Expert systems with applications》2014,41(9):4113-4122
This paper presents an approach to implement vibration, pressure, and current signals for fault diagnosis of the valves in reciprocating compressors. Due to the complexity of structure and motion of such compressor, the acquired vibration signal normally involves transient impacts and noise. This causes the useful information to be corrupted and difficulty in accurately diagnosing the faults with traditional methods. To reveal the fault patterns contained in this signal, the Teager–Kaiser energy operation (TKEO) is proposed to estimate the amplitude envelopes. In case of pressure and current, the random noise is removed by using a denoising method based on wavelet transform. Subsequently, statistical measures are extracted from all signals to represent the characteristics of the valve conditions. In order to classify the faults of compressor valves, a new type of learning architecture for deep generative model called deep belief networks (DBNs) is applied. DBN employs a hierarchical structure with multiple stacked restricted Boltzmann machines (RBMs) and works through a greedy layer-by-layer learning algorithm. In pattern recognition research areas, DBN has proved to be very effective and provided with high performance for binary values. However, for implementing DBN to fault diagnosis where most of signals are real-valued, RBM with Bernoulli hidden units and Gaussian visible units is considered in this study. The proposed approach is validated with the signals from a two-stage reciprocating air compressor under different valve conditions. To confirm the superiority of DBN in fault classification, its performance is compared with that of relevant vector machine and back propagation neuron networks. The achieved accuracy indicates that the proposed approach is highly reliable and applicable in fault diagnosis of industrial reciprocating machinery. 相似文献
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付雄新 《自动化与仪器仪表》2014,(6):62-63
天然气压缩机是压缩站的关键设备,需要不间断正常运转,建立一套天然气压缩机的远程监控系统具有实际的工程意义。本文提出了一种基于GPS定位、GPRS无线双向数据传输技术的天然气压缩机智能监控系统。介绍了天然气压缩机智能监控系统的功能与方案、智能终端及监控中心的结构及实现方法。经现场实践表明,该系统工作性能稳定,能够实现天然气压缩机远程监控、在线检测及智能故障诊断。 相似文献