Our client designs, manufactures, and leases industrial equipment and provides software to remotely monitor equipment operations. They looked to Faststream Technologies to accelerate the prototyping of intelligent features in the software platform that could reduce downtime of machinery by predicting and preventing faults.Working closely with our client’s software engineering team, Faststream built a predictive analytics prototype that consists of 2 wire current sensors, an Anemometer, equipment sensor time-series data to predict and prevent machine downtime. The system alerts field teams about units at risk of faulting, so they can proactively take action before any failure. The client’s team is able to continue the work on their own, maintain the code, and conduct further experiments using the data processing pipeline and machine learning framework we created.One sensor that we used was an inertial sensor that includes a Machine Learning Core (MLC) and a Finite State Machine (FSM). A revolutionary aspect of this sensor is that it has an embedded Machine Learning Core. Our team could configure specific parameters of the decision tree with Weka, an open-source collection of machine learning algorithms. 

Manufacturers rightly focus on improving profit margins and growing revenue. Attracting new customers, selling more products and lean practices can help. However, as equipment sophistication increases, so does the ability to monitor equipment. Manufacturers can now develop revenue from maintenance services. Preventive maintenance has its advantages but to really drive uptime and maintain service levels, predictive maintenance is needed. Seamless IoT and machine sensor data integration is critical as well as a low-latency messaging backbone for scalable, fast and reliable transport. Delivering potentially large quantities of data at sub-second speeds is key to downstream activities. webMethods Integration, featuring Universal Messaging, addresses this need with an enterprise-grade service bus for connectivity, messaging, transformation and security of machine data for advanced real-time analytics.

Egger Ltd at Hexham, Northumberland, UK, manufactures chip board and associated products on highly automated production lines. The programs within numerous manufacturing systems and controllers are vital to the resulting OEE of the company and require frequent back up, version control and automatic administration. Egger standardised on Versiondog software from SolutionsPT to undertake and manage this task.

Predictive maintenance is a technique that uses condition-monitoring sensors and machine learning or rules based algorithms to track the performance of equipment during normal operation and detect possible defects before they result in failure. Predictive maintenance enables the reduction of both schedule-based maintenance and unplanned reactive maintenance by triggering maintenance calls based on the actual status of the equipment. IoT relies on predictive maintenance sensors to capture information, make sense of it, and identify any areas that need attention. Some examples of using predictive maintenance and predictive maintenance sensors include vibration analysis, oil analysis, thermal imaging, and equipment observation. Visit our condition-based maintenance page to learn more about these methods.