condition monitoring

14 posts

Intelligent sensors for higher equipment performance

Powerful, smart sensors and multifunctional solutions are the precondition for increasing the overall system efficiency (OEE). Smart sensors from Balluff provide optimal support because they not only fulfill a primary measurement task, but also provide up to 16 additional condition monitoring features for self-diagnosis and the environmental condition at the installation site.

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SAMS – The Smart Automation and Monitoring System

Increasing the efficiency of machines and plants requires powerful, multifunctional solutions that provide data on the machine condition in addition to the machine control. With the condition monitoring information obtained from this the performance of each machine and thus of the entire plant can be optimized

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Unsupervised anomaly detection using an ensemble of one-class random forest classifiers

Introduction Unsupervised anomaly detection with unlabeled data – is it possible to detect outliers when all we have is a set of uncommented, context-free signals? The short answer is, yes – this is the essence of how one deals with network intrusion, fraud, and other types of low-instance anomaly. In

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Improving black-box process efficiency using Bayesian Optimization

Instead of randomly or exhaustively iterating through combinations of algorithms and parameters, we can use Bayesian Optimization libraries to build up an in-memory approximation to the process we want to fine-tune. We can then make a our selections on prior knowledge.

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Predicting hidden events: inverting the time-to-event paradigm: Part 2

Time-to-event (TTE) use-cases crop up in many places across industries. Some examples would be: the prediction of customer churn (the sales domain), remaining-useful-life or time-to-failure TTF (predictive maintenance), or anomaly detection (machine monitoring). Some events are difficult to predict as they are hidden. We can instead try to look for interim events to improve prediction accuracy.

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chart close up data desk

Predicting hidden events: inverting the time-to-event paradigm: Part 1

Time-to-event (TTE) use-cases crop up in many places across industries. Some examples would be: the prediction of customer churn (the sales domain), remaining-useful-life or time-to-failure TTF (predictive maintenance), or anomaly detection (machine monitoring).Some events are difficult to predict as they are hidden. We can instead try to look for interim events to improve prediction accuracy.

Read more