Damage propagation modeling for aircraft engine run-to-failure simulation

Abstract

This paper describes how damage propagation can be modeled within the modules of aircraft gas turbine engines. To that end, response surfaces of all sensors are generated via a thermo-dynamical simulation model for the engine as a function of variations of flow and efficiency of the modules of interest. An exponential rate of change for flow and efficiency loss was imposed for each data set, starting at a randomly chosen initial deterioration set point. The rate of change of the flow and efficiency denotes an otherwise unspecified fault with increasingly worsening effect. The rates of change of the faults were constrained to an upper threshold but were otherwise chosen randomly. Damage propagation was allowed to continue until a failure criterion was reached. A health index was defined as the minimum of several superimposed operational margins at any given time instant and the failure criterion is reached when health index reaches zero. Output of the model was the time series (cycles) of sensed measurements typically available from aircraft gas turbine engines. The data generated were used as challenge data for the Prognostics and Health Management (PHM) data competition atPHM'08.

Features Selection Procedure for Prognostics: An Approach Based on Predictability

“…The proposed developments are applied on the challenge data set of diagnostics and prognostics of machine faults from first international conference of prognostics and health management (Saxena et al (2008)). This data set consists of multivariate time series signals (26 features) from different degrading instances and contaminated with measurement noise.…”

Reducing Dimensionality of Multi-regime Data for Failure Prognostics

“…The measurements have signified their practical relevance in prognostic designs and have found their way into multi-step predictions. The metrics used in this research are based on the works of [14,19]. Mean Absolute Error MAE calculates an average of the absolute error terms.…”

“…On the other hand, the test subsets are ended at a certain point before the engine reaches the system failure. The challenge is to predict the remaining useful life between the end of each test set and to validate the results with the actual failure point which was given separately by a vector corresponding to true RUL values of the test data [14].…”

“…Since the exact behavior of degradation change is known, the coefficient variables can be calculated with regard to the operational setting, and the differences caused by the noise. The generalized time-varying health index equation can be used as a synthetic wear level index to yield supervised classifications for C-MAPSS datasets [14].…”

Prognostics: a literature review

“…A macro-level model is based on the first principle knowledge about the system to model the relation between its component parts. Modeling is performed by mathematical equations such as modeling degradation of turbofan engines as a function of efficiency loss and flow [35].…”