Failure　modes　and　effects　analysis　is　a　proactive　reliability-management　engineering　technique,　it　has　been　applied　to　improve　reliability　of　processes,　products,　and　services.　Traditional　failure　modes　and　effects　analysis　method　manifests　three　deficiencies　with　regard　to　form　of　risk　assessments,　determination　of　risk　factor　weights,　and　exploitation　of　failure　modes’　risk　levels.　Although　numerous　studies　have　contributed　to　surmount　these　deficiencies,　existing　failure　modes　and　effects　analysis　methods　still　face　such　issues.　Moreover,　a　novel　drawback　has　raised　when　failure　modes　and　effects　analysis　participants　adopt　linguistic　terms　as　risk　assessments　of　failure　modes,　the　reliability　of　linguistic　distribution　assessments　as　well　as　failure　modes　and　effects　analysis　groups　(participants　with　similar　knowledge　background　are　previously　classified　into　a　failure　modes　and　effects　analysis　group)　is　overlooked.　Therefore,　this　paper　introduces　the　transformation　of　linguistic　distribution　assessments　into　fuzzy　preference　relations,　as　more　effective　modeling　for　investigating　consistency,　by　using　the　newly　defined　possibility　degrees　of　linguistic　distribution　assessments　will　be　applied　to　devise　four　algorithms　for　enhancing　reliability　of　assessments.　Hence,　the　three　deficiencies　of　traditional　failure　modes　and　effects　analysis　method　are　solved　by　means　of　a　novel　failure　modes　and　effects　analysis　framework.　Finally,　a　real　example　with　respect　to　ranking　failure　modes　of　worm　wheel　grinding　machine　is　developed　to　verify　the　performance　and　advantages　of　the　proposed　method,　in　which　ten　failure　modes　are　ranked　and　the　one　with　the　highest　risk　level　is　selected,　and　consistency　of　risk　assessments　is　improved　to　be　acceptable.　©　2024