Predict equipment failure, plan cost-effective maintenance
Sudden malfunctions of equipment can stop a business on a dime. It can create dissatisfied customers, unmet delivery expectations, contract penalties, lost revenue, and costly emergency action to set things right. Data science can protect your business against these unexpected misfortunes. Capturing the vast data streams generated by most modern equipment, you can predict when repairs will be needed, schedule maintenance cost-effectively, and keep your business operating smoothly.
Avoid unplanned maintenance
Minimize unplanned downtime – potential disasters in waiting that put your business at risk. Cut down on nasty surprises.
Improve maintenance planning
Optimize your maintenance schedules by thoughtfully allocating service resources and reducing mean time-to-repair.
Lower maintenance costs
Don’t waste money through over-zealous maintenance. Only repair equipment when repairs are actually needed.
Find causes for equipment malfunctions and work with suppliers to switch-off reasons for high failure rates. Increase return on your assets.
Get started on your predictive maintenance project today!
Download RapidMiner Studio and use the “Predictive Maintenance” template to get started quickly. In this template, apply the model to current situations to anticipate machine failures and schedule maintenance preemptively.
Load data of past machine runs, labeled with information about whether there has been a failure or not.
Determine influence factors using various attribute weighting algorithms and averaging their weights results.
Train a k-NN model – optimizing for k (the number of reference situations to take into account for prediction) to produce a maximum failure prediction accuracy.