Machine Learning and advanced image analytics can improve predictive maintenance by eliminating human error and enhancing quality and consistency of operations. Join the virtual chat to learn more about real-world examples from Energy, Manufacturing and Transportation industries.
Real time data from IoT devices give businesses an unprecedented view into how their products are performing. It can help identify potential faults, troubleshoot from afar, and ultimately improve customer satisfaction.
Classification approach - predicts whether there is a possibility of failure in next n-steps. Regression approach - predicts how much time is left before the next failure. We call this Remaining Useful Life (RUL).
It can be extremely challenging to create a model that can accurately predict the lifetime of a machine. However, in reality, such a model is not needed. We can use risk based classification models to predict a failure within the next ’N’ days or cycles.
The objective with any maintenance program is to reduce equipment failures. An unexpected failure interrupts a business process, usually for an extended period of time. Regular maintenance is designed to reduce the chance that failures like this from occurring.