EN 
Українська
Polski
Español
Deutsch
日本語
Italiano
Português (BR)
Français
العربية
Nederlands
中文
한국어
हिन्दी
Türkçe
Tiếng Việt
Русский
Bahasa Indonesia
فارسی
বাংলা
ไทย
Filipino
Kiswahili
اردو
Bahasa Melayu
தமிழ்
עברית
Ελληνικά
ਪੰਜਾਬੀ
አማርኛ
မြန်မာဘာသာ
ខ្មែរ
ქართული
नेपाली
Монгол хэл
Հայերեն
සිංහල
ລາວ
Čeština
Română
Magyar
Български
Svenska
Dansk
Suomi
Norsk
Slovenčina
Hrvatski
Српски
Slovenščina
Lietuvių
Latviešu
Eesti
Íslenska
Shqip
Македонски
Bosanski
Malti
Azərbaycanca
Қазақша
O'zbekcha
Тоҷикӣ
КыргызчаMachine Learning for Predictive Maintenance
This guide provides a complete overview of using machine learning to predict equipment failures and optimize maintenance schedules.
1. Core Principles of Predictive Maintenance
Data Requirements – The Foundation
A key element is the availability of historical data on equipment failures.
The quality of this data (free from noise) and its frequency of collection (at least once per minute) are crucial for effective models.
ML Layer: Models & Inference
The ML layer encompasses the training models and their inference processes.
Application Layer: Dashboards, alerts, and integrations provide a user-friendly interface for accessing insights.
Frequently asked questions
Can machine learning be used with older equipment?
Yes, it can, but you may need additional sensors. Older equipment often has less data available, making model training more challenging.
Is it possible to integrate machine learning with CMMS systems?
Most modern CMMS systems support API integration. This allows for automated work order creation based on ML predictions.
Do most CMMS systems support API access?
Yes, the majority of contemporary CMMS systems offer API integration capabilities, facilitating seamless data exchange and automation.
▶ Try it live
Everything above runs in your browser — open Decision Tree Live and change the parameters while it is running. Nothing is installed, nothing is uploaded, the whole model lives in one tab.