{"id":2569,"date":"2026-02-06T15:57:26","date_gmt":"2026-02-06T14:57:26","guid":{"rendered":"https:\/\/aiquiro-research.de\/2026\/02\/20\/the-silent-lever-in-mechanical-engineering-is-called-condition-based-maintenance-not-calendar-based-maintenance\/"},"modified":"2026-05-18T00:22:02","modified_gmt":"2026-05-17T22:22:02","slug":"the-silent-lever-in-mechanical-engineering-is-called-condition-based-maintenance-not-calendar-based-maintenance","status":"publish","type":"post","link":"https:\/\/aiquiro-research.de\/en\/2026\/02\/06\/the-silent-lever-in-mechanical-engineering-is-called-condition-based-maintenance-not-calendar-based-maintenance\/","title":{"rendered":"The silent lever in mechanical engineering is called “condition-based maintenance” \u2013 not calendar-based maintenance."},"content":{"rendered":"

Downtime is rarely just “bad luck”. There are usually warning signs beforehand.<\/b><\/b><\/p>\n

In German SMEs, maintenance is often planned as follows: fixed intervals, fixed checklists, fixed spare parts. This looks organised. However, it is often expensive. Because real problems do not follow a calendar. They follow stress, wear and process deviations.<\/p>\n

The question is not: \u201cHave we carried out maintenance?\u201d<\/p>\n

The question is: <\/span>\u201cDid we do the right thing at the right time?\u201d<\/b><\/b><\/p>\n

Bosch Rexroth: Condition-based maintenance in its own factory<\/b><\/h3>\n

 <\/p>\n

In a case study from its Homburg plant, Bosch Rexroth describes how condition-based maintenance<\/b><\/span> was implemented on hydraulic test benches. The aim: fewer faults caused by contamination, fewer unnecessary maintenance measures, and more consistent quality. In one specific case, an efficiency increase of 5 %<\/b><\/span> is cited. <\/span><\/p>\n

This is not marketing \u201cwishy-washy\u201d, but precisely the lever that SMEs feel:<\/p>\n

    \n
  • \n

    fewer unplanned stoppages,<\/p>\n<\/li>\n

  • \n

    fewer parts replaced \u2018on a hunch\u2019,<\/p>\n<\/li>\n

  • \n

    greater process stability.<\/p>\n<\/li>\n<\/ul>\n

     <\/p>\n

    What constitutes \u201cknowledge\u201d here \u2013 and why AI makes sense in this context<\/b><\/h3>\n

     <\/p>\n

    Condition-based maintenance only works if you detect deviations early:<\/p>\n

      \n
    • \n

      Pressure profiles change,<\/p>\n<\/li>\n

    • \n

      Temperature drifts,<\/p>\n<\/li>\n

    • \n

      Vibrations increase,<\/p>\n<\/li>\n

    • \n

      Cycle times become \u201cerratic\u201d.<\/p>\n<\/li>\n<\/ul>\n

       <\/p>\n

      AI excels at filtering such patterns from data. It detects deviations faster than the human eye. It recognises combinations that people overlook in everyday life. However: AI does not automatically tell you what<\/b><\/span> you should do. That remains a human decision.<\/p>\n

      The SME toolkit: Getting started can be this simple<\/b><\/h3>\n

       <\/p>\n

      You don\u2019t need a \u2018major Industry 4.0 overhaul\u2019. Start like this:<\/p>\n

        \n
      1. \n

        Choose a bottleneck system.<\/b><\/span> The one that determines the line.<\/p>\n<\/li>\n

      2. \n

        Measure 2\u20134 signals<\/b><\/span> that you already have or can easily add.<\/p>\n<\/li>\n

      3. \n

        Define 3 typical failure scenarios<\/b><\/span> (e.g. leakage, contamination, bearings).<\/p>\n<\/li>\n

      4. \n

        Determine: \u201cWhat does 1 hour of downtime really cost us?\u201d<\/p>\n<\/li>\n

      5. \n

        Decide after 8\u201312 weeks: scale up or stop.<\/p>\n<\/li>\n<\/ol>\n

         <\/p>\n

        This is precisely where \u201cAI-supported, but human-led\u201d comes into play:<\/p>\n

        AI classifies signals. People assess causes. People set priorities.<\/p>\n

        What SMEs can take away from this<\/b><\/h3>\n

         <\/p>\n

          \n
        • \n

          Scheduled maintenance is often more expensive<\/b><\/span> than necessary.<\/p>\n<\/li>\n

        • \n

          A small pilot project can be enough to prove the benefits.<\/p>\n<\/li>\n

        • \n

          AI helps with pattern recognition. People take the action.<\/p>\n<\/li>\n

        • \n

          The goal is not \u2018more data\u2019. The goal is less downtime<\/b><\/span>.<\/p>\n<\/li>\n<\/ul>\n

           <\/p>\n","protected":false},"excerpt":{"rendered":"

          Downtime is rarely just “bad luck”. There are usually warning signs beforehand. In German SMEs, maintenance is often planned as follows: fixed intervals, fixed checklists, fixed spare parts. This looks organised. However, it is often expensive. Because real problems do not follow a calendar. They follow stress, wear and process deviations. The question is not: […]<\/p>\n","protected":false},"author":1,"featured_media":2568,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-2569","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-nicht-kategorisiert"],"_links":{"self":[{"href":"https:\/\/aiquiro-research.de\/en\/wp-json\/wp\/v2\/posts\/2569","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/aiquiro-research.de\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/aiquiro-research.de\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/aiquiro-research.de\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/aiquiro-research.de\/en\/wp-json\/wp\/v2\/comments?post=2569"}],"version-history":[{"count":1,"href":"https:\/\/aiquiro-research.de\/en\/wp-json\/wp\/v2\/posts\/2569\/revisions"}],"predecessor-version":[{"id":4221,"href":"https:\/\/aiquiro-research.de\/en\/wp-json\/wp\/v2\/posts\/2569\/revisions\/4221"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/aiquiro-research.de\/en\/wp-json\/wp\/v2\/media\/2568"}],"wp:attachment":[{"href":"https:\/\/aiquiro-research.de\/en\/wp-json\/wp\/v2\/media?parent=2569"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/aiquiro-research.de\/en\/wp-json\/wp\/v2\/categories?post=2569"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/aiquiro-research.de\/en\/wp-json\/wp\/v2\/tags?post=2569"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}