1. Eric. v. K. Hill Samuel G. Vaughn Christopher L. Rovik
In-Flight Fatigue Crack Monitoring in Aircraft Using Acoustic Emission and Neural Networks
Eric. v. K. Hill
Samuel G. Vaughn
Christopher L. Rovik

2. Fatigue Crack Monitoring
Background
Test Setup & Procedure
Acoustic Emission
Neural Networks
Results
Conclusions & Recommendations

3. Background

4. Fatigue Cracking
Brittle failure in a normally ductile material due to cyclic loads below yield stress
Plastic deformation plus cyclic loads leads to strain hardening, then fatigue cracking
Small cyclic loads can cause significant damage over time

5. Notable Fatigue Failures
1988 Aloha Airlines flight: a piece of a B-737 fuselage tore off during flight due to corrosion/fatigue cracking
Recent F-15E fuselage longeron failure behind cockpit – grounding of fleet
Aging aircraft are progressively accumulating fatigue damage
This leads to costly mandatory inspections and parts replacement at “safe” intervals

6. In-Flight Acoustic Emission Applications
Military: KC-135, C-5A, F-105
Commercial: N/A
Civil: Piper PA-28 Cadet & Cessna T-303 Crusader
Goal: In-flight fatigue crack detection systems promote maintenance schemes based on replacement for cause rather than replacement at conservatively calculated intervals using linear elastic fracture mechanics.

7. Relevant M.S. Theses
1994 A.F. de Almeida: Neural Network Detection of Fatigue Crack Growth in Riveted Joints Using Acoustic Emission
1995 W.P. Thornton: Classification of Acoustic Emission Signals from an Aluminum Pressure Vessel Using a Self-Organizing Map
1996 M.L. Marsden: Detection of Fatigue Crack Growth in a Simulated Aircraft Fuselage
1998 S.G. Vaughn III: In-Flight Fatigue Crack Monitoring of an Aircraft Engine Cowling
1998 C.L. Rovik: Classification of In-Flight Fatigue Cracks in Aircraft Structures Using Acoustic Emission and Neural Networks

8. Test Setup & Procedure

9. Testbed 1: Engine Cowling

10. In-Flight Test Set-Up

11. In-Flight Test Set-Up
4 acoustic emission transducers symmetrically mounted on engine cowling
2 transducers monitoring crack growth and the other 2 recording the noise
3 Flights with 5 particular maneuvers monitored on each flight

12. Testbed 2: Vertical Tail
Cessna Crusader N106ER

13. Equipment Setup (Flight)

14. Equipment Setup (Lab)

15. Acoustic Emission

16. Acoustic Emission (AE)
Definition:
The transient elastic waves generated by the rapid release of energy within a material due to flaw growth mechanisms

17. AE Signal (Voltage vs. Time) Waveform Parameters

18. AE Sources & Characteristics
Fatigue Cracking
Plastic Deformation
Mechanical Noise (Rubbing & Rivet Fretting)
AE Source
Duration
Amplitude
Fatigue Cracking
Short
High
Plastic Deformation
Low-Medium
Mechanical Noise
Long

19. AE Duration vs. Amplitude Plot

20. Source Location

21. Source Location Plot

22. Finite Element Analysis (FEA) Analysis

23. Data Acquisition
AE source (e.g., fatigue crack) emits acoustic emission energy in the form of stress waves
Piezoelectric crystal within AE transducer senses the signal
AE signal amplified and transmitted to a computer where its waveform quantification parameters are digitized and stored
Records signals in the frequency range 100 kHz to 1 MHz

24. Neural Networks

25. Classification Neural Network
Kohonen Self-Organizing Map (SOM) neural network uses mathematical processes to classify “things” based on a set of inputs: six AE quantification parameters (amplitude, duration, counts, energy, rise time, and counts-to-peak)

26. SOM Neural Network Architecture

27. SOM Data Processing
Two primary steps in implementing a Kohonen SOM neural network:
Training the SOM – sample of data
Testing the SOM – remainder of data

28. Training the SOM Create a training file 5 steps to training:
1. Randomly set weights between 0 and 1
2. Introduce first input vector ( 6 signal parameters for AE hit)
3. Find minimal planar distance between the input vector and Kohonen neurons
4. Identify the neuron with the minimal distance
5. Adjust/update the weights

29. Testing the SOM Create testing file
Pass test file through the trained neural network and it will be classified

30. Results

31. Anticipated Results
Neural network classifies lab test data into 3 categories: fatigue cracking, plastic deformation, and rubbing (mechanical noise)
Trained neural network classifies the entire lab test file with a high degree of accuracy
In-flight data verifies fatigue crack growth between Channels 1 & 2 on Piper Cadet cowling
Fatigue crack growth activity associated with stressful maneuvers on Cessna Crusader vertical tail

32. Lab Test Configuration

33. Lab Test Results
Over twenty AE files were recorded during the lab fatigue tests
File twenty: 3 minutes 30 seconds in length; recorded fatigue cracking for the last minute
Duration vs. Amplitude plot of file twenty shows good separation between failure mechanisms

34. Duration vs. Amplitude (File 20)

35. ATPOST Filtering Limits
AE sources filtered into individual files:

36. Training the SOM
100 hits each of fatigue cracking, plastic deformation, and rubbing for a total of 300 hits were used for training
Trained neural network tested 99% accurate when testing the remaining 70,000+ hits
One column by three row (1x3) matrix Kohonen classification layer gave the most concise output

37. Piper PA-28 Cadet Engine Cowling Results

39. In-Flight Test Set-Up

40. Piper Cadet In-Flight Data SOM Output (Channels 1&2)

41. Fatigue Crack (Channels 1&2)

42. Piper Cadet In-Flight Data SOM Output (Channels 3&4)

43. Fatigue Crack (Channels 3&4)

44. Output Observations
Fatigue crack growth unexpectedly detected on both sides of the aircraft cowling
Inspection revealed cracking between Channels as well as 1 - 2
Cracking in the engine cowling occurred predominantly during ground operations: taxi, take-off, and final approach/landing

45. Cessna T-303 Crusader Vertical Tail Results

47. Equipment Setup (Flight)

48. Cessna Crusader In-Flight Data SOM Output

49. Conclusions & Recommendations

50. Conclusions
SOM trained successfully to classify fatigue cracking, plastic deformation, and rubbing noises
Fatigue crack growth successfully detected in-flight from both engine cowling of the Piper PA-28 Cadet and vertical tail of the Cessna T-303 Crusader using AE parameter data
Engine cowling fatigue cracking occurred mostly during ground-based operations while vertical tail fatigue cracking occurred predominantly in-flight, especially during rolls and Dutch rolls
In-flight crack detection systems should help to minimize maintenance costs and extend the service lives of aging aircraft.

51. Problem: Data Overlap in AE Parameter Plots

52. Fatigue Crack Waveform

53. Plastic Deformation Waveform

54. Source Location Plot Second hump indicates two Lamb wave types:
Symmetric (s0) – plane strain crack
Antisymmetric (a0) – plane stress crack

55. Average Frequency vs RA Value Plot
Tearing Cracks
Mode III
Tensile Cracks
Mode I
Mixed Mode Cracks

56. Recommendations Problem: AE parameter data overlap Possible solutions:
Frequency analysis of waveforms using Fast Fourier or Wavelet Transforms
Symmetric and antisymmetric Lamb waves separated using average frequency (favg = C/D) vs. RA parameter (RA = RT/A) plot
High fidelity broadband AE sensors needed for frequency analysis and Lamb wave identification