Predictive maintenance is becoming essential to the smart factory. Predictive maintenance enables users to more accurately anticipate when machine maintenance will be needed based on real-time data from the machines themselves.
Predictive maintenance is the process of tracking the performance of crucial machine components, such as motors, to minimize downtime needed for repairs. 予知保全では、機械から得るリアルタイムのデータを基に、より正確に機械のメンテナンスが必要となるタイミングを知ることができます
Traditionally, plant managers relied on preventative maintenance schedules provided by a machine’s manufacturer, including regularly replacing machine components on a suggested timeline. However, these timelines are only estimates of when the machine will require service, and the actual use of the machine can greatly affect the reliability of these estimates.
For example, if bearings wear prematurely or a motor overheats, a machine may require service sooner than anticipated. Furthermore, if a problem goes undetected for too long, the issue could escalate to further damage the machine and lead to costly unplanned downtime. Predictive maintenance helps avoid these problems, saving time and costs.
Condition monitoring plays a key role in predictive maintenance by allowing users to identify critical changes in machine performance. One important condition to monitor is vibration. 機械の振動の多くは、部品の不均衡、ずれ、ゆるみ、摩耗が原因です。
Vibration sensors typically measure RMS velocity, which provides the most uniform measurement of vibration over a wide range of machine frequencies and is indicative of overall machine health. Another key data point is temperature change (i.e. overheating).
Machine learning takes condition monitoring data and automatically defines a machine's baseline conditions and sets thresholds for acute and chronic conditions so that you know in advance--and with confidence--when your machine will require maintenance.
After mounting the vibration sensor onto your machine, most sensors require you to collect enough data to establish a baseline for the machine. Machine learning removes the chances of human error by automating the data analysis.
A condition monitoring solution with machine learning will recognize the machine’s unique baseline of vibration and temperature levels and automatically set warning and alert thresholds at the appropriate points. This makes the condition monitoring system more reliable and less dependent on error-prone manual calculations.
When a vibration or temperature threhold has been exceeded, a smart condition monitoring system provides both local indication, such as sending a signal to a tower light in a central location, and remote alerts like emails or text messages. This ensures that warnings are addressed quickly.
In addition, a condition monitoring solution that allows you to log the collected data over time enables even more optimization. With a wireless system, vibration and temperature data can be sent to a wireless controller or programmable logic controller (PLC) for in-depth, long-term analysis.
DXMシリーズ産業ワイヤレスコントローラは、イーサネット接続とインダストリアルIoT (IIoT) 用途を容易にするように設計されています。