System which will identify the class to which each

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system, which will identify the class to which each unknown signal belongs. Successful identification of input signals is a good indication of a properly trained system. Signal Classification The data used to train a classifier correspond to a group of points in feature space. If two features are used for a system with three source classes, then the feature space might appear as shown in Fig. 8. The classification mechanism must distinguish among the classes. One simple way to distinguish the classes is to place planes between the classes (hyperplanes in a multidimensional space with many features). The classifier is a linear combination of feature elements that defines a hyperplane to separate one class of signals from another in the feature space. This is called a linear discriminant function classifier. When the classes are distributed in a more complex arrangement, more sophisticated classifiers are required. An example would be the K nearest neighbor classifier that considers every member in the training set as a representation point. It determines the distance of an unknown signal from every pattern in the training set and it thus finds the K nearest patterns to the unknown signal. The unknown is then assigned to the class in which the majority of the K nearest neighbors belongs. In other cases, the pattern recognition process is modeled statistically and statistical discriminant functions are derived. Signal Treatment and Classification System A classification system is shown in Fig. 9. Acoustic emission source characterization and recognition begins with signal treatment. The acoustic emission signals from different sources are first fed into a treatment system that computes the necessary waveform features of each signal. The identity of the signal is tagged to each of the feature vectors. These vectors are then divided into two data sets, one for training the classifier and the other to test its performance. Generally, a normally distributed random variable is used for assigning the signal to the two data sets and this ensures that each set has the same a priori probability. Once the classifier is trained, it is tested by supplying a signal feature vector with a known identity and then 49 Fundamentals of Acoustic Emission Testing F IGURE 8. Feature space for two features used for a system with three source classes: (a) linearly separable; (b) nonlinearly separable; (c) piecewise linear separation of two regions. (a) (b) (c) + + F IGURE 9. A classification system for treatment of acoustic emission signals. Batch control file Batch feature extraction or Preprocessor Extracted feature data Training sample Control Testing sample Feature selection and decision logic Trained information Classifier Trained information Decision library Classification output Signal data
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comparing the known information to the classification output. The training information and the trained classifier setup can be stored in a decision library that can be used to characterize and classify the acoustic emission sources.
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  • Fall '19
  • Nondestructive testing, Acoustic Emission, Acoustic Emission Testing

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