Condition monitoring applications deploying the usage of impact acoustic techniques are mostly done intuitively by skilled personnel. In this article, a pattern recognition approach is taken to automate such intuitive human skills for the development of more robust and reliable testing methods. The focus of this work is to use the approach as a part of a major research project in the rail inspection area, within the domain of intelligent transport systems. Data from impact acoustic tests made on wooden beams have been used. The relation between condition of the wooden beams and respective sounds they make when struck, has been analyzed experimentally. Features were extracted from the acoustic emissions of wooden beams and were used for pattern classification. Features such as magnitude of the signal, natural logarithm of the magnitude and Mel-frequency cepstral coefficients, yielded good results. The extracted feature vectors were used as input to various pattern classifiers for further pattern recognition task. The effect of using classifiers like support vector machines and multi-layer perceptron has been tested and compared. Results obtained experimentally, demonstrate that support vector machines provide good detection rates for the classification of impact acoustic signals in the NDT domain

Collectionfusion is a data fusion problem in which the results of retrieval runs on separate, autonomous document collections must be merged to produce a single, effective result.This paper explores two collection fusion techniques that learn the rmrnber of documents to retrieve from each collection using only the ranked lists of documents returned in response to past queries and those documents!relevance judgments.Retrieval experiments using the TREC test co]lection demonstrate that the effectiveness of the fusion techniques is within 10'?% of the effectiveness of a run in which the entire set of documents is treated as a single collection.