SOA Based Integrated Software to Develop Fault Diagnosis Models Using Machine Learning in Rotating Machinery

Abstract

Fault detection and diagnostic software (FDDS) supports technicians and engineers to deal with operational matters, in major cases related to complicated systems and advanced technology that require higher performance expectation. Information and communication technologies play an importantrole for implementing efficient maintenance software, therefore, the development of FDDS is posed as an industrial necessity. In case of industrial rotating machinery, data-driven FDDS using available vibration signals, or other related signals monitored from sensors, is currently viewed as an industrial informatics requirement. This paper proposes the application of a Service Oriented Architecture (SOA) to implement an integrated tool for automatically developing and testing machine learning based fault diagnosis models in rotating machinery. As a result, a generic architecture is obtained which is able to build and implement diagnosis models in similar devices or processes. A condition monitoring software application, using the proposed SOA, was implemented in Java and deployed on a computational environment to test its performance in a experimental test bed, under realistic fault mechanical conditions in a gearbox.