Kernel-based data compression for internet-based manufacturing monitoring

Global manufacturing chains have become common in the 21st century. To perform remote monitoring and diagnosis in such chains, real-time data compression has become a core factor in the efficient and effective exchange of information via computer networks. This paper presents a new technique for compressing data using a kernel-based method. Overcoming the drawbacks of support vector techniques – that is, fast decompression but slow compression – the new method exhibits high speed in both phases. In addition, the new method can also be applied for pattern classification. Based on strain signal, example tests derived from sheet metal stamping operations, the new method is shown to be very effective. The proposed technology has enormous potential in the application of advanced manufacturing processes monitoring and control through Internet or Intranet.