Adaptive process monitoring using scale cusum for serially correlated processes

We present an adaptive monitoring approach for serially correlated data. This algorithm uses the adaptive linear prediction lattice filter (ALPLF) which makes it compute process parameters and prediction errors in real time and recursively update their estimates. We propose to apply a scale CUSUM control chart to prediction errors as an omnibus method for detecting changes in process parameters. Results of computer simulations demonstrate that the proposed adaptive monitoring approach has great potentials for real-time industrial applications which vary frequently in their control environment.