MATLAB® and Simulink® product managers talked to more than 100 engineers and engineering managers working on predictive maintenance systems to find what these teams had in common.
Four areas came up as common obstacles to predictive maintenance across companies and industries:
- Insufficient data
- Lack of failure data
- Inability to predict failure
- Lack of experience building predictive maintenance algorithms
Read this paper to learn how to overcome these obstacles through best practices, examples from real businesses, and an explanation of the predictive maintenance workflow.