Industry 4.0: Predictive maintenance use cases in detail

Feb 15, 2018

Predictive maintenance involves collecting and evaluating data from your machines to increase efficiency and optimize maintenance processes. Not only can you gage the condition of your equipment, but also more accurately predict when maintenance work is needed.

Industry 4.0: Predictive maintenance for milling machines

Spindles in milling machines are prone to breaking during the production process. What’s more, repairing spindles can be very expensive. Therefore, being able to predict damage and precisely when the spindle will break can greatly reduce costs.

To overcome this challenge, special sensors (e.g. ultrasonic or vibration sensors) identify the patterns of a fragile spindle. Relevant alert settings for the current state of the machine can then be created.

Milling machine working on a piec eof metal. Source: fotolia/Andrey Armyagov

The sensors generate data which is then compared to the information from the machine and the specific workpiece being processed. By analyzing the data, it is possible to identify patterns of behavior that more accurately predict when the spindle is about to break. This enables maintenance schedules to be planned accordingly.

The benefits in detail:

  • Higher process transparency
  • Lower maintenance costs
  • Reduced machine downtime

Industry 4.0: Predictive maintenance for heat exchangers

Deposits in the conduits can cause heat exchangers to clog. A further complicating factor is the fact that it is impossible to measure the flow rate of a heat exchanger directly. A complete blockage can cause serious problems, resulting in manufacturing errors and hours of downtime.

Close-up of a heat exchanger. Source: Fotolia/missisya

One solution to this issue is to measure the temperature differential upstream and downstream of the heat exchanger. After gathering and visualizing the measured values, it is possible to define threshold values. These values can then be input into an alert system to notify employees as soon as the first signs of clogging appear.

The benefits in detail:

  • Early warning of anomalies indicating potential blockages
  • Reduced machine downtime and less wastage of materials

Industry 4.0: Predictive maintenance for the health of robots

It is difficult to plan robot maintenance if the health of a robot is monitored only locally or not at all. But why refrain from gathering relevant machine data? Many parameters can be monitored, including CPU and housing temperature as well as positioning and overload errors. By collecting and displaying this data centrally and then evaluating it, maintenance can be planned before the situation becomes acute.

Close-up of a robot's arm. Source: Fotolia/Sved Oliver

The benefits in detail:

  • Awareness of the health of the machine
  • Intervention before the machine is damaged
  • Increased uptime
  • Early recognition of wear

The post Industry 4.0: Predictive maintenance use cases in detail appeared first on Bosch ConnectedWorld Blog.


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