Unsupervised Machine Condition Monitoring Using Segmental Hidden Markov Models.

The task of machine condition monitoring is to detect machine failures at an early stage such that maintenance can be carried out in a timely manner. Most existing techniques are supervised approaches: they require user annotated training data to learn normal and faulty behaviors of a machine. However, such supervision can be difficult to acquire. In contrast, unsupervised methods don't need much human involvement, however, they face another challenge: how to model the generative (observation) process of sensor signals. We propose an unsupervised approach based on segmental hidden Markov models. Our method has a unifying observation model integrating three pieces of information that are complementary to each other. First, we model the signal as an explicit function over time, which describes its possible non-stationary trending patterns. Second, the stationary part of the signal is fit by an autoregressive model. Third, we introduce contextual information to break down the signal complexity such that the signal is modeled separately under different conditions. The advantages of the proposed model are demonstrated by tests on gas turbine, truck and honeybee datasets.