Predictive maintenance

Our AI-based failure prediction application assesses the remaining life cycle of each machine component. It allows technicians to plan interventions at the right time.

Test for free

Predictive maintenance demonstrator with our AI application
Schneider Electric

Predictive Maintenance features

Discover all the features of our AI application.

Dashboard

Visualisation of the history of failures, life cycles and values of each sensor per machine.

Predicted failures

Prediction of the number of remaining cycles for each machine with an indication of the margin of error.

Explicability of results

Explicability of results to identify the origin of a future failure or the effectiveness of maintenance.

touch

Prioritisation of maintenance operations

Taking into account the cost of each intervention to prioritise and optimise maintenance operations.

Our application is compatible with

twimm intelligent platform
DimoMaint

Our application is compatible with

twimm intelligent platform
DimoMaint

The benefits of Predictive Maintenance

Discover the different benefits of our application for users.

Increased machine availability

Increased machine availability and reduced associated costs

Reduction in the number of breakdowns

Changing the part before the fault occurs

Reduction of maintenance costs

Optimisation of technical interventions

An operational application in record time

We integrate our applications into your company in less than 7 weeks.

Week 1

01

Technical framing

The data available at the customer's premises is audited.

Week 3

02

Data Collection

The data connectors are created in compliance with RGPD constraints.

Week 4

03

Application integration

The application is integrated within your company and into your existing tools.

Week 7

04

Operation of the application

The application is ready to be used in production.

Week 7

05

Evolution and maintenance

The application benefits from all evolutions and permanent technical support.

Our case studies

GRT Gaz

The company wanted to equip itself with a tool that would enable it to optimise the maintenance operations of its 10,000 pieces of gas network equipment.

Issue

Several thousand maintenance operations are carried out each year on the network, which represents a significant cost.

15%

observed failure rate

Solution

The predictive maintenance tool makes it possible to know which equipment is likely to fail and therefore to prioritise operations correctly.

500

recommended operations / year

Result

Eliminate unnecessary maintenance, save time, increase equipment availability.

-25%

Maintenance cost