A PRACTICAL ASSESSMENT OF PROCESS DATA-COMPRESSION TECHNIQUES

Plant data are used to compare the effectiveness of wavelet-based meth ods with other compression techniques. The challenge is to effectively treat the data so that the maximum compression ratio is achieved whil e the important features are retained in the compressed data. Wavelets have properties that are desirable for data compression. They are loc alized in time (or space) and in frequency. This means that important short-lived high-frequency disturbances can be preserved in the compre ssed data, and these disturbances may be differentiated from slower, l ow-frequency trends. Besides discrete wavelet transforms, linear inter polation, discrete cosine transform, and vector quantization are also used to compress data. The transform-based compression algorithms perf orm better than the linear interpolation methods, such as swinging doo r, that have been used traditionally in the chemical process industrie s. Among these techniques, the wavelet-based one compresses the proces s data with excellent overall and best local accuracy.