Faster Handling of Filling Machine Failure
Although predictive maintenance can reduce the frequency with which machines break down, they can’t completely eliminate failures—at least not when it comes to bottle-filling machines in breweries. These machines consist of seven complex sub-machines that often experience breakdowns multiple times per day. However, these failures aren’t the kind of thing that can be predicted ahead of time, so predictive maintenance can’t help.
There’s an obvious cost associated with having the filling machines out of commission, which makes it critical to quickly and correctly identify what’s wrong and repair the problem. Typically, mechanics have to manually check multiple parts of the machine until they find the issue and then fix it, which can take a significant amount of time. With machine learning tools, however, error messages can be refined with the addition of a prediction about what the problem is and where it occurred, speeding triage and repair.
Benefits for the company
The information helps in addressing the breakdowns quickly and effectively, thereby eliminating any delays and downstream problems.
Type of expertise/ AI domain
Classification, Clustering, Advanced Analytics
Internal data required
Maintenance Logs, Historical Repair logs, Real-time machine logs