Operational pattern analysis for predictive maintenance scheduling of industrial systems

Zhang, Yu, Bingham, Chris, Gallimore, Michael and Maleki, Sepehr (2015) Operational pattern analysis for predictive maintenance scheduling of industrial systems. In: IEEE international conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA), 12-14 June, 2015, Shenzhen, China.

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Item Type:Conference or Workshop contribution (Presentation)
Item Status:Live Archive


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:Operational usage pattern, fast Fourier transform, singular spectral analysis
Subjects:G Mathematical and Computer Sciences > G700 Artificial Intelligence
G Mathematical and Computer Sciences > G340 Statistical Modelling
Divisions:College of Science > School of Engineering
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ID Code:17829
Deposited On:09 Jul 2015 09:46

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