Paper machine data analysis and compression using wavelets

This paper describes the analysis of paper machine process data using discrete wavelet transforms. The techniques have been adapted from a general signal analysis theory that has been developed in recent years. It is shown that wavelets are an effective representation for the detection of basis weight and moisture process variations in noisy data and lead to improved estimation and visualization of the machine-direction and cross-direction variations. Using simulated data, it has been shown that the new methods produce results superior to conventional industrially accepted procedures. Industrial data also have been analyzed, and it is apparent that the method has many desirable characteristics. The second main advantage of the method has been to allow significant compression of the process data without diminishing the ability to reconstruct accurate and detailed profiles. It has been shown that the compression method can be embedded into the estimation algorithm, producing excellent results without major expense in computation time. The ability to reduce data storage requirements is of importance in mill-wide process-monitoring systems. Application : wavelet representation is used to identify profile irregularities that might otherwise not be detected and to separate out components that should be eliminated by an effective CD controller, taking into account the actuator spacing. The method can also be used to provide superior on-line operator displays of sheet basis weight and moisture process data.