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by Arun Gowtham Leave a Comment

How AI complements Reliability Engineers

How AI complements Reliability Engineers

The tasks of a Reliability Engineer are long & diverse. While heavily dependent on the industry one is working in, it generally involves all aspects of the Equipment – from Design to Manufacturing to Operation to Maintenance. Even though the responsibility is wide, the resources available for a Reliability Engineer within an organization are limited. Often, there are only a few Reliability Engineers managing hundreds of Equipment. Given this current situation, the arrival of AI seems like a perfect resource to complement the work.

The technological aspect of AI that will be a great fit here is Machine Learning (ML). It is the concept where patterns are learned from the input data without explicit programming. It opens new ways to analyze data that were previously deemed difficult to perform computations. Six common applications of ML in Reliability Engineering are given in this reference link.

Let’s explore some common responsibilities and how AI complements Reliability Engineers:

Reliability Engineer’s TaskDone without AIDone with AI
Equipment Performance MonitoringManually comb through data trends from Automation systems, Warranty database, CMMS, etc.AI monitors the performance & Alerts user of performance deviation
Equipment Reliability PredictionMake Life Cycle predictions (Weibull Analysis) at regular intervals of the programAutomated analysis predicts Reliability % at any given time
Root Cause Failure AnalysisConduct investigation, collect associated data, assess failed component, and discuss with team to determine Root CauseDiagnostic algorithms will detect data changes & highlights the contributing parameter to Failure
Data CollectionData from different systems: CMMS Workorders, Field Warranty Reports, Testing Reports need to be combined for analysisTest extraction from reports (pdf, images), multiple data sources combined to create Training dataset, Data Pre-processing methods
Design OptimizationDesign drafted, built, and iterated after testing. Iteration is linear.Optimization algorithm finds the best design layout given program inputs & constraints

The table above shows that certain sections of the tasks are executed best by an algorithm. They are precise, accurate, reliable, and faster in processing data of high dimension & size. But it is important to note that the algorithm will not replace the Reliability Engineer. It is a perfect companion to them just like a calculator or an analysis software, but the final interpretation of the results and decision-making must be done by an experienced Reliability Engineer.

This is not an exhaustive list. New algorithms are being researched and new applications are explored by industry & academia. One of the lesser appreciated tasks of Reliability Engineer is to communicate the findings to other teams and Management. Can we adapt ChatGPT, MS CoPilot, Vimeo One-Take to make persuasive presentations & reports? Yes, we can.

The only real limitation of complementing AI in Reliability Engineering work seems to be in the imagination!

Filed Under: AI & Predictive Maintenance, Articles, on Maintenance Reliability

About Arun Gowtham

Arun Gowtham is the Founder/Lead Reliability Engineer at Owtrun. He works on accelerating the adoption of digital tools to support Reliability Engineers. Writes about all things Reliability, AI/ML, and Project Management.

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