Operational pattern analysis for predictive maintenance scheduling of industrial systems

Author

Yu Zhang ; Bingham, Chris ; Gallimore, Michael ; Maleki, Sepehr

Author_Institution

Sch. of Eng., Univ. of Lincoln, Lincoln, UK

fYear

2015

fDate

12-14 June 2015

Firstpage

1

Lastpage

5

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.

Keywords

fast Fourier transforms; gas turbines; image segmentation; maintenance engineering; mechanical engineering computing; power measurement; scheduling; spectrometers; turbogenerators; FFT; SSA; fast Fourier transform; image segmentation; industrial gas turbine generator; industrial systems; long-term unit startups pattern; operational pattern analysis; operational trend extraction; power measurements; power requirements duty cycle; predictive maintenance scheduling; singular spectral analysis; spectral power indices; spectrogram; Forecasting; Generators; Job shop scheduling; Market research; Predictive maintenance; Spectrogram; Time series analysis; Operational usage pattern; fast Fourier transform; singular spectral analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA), 2015 IEEE International Conference on

Conference_Location

Shenzhen

Type

conf

DOI

10.1109/CIVEMSA.2015.7158599

Filename

7158599