Dealing with missing data for prognostics purposes

Panagiotis Loukopoulos, Suresh Sampath, Pericles Pilidis, George Zolkiewsk, Ian Bennett, Fang Duan, David Mba

Research output: Chapter or section in a book/report/conference proceedingChapter or section

5 Citations (SciVal)


Centrifugal compressors are considered one of the most critical components in oil industry, making the minimisation of their downtime and the maximisation of their availability a major target. Maintenance is thought to be a key aspect towards achieving this goal, leading to various maintenance schemes being proposed over the years. Condition based maintenance and prognostics and health management (CBM/PHM), which is relying on the concepts of diagnostics and prognostics, has been gaining ground over the last years due to its ability of being able to plan the maintenance schedule in advance. The successful application of this policy is heavily dependent on the quality of data used and a major issue affecting it, is that of missing data. Missing data’s presence may compromise the information contained within a set, thus having a significant effect on the conclusions that can be drawn from the data, as there might be bias or misleading results. Consequently, it is important to address this matter. A number of methodologies to recover the data, called imputation techniques, have been proposed. This paper reviews the most widely used techniques and presents a case study with the use of actual industrial centrifugal compressor data, in order to identify the most suitable ones.
Original languageEnglish
Title of host publicationProceedings of 2016 Prognostics and System Health Management Conference (PHM-Chengdu)
EditorsMing J Zuo, Liudong Xing, Zhaojun Li, Zhigang Tian, Qiang Miao
Place of PublicationChengdu, Sichuan, China
Number of pages5
ISBN (Electronic)978-1-5090-2778-1
ISBN (Print)978-1-5090-2779-8
Publication statusPublished - 19 Jan 2017
Event2016 Prognostics and System Health Management Conference (PHM-Chengdu) - Chengdu, China
Duration: 19 Oct 201621 Oct 2016

Publication series

NamePrognostics and System Health Management Conference (PHM)
ISSN (Electronic)2166-5656


Conference2016 Prognostics and System Health Management Conference (PHM-Chengdu)


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