Contact person : Justine Gazikian, Marketing Manager – Justine.email@example.com
Location: Grenoble, France
Year of creation: 2018
Type of technology:
Amiral Technologies develops a blind failure detection solution without the need of historical failure data.
Other possible applications of their technology:
Cardiology and Neurology using EEG and ECG time series data
Disruptive innovation (s) :
Automatic generation of health indicators from industrial time series allowing an high performance unsupervised approach to failure prediction
Experience and/or interest in financing their product development through publicly funded projects:
We are i-Lab 2019 laureate, are bidding for i-Nov 2020 program (we are shortlisted), and more generally open to publicly funded projects
DiagFit fills the gap of the predictive maintenance workflow: the processing of IIoT data to provide actionable predictions. It contains algorithms consisting in an automatic feature generation for time series, these are mathematical properties that describe time-dependent data series originating from an equipment which behavior is governed by laws, and that are captured by physical sensors.
These properties are extremely rich to the point that they significantly increase the odds of finding solutions to problems such as the detection of signs of failure, ageing or nearing end-of-life for an equipment.
3 main markets: Energy, Transport and Manufacturing
The energy sector is a resource intensive activity involving several critical and expensive equipment to monitor in order to achieve highest possible uptimes. The market is undergoing a rapid transition driven by new energy types, changing distribution models and shifts in supply and demand. Companies must adapt quickly. In addition, this sector is living a rapid transformation due to environmental constraints. Diversity of equipment and data types, combined to the lack of historical failure data, bring huge complexity to designing cost effective predictive modelling solutions.
Because transportation is an ever-increasing connected sector – connected cars, connected aircrafts and helicopters, connected trains, connected ships -, operators and manufacturers must leverage huge investments in sensors to provide low-carbon vehicles ideally 100% secure.
Capacity of predictive software solutions to adapt to different operating contexts, to be embedded within devices/sensors, to provide versatile models, is a must have for these companies.
Manufacturing has been for several years at the forefront of industries high expectations to implement the number 1 application of the Industry 4.0 paradigm: predictive maintenance. However, customers are not yet fully satisfied. Why? Mainly, because of the lack of historical failure data to train machine learning algorithms, or of time-consuming condition-based monitoring approach, expensive digital twins, cloud-based only and non-versatile AI-based solutions. Manufacturers expect software solutions capable to remove these major roadblocks to reaching high ROI.
Amiral Technologies has the technology to address all these needs.