This paper concerns the analysis of experimental data, verifying the applicability of signal analysis techniques for condition monitoring of a packaging machine. In particular, the activity focuses on the cutting process that divides a continuous flow of packaging paper into single packages. The cutting process is made by a steel knife driven by a hydraulic system. Actually, the knives are frequently substituted, causing frequent stops of the machine and consequent lost production costs. The aim of this paper is to develop a diagnostic procedure to assess the wearing condition of blades, reducing the stops for maintenance. The packaging machine was provided with pressure sensor that monitors the hydraulic system driving the blade. Processing the pressure data comprises three main steps: the selection of scalar quantities that could be indicative of the condition of the knife. A clustering analysis was used to set up a threshold between unfaulted and faulted knives. Finally, a Support Vector Machine (SVM) model was applied to classify the technical condition of knife during its lifetime.
Anomaly detection in a cutting tool by K-means clustering and Support Vector Machines / Lahrache, Achraf; Cocconcelli, Marco; Rubini, Riccardo. - In: DIAGNOSTYKA. - ISSN 1641-6414. - 18:3(2017), pp. 21-29.
|Data di pubblicazione:||2017|
|Data di prima pubblicazione:||31-lug-2017|
|Titolo:||Anomaly detection in a cutting tool by K-means clustering and Support Vector Machines|
|Autore/i:||Lahrache, Achraf; Cocconcelli, Marco; Rubini, Riccardo|
|Codice identificativo Scopus:||2-s2.0-85030166297|
|Citazione:||Anomaly detection in a cutting tool by K-means clustering and Support Vector Machines / Lahrache, Achraf; Cocconcelli, Marco; Rubini, Riccardo. - In: DIAGNOSTYKA. - ISSN 1641-6414. - 18:3(2017), pp. 21-29.|
|Tipologia||Articolo su rivista|
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