In contrast to traditional corrective maintenance or scheduled maintenance, CBM is an approach whereby maintenance is performed proactively on evidence of need identified through direct or indirect monitoring and predictive analytics of the assets of interest.
Taking a predictive approach to maintenance is key to maximising uptime, and with Industry 4.0 transforming the manufacturing landscape, now is the time for design engineers to look to digital solutions to reduce machine downtime and ensure consistent levels of efficiency.
Metal AM powders, such as titanium, aluminium and Inconel are used to produce parts for F1 cars and to ensure their quality, the powder is qualified before use and, when the build process is complete, unused powder is reclaimed and requalified.
Not investing in the right lubricant is clearly a costly mistake especially if you are buying old machinery for business use. This is because a good lubricant can maximize the returns on your investment by ensuring that your old machinery runs at optimal efficiency...
Addressing the wear and tear on equipment is essential for any manufacturing operation, and in this article, we'll detail everything you need to know. We'll walk you through four effective methods of cleaning and maintenance so you can avoid expensive downtime
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 robots 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 thats 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.
The Russell AMPro Sieve Station™ guarantees the quality of your additive manufacturing (AM) powder, and has been designed to provide optimum sieving efficiency, ensuring your powder is ready for use or reuse as and when you require it. With a simple one-button operation and mobile design, this automated check screener ensures your powder at every stage of the process is qualified for use quickly and safely. The flexibility of the Russell AMPro Sieve Station™ means you can use the system for numerous powder handling tasks - being a modular design ensures the machine can be configured to meet your exact requirements.