Design for Machine Learning

Machine Learning (ML) is necessary for the future of designing systems and products for maintainability. This chapter discusses the meaning of ML and Deep Learning (DL), and the differences between ML, Artificial Intelligence, and DL. Model‐based testing may apply ML techniques in considering the potential for automation that it offers, but it depends on how effectively the model‐based testing is applied in the system. When models of the observed system are used for anomaly detection, fault detection, and failure diagnosis, this is often referred to as model‐based testing. Since model‐based testing is very important in Design for Maintainability (DfMn), some explanation of model‐based testing is warranted before continuing with ML designs for maintainability. The chapter explains what ML is and how it supports DfMn activities that facilitate Preventative Maintenance Checks and Services, Digital Prescriptive Maintenance, Prognostics and Health Management, Condition‐based Maintenance, Reliability‐centered Maintenance, Remote Maintenance Monitoring, Long Distance Support, and Spares Provisioning.