Manufactures & Factories
❯
Predictive Maintenance
❯Jet engine predictive maintenance service
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
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
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
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
Jet engine predictive maintenance service
For:
Airline industry, Jet engine industry, Airline maintenance industry, cloud-based
AI providers, airline insurance industryGoal:
OtherScope of use case
Use of jet engine telemetry data to train predictive maintenance algorithms
Description
By collecting large quantities of telemetry data from jet
engines installed on commercial airliners as well as their
maintenance history, machine learning algorithms can be
trained to predict how those engines can fail in the future.
Having made such predictions, maintenance can be
performed proactively on the airliner engines before the
problems actually occur, improving safety and lowering cost
by having more reliable and predictable equipment, making
airline flights less prone to disruption.
To allow collection of large quantities of jet engine telemetry
and maintenance logs (big data) for use in ML model training,
both airlines operating the planes as well as jet engine
manufacturers are required to participate. But jet engine
telemetry data or maintenance logs can contain proprietary
and confidential corporate data under exclusive control of
the jet engine manufacturers.
Therefore, the use of the proprietary data in model training
by the company that develops the maintenance service needs
to be explained and be transparent so the airlines and engine
manufacturers know how their data is used, and to ensure
that their proprietary data is not shared with their
competition.
The process of training models and how the data is used
needs to be explainable and transparent, and use of de-
identification techniques applied to parts of the data that
contain proprietary information is expected to be described
to ensure trustworthiness. Such level of transparency and
explainability can then be used in contracts necessary to
enable data sharing across the industry. Without such
transparency and explainability of data use in ML model
training, data sharing would not proliferate and adoption of
ML technologies would be hindered.