Manufactures & Factories
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Predictive Maintenance
❯Manufacturing and Factories – Predictive Maintenance
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
Large Companies
Manufacturing and Factories – Predictive Maintenance
For:
Operation, R&DGoal:
Anticipate Risks, Improve Operation EfficiencyProblem addressed
Within manufacturing an ‘unplanned shutdown’, due to asset failure, is unacceptable. And two potential approaches to deal with this situation are: -
Reactive Maintenance: Not Scheduled – targeted replacement of parts in response only to their failure. Occurs during the subsequent unplanned shutdown.
Inefficient (need stock of replacement parts, costly line stoppages).
Preventative Maintenance: Scheduled – bulk replacement of parts, which are within a margin of their end of useful life. Performed at set intervals during planned shutdown.
Inefficient (all service parts replaced, some still useable).
“Users require timely notification of the potential failure of an asset. Allowing maintenance to be performed, at a time convenient to operations, before the failure occurs.”
Scope of use case
Avoiding unplanned shutdowns in Manufacturing using Machine Learning to predict failure states in equipment.
Description
Predictive Maintenance: sensors, applicable to condition monitoring (e.g., temp, pressure & vibration) are mounted on the asset. An ‘Internet of Things’ type set up provides for highest levels of functionality. The DataStream from said sensors allows capture & storage of an asset’s state, in real time.
Predictive Analytics, i.e., Machine/Deep Learning Algorithms applied to the data detects the minutiae present during the transition from the Normal State, into a Failure State and allows predictions identifying the failing subsystem and ‘Time till Failure’. Before o deployment, these algorithms require training with a mixture of data, between the Normal & Failure states. A technique known as ‘anomaly detection’; in which novel data, not being outliers, allows classification between Good/No-Good.
By detecting the assets degradation into a failure condition, before it occurs, users can plan for targeted part replacement at an appropriately scheduled downtime.
Common challenges: Issues associated with appropriate sensor availability, employee knowledge/training and data quality.
Sensor Network - IOT
AI: Perceive
Deep Learning
Machine Learning
AI: Understand
Detect Anomaly & Fraud
Predict / Forecast
AI: Act