Principles of Classification

Abstract

This chapter discusses the main principles of the creation and use of a classifier in order to predict the interpretation of an unknown data sample. Classification offers the possibility to learn and use learned information received from previous occurrences of various normal and fault modes. This process is continuous and can be generalized to cover the diagnostics of all objects that are substantially of the same type. The effective use of a classifier includes initial training with known data samples, anomaly detection, retraining, and fault detection. With these elements an automated, a continuous learning machine diagnostics system can be developed. The main objective of such a system is to automate various time intensive tasks and allow more time for an expert to interpret unknown anomalies. A secondary objective is to utilize the data collected from previous fault modes to predict the re-occurrence of these faults in a substantially similar machine. It is important to understand the behaviour and functioning of a classifier in the development of software solutions for automated diagnostic methods. Several proven methods that can be used, for instance in software development, are disclosed in this chapter.