The term manufacturing company normally brings to mind images of large industrial plants with thousands of workers. However, small family-owned businesses make up the largest proportion of the sector. According to AMT – The Association for Manufacturing Technology, 97% of the manufacturing firms in the United States have less than 500 employees, and 73% have
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AI4EU is a European Union Artificial Intelligence project which aims to bring together the AI community and facilitate knowledge transfer from research to business application. It provides an experiments platform that promotes the sharing and deployment of AI models. In 2021, Viking Analytics has been selected by AI4EU as a solution provider in AI technologies.
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The Industrial Internet of Things (IIoT) market is in the early stages of a growth curve comparable to the evolution of the mobile telephony, especially when it comes to IIoT vibration sensors. In this scenario, is the current business model of keeping systems closed compatible with the future of IIoT? We believe sensor expansion will inevitably lead
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Detecting and monitoring operational modes in a machine is a very effective way of getting a precise view of the asset’s health conditions. In a machine, different operational modes can reflect different usages, a production process, an on-off state, or a developing failure. But besides reflecting the overall state of the machine, Mode Identification can
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One of the fundamentals of condition monitoring is being able to trust the data you get from your sensors. Normally, there is a set of checks that an IIoT installation goes through to become fully functional, like connectivity and authentication tests to ensure that the sensor is in the right operational state or, in other
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With IIoT democratizing asset and process information for remote condition monitoring, the next grand battle in the PdM market will be between OEMs and maintenance companies. The maintenance field has been greatly impacted by digitalization. As data collection and analysis capacities increased, companies were able to move from reactively performing maintenance in their equipment to
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Even companies that are playing in the advanced league of the condition monitoring game may face the challenge of knowing which machines need to be prioritized for maintenance. For example, how can a professional responsible for the maintenance of compressors in a company realize which ones need to go to the top of priorities list?
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Vibration monitoring is key to performing condition monitoring-based maintenance in rotating equipment such as engines, compressors, turbines, pumps, generators, blowers, and gearboxes. However, periodic route-based vibration monitoring programs are not enough to prevent breakdowns, as they normally offer a narrower view of the machines’ conditions. Adding Machine Learning algorithms to this process makes it scalable,
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Predictive maintenance, despite its measurable results and successes, has been suffering from lack of scalability. Models designed and trained for a given machine with a particular set of sensors often had to be retrained before being used with other similar machines. In addition, the quantity and variety of machines in a factory makes developing, training,
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