Statistical signal processing methods for acoustic emission monitoring of dense medium cyclones

Abstract

An Acoustic Emission (AE) signal processing and analysis system is developed for process and condition monitoring of Dense Medium Cyclones (DMCs), key units in coal preparation plants. This paper describes the use of a combination of statistical signal processing, multivariate statistical analysis and pattern classification methods to formulate robust soft-sensor predictive models for DMC process variables and wear state based on passive contact AE signals. Power spectral density and statistical moment ‘feature’ variables are extracted from AE signals. Principal Component Analysis (PCA) is used to evaluate the sensitivity of the AE ‘features’ to process variables and wear state. Principal Component Regression (PCR) and Partial Least Squares (PLS) are used to quantitatively model process variables. Hierarchical clustering is used to qualitatively model wear state and process variables. The AE hardware, signal processing and soft-sensor modelling system can be used for process and condition monitoring of other mineral processing equipment.