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

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

Background

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

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

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.

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

Test Setup & Procedure

Testbed 1: Engine Cowling

In-Flight Test Set-Up

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

Testbed 2: Vertical Tail Cessna Crusader N106ER

Equipment Setup (Flight)

Equipment Setup (Lab)

Acoustic Emission

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

AE Signal (Voltage vs. Time) Waveform Parameters

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

AE Duration vs. Amplitude Plot

Source Location

Source Location Plot

Finite Element Analysis (FEA) Analysis

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

Neural Networks

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)

SOM Neural Network Architecture

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

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

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

Results

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

Lab Test Configuration

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

Duration vs. Amplitude (File 20)

ATPOST Filtering Limits AE sources filtered into individual files:

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

Piper PA-28 Cadet Engine Cowling Results

In-Flight Test Set-Up

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

Fatigue Crack (Channels 1&2)

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

Fatigue Crack (Channels 3&4)

Output Observations Fatigue crack growth unexpectedly detected on both sides of the aircraft cowling Inspection revealed cracking between Channels 3 - 4 as well as 1 - 2 Cracking in the engine cowling occurred predominantly during ground operations: taxi, take-off, and final approach/landing

Cessna T-303 Crusader Vertical Tail Results

Equipment Setup (Flight)

Cessna Crusader In-Flight Data SOM Output

Conclusions & Recommendations

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.

Problem: Data Overlap in AE Parameter Plots

Fatigue Crack Waveform

Plastic Deformation Waveform

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

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

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