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Application of Machine Learning to Mechanical Equipment Diagnostics

Machines of all varieties have failure modes which if predicted can reduce business costs, reduce downtime and increase safety. Failure prediction can be model- or data-based. Both methods require that patterns be recognized which provide an indication of the failure mode. Machine learning methods such as convolutional neural networks are being used to increase the speed and accuracy of failure predictions. This course is an introduction to the application of machine learning to equipment diagnostics. Data requirements and sources, types of applicable machine learning, implementation approaches to machine learning and use cases demonstrating the application of machine learning for failure prediction will be discussed.

Instructor

Dennis Miller

Dennis Miller

Dennis Miller has a BS and MS in electrical engineering with additional graduate work in computer science. He worked for 35 years for Johnson Controls, Inc. as an engineer and manager in building controls research, product development and software testing. ... read more

Benefits and Learning Outcomes

  • Learn the steps necessary to apply machine learning to equipment diagnostics
  • Understand requirements for equipment operating data and sources of data to support the implementation of equipment diagnostics
  • Examine examples of machine learning applied to diagnostics which can be used as references for action toward its application

Date: Tue, May 4, 2021

Delivery Method: Live Online

Time: 8am-4:30pm CT

Platform: Zoom

Instructor: Dennis Miller

Fee: $595

CEUs: 0.7, PDHs: 7.0

Enrollment Limit: 40

Program Number: 4860-13473

Registration Deadline: Apr 30, 2021

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