학술논문
Operational pattern analysis for predictive maintenance scheduling of industrial systems
Document Type
Conference
Source
2015 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA) Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA), 2015 IEEE International Conference on. :1-5 Jun, 2015
Subject
Language
ISSN
2377-9314
2377-9322
2377-9322
Abstract
The paper presents a method to identify the operational usage patterns for industrial systems. Specifically, power measurements from an industrial gas turbine generator are studied. A fast Fourier transform (FFT) and image segmentation is used to develop an intuitive representation of operation. A spectrogram is adopted to study the average usage through the use of spectral power indices, with singular spectral analysis (SSA) applied for operational trend extraction. Through use of these techniques, two fundamental inputs for predictive maintenance scheduling viz. the users behaviour with regard to long-term unit startups patterns, and the duty cycle of power requirements, can be readily identified.