By tapping into existing maintenance or equipment logs, a manufacturer can apply machine learning to predict which connected devices or machines will be in need of servicing or forecast required inventory levels across warehouse locations...
Predictive maintenance is revolutionizing the industry and transforming the entire operation. This technology predicts problems in a factory before they even happen, changing the paradigm and preventing disasters.
Many businesses are already using continuous monitoring technologies - like Internet of Things (IoT) connected devices - which is a good start; but the key lies in not just simply monitoring the output of various data (which is how many companies use it today), but by taking the next step and employing advanced algorithms and machine learning to take action from real-time insights and anticipate future outcomes.
Unsupervised Machine Learning is much more cost effective than Supervised Machine Learning because the algorithm does not need blueprints or understanding of the physical process to "learn" the behavior of the asset.
A robot's abnormal condition can be detected early via data comparison, so a high-speed equipment monitoring solution must be able to acquire analog signals at the rate of 1000 signals per second. Then the collected signals need to be delivered to the control room for monitoring management and data analysis.
The latest in automated lubrication systems ensure optimum equipment performance, even in harsh plant environments, and reduce unscheduled maintenance. Corrosive plant environments are among the most serious threats to many industries today, contributing to extensive production downtime and exorbitant maintenance costs.
The automation and collection of information that's available from machine to machine communication enables manufacturers to transition from corrective to preventive maintenance and ultimately to predictive technologies which rely on more information and data collection.
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