MATLAB® and Simulink® product managers talked to more than 100 engineers and engineering managers working on predictive maintenance systems to find what their teams had identified as obstacles to a successful implementation.

They identified four common obstacles:

  • Insufficient data
  • Lack of failure data
  • Inability to predict failure
  • Lack of experience building predictive maintenance algorithms

Read this white paper to learn how to overcome these obstacles through best practices, examples from real businesses, and an explanation of the predictive maintenance workflow.

Reduce Downtime and Operational Costs Using Condition Monitoring

Learn how you can work with MathWorks Consulting to design and implement your predictive maintenance, anomaly detection, and digital twin projects.

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