Manufactures & Factories
❯
Predictive Maintenance
❯Solution to detect signs of failures in wind power generation system
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
Solution to detect signs of failures in wind power generation system
Goal:
Improve Operation EfficiencyProblem addressed
Detect signs of failure in wind power generation earlier than detection by human
specialists.
Scope of use case
Detect signs of malfunction (failure) in wind power generators
Description
We present a method for detecting anomalies in vibration
signals of wind turbine components.
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The predominant characteristics of wind turbine vibration
signals are extracted by applying a time-frequency feature
extraction method based on Fourier local autocorrelation
(FLAC) features. For anomaly detection, one-class
classification based on an unsupervised clustering approach
is applied in consideration of the wind turbines dynamic
operating conditions and environment. To validate the
proposed system, we conducted experiments using the
vibration data of actual 2 Mega Watt (MW) wind turbines.
The results showed the effectiveness of using the FLAC
features, particularly in the case of the low-speed main
bearing where the conventional method with traditional
features cannot detect the anomalies.