|Author||Ypma, Alexander ♦ Ypma, Er ♦ Duin, Robert P. W.|
|Subject Domain (in DDC)||Computer science, information & general works ♦ Data processing & computer science|
|Subject Keyword||Machine Wear ♦ Feature Space ♦ Clustering Structure ♦ High-dimensional Space ♦ Support Object ♦ Novel Algorithm ♦ Data Set ♦ Fault Class ♦ Parsimonious Description ♦ Normal Behaviour ♦ Normal Machine Behaviour ♦ Dissimilar Data ♦ Computational Feasibility ♦ Domain Approximation ♦ Calibration Measurement ♦ Extremal Point ♦ Small Sample Size ♦ Extremal Operating Condition ♦ Introduction Automatic Recognition|
|Description||We propose a novel algorithm for extracting samples from a data set supporting the extremal points in the set. Since the density of the data set is not taken into account, the method could enable adaptation to novel (e.g. machine wear) data. Knowledge about the clustering structure of the data can aid in determination of the complexity of the solution. The algorithm is evaluated on its computational feasibility and performance with progressively more dissimilar data. 1 Introduction Automatic recognition of machine wear and failure calls for methods that can deal with small sample sizes in high-dimensional spaces, undersampled fault classes and dynamically changing environments. Since normal machine behaviour is typically determined in a few calibration measurements of extremal operating conditions (e.g. when putting the machine into practice), an accurate but parsimonious description of the borders of the domain in the feature space indicating normal behaviour is expected to emerge. F...|
|Educational Role||Student ♦ Teacher|
|Age Range||above 22 year|
|Education Level||UG and PG ♦ Career/Technical Study|
|Learning Resource Type||Article|
|Publisher Institution||In ICANN’98, Skovde (Sweden|
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