Abstract
With the development of the sensor network and manufacturing technology, multivariate processes face a new challenge of high-dimensional data. However, traditional statistical methods based on small- or medium-sized samples such as T 2 monitoring statistics may not be suitable because of the “curse of dimensionality” problem. To overcome this shortcoming, some control charts based on the variable-selection (VS) algorithms using penalized likelihood have been suggested for process monitoring and fault diagnosis. Although there has been much effort to improve VS-based control charts, there is usually a common distributional assumption that in-control observations should follow a single multivariate Gaussian distribution. However, in current manufacturing processes, processes can have multimodal properties. To handle the high-dimensionality and multimodality, in this study, a VS-based control chart with a Gaussian mixture model (GMM) is proposed. We extend the VS-based control chart framework to the process with multimodal distributions, so that the high-dimensionality and multimodal information in the process can be better considered.
Original language | English |
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Pages (from-to) | 1263-1275 |
Number of pages | 13 |
Journal | Quality and Reliability Engineering International |
Volume | 35 |
Issue number | 4 |
DOIs | |
State | Published - Jun 2019 |
Keywords
- Gaussian mixture model
- high dimensionality
- multimodality
- penalized likelihood
- statistical process control
- variable selection