IEEE Boston/Providence/New Hampshire Reliability Chapter
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Speaker: Arun Gowtham of Apex Ridge Reliability
Artificial Intelligence (AI) is upending businesses by unlocking value & drastically changing operating models. Will Reliability Engineering be transformed? The data-intensive exercises of reliability testing, verification & validation, and fleet monitoring can be improved vastly by deploying AI tools. How should the AI implementation be done to get the maximum benefits?
The presentation explores answers to these questions in the context of implementing a Predictive Maintenance (PdM) program where repair work is always planned & executed at optimal times. A strategic framework is needed to plan, execute, and sustain an AI program. Utilizing algorithms to predict machine failures empowers Reliability Engineers with information that they use to manage design iterations, repair work, spare parts, and production. No more reacting to failures after a breakdown resulting in huge losses; project teams will be proactive & efficient. Whether you’re an Executive, an Engineer, or a student, the talk will have key takeaways to set you up on the reliability transformation journey. Date and Time
At registration, you must provide a valid e-mail address to receive the Webinar Session link approximately 15 hours before the event. The link will only be sent to the e-mail address entered with your registration. Please double-check for spelling errors. If you haven’t received the e-mail as scheduled, please check your spam folder and alternate e-mail accounts before contacting the host.
Agenda:
11:00 AM Technical Presentation
11:45 AM Questions and Answers
12:00 PM Adjournment
Speaker: Arun Gowtham of Apex Ridge Reliability
Artificial Intelligence (AI) is upending businesses by unlocking value & drastically changing operating models. Will Reliability Engineering be transformed? The data-intensive exercises of reliability testing, verification & validation, and fleet monitoring can be improved vastly by deploying AI tools. How should the AI implementation be done to get the maximum benefits?
The presentation explores answers to these questions in the context of implementing a Predictive Maintenance (PdM) program where repair work is always planned & executed at optimal times. A strategic framework is needed to plan, execute, and sustain an AI program. Utilizing algorithms to predict machine failures empowers Reliability Engineers with information that they use to manage design iterations, repair work, spare parts, and production. No more reacting to failures after a breakdown resulting in huge losses; project teams will be proactive & efficient. Whether you’re an Executive, an Engineer, or a Student, the talk will have key takeaways to set you up on the reliability transformation journey.
Biography:
Arun Gowtham is a Reliability Consultant at Apex Ridge Reliability. Arun is a Certified Reliability Engineer (CRE) with international experience in leading organizational Reliability programs at Hydrogen Fuel Cells, Semiconductors, Pharmaceuticals, and Composite manufacturing industries. His journey into the exciting field of Reliability Engineering started with thesis work ‘Predictive Modeling & IETM support for RCM’ and since then has extended into various Industry 4.0 technologies such as Industrial Internet of Things (IIoT), Automation, Remote Fleet Management, and Predictive Maintenance. Arun’s current focus is on applying AI to augment Reliability and maintenance tasks. His research work on ML-based Predictive Maintenance was well received at RAMS & MainTrain conferences, leading to a patented approach. He holds an MS in Mechanical Engineering from Drexel University, PA, USA. He volunteers as President at PEMAC Toronto Chapter; and is a licensed Engineer in Ontario, Canada (PEO).
Starts 02 January 2024 12:00 AM
Ends 16 January 2024 05:30 PM
All times are (UTC-05:00) Eastern Time (US & Canada)
No Admission Charge
The meeting is open to all. You do not need to belong to the IEEE to attend this event; however, we welcome your consideration of IEEE membership as a career enhancing technical affiliation.
There is no cost to register or attend, but registration is required.