Exploring predictive maintenance applications in industry

Wieger Willem Tiddens, Jan Braaksma, Tiedo Tinga

Research output: Contribution to journalArticleAcademicpeer-review

21 Citations (Scopus)
787 Downloads (Pure)


Purpose - Asset owners and maintainers need to make timely and well-informed maintenance decisions, based on the actual or predicted condition of their physical assets. However, only few companies have succeeded to implement Predictive Maintenance (PdM) effectively. This paper aims to identify why.
Design/Methodology/Approach - A multiple-case study including thirteen cases in various industries in the Netherlands is conducted. This paper examines the choices made in practice to achieve PdM, and possible dependencies between and motivations for these choices.
Findings – An implementation process for predictive maintenance appears to contain four elements: a trigger, data collection, maintenance technique selection and decision making. For each of these elements, several options are available. By identifying the choices made by companies in practice, and mapping these on the proposed elements, logical combinations appear. These combinations provide insight in the PdM implementation process, and may lead to guidance on this topic. Further, while successful companies typically combine various techniques, the mostly applied techniques are still those based on (previous) experiences.
Research implications - This research calls for better methods or procedures to guide the selection and use of suitable types of PdM, directed by the firm’s ambition level and the available data.
Originality/value - While it is important for firms to make suitable choices during implementation, the literature often only focuses on developing additional techniques for PdM. This paper provides new insights in the application and selection of techniques for PdM in practice and helps practitioners reduce the often applied trial-and-error process.
Original languageEnglish
Pages (from-to)68-85
Number of pages18
JournalJournal of quality in maintenance engineering
Issue number1
Early online date15 Sept 2020
Publication statusPublished - 11 Feb 2022


  • Case studies
  • Condition-based maintenance
  • Diagnostics
  • Implementation
  • Maintenance decision-making
  • Prognostics


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