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Operation

Predictive Maintenance

Active antenna array satellite

Deep learning technology combined with topological data analysis successfully estimates degree of internal damage to bridge infrastructure
Scope:
Estimate and detect the risk of catastrophic collapse of old bridges.
Goal:
Improve Operation Efficiency
Large Companies
Knowledge representation - Bayesian Network
For:
Process Engineers.
Scope:
Applying Bayesian Networks to root cause analysis of industrial processes.
Goal:
Improve Operation Efficiency
Predictive maintenance of public housing lifts
For:
FM company, residents in public housing
Scope:
Build an AI solution that can predict malfunction in a lift
Goal:
Other
Product failure prediction for critical IT infrastructure
For:
QA engineers, manufacturing line technicians, technical sales
Scope:
Building an AI solution to augment QA engineers
Goal:
Other
Automated defect classification on product surfaces
For:
Sanitary industries
Scope:
Image analytics for water taps in sanitary industries.
Goal:
Other
Analysing and predicting acid treatment effectiveness on bottom hole zone
For:
Manufacturer
Scope:
Mining of oil and gas; digital assistant for analysing and predicting the effectiveness of acid treatments of the bottom hole zone.
Goal:
Other
Machine learning-driven approach to identify weak spots in the manufacturing of circuit breakers.
For:
Manufacturer of high-voltage (HV) circuit breakers
Scope:
Detecting issues in the manufacturing process that lead to early failure of the circuit breakers through data mining related to the manufacturing process.
Goal:
Other
Active antenna array satellite
For:
Operators of satellite communication systems Users of satellite communication systems Regulation authorities Space agencies
Scope:
Determine optimal spot beam patterns for communication satellites in order to react to changing geographic distribution and bandwidth requirements of terminals.
Goal:
Other
Intelligent technology to control manual operations via video Norma
For:
Industrial enterprises, repair enterprises, repair shops, operators of engineering products.
Scope:
Tooltip visualization technology (augmented reality) based on technological process and manual operations control in the assembly, maintenance, and repair of engineering products.
Goal:
Other
Jet engine predictive maintenance service
For:
Airline industry, Jet engine industry, Airline maintenance industry, cloud-based AI providers, airline insurance industry
Scope:
Use of jet engine telemetry data to train predictive maintenance algorithms
Goal:
Other
Carrier interference detection and removal for satellite communication
For:
Operators of satellite communication systems Operators of other communication systems (satellite or non-satellite) that are potential sources of interference Users of satellite communication systems Regulation authorities Space agencies
Scope:
Machine-learning-based detection, classification and removal of interference signals for satellite communication systems.
Goal:
Other
Large Companies
Manufacturing and Factories – Predictive Maintenance
For:
Operation, R&D
Scope:
Avoiding unplanned shutdowns in Manufacturing using Machine Learning to predict failure states in equipment.
Goal:
Anticipate Risks, Improve Operation Efficiency
Solution to detect signs of failures in wind power generation system
Scope:
Detect signs of malfunction (failure) in wind power generators
Goal:
Improve Operation Efficiency
Machine learning-driven analysis of batch process operation data to identify causes of poor batch performance
For:
Batch manufacturers such as milk pasteurizers, pharmaceutical makers, paint manufacturers, etc.
Scope:
Detecting issues in a batch manufacturing process that lead to bad quality products or longer cycle times for batch processing.
Goal:
Other

Active antenna array satellite

For:
Operators of satellite communication systems Users of satellite communication systems Regulation authorities Space agencies
Goal:
Other
Problem addressed
Optimise service quality and bandwidth allocation for users of a satellite system.
Scope of use case
Determine optimal spot beam patterns for communication satellites in order to
react to changing geographic distribution and bandwidth requirements of
terminals.
Description
The problem tackled in this use case is to find the optimum
setup of the spot beams with respect to non-uniform
distributed users on the service area.
For training purposes, the ML algorithm would be fed with
different, e.g. randomly generated, terminal distributions,
and a set of spot beam parameters. The performance of the
solution is assessed by analyses of the possible network-
wide throughput. This takes into account:
the contours losses of the terminals at their position;
the interference from spot beams transmitting on the
same frequency band (4-coloring scheme assumed);
the HTS multi-spot beam antenna pattern.
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Machine Learning
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