{"id":388,"date":"2026-05-08T12:33:42","date_gmt":"2026-05-08T10:33:42","guid":{"rendered":"https:\/\/thingslog.com\/de\/?p=388"},"modified":"2026-05-10T21:27:07","modified_gmt":"2026-05-10T19:27:07","slug":"predictive-analytics-iot-wassermonitoring-thingslog","status":"publish","type":"post","link":"https:\/\/thingslog.com\/de\/2026\/05\/08\/predictive-analytics-iot-wassermonitoring-thingslog\/","title":{"rendered":"Von reaktiver Wartung zu Predictive Analytics: IoT-Wassermonitoring mit ThingsLog"},"content":{"rendered":"\n<p>Viele Wasserversorgungsunternehmen reagieren heute noch auf St\u00f6rungen, wenn sie eingetreten sind. Rohrbr\u00fcche werden repariert, nachdem sie aufgetreten sind. Pumpausf\u00e4lle werden behoben, nachdem der Betrieb stillgestanden hat. ThingsLog und fortschrittliche Prognosealgorithmen erm\u00f6glichen einen grundlegend anderen Ansatz: Probleme erkennen und l\u00f6sen, bevor sie entstehen.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Der Weg von reaktiv zu vorausschauend<\/h2>\n\n\n\n<p>Der \u00dcbergang zu Predictive Analytics vollzieht sich in vier Stufen:<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li><strong>Deskriptiv:<\/strong> Was ist passiert? \u2013 Historische Berichte und Alarme nach Ereignissen<\/li>\n<li><strong>Diagnostisch:<\/strong> Warum ist es passiert? \u2013 Ursachenanalyse mit historischen Sensordaten<\/li>\n<li><strong>Pr\u00e4diktiv:<\/strong> Was wird passieren? \u2013 Anomalieerkennung und Trendanalysen in Echtzeit<\/li>\n<li><strong>Pr\u00e4skriptiv:<\/strong> Was soll getan werden? \u2013 Automatische Handlungsempfehlungen und Steuerungseingriffe<\/li>\n<\/ol>\n\n\n\n<p>ThingsLog beginnt auf Stufe 1 und 2 \u2013 und entwickelt sich gemeinsam mit dem Versorger in Richtung Stufe 3 und 4.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Algorithmen, die im Hintergrund arbeiten<\/h2>\n\n\n\n<p>Die ThingsLog-Plattform analysiert kontinuierlich alle eingehenden Sensordaten auf Anomalien. Im Einsatz sind dabei:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Nachtmindestdurchfluss-Analyse:<\/strong> Erkennt Lecks durch ungew\u00f6hnlich hohe Mindestdurchfl\u00fcsse zwischen 2 und 4 Uhr morgens<\/li>\n<li><strong>Druckgradienten-Analyse:<\/strong> Identifiziert Leckbereiche durch Vergleich von Druckprofilen benachbarter Messpunkte<\/li>\n<li><strong>Saisonale Baseline-Modelle:<\/strong> Vergleicht aktuelle Verbrauchsdaten mit historischen Durchschnittswerten desselben Zeitraums<\/li>\n<li><strong>Maschinenlernmodelle f\u00fcr Pump-Anomalien:<\/strong> Erkennt Abweichungen in Leistung und Energieverbrauch, die auf Verschlei\u00df hinweisen<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">Praxisbeispiel: 72 Stunden Fr\u00fchwarnung vor Rohrbruch<\/h2>\n\n\n\n<p>Bei einem Wasserversorger in S\u00fcdeuropa erkannte die ThingsLog-Plattform einen anomalen Druckabfall in einer Hauptleitung \u2013 72 Stunden vor dem tats\u00e4chlichen Rohrbruch. Das Betriebsteam konnte prophylaktisch eingreifen, den Druck in der betroffenen Zone reduzieren und die Reparatur planm\u00e4\u00dfig durchf\u00fchren \u2013 ohne ungeplante Versorgungsunterbrechung.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Integration mit bestehenden Asset-Management-Systemen<\/h2>\n\n\n\n<p>ThingsLog-Prognosedaten lassen sich in bestehende Asset-Management- und Instandhaltungssysteme integrieren. Wartungsauftr\u00e4ge werden automatisch generiert, wenn Algorithmen kritische Zust\u00e4nde erkennen \u2013 der \u00dcbergang zu datengetriebenem Asset Management wird so schrittweise m\u00f6glich.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Fazit: Daten sind das neue Werkzeug des Betriebsleiters<\/h2>\n\n\n\n<p>Predictive Analytics in der Wasserversorgung ist heute kein Zukunftsszenario mehr \u2013 es ist eine verf\u00fcgbare, bew\u00e4hrte Technologie. ThingsLog macht sie zug\u00e4nglich, ohne umfangreiche IT-Projekte oder Datenwissenschaftler im Team vorauszusetzen. Der erste Schritt ist ein IoT-Sensor. Der n\u00e4chste ist die Analyse seiner Daten.<\/p>\n\n\n\n<p><a href=\"https:\/\/thingslog.com\/de\/kontakt\">Erfahren Sie<\/a>, wie ThingsLog Predictive Analytics f\u00fcr Ihr Netz erm\u00f6glicht.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Wie Wasserversorger mit ThingsLog und fortschrittlichen Prognose-Algorithmen von der reaktiven St\u00f6rungsbeseitigung zur vorausschauenden Betriebsf\u00fchrung wechseln.<\/p>\n","protected":false},"author":1,"featured_media":377,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","footnotes":""},"categories":[169,112],"tags":[164,165,166,167,168,132],"class_list":["post-388","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ki-im-wasser","category-ressourcen","tag-advanced-forecasting-algorithms","tag-ai-in-water","tag-iot-water-monitoring","tag-predictive-analytics","tag-predictive-maintenance","tag-thingslog"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.4 - 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