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Title:
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MULTI-SENSOR CONDITION MONITORING USING SPIKING NEURON NETWORKS |
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Author(s):
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Rui G. Silva , Steven Wilcox , António A.m.m. Araújo |
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ISBN:
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978-972-8924-30-0 |
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Editors:
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Nuno Guimarães and Pedro Isaías |
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Year:
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2007 |
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Edition:
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Single |
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Keywords:
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Spiking Neuron Networks; Machining; Condition Monitoring; Tool Wear. |
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Type:
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Full Paper |
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First Page:
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176 |
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Last Page:
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182 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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The paper presents an intelligent system for on-line monitoring of the cutting process. The monitoring apparatus is developed both in hardware and software. The system is based on a PC which is connected to a set of sensors, via a data acquisition card, for on-line post-processing and classification. The proposed monitoring system takes advantage of most attractive features of neural networks, such as abstraction of hardly accessible knowledge and generalisation from distorted sensor signals, to give a reliable prediction on tool condition. It consists of six components: data collection, feature extraction, multi-sensor integration, pattern recognition, tool wear estimation, and outlier detection. The proposed architecture has a built-in Self Organizing neural architecture component based on Spiking Neurons and it is demonstrated that these computational architectures have a greater potential to unveil embedded information in tool wear monitoring data sets, and that smaller structures, compared to sigmoidal neural networks, are needed to capture and model the inherent complexity embedded in tool wear monitoring data. |
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