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

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Presentation on theme: "In-Flight Fatigue Crack Monitoring in Aircraft Using Acoustic Emission and Neural Networks Eric. v. K. Hill Samuel G. Vaughn Christopher L. Rovik."— Presentation transcript:

1 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 Background Test Setup & Procedure Test Setup & Procedure Acoustic Emission Acoustic Emission Neural Networks Neural Networks Results Results Conclusions & Recommendations Conclusions & Recommendations

3 Background

4 Fatigue Cracking Brittle failure in a normally ductile material due to cyclic loads below yield stress 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 Plastic deformation plus cyclic loads leads to strain hardening, then fatigue cracking Small cyclic loads can cause significant damage over time 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 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 Recent F-15E fuselage longeron failure behind cockpit – grounding of fleet Aging aircraft are progressively accumulating fatigue damage Aging aircraft are progressively accumulating fatigue damage This leads to costly mandatory inspections and parts replacement at safe intervals 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 Military: KC-135, C-5A, F-105 Commercial: N/A Commercial: N/A Civil: Piper PA-28 Cadet & Cessna T-303 Crusader 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 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 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 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 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 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 4 acoustic emission transducers symmetrically mounted on engine cowling 4 acoustic emission transducers symmetrically mounted on engine cowling 2 transducers monitoring crack growth and the other 2 recording the noise 2 transducers monitoring crack growth and the other 2 recording the noise 3 Flights with 5 particular maneuvers monitored on each flight 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: Definition: The transient elastic waves generated by the rapid release of energy within a material due to flaw growth mechanisms 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 Fatigue Cracking Plastic Deformation Plastic Deformation Mechanical Noise (Rubbing & Rivet Fretting) Mechanical Noise (Rubbing & Rivet Fretting) AE Source DurationAmplitude Fatigue Cracking ShortHigh Plastic Deformation ShortLow-Medium Mechanical Noise LongLow-Medium

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 AE source (e.g., fatigue crack) emits acoustic emission energy in the form of stress waves Piezoelectric crystal within AE transducer senses the signal 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 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 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) 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: Two primary steps in implementing a Kohonen SOM neural network: Training the SOM – sample of data Training the SOM – sample of data Testing the SOM – remainder of data Testing the SOM – remainder of data

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

29 Testing the SOM Create testing file Create testing file Pass test file through the trained neural network and it will be classified 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) 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 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 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 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 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 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 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: 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 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 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 One column by three row (1x3) matrix Kohonen classification layer gave the most concise output

37 Piper PA-28 Cadet Engine Cowling Results

38

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

46

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 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 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 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. 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 (s 0 ) – plane strain crack Symmetric (s 0 ) – plane strain crack Antisymmetric (a 0 ) – plane stress crack Antisymmetric (a 0 ) – plane stress crack

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

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


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