How it works

Typically captured from sensors when machines are in a consistent operating state. Senseye PdM can also deal with operational parameters and more complex data sources including high-end condition monitoring hardware. If raw data requires transformation into usable condition monitoring data, this can be achieved using Senseye.

This provides Senseye with information on the operating context of an asset. Using operational data, Senseye can account for changes in machine behavior due to different workpieces, programs, or recipes.

Obtained from existing maintenance management systems or entered directly into the Senseye PdM app, this identifies maintenance activity or functional failures affecting a machine being monitored.

Senseye PdM is developed for scale and is underpinned by a cloud-based platform capable of processing huge volumes of data – typically tens of thousands of related measures per hour. This enables you to reap the benefits of predictive maintenance at scale, applying it to every asset in every one of your facilities.

Unlike other solutions, Senseye PdM doesn’t require the development of custom models for each type of asset you operate. Custom models are typically restricted to critical assets due to the high cost of initial development. With Senseye PdM, models are constructed automatically with no user intervention – this means you can apply predictive maintenance to all of your assets, including lower criticality, covering the entire balance of plant.

A common challenge when you attempt to scale condition monitoring is the corresponding increase in user notifications. This challenge becomes even greater as you focus on applying predictive maintenance at scale across thousands of assets.

Senseye PdM uses a unique approach to direct your maintenance effort where it’s needed most. Central to this is the Attention Engine – a proprietary algorithm that estimates an Attention Index^® for each of your assets, based on the recent maintenance data and patterns.

 If the Attention Index^® is sufficiently high, the Attention Engine creates a Case to direct the user’s attention to the asset in question.

The calculation of the Attention Index^® factors in user feedback on previous patterns, asset criticality, and production schedule. The system learns and adapts to this user feedback and, as a result, generates more relevant notifications that are always prioritized consistently.