t cht lk volume of inspections can improve overall reliability. Automated alerts can also prompt timely human intervention from maintenance teams. datasets over long periods will result in more precision and the identification of longer-term trends and patterns in equipment behavior.
t cht lk volume of inspections can improve overall reliability. Automated alerts can also prompt timely human intervention from maintenance teams. datasets over long periods will result in more precision and the identification of longer-term trends and patterns in equipment behavior.
As manufacturing accelerates ever faster towards what has been dubbed Industry 4.0, the push to leverage data across these different areas is resulting in the wide-scale deployment of sensors. Through the use of sensors, ML in manufacturing doesn’ t need a single, fixed location. Instead, it is possible to apply predictive maintenance to remote or distributed ecosystems such as vehicle fleets, industrial machinery, construction, power generation and grid management. Implementation might disrupt established workflows for the teams that manage these resources, but the gains are worth it.
Upfront investment, an ounce of prevention
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.
While it has many virtues, predictive maintenance works in tandem with preventative and reactive maintenance, which remain an absolute necessity.
The upfront costs of investing in ML might seem high, particularly if you have systems and processes in 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
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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