Case Study: Vibration trip and post-event Analysis with Auto-Associative Neural Networks on a Large Steam Turbine
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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
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applications: industrial, Condition Based Maintenance, Auto-Associative Neural Network, Experience Feedback
Fromaigeat, L. (2000). Protection and Condition Monitoring of Hydropower Units using Vibration and Airgap information. Hydropower Bern. Berne: Acqua~Media International.
ISO-comitee. (1986, 09 01). ISO-1940 Mechanical Vibration - Balance quality requirements of rigid rotors -1- Determination of permissible unbalance. Geneva, GE, SWITZERLAND.
ISO-comitee. (1998, 12 15). ISO 10817-1. Rotating shaft Vibration Measuring Systems -1: Relative and absolute sensing of radial vibration. Geneva, GE, SWITZERLAND: ISO.
Kramer, M. A. (1992). Autoassociative neural networks. Computers & chemical engineering, 16(4), 313-328.
Lu, P. J., C., Z. M., Hsu, T. C., & Zhang, J. (2000). An evaluation of engine faults diagnostics using artificial neural networks. ASME Turbo Expo 2000: Power for Land, Sea, and Air.
McCloskey. (2002). Troubleshooting Turbine Steam path damage mechanisms. TURBOMACHINERY SYMPOSIUM, (pp. 105 - 143).
nPower-RWE. (2007). Forces on Large Steam Turbine Blades. London: The Royal Academy of Engineering.
Sanz, J., Perera, R., & Huerta, C. (2007). Fault diagnosis of rotating machinery based on auto-associative neural networks and wavelet transforms. Journal of Sound and Vibration 302, 981-999.
Singh, R., & Katakey, R. (2012). Worst India Outage Highlights 60 Years of Missed Targets. Retrieved March 14, 2016, from Bloomberg: http://www.bloomberg.com/news/articles/2012-08-01/worst-india-outage-highlights-60-years-of-missed-targets-energy
Worden, K. (1997). Structural fault detection using a novelty measure. Journal of Sound and vibration, 201(1), 85-101.
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