Support vector machines for recognizing shifts in correlated and other manufacturing processes

Traditional statistical process control (SPC) techniques of control charting are not applicable in many process industries where the data from the facilities are often autocorrelated. This is often true in piece-part manufacturing industries that are highly automated and integrated. Several attempts have been made in the literature to extend traditional SPC techniques to deal with autocorrelated parameters. However, these extensions pose several serious limitations. The literature discusses several machine-learning methods based on radial basis function (RBF) networks and multi-layer perceptron (MLP) networks to address the limitations, with some success. This paper demonstrates that support vector machines (SVMs) can be extremely effective in minimizing both Type-I errors (probability that the method would wrongly declare the process to be out of control or generate a false alarm) and Type-II errors (probability that the method will be unable to detect a true shift or trend present in the process) in these autocorrelated processes. Even while employing the simplest type of polynomial kernels, the SVMs were extremely good at detecting shifts in papermaking and viscosity datasets (available in the literature) and performed as well or better than traditional as well as machine learning methods. It was also observed that SVMs are good at minimizing both Type-I and Type-II errors even in monitoring non-correlated processes. When tested on datasets available in the literature, they once again performed as well or better than the classical Shewhart control charts and other machine learning methods.