Optimal classifier design based on pairwise statistical separability maximisation of timefrequency features
This paper presents a novel classification algorithm based on the timefrequency features extracted from multiple-sensor signals. Multiple-sensor signals are difficult to handle for classification purpose since each signal may have a different separability measure between classes and, hence, it may be difficult to pick a set of best sensors for classification. This paper provides a new separability measure, the so-called miss-classification probability, in order to overcome such a difficulty. A mathematical representation of the statistical aspect of the timefrequency features is introduced for efficient calculation of the miss-classification probability. Yet, another difficulty may be encountered in extracting a set of timefrequency features, which may best represent the difference among classes. This paper also proposes a pairwise statistical separability maximisation scheme to overcome this difficulty. The resultant classification algorithm based on these new developments is validated through seeded-fault tests with rotary compressors.
statistical separability maximisation timefrequency feature miss-classification probability multiple sensing