t cht lk place that work well, but once finalized, an ML system will run 24 / 7 and become increasingly more effective over time. Using a time series database designed specifically to store and analyze high-resolution datasets over long periods will result in more precision and the identification of longer-term trends and patterns in equipment behavior.
t cht lk place that work well, but once finalized, an ML system will run 24 / 7 and become increasingly more effective over time. Using a time series database designed specifically to store and analyze high-resolution datasets over long periods will result in more precision and the identification of longer-term trends and patterns in equipment behavior.
Taken a step further, given that some of the more advanced deployments of ML in manufacturing are effectively self-calibrating, a scalable ML approach will be more cost-efficient and more effective than relying 100 % on manual intervention. The upfront cost represents 90 % of the initial investment vs running the system and storing the data. This figure also doesn’ t account for the savings from reduced downtime and reduction in TCO from optimizing staffing and repairs.
Cameras and AI image scanners enable near real-time inspections.
While it has many virtues, predictive maintenance works in tandem with preventative and reactive maintenance, which remain an absolute necessity.
For manufacturing, it is the ounce of prevention that saves the pound of cure most maintenance teams are used to applying. As industries move towards Industry 4.0 this is just one of the technologies, enabled by better control and manipulation of data, that will improve productivity for workers and efficiency for organizations. p
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