Classification of acoustic emission signals using wavelets and Random Forests: application to localized corrosion

This work aims at proposing a novel approach to classify acoustic emission (AE) signals deriving from corrosion damage, even if embedded into a noisy environment. Tests involving noise and crevice corrosion are conducted, by preprocessing the waveforms including wavelet denoising and extracting a rich set of features as input of the Random Forest algorithm. To this end, a software called RF-CAM has been developed. Results show this approach is very efficient on ground truth data and is also very promising on real data, especially for its reliability, performance and speed, which are serious criteria for the chemical industry. 1" GENERAL SPECIFICATIONS Considering the crevice corrosion process, emitted gas (i.e. bubbles) coming from chemical reactions generate AE activity, which can be recorded by sensors located on the surface of the specimen. Since AE signals associated to crevice corrosion are characterized by low energy content, it is very difficult to separate those signals from the environmental noise [1, 2]. Thus, an in-depth work has been realized to preprocess the corresponding waveforms and a major motivation was to find the most relevant set of features. Chosen classification algorithm must be fast, reliable and not very sensitive to a mislabeled learning database (due to real-time and reliability industrial constraints). Moreover, it is preferable to provide a confidence level for the final decision. A whole approach combining the waveform preprocessing and Random Forest supervised classification has been implemented. To validate this new methodology, synthetic data were first used throughout an in-depth analysis, comparing Random Forests (RF) to the k-Nearest Neighbor (k-NN) algorithm, in terms of accuracy and speed processing. Then, tests on real cases involving noise and crevice corrosion were conducted. In order to build up various data sets, pH, temperature, NaCl concentration and H2O2 addition were controlled to obtain controlled crevice corrosion for some experiments and no corrosion for the others. The purpose of the classification is to isolate AE signals from corrosion to noise. 2" WAVEFORM PROCESSING This important preliminary step is performed on waveforms directly acquired from sensors. The motivation here is to normalize those AE signals for consistent comparison. It is possible to discard useless information, numerically store the waveforms for further analysis and denoise them. The waveform preprocessing consists in the three following steps : Pre-trigger removing, tail cutting, Shape Preserving Interpolation (SPI) resampling. Tail cutting resides in dynamically cutting the end of the waveform according to an energy criterion. For each point in the waveform, the cumulative energy computed from the beginning is compared to the energy contained in a 10 os length window following that point. If this energy is less than a certain threshold T (in %) of the cumulative energy, then the corresponding point represents the end of the signal. Wavelet denoising [3] can also be performed and uses the wden function from the Matlab Wavelet Toolbox. Specific parameters have been set: the universal threshold of Donoho [4] is used to select the wavelet coefficients in combination with a soft thresholding being rescaled using level dependent estimation of level noise. Decomposition is made at level 3 with the symmlet8 as the mother wavelet. 3" FEATURES EXTRACTION AND RANDOM FOREST CLASSIFICATION " Thus, each waveform is turned into a compact representation through a set of 30 features, in time, frequency and wavelet domains (Tab. 1). Besides common features such as amplitude, duration, energy, rise time, partial powers or peak frequency, other features derive from speech recognition and sound description studies. Wavelet features are actually a specific set of features using wavelet packet energy [5]. The energy percentage of the terminal nodes of the wavelet packet tree is computed, leading to 8 wavelet packet energy features. ḾS20Ḿ PROGRESS in ACOUSTIC EMISSION XVIII, JSNDI & IIIAE M or e in fo a bo ut th is a rt ic le : ht tp :// w w w .n dt .n et /? id = 21 55 5