{"id":1551,"date":"2026-03-17T18:47:16","date_gmt":"2026-03-17T18:47:16","guid":{"rendered":"https:\/\/bulbthings.com\/blog\/?p=1551"},"modified":"2026-03-17T18:47:16","modified_gmt":"2026-03-17T18:47:16","slug":"ai-cmms-parts-prediction-anomaly-detection-recurring-failures","status":"publish","type":"post","link":"https:\/\/bulbthings.com\/blog\/ai-cmms-parts-prediction-anomaly-detection-recurring-failures","title":{"rendered":"From breakdown to insight: AI in CMMS for parts prediction, anomaly detection, and recurring failures"},"content":{"rendered":"<p><i><span style=\"font-weight: 400;\">If your CMMS is full of work orders but your team still gets surprised by breakdowns, you\u2019re not alone. The missing piece is usually \u201clearning\u201d: turning history into early warnings, smarter stocking, and fewer repeat failures. Here\u2019s how AI can help.<\/span><\/i><\/p>\n<h2><b>CMMS data is valuable if you can actually use it<\/b><\/h2>\n<p><span style=\"font-weight: 400;\">Growing businesses often reach a point where they have <\/span><i><span style=\"font-weight: 400;\">lots<\/span><\/i><span style=\"font-weight: 400;\"> of maintenance data: notes, downtime, parts used, meter readings, and maybe some sensor data. Yet the same problems keep coming back because:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Failure patterns are hidden across hundreds of work orders<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Symptoms are described inconsistently (\u201cvibration,\u201d \u201cnoise,\u201d \u201cshaking\u201d)<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Parts usage feels unpredictable (too much stock here, stockouts there)<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">By the time a KPI changes, the damage is already done<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">AI helps by finding patterns earlier and making recommendations that your CMMS can operationalize: what to inspect, what to stock, and what to prevent next.<\/span><\/p>\n<h2><b>Three practical AI use-cases that fit a modern CMMS<\/b><\/h2>\n<h3><b>1) Parts prediction: stock what you\u2019ll need (not everything)<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Spare parts demand is tricky: it can be intermittent, seasonal, and tied to specific failure modes. Research shows machine learning can improve spare parts forecasting by using more signals than basic averages\u2014things like lifecycle, failure rates, and usage patterns.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">In a CMMS context, \u201cparts prediction\u201d looks like:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Forecasting which parts are likely to be consumed next month\/quarter<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Flagging parts at high stockout risk based on upcoming PM + known weak components<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Recommending min\/max changes per storeroom or site<\/span><\/li>\n<\/ul>\n<h3><b>2) Anomaly detection: catch weird behavior before it becomes downtime<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Anomaly detection is simply \u201cspot what doesn\u2019t look normal.\u201d It can use:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Condition data (temperature, vibration, pressure, energy draw)<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Operational patterns (runtime, starts\/stops)<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Maintenance signals (repeat callouts, growing time-to-repair)<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Many predictive maintenance studies use benchmark <a href=\"https:\/\/www.nasa.gov\/intelligent-systems-division\/discovery-and-systems-health\/pcoe\/pcoe-data-set-repository\/\">datasets and time-series approaches<\/a> to detect degradation and estimate remaining useful life, exactly the kind of thinking you can apply when you connect asset signals back to your CMMS work history.<\/span><\/p>\n<h3><b>3) Recurring failure insights: stop fixing the same problem twice<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">This is where CMMS + AI gets very \u201cbusiness friendly.\u201d AI can review your history and answer questions like:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Which assets drive the most repeat work in the last 60\u201390 days?<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">What failure modes cluster together (e.g., \u201coverheat\u201d + \u201cbearing\u201d + \u201cmisalignment\u201d)?<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Which vendors\/parts lots correlate with abnormal failure rates? (if you track it)<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Which PM tasks are not reducing breakdowns (so you can redesign them)?<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">The result isn\u2019t a fancy report, it\u2019s a targeted list of preventive actions that reduce repeat labor and customer-facing downtime.<\/span><\/p>\n<h2><b>How to implement this without a data science team<\/b><\/h2>\n<ol>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Start with data you already trust:<\/b><span style=\"font-weight: 400;\"> downtime, parts used, problem\/cause codes, and asset criticality.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Pick one outcome KPI:<\/b><span style=\"font-weight: 400;\"> unplanned downtime hours, repeat work, or parts stockouts.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Use AI for \u201crecommendations,\u201d not autopilot:<\/b><span style=\"font-weight: 400;\"> let it suggest PM changes, inspections, and stocking levels, then approve.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Track what changed:<\/b><span style=\"font-weight: 400;\"> a good CMMS should show before\/after outcomes by asset class.<\/span><\/li>\n<\/ol>\n<h2><b>Where Bulbthings fits<\/b><\/h2>\n<p data-start=\"80\" data-end=\"363\"><a href=\"https:\/\/bulbthings.com\/\">Bulbthings<\/a> is an AI-powered automation platform for asset management. It brings together CMMS workflows\u2014work orders, preventive maintenance, assets, and parts\u2014while using AI to streamline operations, standardize data, and support better decision-making.<\/p>\n<p data-start=\"612\" data-end=\"771\" data-is-last-node=\"\" data-is-only-node=\"\">If you\u2019re looking to simplify maintenance operations today and prepare for smarter, AI-driven workflows tomorrow, visit Bulbthings and request a demo.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>If your CMMS is full of work orders but your team still gets surprised by breakdowns, you\u2019re not alone. The missing piece is usually \u201clearning\u201d: turning history into early warnings, smarter stocking, and fewer repeat failures. Here\u2019s how AI can help. CMMS data is valuable if you can actually use it Growing businesses often reach [&hellip;]<\/p>\n","protected":false},"author":3,"featured_media":1560,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-1551","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.3 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>How AI-powered CMMS helps businesses prevent downtime.<\/title>\n<meta name=\"description\" content=\"Use AI in your CMMS to forecast parts needs, flag anomalies early, and surface recurring failure patterns that drive downtime.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/bulbthings.com\/blog\/ai-cmms-parts-prediction-anomaly-detection-recurring-failures\" \/>\n<meta property=\"og:locale\" content=\"en_GB\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"How AI-powered CMMS helps businesses prevent downtime.\" \/>\n<meta property=\"og:description\" content=\"Use AI in your CMMS to forecast parts needs, flag anomalies early, and surface recurring failure patterns that drive downtime.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/bulbthings.com\/blog\/ai-cmms-parts-prediction-anomaly-detection-recurring-failures\" \/>\n<meta property=\"og:site_name\" content=\"The Blog\" \/>\n<meta property=\"article:published_time\" content=\"2026-03-17T18:47:16+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/blog.bulbthings.com\/wp-content\/uploads\/2026\/03\/2151615111-1.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"1000\" \/>\n\t<meta property=\"og:image:height\" content=\"375\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"Marketing team\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Marketing team\" \/>\n\t<meta name=\"twitter:label2\" content=\"Estimated reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"3 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\\\/\\\/bulbthings.com\\\/blog\\\/ai-cmms-parts-prediction-anomaly-detection-recurring-failures#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/bulbthings.com\\\/blog\\\/ai-cmms-parts-prediction-anomaly-detection-recurring-failures\"},\"author\":{\"name\":\"Marketing team\",\"@id\":\"https:\\\/\\\/bulbthings.com\\\/blog\\\/#\\\/schema\\\/person\\\/d895c3aed7b1c0677d96dd428e8cf362\"},\"headline\":\"From breakdown to insight: AI in CMMS for parts prediction, anomaly detection, and recurring failures\",\"datePublished\":\"2026-03-17T18:47:16+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\\\/\\\/bulbthings.com\\\/blog\\\/ai-cmms-parts-prediction-anomaly-detection-recurring-failures\"},\"wordCount\":557,\"commentCount\":0,\"image\":{\"@id\":\"https:\\\/\\\/bulbthings.com\\\/blog\\\/ai-cmms-parts-prediction-anomaly-detection-recurring-failures#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/blog.bulbthings.com\\\/wp-content\\\/uploads\\\/2026\\\/03\\\/2151615111-1.jpg\",\"articleSection\":[\"Uncategorized\"],\"inLanguage\":\"en-GB\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\\\/\\\/bulbthings.com\\\/blog\\\/ai-cmms-parts-prediction-anomaly-detection-recurring-failures#respond\"]}]},{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/bulbthings.com\\\/blog\\\/ai-cmms-parts-prediction-anomaly-detection-recurring-failures\",\"url\":\"https:\\\/\\\/bulbthings.com\\\/blog\\\/ai-cmms-parts-prediction-anomaly-detection-recurring-failures\",\"name\":\"How AI-powered CMMS helps businesses prevent downtime.\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/bulbthings.com\\\/blog\\\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\\\/\\\/bulbthings.com\\\/blog\\\/ai-cmms-parts-prediction-anomaly-detection-recurring-failures#primaryimage\"},\"image\":{\"@id\":\"https:\\\/\\\/bulbthings.com\\\/blog\\\/ai-cmms-parts-prediction-anomaly-detection-recurring-failures#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/blog.bulbthings.com\\\/wp-content\\\/uploads\\\/2026\\\/03\\\/2151615111-1.jpg\",\"datePublished\":\"2026-03-17T18:47:16+00:00\",\"author\":{\"@id\":\"https:\\\/\\\/bulbthings.com\\\/blog\\\/#\\\/schema\\\/person\\\/d895c3aed7b1c0677d96dd428e8cf362\"},\"description\":\"Use AI in your CMMS to forecast parts needs, flag anomalies early, and surface recurring failure patterns that drive downtime.\",\"breadcrumb\":{\"@id\":\"https:\\\/\\\/bulbthings.com\\\/blog\\\/ai-cmms-parts-prediction-anomaly-detection-recurring-failures#breadcrumb\"},\"inLanguage\":\"en-GB\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\\\/\\\/bulbthings.com\\\/blog\\\/ai-cmms-parts-prediction-anomaly-detection-recurring-failures\"]}]},{\"@type\":\"ImageObject\",\"inLanguage\":\"en-GB\",\"@id\":\"https:\\\/\\\/bulbthings.com\\\/blog\\\/ai-cmms-parts-prediction-anomaly-detection-recurring-failures#primaryimage\",\"url\":\"https:\\\/\\\/blog.bulbthings.com\\\/wp-content\\\/uploads\\\/2026\\\/03\\\/2151615111-1.jpg\",\"contentUrl\":\"https:\\\/\\\/blog.bulbthings.com\\\/wp-content\\\/uploads\\\/2026\\\/03\\\/2151615111-1.jpg\",\"width\":1000,\"height\":375},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\\\/\\\/bulbthings.com\\\/blog\\\/ai-cmms-parts-prediction-anomaly-detection-recurring-failures#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\\\/\\\/www.bulbthings.com\\\/blog\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"From breakdown to insight: AI in CMMS for parts prediction, anomaly detection, and recurring failures\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\\\/\\\/bulbthings.com\\\/blog\\\/#website\",\"url\":\"https:\\\/\\\/bulbthings.com\\\/blog\\\/\",\"name\":\"The Blog\",\"description\":\"Tips, Trends And Success Stories\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\\\/\\\/bulbthings.com\\\/blog\\\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"en-GB\"},{\"@type\":\"Person\",\"@id\":\"https:\\\/\\\/bulbthings.com\\\/blog\\\/#\\\/schema\\\/person\\\/d895c3aed7b1c0677d96dd428e8cf362\",\"name\":\"Marketing team\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-GB\",\"@id\":\"https:\\\/\\\/secure.gravatar.com\\\/avatar\\\/73dab8c6dae75b80ac04bed9e70500525a7e57caa4f9f7c80b3c89ee4677755a?s=96&d=mm&r=g\",\"url\":\"https:\\\/\\\/secure.gravatar.com\\\/avatar\\\/73dab8c6dae75b80ac04bed9e70500525a7e57caa4f9f7c80b3c89ee4677755a?s=96&d=mm&r=g\",\"contentUrl\":\"https:\\\/\\\/secure.gravatar.com\\\/avatar\\\/73dab8c6dae75b80ac04bed9e70500525a7e57caa4f9f7c80b3c89ee4677755a?s=96&d=mm&r=g\",\"caption\":\"Marketing team\"},\"url\":\"https:\\\/\\\/bulbthings.com\\\/blog\\\/author\\\/leslie-depondbulbthings-com\"}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"How AI-powered CMMS helps businesses prevent downtime.","description":"Use AI in your CMMS to forecast parts needs, flag anomalies early, and surface recurring failure patterns that drive downtime.","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/bulbthings.com\/blog\/ai-cmms-parts-prediction-anomaly-detection-recurring-failures","og_locale":"en_GB","og_type":"article","og_title":"How AI-powered CMMS helps businesses prevent downtime.","og_description":"Use AI in your CMMS to forecast parts needs, flag anomalies early, and surface recurring failure patterns that drive downtime.","og_url":"https:\/\/bulbthings.com\/blog\/ai-cmms-parts-prediction-anomaly-detection-recurring-failures","og_site_name":"The Blog","article_published_time":"2026-03-17T18:47:16+00:00","og_image":[{"width":1000,"height":375,"url":"https:\/\/blog.bulbthings.com\/wp-content\/uploads\/2026\/03\/2151615111-1.jpg","type":"image\/jpeg"}],"author":"Marketing team","twitter_card":"summary_large_image","twitter_misc":{"Written by":"Marketing team","Estimated reading time":"3 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Article","@id":"https:\/\/bulbthings.com\/blog\/ai-cmms-parts-prediction-anomaly-detection-recurring-failures#article","isPartOf":{"@id":"https:\/\/bulbthings.com\/blog\/ai-cmms-parts-prediction-anomaly-detection-recurring-failures"},"author":{"name":"Marketing team","@id":"https:\/\/bulbthings.com\/blog\/#\/schema\/person\/d895c3aed7b1c0677d96dd428e8cf362"},"headline":"From breakdown to insight: AI in CMMS for parts prediction, anomaly detection, and recurring failures","datePublished":"2026-03-17T18:47:16+00:00","mainEntityOfPage":{"@id":"https:\/\/bulbthings.com\/blog\/ai-cmms-parts-prediction-anomaly-detection-recurring-failures"},"wordCount":557,"commentCount":0,"image":{"@id":"https:\/\/bulbthings.com\/blog\/ai-cmms-parts-prediction-anomaly-detection-recurring-failures#primaryimage"},"thumbnailUrl":"https:\/\/blog.bulbthings.com\/wp-content\/uploads\/2026\/03\/2151615111-1.jpg","articleSection":["Uncategorized"],"inLanguage":"en-GB","potentialAction":[{"@type":"CommentAction","name":"Comment","target":["https:\/\/bulbthings.com\/blog\/ai-cmms-parts-prediction-anomaly-detection-recurring-failures#respond"]}]},{"@type":"WebPage","@id":"https:\/\/bulbthings.com\/blog\/ai-cmms-parts-prediction-anomaly-detection-recurring-failures","url":"https:\/\/bulbthings.com\/blog\/ai-cmms-parts-prediction-anomaly-detection-recurring-failures","name":"How AI-powered CMMS helps businesses prevent downtime.","isPartOf":{"@id":"https:\/\/bulbthings.com\/blog\/#website"},"primaryImageOfPage":{"@id":"https:\/\/bulbthings.com\/blog\/ai-cmms-parts-prediction-anomaly-detection-recurring-failures#primaryimage"},"image":{"@id":"https:\/\/bulbthings.com\/blog\/ai-cmms-parts-prediction-anomaly-detection-recurring-failures#primaryimage"},"thumbnailUrl":"https:\/\/blog.bulbthings.com\/wp-content\/uploads\/2026\/03\/2151615111-1.jpg","datePublished":"2026-03-17T18:47:16+00:00","author":{"@id":"https:\/\/bulbthings.com\/blog\/#\/schema\/person\/d895c3aed7b1c0677d96dd428e8cf362"},"description":"Use AI in your CMMS to forecast parts needs, flag anomalies early, and surface recurring failure patterns that drive downtime.","breadcrumb":{"@id":"https:\/\/bulbthings.com\/blog\/ai-cmms-parts-prediction-anomaly-detection-recurring-failures#breadcrumb"},"inLanguage":"en-GB","potentialAction":[{"@type":"ReadAction","target":["https:\/\/bulbthings.com\/blog\/ai-cmms-parts-prediction-anomaly-detection-recurring-failures"]}]},{"@type":"ImageObject","inLanguage":"en-GB","@id":"https:\/\/bulbthings.com\/blog\/ai-cmms-parts-prediction-anomaly-detection-recurring-failures#primaryimage","url":"https:\/\/blog.bulbthings.com\/wp-content\/uploads\/2026\/03\/2151615111-1.jpg","contentUrl":"https:\/\/blog.bulbthings.com\/wp-content\/uploads\/2026\/03\/2151615111-1.jpg","width":1000,"height":375},{"@type":"BreadcrumbList","@id":"https:\/\/bulbthings.com\/blog\/ai-cmms-parts-prediction-anomaly-detection-recurring-failures#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/www.bulbthings.com\/blog"},{"@type":"ListItem","position":2,"name":"From breakdown to insight: AI in CMMS for parts prediction, anomaly detection, and recurring failures"}]},{"@type":"WebSite","@id":"https:\/\/bulbthings.com\/blog\/#website","url":"https:\/\/bulbthings.com\/blog\/","name":"The Blog","description":"Tips, Trends And Success Stories","potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/bulbthings.com\/blog\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-GB"},{"@type":"Person","@id":"https:\/\/bulbthings.com\/blog\/#\/schema\/person\/d895c3aed7b1c0677d96dd428e8cf362","name":"Marketing team","image":{"@type":"ImageObject","inLanguage":"en-GB","@id":"https:\/\/secure.gravatar.com\/avatar\/73dab8c6dae75b80ac04bed9e70500525a7e57caa4f9f7c80b3c89ee4677755a?s=96&d=mm&r=g","url":"https:\/\/secure.gravatar.com\/avatar\/73dab8c6dae75b80ac04bed9e70500525a7e57caa4f9f7c80b3c89ee4677755a?s=96&d=mm&r=g","contentUrl":"https:\/\/secure.gravatar.com\/avatar\/73dab8c6dae75b80ac04bed9e70500525a7e57caa4f9f7c80b3c89ee4677755a?s=96&d=mm&r=g","caption":"Marketing team"},"url":"https:\/\/bulbthings.com\/blog\/author\/leslie-depondbulbthings-com"}]}},"_links":{"self":[{"href":"https:\/\/bulbthings.com\/blog\/wp-json\/wp\/v2\/posts\/1551","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/bulbthings.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/bulbthings.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/bulbthings.com\/blog\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/bulbthings.com\/blog\/wp-json\/wp\/v2\/comments?post=1551"}],"version-history":[{"count":3,"href":"https:\/\/bulbthings.com\/blog\/wp-json\/wp\/v2\/posts\/1551\/revisions"}],"predecessor-version":[{"id":1554,"href":"https:\/\/bulbthings.com\/blog\/wp-json\/wp\/v2\/posts\/1551\/revisions\/1554"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/bulbthings.com\/blog\/wp-json\/wp\/v2\/media\/1560"}],"wp:attachment":[{"href":"https:\/\/bulbthings.com\/blog\/wp-json\/wp\/v2\/media?parent=1551"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/bulbthings.com\/blog\/wp-json\/wp\/v2\/categories?post=1551"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/bulbthings.com\/blog\/wp-json\/wp\/v2\/tags?post=1551"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}