Reliability　and　maintenance　(R&M)　engineering　is　conventionally　notorious　for　a　lack　of　sufficient　failure　data　to　develop　robust　statistical　models.　The　increasing　miniaturization　of　data　collection　devices　such　as　wireless　sensors　has　provided　a　promising　infrastructure　for　gathering　information　about　parameters　of　the　physical　systems,　which　enable　practitioners　and　researchers　to　apply　machine　learning　(ML)　algorithms　to　improve　the　efficiency　of　R&M　analysis.　The　number　of　published　papers　on　ML　in　R&M　is　enormous,　this　paper　will　therefore　categorizes　those　papers　that　were　published　between　2017　to　16/May/2020,　that　are　written　in　English,　that　have　received　a　top　5%　number　of　citations　in　the　year　published,　and　that　use　support　vector　methods,　random　forests,　and　cluster　analysis.