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CONDITION MONITORING

Condition monitoring is the process of monitoring a parameter of condition in machinery (vibration, temperature etc.), in order to identify a significant change which is indicative of a developing fault. It is a major component of predictive maintenance.

The use of condition monitoring allows maintenance to be scheduled, or other actions to be taken to prevent consequential damages and avoid its consequences.

Condition monitoring has a unique benefit in that conditions that would shorten normal lifespan can be addressed before they develop into a major failure.

PREDICTIVE MAINTENANCE: CONDITION MONITORING

For optimal performance, all factory machinery needs to be maintained and companies often have maintenance agreements in place, usually with the original machine manufacturers or their approved service network. Whilst useful, these agreements are largely service-interval schedules, which don’t take into account actual usage and do little to prevent unplanned downtime. This is the widely-used preventative and reactive maintenance model.

Predictive maintenance is more proactive, analysing the huge volumes of available machinery data to give you a better understanding of the ongoing health of your machines and pre-empt failure. This maintenance approach opens up opportunistic possibilities in machinery management, allowing you to adapt and enhance your existing maintenance arrangements. By analysing data to predict when machinery will break down, companies can remove surprise failures and reduce downtime, scheduled maintenance and the routine replacement of parts that may in-fact be perfectly healthy.

CONDITION-BASED MAINTENANCE

Condition-based maintenance is work that is performed at the exact moment when measured parameters reach unacceptable levels.

It relies only on real-time sensor measurements. Once a parameter reaches an unacceptable level, maintenance workers are dispatched. This means that condition-based maintenance systems perform work only in the moment it is needed.

Condition-based maintenance can be expensive in comparison to Predictive Maintenance due to the cost of maintaining sensor devices and its reactionary nature.

READ ABOUT PREDICTIVE MAINTENANCE & CONDITION MONITORING

Download the “Harness The Power of Prediction: Maximize ROI With The Right Condition Monitoring Solution” Whitepaper to learn:

  • The obstacles manufacturers need to overcome
  • How the arrival of smart technologies has helped tackle some of these challenges
  • The potential impact predictive maintenance can have on ROI

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WATCH HOW PREDICTIVE MAINTENANCE WORKS

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SKF, Siemens, GE, Schneider Electric, Nissan, Tata Steel, Smufit Kappa and many more…

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THE RESULTS

  • 85% improvement in downtime forecasting accuracy
  • 50% reduction in unplanned machine downtime
  • 55% increase in maintenance staff productivity
  • 40% reduction in maintenance costs

Read about Predictive Maintenance & Condition Monitoring

Download the “Harness The Power of Prediction: Maximize ROI With The Right Condition Monitoring Solution” Whitepaper

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