Throughout the United States Navy, marine gas turbines (MGT) are used for the production of propulsive and electric power aboard many surface ships. The operational availability of these ships during deployments is contingent on the health and reliability of the installed MGTs. Currently, to ensure the health and proper functionality of these turbines, a series of manual evaluations are employed with varying success (e.g., pre-deployment visual inspections, periodic inspections of predefined wear out modes, characteristic vibration surveys, Integrated Performance Analysis Reports). This paper examines historical records associated with the General Electric LM2500 MGT installed for propulsion aboard Guided Missile Destroyers (DDG) and Guided Missile Cruisers (CG) to develop a deployable model of a healthy engine for the automated, near-real time comparison of sensed data. In traditional gas-path analysis (GPA), parameters such as compressor discharge pressure (CDP), compressor inlet temperature (CIT), and exhaust gas temperature (EGT) are predicted as a function of engine speed using baseline engine data and correction factors. Implementation of GPA in the MGT environment is particularly challenging, as analysis will be limited to narrow operation bands and not account for influence factors such as engine load or the performance of critical subsystems on the engine. In this work, a multi-layer perceptron (MLP) regression model is developed in order to capture the nonlinear relationships between engine controller inputs, external loads, and ambient conditions to selected sensor outputs such as gas generator speed (NGG), low pressure turbine speed (NPT ), and power turbine inlet pressure (P54). Optimal inputs and outputs are chosen using both mutual information (MI) scores and input from subject matter experts. A healthy data set was created using data from 60 MGT’s in service and time-synchronized failure records over a five year period from 2012 to 2017. An inter-engine model was trained from the healthy data set and used to generate model residuals, which show strong correlation with a variety of critical failure modes reported in maintenance history, thus enabling automatic fault detection and remote identification of asset health and reliability.
How to Cite
Gas turbine diagnostics
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.