Authors:
Alexander Wurl
1
;
Andreas Falkner
1
;
Alois Haselböck
1
;
Alexandra Mazak
2
and
Simon Sperl
1
Affiliations:
1
Siemens AG Österreich, Corporate Technology, Vienna and Austria
;
2
TU Wien, Business Informatics Group, Vienna and Austria
Keyword(s):
Predictive Asset Management, Obsolescence Management, Partial Least Squares Regression, Data Analytics.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Business Analytics
;
Cardiovascular Technologies
;
Computing and Telecommunications in Cardiology
;
Data Analytics
;
Data Engineering
;
Decision Support Systems
;
Decision Support Systems, Remote Data Analysis
;
Health Engineering and Technology Applications
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Statistics Exploratory Data Analysis
;
Symbolic Systems
Abstract:
As wrong estimations in hardware asset management may cause serious cost issues for industrial systems, a precise and efficient method for asset prediction is required. We present two complementary methods for forecasting the number of assets needed for systems with long lifetimes: (i) iteratively learning a well-fitted statistical model from installed systems to predict assets for planned systems, and - using this regression model - (ii) providing a stochastic model to estimate the number of asset replacements needed in the next years for existing and planned systems. Both methods were validated by experiments in the domain of rail automation.