Operation
❯
Predictive Maintenance
❯Product failure prediction for critical IT infrastructure
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
Product failure prediction for critical IT infrastructure
For:
QA engineers, manufacturing line technicians, technical salesGoal:
OtherProblem addressed
Reduce the likelihood of releasing defective batches of hardware
Scope of use case
Building an AI solution to augment QA engineers
Description
The hardware manufacturing company was using a few QA
engineers to make subjective calls on whether or not a
specific batch was good enough to be released into the
market. The graphical representation of the shortfalls and
defects was also done manually. This led to inconsistent
labelling and many unsatisfied customers. To augment the
QA engineers, a deep learning AI model was developed to do
more accurate and consistent labelling of which batches
were likely to be most defective and the major type of
defects.