This paper presents a novel approach to detecting and localising structural defects based on a novelty detection method, outlier analysis (OA), and a multi-layer perceptron (MLP) neural network.
Segmentation of original audio is done using novelty detection method applied on the similarity matrix; sequencing of segments is based on transition probabilities.
This paper proposes a novelty detection-based method for machine condition monitoring (MCM) using vibration signals and a new feature extraction method based on higher-order statistics of the power spectral density.
It is also possible that the posterior parietal cortex is an automatic processor of somatosensory information, leaving it difficult to detect any additional role in novelty detection using the current methods given the close sensory match across Groups Novel and Familiar.
To illustrate the effectiveness of the proposed method, novelty detection was applied to a particular kind of tapered roller bearing used in an industrial robot, which is investigated as a case study.
The method of novelty detection is applied to diagnose damage in a simple simulated lumped-parameter mechanical system.
An SVM approach to release detection is the so-called "one-class SVM" method of novelty detection (Schölkopf et al. 2001).
In order to identify whether a lightning occurrence could have caused a network fault, two pattern recognition methods based on novelty detection approaches have been implemented and compared using the collected data: Parzen density estimator (PDE) and support vector data description (SVDD).
This paper introduces such pattern recognition methods into the wind energy field and attempts to address the effectiveness of such methods by combining vibration response data with novelty detection techniques.
The experimental results, in comparison to other methods, demonstrate that the proposed SVDD can conduct novelty detection of the monitored mechanical component effectively with higher accuracy.
