Data acquired from industrial processes, usually via sensors, are generally noisy, correlated in time and nonstationary; this makes the implementation of the monitoring process difficult, as most techniques are designed for Gaussian and uncorrelated observations. As conventional monitoring methods, their efficiency may be significantly affected by typical uncertainties in industrial processes. Assumptions of Gaussianity, dependence in time, and stationarity are typically not verified in industrial processes. These properties make wavelet-based fault detection approaches especially appropriate. Wavelet methods are also helpful when the characteristics of the fault are unknown. This chapter discusses wavelet-based monitoring approaches that are flexible and designed with fewer structural assumptions. In this chapter, we present a brief overview of wavelets and their desirable characteristics, as well as the discrete wavelet transform. We then assess the effect of violating these assumptions (in addition to the effect of noise levels), based on the performances of the univariate monitoring methods, provide an overview of the univariate wavelet-based technique. And then discuss and illustrate the wavelet-based multivariate extension of LVR methods. At the end of the chapter, the methods are demonstrated on distillation column data.
|Original language||English (US)|
|Title of host publication||Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches|
|Number of pages||37|
|State||Published - 2021|