An Unseen Fault Classification Approach for Smart Appliances Using Ongoing Multivariate Time Series

Abstract

Getting real-time information about the operational behavior of industrial or domestic appliances becomes effortless with the availability of sensors. The sensors generate multiple streams of measurements, called as multivariate time series, corresponding to an operation of the appliance. An appliance can go through various types of faults during its lifetime. Such a fault can be identified by classifying the multivariate time series (MTS), which is generated by the sensors corresponding to this fault. As it is also unfeasible to have prior knowledge about all types of faults, the classification approach should also be able to identify an unseen (unknown) fault using its MTS. In this article, we propose a semantic-information-based early classification approach for MTS. The approach uses a concept of zero-shot learning to classify an unseen fault. This work conducts a case study to evaluate the approach by classifying different faults of a washing machine using sensory data.