Data Driven Prognostics from Machine Learning to Deep Learning: A survey

Abstract In the age of Industry 4.0, prognostics and health management (PHM) is critical. For proactive and scheduled maintenance, the life of a tool, part, or component of a system must be tracked. This increases productivity while reducing human effort and saving lives. The PHM application could be used for the detection of anomalies, fault diagnosis and/or failure prognosis. Data driven prognostics is highly relying on machine learning (ML), statistical methods and deep learning (DL) techniques. DL is a massive enlarging field with encouraging outcomes in prognostics for modelling of data with complex representations and temporal dependencies. This paper provides review of some of the latest contributions of ML in prognostic applications. Description of different deep learning architectures is given. A literature review of DL applications in prognostics is presented in an analytical and comparative view. Then a revisit to PHM and deep learning terminologies is then highlighted. Finally, challenges and opportunities are emphasized.