Addressing missing data for diagnostic and prognostic purposes

被引：0

作者：

Loukopoulos P. ^{[1
]}

Zolkiewski G. ^{[2
]}

Bennett I. ^{[2
]}

Sampath S. ^{[1
]}

Pilidis P. ^{[1
]}

Duan F. ^{[3
]}

Mba D. ^{[3
]}

机构：

[1] School of Aerospace, Transport and Manufacturing, Cranfield University, Cranfield, Bedfordshire

[2] Shell Global Solutions, Rijswijk

[3] School of Engineering, London South Bank University, 103 Borough Road, London

来源：

Lecture Notes in Mechanical Engineering | 2017年 / 0卷 / 9783319622736期

关键词：

Centrifugal compressor; Condition monitoring data; Imputation techniques; Missing data;

D O I：

10.1007/978-3-319-62274-3_17

中图分类号：

学科分类号：

摘要：

One of the major targets in industry is minimising the downtime of a machine while maximising its availability, with maintenance considered as a key aspect towards achieving this objective. Condition based maintenance and prognostics and health management, which relies on the concepts of diagnostics and prognostics, is a policy that has been gaining ground over several years. The successful implementation of this methodology is heavily dependent on the quality of data used which can be undermined in scenarios where there is missing data. This issue may compromise the information contained within a data set, thus having a significant effect on the conclusions that can be drawn, hence it is important to find suitable techniques to address this matter. To date a number of methods to recover such data, called imputation techniques, have been proposed. This paper reviews the most widely used methodologies and presents a case study using actual industrial centrifugal compressor data, in order to identify the most suitable technique. © Springer International Publishing AG 2018.

引用

页码：197 / 205

页数：8

共 38 条

[1]

Kothamasu R., Huang S.H., Verduin W.H., System health monitoring and prognostics—a review of current paradigms and practices, Int J Adv Manuf Technol, 28, 910, pp. 1012-1024, (2006)

[2]

Lee J., Wu F., Zhao W., Ghaffari M., Liao L., Siegel D., Prognostics and health management design for rotary machinery systems—reviews, methodology and applications, Mech Syst Signal Process, 42, 12, pp. 314-334, (2014)

[3]

Vachtsevanos G., Lewis F., Roemer M., Hess A., Wu B., Intelligent Fault Diagnosis and Prognosis for Engineering Systems, (2006)

[4]

Jardine A.K.S., Lin D., Banjevic D., A review on machinery diagnostics and prognostics implementing condition-based maintenance, Mech Syst Signal Process, 20, 7, pp. 1483-1510, (2006)

[5]

Peng Y., Dong M., Zuo M.J., Current status of machine prognostics in condition-based maintenance: A review, Int J Adv Manuf Technol, 50, 14, pp. 297-313, (2010)

[6]

Sikorska J.Z., Hodkiewicz M., Ma L., Prognostic modelling options for remaining useful life estimation by industry, Mech Syst Signal Process, 25, 5, pp. 1803-1836, (2011)

[7]

Brown M.L., Kros J.F., Data mining and the impact of missing data, Ind Manag Data Syst, 103, 8, pp. 611-621, (2003)

[8]

Pantanowitz A., Marwala T., Evaluating the impact of missing data imputation. In: Advanced data mining and applications, Lecture Notes in Computer Science, Vol, 5678, pp. 577-586, (2009)

[9]

McKnight P.E., McKnight K.M., Souraya Sidani A.J.F., Missing Data: A Gentle Introduction, (2007)

[10]

Acock A.C., Working with missing values, J Marriage Fam, 67, 4, pp. 1012-1028, (2005)

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