How to Put CBM to Work Using Edge AI
While predictive maintenance in machinery promises many benefits to businesses, there are pitfalls when putting it in practice. In this webinar, we will examine the key challenges for CBM and look at how edge AI and high frequency vibration data can help you break the walls. The session will cover the best practices for selecting sensors and utilizing the data to perform repair or countermeasure before abnormality occurs, hence reducing the cost of maintenance.
1. Cost reduction in maintenance (CBM vs TBM)
2. Solutions used for implementing CBM
3. Optimal sensor selection for accurate implementation of CBM
4. Easy steps to implement AI, edge computing for automated CBM
About the Presenter
Vikas Kumar Singh is a multi-lingual professional from India. He is an engineer at Macnica, a leading global company in the field of next generation technologies such as AI, edge computing and predictive maintenance. Vikas has over 10 years of experience in sound and vibration analysis for factory automation and for automotive industry. Vikas is keenly interested in combining core physics of vibration analysis with AI and edge computing for the automation of predictive maintenance. Vikas won a scholarship in 2009 to study in National Japanese University. He was a Researcher at Tohoku University, Japan and earned M.E degree from Hiroshima University.
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