Systematic study to assess neural networks' capability in classifying control chart patterns
Conference proceedings article
ผู้เขียน/บรรณาธิการ
กลุ่มสาขาการวิจัยเชิงกลยุทธ์
ไม่พบข้อมูลที่เกี่ยวข้อง
รายละเอียดสำหรับงานพิมพ์
รายชื่อผู้แต่ง: Lavangnananda K., Kasikitsakulphol S.
ผู้เผยแพร่: Hindawi
ปีที่เผยแพร่ (ค.ศ.): 2010
Volume number: 2
หน้าแรก: 718
หน้าสุดท้าย: 722
จำนวนหน้า: 5
ISBN: 9781424465835
นอก: 0146-9428
eISSN: 1745-4557
ภาษา: English-Great Britain (EN-GB)
บทคัดย่อ
Monitoring a process of a certain machine/device is an essential. Abnormality ought to be detected as soon as possible. This is done by means of monitoring the Control Chart Patterns (CCPs). At present, there are six well recognized patterns. A system which can accurately classify these patterns is advantageous to manufacturing processes. Many techniques in Statistical Process Control (SPC), Artificial Intelligence (AI) have been utilized in implementing of such system. Among these existing techniques, neural networks receive much attention lately due to its learning capability where precise equations and algorithms do not exist. CCPs in this are relies on Generalized Autoregressive Conditional Heteroskedasticity Model (GARH) model to generate synthetic patterns with varied degrees of noise in them. This research is the first work to systematically study the maximum level of noise in CCPs in which neural networks can classify them satisfactory within certain degrees of accuracy. Results reveal the noise levels in which neural networks can tolerate up to 90% and 95% level of accuracy. They are also discussed in terms of Signal to Noise Ratio (SNR). ฉ2010 IEEE.
คำสำคัญ
Generalized autoregressive conditional heteroskedasticity model (GARH), Signal to noise ratio (SNR), Statistical process control (SPC)
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