A Cloud-based Architecture for Condition Monitoring based on Machine Learning

Abstract

In the framework of the digitalization of the industry, there is an increasing trend to use machine learning techniques to assess condition monitoring, fault detection, and process optimization. Traditional approaches use a local Information Technology (IT) framework centralized in a server in order to provide these services. Cost of equipment and IT manpower are associated with the implementation of a condition monitoring based on machine learning. Nowadays, cloud computing can replace local IT frameworks with a remote service, which can be paid according to the customer needs. This paper proposes a cloud-based architecture for condition monitoring based on machine learning, which the end-user can assess through a web application. The condition monitoring is implemented using a fusion of classification methods. The fusion is implemented using Dempster-Shafer Evidence Theory (DSET). The results show that the use of DSET improves the overall result.