Start-up vibration analysis for novelty detection on industrial gas turbines

Zhang, Yu, Cruz-Manzo, Samuel and Latimer, Anthony (2016) Start-up vibration analysis for novelty detection on industrial gas turbines. In: XI International Symposium on Industrial Electronics - INDEL 2016, 3-5 November 2016, Banja Luka, Bosnia and Herzegovina.

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


This paper focuses on industrial application of start-up vibration signature analysis for novelty detection with experimental trials on industrial gas turbines (IGTs). Firstly, a representative vibration signature is extracted from healthy start-up vibration measurements through the use of an adaptive neuro-fuzzy inference system (ANFIS). Then, the first critical speed and the vibration level at the critical speed are located from the signature. Finally, two (s- and v-) health indices are introduced to detect and identify different novel/fault conditions from the IGT start-ups, in addition to traditional similarity measures, such as Euclidean distance and cross-correlation measures. Through a case study on IGTs, it is shown that the presented approach provides a convenient and efficient tool for IGT condition monitoring using start-up field data.

Keywords:Start-up vibration signature, adaptive neuro-fuzzy inference system, s- & v- health indices, novelty/fault detection, industrial gas turbine
Subjects:G Mathematical and Computer Sciences > G510 Information Modelling
H Engineering > H342 Vibration
Divisions:College of Science > School of Engineering
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ID Code:25216
Deposited On:22 Nov 2016 14:45

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