Neural Network Models for Usage Based Remaining Life Computation

Abstract

In engine structural life computations, it is common practice to assign a life of certain number of start-stop cycles based on a standard flight or mission. This is done during design through detailed calculations of stresses and temperatures for a standard flight, and the use of material property and failure models. The limitation of the design phase stress and temperature calculations is that they cannot take into account actual operating temperatures and stresses. This limitation results in either very conservative life estimates and subsequent wastage of good components or in catastrophic damage because of highly aggressive operational conditions, which were not accounted for in design. In order to improve significantly the accuracy of the life prediction, the component temperatures and stresses need to be computed for actual operating conditions. However, thermal and stress models are very detailed and complex, and it could take on the order of a few hours to complete a stress and temperature simulation of critical components for a flight. The objective of this work is to develop dynamic neural network models that would enable us to compute the stresses and temperatures at critical locations, in orders of magnitude less computation time than required by more detailed thermal and stress models. The current paper describes the development of a neural network model and the temperature results achieved in comparison with the original models for Honeywell turbine and compressor components. Given certain inputs such as engine speed and gas temperatures for the flight, the models compute the component critical location temperatures for the same flight in a very small fraction of time it would take the original thermal model to compute.

Integrated Prognostics Observer for Condition Monitoring of an Automated Manual Transmission Dry Clutch System

“…Refer Figure 3 which shows the wear & tear parts of the clutch assembly which is commonly used on both MT &AMT. Parthasarathy, Menol, Richardson, Jameel, McNamee, Desper, Gorelik, and Hickenbottom (2008) demonstrated model reduction technique for computing critical component parameters for Remaining Useful Life (RUL). Dynamic neural network model reduces the original model.…”

Aircraft auxiliary power unit performance assessment and remaining useful life evaluation for predictive maintenance

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Off-design performance improvement of twin-shaft gas turbine by variable geometry turbine and compressor besides fuel control

“…In some of recent research, the Artificial Neural Network has been used for estimating the gas turbine blade life cycle. [11][12][13][14] In this paper, a novel procedure for optimizing the performance of an industrial twin-shaft gas turbine at off-design conditions (due to various ambient temperatures) is addressed. The above-mentioned procedure is based on maximizing the power output as well as keeping the gas generator (GG) turbine rotor blade life cycle in an acceptable range.…”