Artificial intelligence for predictive 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.

Manufacturing, energy, and utilities verticals are among the biggest demand drivers for predictive maintenance, and the technology is growing in aerospace as manufacturers look to control maintenance and downtime costs. So, it’s critical for equipment manufacturers and owners/operators to adopt a predictive maintenance solution to maintain a competitive advantage.

The bigger players have been using this methodology for more than a decade. Small- and medium-sized companies in the manufacturing sector also can reap its advantages by keeping repair costs low and meeting initial operational costs for new operations.

Offering more business benefits than corrective and preventative maintenance programs, predictive maintenance is a step ahead of preventive maintenance. As maintenance work is scheduled at preset intervals, maintenance technicians are informed of the likelihood of parts and components failing during the next work cycle and can act to minimize downtime.

It’s critical for equipment manufacturers and owners/operators to adopt a predictive maintenance solution to maintain a competitive advantage.

Performance benefits

Predictive maintenance employs non- intrusive testing techniques to evaluate and compute asset performance trends. Additional methods used can include thermodynamics, acoustics, vibration analysis, and infrared analysis.

The continuous developments in Big Data, machine-to-machine communication, and cloud technology have created new possibilities for investigating information derived from industrial assets. Condition monitoring in real-time is viable from sensors, actuators, and other control parameters. What stakeholders need is a bankable analytics and engineering service partner who can help them leverage data science to predict embryonic asset failures, eliminate them, and act in a timely manner.

Cyient
https://www.cyient.com

About the author: Dr. Sean Otto leads business development for Cyient’s Advanced Analytics team, focused on designing AI and machine learning models to improve the functionality and reliability of equipment and systems.