Data-driven models for reliability prognostics of gas turbines

This thesis develops three data-driven models of a commercially operating gas turbine, and applies inference techniques for reliability prognostics. The models focus on capturing feature signals (continuous state) and operating modes (discrete state) that are representative of the remaining useful life of the solid welded rotor. The first model derives its structure from a non-Bayesian parametric hidden Markov model. The second and third models are based on Bayesian nonparametric methods, namely the hierarchical Dirchlet process, and can be viewed as extensions of the first model. For all three approaches, the model structure is first prescribed, parameter estimation procedures are then discussed, and lastly validation and prediction results are presented, using proposed degradation metrics. All three models are trained using five years of data, and prediction algorithms are tested on a sixth year of data. Results indicate that model 3 is superior, since it is able to detect new operating modes, which the other models fail to do. The turbine is based on a sequential combustion design and operates in the 50Hz wholesale electricity market. The rotor is the most critical asset of the machine and is subject to nonlinear loadings induced from three sources: i) day-to-day variations in total power generated by the turbine; ii) machine trips in high and low loading conditions; iii) downtimes due to scheduled maintenance and inspection events. These sources naturally lead to dynamics, where random (resp. forced) transitions occur due to switching in the operating mode (resp. trip and/or maintenance events). The degradation of the rotor is modeled by measuring the abnormality witnessed by the cooling air temperature within different modes. Generation companies can utilize these indicators for making strategic decisions such as maintenance scheduling and generation planning.

Department dc:contributor.department

Massachusetts Institute of Technology. Computation for Design and Optimization Program.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2015

Author dc:creator

• Kumar, Gaurev

Advisor dc:contributor.advisor

• Saurabh Amin.

• Computation for Design and Optimization Program.

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/106960

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/106960