Predictive maintenance (PdM) within asset management improves savings in operational cost, productivity, and safety management capabilities. While PdM can be administered using various methods, growing interest in Arti cial Intelligence (AI) has lead to current state of the art PdM relying on machine learning (ML) technology. Like other tools used in PdM for asset management, standards for applying ML technology for PdM are required. This work introduces a standard of practice in regards to usage of asset data to develop ML analytic tools for PdM. It provides a standard method for ensuring asset data is in a form conducive to ML algorithms, and ensuring retention of asset information necessary for optimum PdM during the data transform. In the ML domain, it has been proven through research initiatives that the data structure used to train and test ML algorithms has a great impact on their performance and accuracy. Using poorly trained models for estimation due to improper data usage, can leave some AI-based PdM tools vulnerable to high rates of inaccurate estimations. Thus, leading to value loss during an asset's life cycle.This research was supported by
the Australian Government Research Training Program Scholarship,
American Australian Association Education Fund Fellowship, National
GEM Consortium, Sustain-Owner (Sustainable Design and
Management of Industrial Assets through Total Value and Cost of
Ownership), a project sponsored by the EU Framework Programme
Horizon 2020, MSCA-RISE-2014: Marie Sk lodowska-Curie Actions
Research and Innovation Staff Exchange (Rise) (grant agreement
number 645733 Sustain-owner H2020-MSCA-RISE-2014
