Master of Science / Department of Industrial and Manufacturing Systems Engineering / Shing I. Chang / This research studies a few methodologies for real-time detection of wave profile changes. In regular profile monitoring, change detection takes place at the end of time period when a complete profile is available. In real-time change detection of profiles, a potential profile change takes place between the beginning and the end of the time period. The decision involves the identification whether a process is in control or out of control before the entire profile is generated. In this regard, five proposed methodologies were developed and tested in this thesis.
Earthquake waves, manufacturing processes, and heart beat rate are a few examples of profiles with different natures that the proposed methodologies can be applied to. Water temperature profiles generated during a curing process are considered as an example in this study. Successful implementation of the proposed work on these profiles would cause saving great amounts of time and money.
Five methods are studied for monitoring the water control process of a curing process. The first four proposed methodologies are based on an univariate approach where the statistic used for process monitoring is the enclosed area between the profiles and their fitted cutting lines. A multivariate approach is also proposed.
A simulation study is also conducted when the best method is chosen based on it performance and simplicity of operations. Various types of acceptable and unacceptable profiles are simulated for the best proposed method identified in the preliminary study. The best method has a satisfactory performance in detecting the changes in the unacceptable profiles. In addition, the false alarm rate in identifying acceptable profiles as bad profiles is lower than 10%.
| Identifer | oai:union.ndltd.org:KSU/oai:krex.k-state.edu:2097/14135 |
| Date | January 1900 |
| Creators | Tavakkol, Behnam |
| Publisher | Kansas State University |
| Source Sets | K-State Research Exchange |
| Language | en_US |
| Detected Language | English |
| Type | Thesis |
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