Affiliation:
1. Control Engineering, Faculty of Technology Pentti Kaiteran katu 1, 90014 Oulu , Finland
Abstract
Abstract
Performance improvement is taken as the primary goal in the asset management. Advanced data analysis is needed to efficiently integrate condition monitoring data into the operation and maintenance. Intelligent stress and condition indices have been developed for control and condition monitoring by combining generalized norms with efficient nonlinear scaling. These nonlinear scaling methodologies can also be used to handle performance measures used for management since management oriented indicators can be presented in the same scale as intelligent condition and stress indices. Performance indicators are responses of the process, machine or system to the stress contributions analyzed from process and condition monitoring data. Scaled values are directly used in intelligent temporal analysis to calculate fluctuations and trends. All these methodologies can be used in prognostics and fatigue prediction. The meanings of the variables are beneficial in extracting expert knowledge and representing information in natural language. The idea of dividing the problems into the variable specific meanings and the directions of interactions provides various improvements for performance monitoring and decision making. The integrated temporal analysis and uncertainty processing facilitates the efficient use of domain expertise. Measurements can be monitored with generalized statistical process control (GSPC) based on the same scaling functions.
Subject
Management of Technology and Innovation,Industrial and Manufacturing Engineering,Management Information Systems
Reference52 articles.
1. [1] S. Lahdelma and E.K. Juuso, ”Advanced signal processing and fault diagnosis in condition monitoring”, Insight, vol. 49, no. 12, pp. 719-725, 2007.
2. [2] S. Lahdelma and E.K. Juuso, “Signal processing and feature extraction by using real order derivatives and generalised norms. Part 1: Methodology”, The International Journal of Condition Monitoring, vol. 1, no. 2, pp. 46-53, 2011.
3. [3] S. Lahdelma and E.K. Juuso, “Signal processing and feature extraction by using real order derivatives and generalised norms. Part 2: Applications”, The International Journal of Condition Monitoring, vol. 1, no. 2, pp. 54-66, 2011.
4. [4] E.K. Juuso and S. Lahdelma, “Intelligent scaling of features in fault diagnosis”, in 7th Int. Conf. on Condition Monitoring and Machinery Failure Prevention Technol- ogies, Stratford-upon-Avon, United Kingdom, vol. 2, 2010, pp. 1358-1372.
5. [5] E.K. Juuso and S. Lahdelma, “Intelligent performance measures for condition-based maintenance”, Journal of Quality in Maintenance Engineering, vol. 19, no.3, pp. 278-294, 2013.
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