AIMaintenance.net

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 – Artificial intelligence Maintenance –

 

 

Leveraging artificial intelligence (AI) models to identify anomalous behavior turns equipment sensor data into meaningful, actionable insights for proactive asset maintenance – preventing downtime or accidents. Commonly known as predictive maintenance, this intelligence forecasts when or if functional equipment will fail so its maintenance and repair can be scheduled before the failure occurs.
Considering the aggressive time-to-market required for aerospace products and services, identifying causes of potential faults allows companies to deploy maintenance services more effectively, improving equipment up-time.

Critical features that help predict faults or failures are often buried in structured data, such as year of production, make, model, and warranty details, as well as unstructured data such as maintenance history and repair logs. However, emerging technologies such as the Internet of Things (IoT), Big Data, analytics, and cloud data storage are enabling more equipment to share condition-based data with a centralized server, making fault detection easier, more practical, and more direct.

 

Predictive maintenance model

The underlying architecture of a preventive maintenance model is fairly uniform irrespective of applications. Analytics usually reside on various IT platforms, with layers systematically described as:

  • Data acquisition, storage – Cloud or edge systems
  • Data transformation – Conversion of raw data for machine learning models
  • Condition monitoring – Alerts based on asset operating limits
  • Asset health evaluation – Diagnostic records based on trend analysis if asset health declines
  • Prognostics – Failure predictions through machine learning models, estimate remaining life
  • Decision support system – Best action recommendations
  • Human interface layer – Information accessible in easy-to-understand format

Failure prediction, fault diagnosis, failure-type classification, and recommendation of relevant maintenance actions are all a part of predictive maintenance methodology.

 

 

 

 

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