A rules-based approach to classification of chemical acoustic emission signals

Abstract

Abstract Soulsbury, K.A., Wade, A.P., and Sibbald, D.B., 1992. A rules-based approach to classification of chemical acoustic emission signals. Chemometrics and Intelligent Laboratory Systems, 15: 87–105 Chemical acoustic emission studies pose unique data analysis problems, since a single experiment can result in capture of several thousand signals, each of at least 1 Kb in length, at rates of more than 10/s. In studies where waveform analysis is considered valuable, automated methods for data reduction and information extraction are therefore essential. This paper presents a rules-based approach to the automated classification of chemical acoustic emission signals. Signal classification rules are generated by analysis of data from preliminary experiments which form a training set. Rule clauses indicate the expected values for the descriptive statistical factors (descriptors) which best characterize signals as belonging to recognized classes (e.g. gas evolution, crystallization, background noise). The rules may then be applied to classify signals in further data sets. Modification of the rules to include only those descriptors which best differentiate the classes of interest provides optimal performance. This approach has several advantages: since only the signal class, time of acquisition (and possibly descriptor values) need to be stored, long-term data storage is greatly decreased, whilst retaining analytical information; automated rejection of signals due to background noise results in sensitivity improvements, since lower trigger levels may be used; furthermore, the improved selectivity provides greater confidence that signals captured can be attributed to processes of interest. Thus, the approach opens the door to intelligent, real-time data reduction, more accurate measurements of emission rates, and automatic identification of dominant physicochemical emission mechanisms. Once trained, the automated signal classifier performed as well as or better than human pattern recognition, and required only a fraction of the time. To illustrate the utility of the approach, acoustic emission waveform characterization rules were obtained for and applied to three chemical systems. For pyrolysis of poly(vinyl chloride), noise signals were effectively discriminated from signals from the chemical process. Rules for copper(II) sulfate recrystallization resulted in improved certainty in determining the time of the onset and the rate of crystal fracture. In hydration of silica gel, rules automatically classified signals as being from fracture or gas evolution.