Solar-assisted　air　source　heat　pumps　(SAASHP)　offer　promising　solutions　for　energy-efficient　heating　applications.　Despite　extensive　research　on　SAASHP　system　design　and　optimization,　significant　gaps　remain　in　understanding　the　failure　modes　and　their　effects　on　system　performance　and　reliability.　This　paper　presents　an　improved　fuzzy　logic-based　failure　modes　and　effects　analysis　(FMEA)　model　to　evaluate　failure　effects　in　SAASHP　systems,　with　a　particular　focus　on　how　these　risks　affect　system　reliability　while　enhancing　energy　management.　The　model　incorporates　weighted　expert　judgment　with　fuzzy　logic　rules　to　achieve　more　precise　assessments.　Forty-three　failure　modes　are　identified　for　SAASHP,　forming　the　foundation　for　designing　reliability　improvements　and　preventive　maintenance.　The　study　identifies　critical　components　and　failure　modes　with　the　highest　risk　priority　numbers　(RPN),　including　structural　instability,　connection　integrity,　sensor　accuracy,　and　circuit　reliability.　Results　show　that　Fuzzy-RPN　scores　generally　follow　a　normal　distribution.　The　scores　are　categorized　into　five　risk　levels:　minimal,　low,　moderate,　high,　and　extreme　risk.　Moderate　risk　accounted　for　the　highest　proportion,　at　34.88　%　of　the　total　risk.　Additionally,　an　outlier　with　an　RPN　score　of　960.0　in　the　extreme　risk　category　indicates　a　need　for　immediate　attention.　To　mitigate　these　risks　and　improve　energy　management,　targeted　maintenance　strategies　and　preventive　measures　are　recommended,　including　regular　monitoring　of　high-risk　areas,　robust　design　elements,　and　enhanced　control　systems.　The　findings　confirm　that　improving　system　reliability　and　proactively　managing　energy　use　can　extend　the　lifespan　of　SAASHP　systems,　reduce　maintenance　costs,　enhance　operational　efficiency　and　support　broader　adoption.　©　2025　Elsevier　Ltd