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A TOPSIS-Assisted Feature Selection Scheme and SOM-Based Anomaly Detection for Milling Tools Under Different Operating Conditions by Assafo, Maryam (author);Langendorfer, Peter (author) is a Engineering article available to read on EtoBox.

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Anomaly detection modeled as a one-class classification is an essential task for tool condition monitoring (TCM) when only the normal data are available. To confront with the real-world settings, it is crucial to take the different operating conditions, e.g., rotation speed, into account when approaching TCM solutions. This work mainly addresses issues related to multi-operating-condition TCM models, namely the varying discriminability of sensory features with different operating conditions; the overlap between normal and anomalous data; and the complex structure of input data. A feature selection scheme is proposed in which the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is presented as a tool to aid the multi-objective selection of sensory features. In addition, four anomaly detection approaches based on Self-Organizing Map (SOM) are studied. To examine the stability of the four approaches, they are applied on different single-operating-condition models. Further, to examine their robustness when dealing with complex data structures, they are applied on multi-operating-condition models. The experimental results using the NASA Milling Data Set showed tha

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Author

Assafo, Maryam (author);Langendorfer, Peter (author)

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Published

2021

Language

EN

Field

Engineering (Physical Sciences)