Intelligent performance measures for condition‐based maintenance

Abstract

Purpose

The purpose of this paper is to develop a comprehensive approach to efficiently integrate maintenance and operation by combining process and condition monitoring data with performance measures.

Design/methodology/approach

Intelligent stress, condition and health indicators have been developed for control and condition monitoring by combining generalised moments and norms with efficient nonlinear scaling. The data analysis resulting nonlinear scaling functions can also be used to handle performance measures used for management. The generalised norms provide limits for an advanced statistical process control.

Findings

The data‐driven analysis methodology demonstrates that management‐oriented indicators can be presented in the same scale as intelligent condition and stress indices. Control, condition monitoring, maintenance and performance monitoring are represented as interactive feedback loops.

Practical implications

Performance analysis can be based on real‐time information by using various stress, condition and health indices as inputs. Similar approaches can be used for outputs: quality indices, harmonised indices, key performance indicators, process capability indices and overall equipment effectiveness. Since consistent linguistic explanations based on nonlinear scaling are available for all these indices, the analysis can be further deepened with LE modelling. Efficient monitoring with intelligent indices provides a good basis for control and condition‐based maintenance and performance monitoring.

Originality/value

The paper extends the nonlinear scaling methodology and linguistic equations to intelligent performance measures. The methodology provides a consistent way to also represent all information with linguistic terms.

Citation

Juuso, E.K. and Lahdelma, S. (2013), "Intelligent performance measures for condition‐based maintenance", Journal of Quality in Maintenance Engineering, Vol. 19 No. 3, pp. 278-294. https://doi.org/10.1108/JQME-05-2013-0026