The role of subclasses in machine diagnostics

In machine diagnostics it is difficult to collect for learning all possible operating modes of machine functioning. Some of the operating modes are often missing. In these circumstances, it is important to know which modes (subclasses) are the most valuable for successful machine diagnosis. It is also of interest to investigate the usefulness of noise injection to cover the missing operating modes in the data. In this paper, we study the importance of selecting different operating modes of a water-pump and using them for learning in both 2-class and 4-class problems. We show that the operating modes representing different running speeds are more valuable than those representing machine loads. We also demonstrate that the 2-nearest neighbours directed noise injection is useful when filing in missing operating modes in the data.