Condition-based filter monitoring in production

Use filters optimally and consider real wear

Super simple – with the moneo software solution. The software monitors the filter and records the resulting data directly in the production process. Based on the recorded sensor values, the running time of a filter can be adjusted according to the degree of contamination, ensuring optimum filter utilisation. Permanent filter monitoring results in the optimisation of the entire process by avoiding unplanned downtimes – changing from time-based to condition-based maintenance. The responsible personnel receives the warnings and alerts directly via email or a ticket system, so that they can react quickly to changes.

Contamination and filter defects are detected promptly and expensive consequential costs for process and machine are therefore prevented. Timely detection of errors and alerts for filter replacements help maintain machine uptime and enhance process quality. With LifetimeEstimator, you can create the corresponding maintenance order with a custom lead time, ensuring the filter replacement is incorporated into maintenance planning. The environmental impact and the operating costs are sustainably reduced by the new maintenance strategy. The personnel expenditure for condition evaluation and filter replacement is reduced to a minimum. moneo is also easy to use and can be adapted to customer-specific requirements without any difficulty. This ensures that filters are always changed at the right time – neither too early nor too late – so that any associated costs can be avoided.

Value proposition

Condition-based instead of time-based maintenance

Filter monitoring plays an essential role in the smooth operation of production plants. Filters are an often overlooked component in production or building systems. They are usually replaced after a specific time interval, without considering the actual soiling of the filter. Machine downtime due to defective or clogged filters and unplanned production standstills due to maintenance work are the norm. This results in additional costs due to replacing the filter too early or too late.

To avoid additional costs, monitoring and visualisation of the filter taking the actual condition into account should be implemented. The aim is to replace the filter as needed to enable optimal use. moneo offers an ideal solution for optimal filter monitoring. The tool makes it possible to change from time-based to condition-based maintenance.

moneo simulation video

Cost-optimised maintenance of various filter systems

We distinguish between three different filter systems, because each filter system is subject to different requirements and specifications.

• Air filters
• Water filters
• Oil filters

Operating principle of

Air filters

Air quality in the workplace and at the respective machines is crucial. For this reason, air filters are used in extraction systems in production facilities. The resulting vapours should be extracted from the machines to prevent quality defects caused by dust and vapours. Air in the factory must also be constantly cleaned to prevent damaging employees’ health due to contaminated air. Legal regulations on air quality (in Germany: Technical Instructions on Air Quality Control) must also be adhered to. To record the air quality or soiling of the filters, pressure sensors are installed before and after the filter to ensure continuous monitoring. The corresponding tool evaluates the data and indicates necessary filter maintenance at an early stage.

Water filters

Microfilters are installed in cooling circuits to ensure that systems with heat exchangers run smoothly. These water filters take on the important task of filtering out impurities in the cooling water, thereby protecting the heat exchangers in the connected machines. With water filters, the cooling capacity of a machine is also important. If the filters are dirty, the machine or parts of the machine are no longer cooled properly, resulting in defects in the machine and the manufactured component.

Oil filters

In addition to air filters and water filters, condition monitoring of oil filters is of particular importance. Oil filters are an important component in hydraulic and lubrication units. Undetected soiling leads to damage with possibly high follow-up costs. If a filter becomes clogged sooner than expected, there is likely to be an issue with the lubrication of the bearings or gears, and failure of these components is inevitable. In hydraulics, the oil must have a certain level of purity. This can only be achieved using filters installed in the return line. Appropriate filter monitoring is therefore absolutely necessary to ensure the process and operation of the plant.

With ideal use of filter monitoring, numerous factors are taken into account to prevent unplanned failures, reduce costs and use resources optimally. At the same time, machine availability is increased and employees’ health is protected. Artificial intelligence can help to monitor these processes.

AI-assisted plant monitoring: How moneo LifetimeEstimator maximises machine availability

Everyone is talking about Artificial Intelligence (AI), but how can it really be used? AI can be applied in many different areas, including production. For example, moneo LifetimeEstimator is a powerful AI tool that can accurately monitor and predict the remaining service life of machinery and equipment, in our case filters, by analysing historical data and selecting the best calculation model.

This leads to an improved maintenance strategy and more efficient use of resources.

Automated maintenance interfaces

The comprehensive solution from ifm sensors in conjunction with the moneo software opens up a wide range of options and offers numerous interfaces. Data processed in moneo can be exported using different protocols.

It is possible to use data via MQTT or OPC UA in a third-party system or to transmit data directly to AWS, Azure or SAP via a specific connector.

• moneo
  MQTT or OPC UA → AWS, Azure, SAP
• SAP Integration
  SFI (shop floor integration) → SAP PM
• Alarm
  Email notification (threshold)
• Spare parts
  Automatic ordering through SAP
• Cloud
  Filter monitoring possible in the cloud

By integrating ifm’s own SFI (Shop Floor Integration) interface, a direct connection to SAP PM is possible. The on-premises solution serves as an interface between production and business levels and offers the possibility of automatically triggering further follow-up processes when limits are exceeded. The maintenance engineer receives an email notification when the specified threshold values are exceeded and warnings and alerts are sent out. This makes it possible for them to plan a suitable replacement or cleaning at an early stage and to order spare parts. Depending on customer requirements, alarms can also trigger ordering directly through SAP.

Filter monitoring is also possible in conjunction with the cloud. ifm provides the necessary interfaces to store your data in the cloud and, if necessary, use it for further analysis. moneo LifetimeEstimator can also be used directly in moneo|cloud.

Implement filter monitoring to reduce costs

For filter monitoring, the moneo condition monitoring tool and the moneo LifetimeEstimator as part of the Industrial AI Assistant package are installed centrally on a server. An IO-Link master is connected to the server via an internally secured network (VLAN). Two sensors each are installed on the water or oil filter. One sensor detects the pressure before the filter and the second sensor detects the pressure after the filter. Using these two pressure values it is possible to determine a pressure difference that describes the filter condition.

A differential pressure sensor is connected to the air filter. The most accurate pressure measurement possible is required for this. All pressure sensors feature IO-Link interfaces, which allow data transfer to an IO-Link master. With differential pressure sensors on air filters, the signal is similarly converted to IO-Link. The IO-Link master then transmits the sensor values to moneo.

moneo is responsible for pre-processing the data. This includes calculating the pressure difference, saving historical data, visualising the data and monitoring the threshold values. To monitor the filters, the respective threshold values are defined for the warning and alarm thresholds. Threshold violations are transmitted to the SAP system via the SFI interface.

System structures

Air filter system structure

1. IO-Link master (VLAN)
2. IO-Link pressure sensor (before filter)
3. IO-Link pressure sensor (after filter)
4. IO-Link pressure difference sensor

moneo software

• Pressure difference
• Historical data
• Visualisation
• Monitoring
• Alarm function
• Threshold violations via SFI to SAP

Water filter system structure

moneo software

• Pressure difference
• Historical data
• Visualisation
• Monitoring
• Alarm function
• Threshold violations via SFI to SAP

Oil filter system structure

moneo software

• Pressure difference
• Historical data
• Visualisation
• Monitoring
• Alarm function
• Threshold violations via SFI to SAP

moneo LifetimeEstimator helps you plan necessary maintenance activities well in advance with sufficient lead time. In the first step, two replacement intervals of the filter are recorded in moneo. In the second step, moneo LifetimeEstimator is trained and set up based on the recorded cycles. The above-mentioned threshold from the data sheet is used as the end point of a cycle.

The advance notification for the upcoming maintenance activity is set to 7 days before the threshold is reached, allowing the necessary filter replacement to be incorporated into the weekly personnel planning. Based on this database, it can be seen whether the filter is clogged according to the measurement results or whether the filter’s life can even be significantly extended. Initial success will be noticeable within just a few weeks. The sensors record the actual use and therefore soiling of the respective filters.

• Downtimes are avoided and maintenance periods for filter replacement can be planned optimally.