Key Data Quality Pitfalls for Condition Based Maintenance

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Standard

Key Data Quality Pitfalls for Condition Based Maintenance. / Madhikermi, Manik; Buda, Andrea; Dave, Bhargav; Främling, Kary.

2017 2nd International Conference on System Reliability and Safety, ICSRS 2017. IEEE, 2018. p. 474-480.

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Harvard

Madhikermi, M, Buda, A, Dave, B & Främling, K 2018, Key Data Quality Pitfalls for Condition Based Maintenance. in 2017 2nd International Conference on System Reliability and Safety, ICSRS 2017. IEEE, pp. 474-480, International Conference on System Reliability and Safety, Milan, Italy, 20/12/2017. https://doi.org/10.1109/ICSRS.2017.8272868

APA

Madhikermi, M., Buda, A., Dave, B., & Främling, K. (2018). Key Data Quality Pitfalls for Condition Based Maintenance. In 2017 2nd International Conference on System Reliability and Safety, ICSRS 2017 (pp. 474-480). IEEE. https://doi.org/10.1109/ICSRS.2017.8272868

Vancouver

Madhikermi M, Buda A, Dave B, Främling K. Key Data Quality Pitfalls for Condition Based Maintenance. In 2017 2nd International Conference on System Reliability and Safety, ICSRS 2017. IEEE. 2018. p. 474-480 https://doi.org/10.1109/ICSRS.2017.8272868

Author

Madhikermi, Manik ; Buda, Andrea ; Dave, Bhargav ; Främling, Kary. / Key Data Quality Pitfalls for Condition Based Maintenance. 2017 2nd International Conference on System Reliability and Safety, ICSRS 2017. IEEE, 2018. pp. 474-480

Bibtex - Download

@inproceedings{6fd1a97e3460437a952682c60c04dc57,
title = "Key Data Quality Pitfalls for Condition Based Maintenance",
abstract = "In today's competitive and fluctuating market, original equipment manufacturers (OEMs) must be able to offer aftersales services along with their products, such as condition based maintenance, extended warranty services etc. Condition based maintenance requires detailed understanding about products' operational behaviour, to detect problems before they occur, and react accordingly. Typically, Condition based maintenance consists of data collection, data analysis, and maintenance decision stages. Within this context, data quality is one of the key drivers in the knowledge acquisition process since poor data quality impacts the downstream maintenance processes, and reciprocally, high data quality will foster good decision making. The prospect of new business opportunities and better services to customers encourages companies to collect large amounts of data that have been generated in different stages of product lifecycle. Despite of availability of data, as well as advanced statistical and analytical tools, companies are still struggling to provide effective service by reducing maintenance cost and improving uptime. This paper highlights data related pitfalls that hinder organisations to improve maintenance services. These pitfalls are based on case studies of two globally operating Finnish manufacturing companies where maintenance is one of the major streams of income.",
keywords = "condition based maintenance, data analysis, data quality, data reliability, after-sales service, statistics",
author = "Manik Madhikermi and Andrea Buda and Bhargav Dave and Kary Fr{\"a}mling",
note = "| openaire: EC/H2020/688203/EU//BIoTope",
year = "2018",
doi = "10.1109/ICSRS.2017.8272868",
language = "English",
isbn = "978-1-5386-3322-9",
pages = "474--480",
booktitle = "2017 2nd International Conference on System Reliability and Safety, ICSRS 2017",
publisher = "IEEE",

}

RIS - Download

TY - GEN

T1 - Key Data Quality Pitfalls for Condition Based Maintenance

AU - Madhikermi, Manik

AU - Buda, Andrea

AU - Dave, Bhargav

AU - Främling, Kary

N1 - | openaire: EC/H2020/688203/EU//BIoTope

PY - 2018

Y1 - 2018

N2 - In today's competitive and fluctuating market, original equipment manufacturers (OEMs) must be able to offer aftersales services along with their products, such as condition based maintenance, extended warranty services etc. Condition based maintenance requires detailed understanding about products' operational behaviour, to detect problems before they occur, and react accordingly. Typically, Condition based maintenance consists of data collection, data analysis, and maintenance decision stages. Within this context, data quality is one of the key drivers in the knowledge acquisition process since poor data quality impacts the downstream maintenance processes, and reciprocally, high data quality will foster good decision making. The prospect of new business opportunities and better services to customers encourages companies to collect large amounts of data that have been generated in different stages of product lifecycle. Despite of availability of data, as well as advanced statistical and analytical tools, companies are still struggling to provide effective service by reducing maintenance cost and improving uptime. This paper highlights data related pitfalls that hinder organisations to improve maintenance services. These pitfalls are based on case studies of two globally operating Finnish manufacturing companies where maintenance is one of the major streams of income.

AB - In today's competitive and fluctuating market, original equipment manufacturers (OEMs) must be able to offer aftersales services along with their products, such as condition based maintenance, extended warranty services etc. Condition based maintenance requires detailed understanding about products' operational behaviour, to detect problems before they occur, and react accordingly. Typically, Condition based maintenance consists of data collection, data analysis, and maintenance decision stages. Within this context, data quality is one of the key drivers in the knowledge acquisition process since poor data quality impacts the downstream maintenance processes, and reciprocally, high data quality will foster good decision making. The prospect of new business opportunities and better services to customers encourages companies to collect large amounts of data that have been generated in different stages of product lifecycle. Despite of availability of data, as well as advanced statistical and analytical tools, companies are still struggling to provide effective service by reducing maintenance cost and improving uptime. This paper highlights data related pitfalls that hinder organisations to improve maintenance services. These pitfalls are based on case studies of two globally operating Finnish manufacturing companies where maintenance is one of the major streams of income.

KW - condition based maintenance

KW - data analysis

KW - data quality

KW - data reliability

KW - after-sales service

KW - statistics

U2 - 10.1109/ICSRS.2017.8272868

DO - 10.1109/ICSRS.2017.8272868

M3 - Conference contribution

SN - 978-1-5386-3322-9

SP - 474

EP - 480

BT - 2017 2nd International Conference on System Reliability and Safety, ICSRS 2017

PB - IEEE

ER -

ID: 16075795