MANTIS for Compressor maintenance

With SMARTLINK monitoring program, Atlas Copco makes use of connectivity data and data intelligence to help customer to keep up their production uptime and to improve, when possible, energy efficiency.

With approximately 100,000 machines connected with SMARTLINK, Atlas Copco makes compressors in the field communicate directly with the back office and their service technicians.

Atlas Copco’s SMARTLINK technology allows for remote monitoring of compressors in the fields.
Atlas Copco’s SMARTLINK technology allows for remote monitoring of compressors in the fields.

Customers become more proactive, planning is more efficient and reliability of the compressed air installations is better than ever before.

Customers of SMARTLINK get a monthly overview of machine information, including running hours and the time left before service, thus allowing them to order a service visit at the right time, maintaining maximum uptime and energy efficiency.

With SMARTLINK they can closely follow up on machine warnings via email or SMS. With this information they can take the necessary actions to prevent a breakdown.

With the MANTIS project, Atlas Copco will take proactive maintenance to the next level, by:

  • predicting the remaining useful life of consumables and components that are subject to wear
  • detecting upcoming problems or inefficiencies before they deteriorate
  • remotely diagnosing the root cause of an unplanned shutdown
During the MANTIS project, Atlas Copco will take proactive maintenance to the next level by predicting component lifetime, by detecting anomaly and by perform remote diagnosis of the compressed air installation.
During the MANTIS project, Atlas Copco will take proactive maintenance to the next level by predicting component lifetime, by detecting anomaly and by perform remote diagnosis of the compressed air installation.

Moreover, in order to reduce communication costs, smart sensing technology is being investigated, or how local preprocessing of information can significantly reduce the amount of data to be transmitted.

A major challenge for Atlas Copco is the huge variety of compressor types and operating conditions. To process this enormous amount of information, self-learning techniques are combined with physics-based compressor models. Eventually, these will enable the discovery of new patterns in data, collected on a worldwide scale.

The ultimate goal is to translate these data into actionable information for the global service network.

Service interventions will be planned even better and will be shorter and more efficient. Problems will be fixed in one visit, as technicians will know in advance what to do and what parts to bring.

The results of the project will allow better service planning, shorter visits and first time fix, thus reducing downtime for the end customers and ensuring sustainable productivity
The results of the project will allow better service planning, shorter visits and first time fix, thus reducing downtime for the end customers and ensuring sustainable productivity

For the customer, this means no unnecessary maintenance, less planned or unplanned downtime, therefore achieving maximum productivity.