Viva Technology 2019, the worldwide meeting for technological innovation, closed its doors on May 18 after hosting 8000 startups and
Winner of the Digital Industry Challenge 2018 organised by General Electric and NUMA
Why predictive maintenance?
Our Vision: Build the future industry with Zero Defects and Zero Downtime.
Current maintenance strategies are mostly based on corrective and preventative methods
Preventative maintenance processes and downtime duration are intentionally over-estimated to minimize risks. However failure and production downtime is still a costly risk.
Equipment are getting more and more connected
- 13 billion connected objects in 2016 / 29 billion in 2020. (IDATE)
- Less than 20% of the assets are connected today and 70% of the collected data is still not used (World Economic Forum)
- Predictive maintenance is the #1 B2B application with 250 million connected industrial equipment in 2020 (ABI Research)
- An innovative Automatic Feature Generation algorithm for time series
- Powerful and performant Predictive Maintenance Models
Good to know
10 to 40% win on maintenance costs
50% win on downtime
3 to 5% win on capital investment
We just published a new white paper in our document library « DiagSign Automatic Feature Generation and the State of the
What is unsupervised learning? Unsupervised Learning (see wikipedia) is used when no historical data is available that involves past faulty behavior