Some change detection and time-series forecasting algorithms for an electronics manufacturing process

In a sequential manufacturing process, a product unit proceeds through different manufacturing stages. At these stages, sensors monitor the features of the unit. In this work, the information produced by the sensors is employed to detect abrupt changes in process variables as well as to forecast their future behaviour. The developed algorithms were implemented as an on-line application to a manufacturing system. A literature survey was performed to study the most common methods utilised in change detection and time-series forecasting. The most promising methods were selected on the basis of their on-line applicability and transferability to new manufacturing lines. These methods were further evaluated with off-line data. Finally, the difference method was applied for change detection and linear regression-based method for forecasting in this case. During both on-line and off-line tests, some satisfactory results were attained. Real-time, on-line manufacturing environment set also its requirements for the applications. In the future, the possibility of combining expert knowledge with the aforementioned methods should be examined. The information thus received could be further utilised in the preventive maintenance and quality control.