Case Study: Vibration trip and post-event Analysis with Auto-Associative Neural Networks on a Large Steam Turbine

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Luc Fromaigeat Gianluca Nicchiotti

Abstract

This 300 MW steam turbine at a coal-based thermal power plant is equipped with a protection system, a condition monitoring analysis software and an automatic diagnostic tool. The Machine Protection System (MPS) and Condition Monitoring System (CMS) configuration combines sensors, electronic hardware, firmware and software specific to this application. The protection system initiated a trip having identified high vibration. The trip prevented further damage. Subsequent analysis of the data using the condition monitoring software established the bearings most affected and pin pointed the source of high vibration. The data is post processed using an Auto-Associative Neural Networks (AANN) that has been trained with healthy data recorded several hours prior to the trip. AANN are methodologies widely used for novelty and anomaly detection. The AANN results indicates that such approach would be capable of detecting the failure event in advance compared to the automatic diagnostic system based on rules, demonstrating the validity of the approach in this context. Various aspects related to vibration: protection, condition monitoring, analysis, automatic diagnostics using rules and Neural Networks are presented and their results discussed.

How to Cite

Fromaigeat, L., & Nicchiotti, G. (2016). Case Study: Vibration trip and post-event Analysis with Auto-Associative Neural Networks on a Large Steam Turbine. PHM Society European Conference, 3(1). https://doi.org/10.36001/phme.2016.v3i1.1611
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Keywords

applications: industrial, Condition Based Maintenance, Auto-Associative Neural Network, Experience Feedback

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Section
Technical Papers

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