Model-based Reasoning Approach for Automated Failure Analysis: An Industrial Gas Turbine Application

Gulnar Mehdi, Davood Naderi, Giuseppe Ceschini, and Mikhail Roshchin
Submission Type: 
Full Paper
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phmc_15_053.pdf765.05 KBAugust 25, 2015 - 8:56am

Keeping up with the technological advances, turbo-machinery industry aspires to integrate manufacturing, servicing and maintenance of their plants. Typically, these objectives may be accomplished by adoption of condition monitoring services and diagnostic solutions, resulting in improved plant operations, lower maintenance cost, and impart safety and reliability. Specifically, failure analysis, within systematic diagnostics, is a fundamental feature of design and maintenance phase, as it allows fault identification, and its causes and effects that propagate at different system levels. With the large number of subsystems and process flows, failure analysis for industrial gas turbines is non-trivial, and requires expertise of system mechanics, aerodynamics, thermodynamics, etc. Consequently, in order to realize an efficient system analysis, we device an automated model-based approach to failure analysis for industrial gas turbine applications. This paper presents context-independent qualitative models of key turbine components, which are most error-prone, together with their potential failure mode descriptions, and their impact at different system levels. Using an existing reasoning engine, we present behavior models and results for two most vulnerable turbine subsystems i.e. Lubrication Oil System and the Core Gas Turbine Engine. Finally, we evaluate the practical use-cases of this model-based solution implemented for diagnostic services at Siemens AG.

Publication Year: 
2015
Publication Volume: 
6
Publication Control Number: 
053
Submission Keywords: 
model based reasoning
Failure Modes and Effect Analysis (FMEA)
qualitative modeling
component level model
failure analysis
gas turbines
Submission Topic Areas: 
Component-level PHM
Industrial applications
Model-based methods for fault detection, diagnostics, and prognosis
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