Statistical approach for finding sensitive positions for condition based monitoring of reciprocating air compressors

2011 IEEE International Conference on System Engineering and Technology Pub Date : 2011-06-27 DOI:10.1109/ICSENGT.2011.5993411

N. Verma, J. Kadambari, B. Abhijit, S. Tanu, Tejas Subramaniam

{"title":"Statistical approach for finding sensitive positions for condition based monitoring of reciprocating air compressors","authors":"N. Verma, J. Kadambari, B. Abhijit, S. Tanu, Tejas Subramaniam","doi":"10.1109/ICSENGT.2011.5993411","DOIUrl":null,"url":null,"abstract":"Reciprocating air compressors are one of the most popular and widely used machines in industry today. Timely detection of fault occurring in these machines becomes very critical since it influences the plant performance by virtue of system reliability, operating efficiency and maintenance cost. Monitoring of faults by identifying sensitive positions through sensory output forms vital part of everyday manufacturing. Health monitoring of a reciprocating air compressor using PC based data acquisition system and timely identification of potential faults can prevent failures of the entire system. Various transducers, data acquisition DAQ hardware and relevant software forms the basic components of the health monitoring system. Prior to acquiring the data for health monitoring and consequently the fault diagnosis, it is crucial to locate the sensitive positions on the machine. This paper proposes a scheme to determine the sensitive positions on a machine in healthy condition, based on computation of statistical parameters such as Peak value, Standard Deviation, RMS value, Variance and Cross Correlation.","PeriodicalId":346890,"journal":{"name":"2011 IEEE International Conference on System Engineering and Technology","volume":"70 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"16","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 IEEE International Conference on System Engineering and Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSENGT.2011.5993411","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 16

Abstract

Reciprocating air compressors are one of the most popular and widely used machines in industry today. Timely detection of fault occurring in these machines becomes very critical since it influences the plant performance by virtue of system reliability, operating efficiency and maintenance cost. Monitoring of faults by identifying sensitive positions through sensory output forms vital part of everyday manufacturing. Health monitoring of a reciprocating air compressor using PC based data acquisition system and timely identification of potential faults can prevent failures of the entire system. Various transducers, data acquisition DAQ hardware and relevant software forms the basic components of the health monitoring system. Prior to acquiring the data for health monitoring and consequently the fault diagnosis, it is crucial to locate the sensitive positions on the machine. This paper proposes a scheme to determine the sensitive positions on a machine in healthy condition, based on computation of statistical parameters such as Peak value, Standard Deviation, RMS value, Variance and Cross Correlation.

查看原文本刊更多论文

往复式空气压缩机状态监测灵敏位置的统计方法

往复式空气压缩机是当今工业中最受欢迎和广泛使用的机器之一。及时发现这些机器发生的故障至关重要，因为它影响到系统的可靠性、运行效率和维护成本。通过感官输出识别敏感位置来监测故障是日常制造的重要组成部分。利用基于PC机的数据采集系统对往复式空压机进行健康监测，及时发现故障隐患，防止整个系统出现故障。各种传感器、数据采集硬件和相关软件构成了健康监测系统的基本组成部分。在获取健康监测数据并进行故障诊断之前，对机器上的敏感位置进行定位是至关重要的。本文提出了一种基于峰值、标准差、均方根值、方差和相互关系等统计参数计算的健康状态下机器敏感位置确定方案。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

2011 IEEE International Conference on System Engineering and Technology

自引率

0.00%

发文量