Predictive Maintenance and Downtime Reduction in Industry 4.0
Industries and manufacturing businesses worldwide have heavily committed to digitalising their operations, from which they expect benefits such as increased productivity and reduced costs. Nonetheless, it has also become clear that there are some challenges along the way: this is where Adaptix comes in to support you in overcoming the hurdles of complex data analysis to maintain your competitive edge.
Combine sensor and machine data with CMMS data
Adaptix connects with sensor data historians and operation asset data properties and configurations and maps it with computerised maintenance management system (CMMS) data in order to build a holistic perspective of machinery condition.
Detect the unanticipated
Although some failures may exhibit well known (or simply, documented) patterns, it also happens that problematic situations may arise that have not been seen in the past, or not for a given asset. Adaptix’s unsupervised analysis help cope with such situations by building and tracking abnormality indicators autonomously, without requiring an exhaustive knowledge base for all possible failures. This allows detection of drifts that other artificial intelligence based condition monitoring systems will miss.
Just as the behavior of machine changes over time, insights describing associated risks should do too. Adaptix revisits its assessment whenever new information is available to update its analysis, be it diagnosis or prediction. It also provides comprehensive confidence indicators, so that operators can mitigate between early signs of failure potential and immediate and dangerous exposure.
Unique machine behaviour modelling
Even with identical machines sitting on same production line, behaviour and risk depends on the task they are set to perform. Therefore, similar behavior across a fleet might result in failure to different failures, at different times and for different reasons. Adaptix is able to build an individual model for each monitored machine, so that contextual and asset characteristics like past performance or operational programs are accounted for in the modeling process in order to elaborate tailor-made predictions and diagnosis.
No black box software/Transparent decision helper tools
We believe the industry adoption of predictive analytics depends on its ability to work collaboratively with teams on the field. But this also means that underlying algorithms need to be able to communicate their reasoning to support decision making transparently. This is actually far from being trivial with most machine learning technologies. On the contrary, Adaptix justifies its results so that its user can easily understand the relationship between a given failure scenario and the current condition of a machine.
Adaptive and evolutive predictions
It is not uncommon for the digitisation process to be fairly recent, and as a result failure examples may be too few or too scarce for traditional prediction tools. Adaptix employs a continuous learning approach, which enables the system to model not only based on the existing historian but also “on the go”, which allows it to reinforce its prediction accuracy with time.
Integration with existing sensors and systems
Adaptix works with the sensors, data and platforms from solutions provider like Siemens or IBM that you already have in place, and does not require additional instrumentation to get started.
Cross sector analytics platform
Adaptix’s engine has been crunching data from manufacturing assets in industries as diverse as automotive, aerospace or HVAC. We work with you to configure our platform to deliver you high value insights and support your digitization strategy.