Predictive maintenance is more about algorithms

Most experienced maintenance engineers already have a detailed mental picture of the machinery they care for. They know whether a rattling valve means a breakdown is imminent, for example, or if it is safe to ignore it until the next scheduled shutdown. If a predictive maintenance system can tap into this pool of knowledge, it can learn to provide the best possible support in the decision-making process.

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  • The importance of a user-centric approach to predictive maintenance
  • What lessons we can learn from other industries
  • How Senseye focuses on meaningful outcomes for users

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