This paper concerns the health monitoring of pipelines and tubes. It proposes the k-means clustering algorithm as a simple tool to monitor the integrity of a structure (i.e., detecting defects and assessing their growth). The k-means algorithm is applied on data collected experimentally, by means of an ultrasonic guided waves technique, from healthy and damaged tubes. Damage was created by attaching magnets to a tube. The number of magnets was increased progressively to simulate an increase in the size of the defect and also, a change in its shape. To test the performance of the proposed method for damage detection, a statistical population was created for the healthy state and for each damage step. This was done by adding white Gaussian noise to each acquired signal. To optimize the number of clusters, many algorithms were run, and their results were compared. Then, a semi-supervised based method was proposed to determine an alarm threshold, triggered when a defect becomes critical.

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The detection of cracks in spot welding is a major issue especially on-line. This paper deals with this topic using acoustic emission monitoring. It proposes an approach based on Shannon entropy with a novel criterion that allows increasing the detection sensitivity. A data-based model is built, coded and then applied on acoustic emission signals. The obtained crack detection results are satisfactory.The cracks are revealed using fluorescent dye penetrant, which enables validating the method. A flowchart of whole the proposed approach is described so that practitioners can benefit from the authors experience, towards successful implementation in laboratory as well as in production-line.The current approach is applied on resistance spot welding as a case of study, but it could be applicable in non-stationary environment such as other kinds of spot welding, and more hopefully for other manufacturing processes.

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