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.

Root-cause analyses

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.

Step 1:

Load data of past machine runs, labeled with information about whether there has been a failure or not.

Step 2:

Determine influence factors using various attribute weighting algorithms and averaging their weights results.

Step 3:

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.

Step 4:

Load new data and apply the machine failure model to current machine runs to predict potential machine failures.

View Other Use Cases

Churn Prevention

Identify customers likely to leave, take preventative action.

Customer Lifetime Value

Distinguish between customers based on business value.

Customer Segmentation

Create meaningful customer groups for more relevant interactions.

Demand Forecasting

Know what volumes to expect to improve planning.

Fraud Detection

Identify fraudulent activity quickly, and end it.

Next Best Action

The right action at the right time for the right customer.

Price Optimization

Set prices that balance demand, profit, and risk.

Product Propensity

Predict what your customers will buy, before even they know it.

Quality Assurance

Resolve quality issues before they become a problem.

Risk Management

Understand risk to manage it.

Text Mining

Extract insight from unstructured content.

Up- and Cross-Selling

Convince customers to buy more.