Research Papers

Analysis of the Reliability of a Jet Engine Compressor Rotor Blade Containing a Fatigue Crack

[+] Author and Article Information
Dooyoul Lee, Jan D. Achenbach

Department of Mechanical Engineering,
McCormick School of Engineering and
Applied Science,
Northwestern University,
Evanston, IL 60208

Contributed by the Applied Mechanics Division of ASME for publication in the JOURNAL OF APPLIED MECHANICS. Manuscript received December 7, 2015; final manuscript received December 16, 2015; published online January 18, 2016. Editor: Yonggang Huang.

J. Appl. Mech 83(4), 041004 (Jan 18, 2016) (9 pages) Paper No: JAM-15-1657; doi: 10.1115/1.4032376 History: Received December 07, 2015; Revised December 16, 2015

The reliability of a jet engine compressor rotor blade containing a fatigue crack has been assessed based on the eddy current inspection (ECI) response of both the actual rotor blade and bolt hole specimens containing cracks of known lengths. The detection threshold and the probability of detection (POD) curve have been determined. A dynamic Bayesian network (DBN) model was used to quantify uncertainties. The model encompasses a realistic ECI response model, so that it is possible to consider all relevant inspection data types. Factors which contribute the most to the variation of crack length have been determined by sensitivity analysis and have been calibrated using the field inspection data. Part of the inspection data was used to validate the calibrated model, and a Bayes factor of 9.93 which corresponds to a confidence level of 91% has been obtained. Based on the control level for the reliability index βctrl = 3, and the reliability indices calculated from the calibrated model, the recommended interval for the first inspection has been determined as 1600 hrs. This interval is smaller than the current interval which is 3200 hrs.

Copyright © 2016 by ASME
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Fig. 1

(a) View of the compressor rotor blade and (b) magnified view of blade tangs that are held by a pin. The dashed circle indicates the location of the fatigue crack.

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Fig. 2

(a) ECI response versus inspection time and (b) ECI response versus crack length

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Fig. 3

(a) Posterior mean ECI response and 90% credible bounds with detection threshold and posterior noise mean, and (b) posterior mean POD and 95% lower credible bound

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Fig. 4

The mesh for coarse global model and refined submodel. Stress intensity factor has been obtained from the refined submodel.

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Fig. 5

The surface of stress intensity factor obtained from the submodel

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Fig. 6

Fishbone diagram shows the sources of variability for fatigue growth

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Fig. 7

(a) Sensitivity index of a0 versus N, (b) sensitivity index of m versus N, (c) sensitivity index of C versus N, and (d) sensitivity index of Δσ versus N

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Fig. 8

Bayesian network for fatigue crack growth analysis

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Fig. 9

Reliability index calculated from both the DBN model and MCS

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Fig. 10

Calibrated (a) Δσ and (b) m

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Fig. 11

Comparison between before and after the calibration




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