Industrial Internet of Things

11 posts

Tracking of transport trolleys in production of RUCH NOVAPLAST

Manual, paper-based processes in intralogistics are inefficient, inaccurate and not scalable. RUCH NOVAPLAST, a manufacturer of product solutions made of particle foams, now uses RFID systems to track transport trolleys at every stage of production. This provides greater transparency in the manufacturing process.

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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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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.

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The Emergence of Device-level Safety Communications in Manufacturing

Manufacturing is rapidly changing, driven by trends such as low volume/high mix, shorter lifecycles, changing labor dynamics and other global factors. One way industry is responding to these trends is by changing the way humans and machines safely work together, enabled by updated standards and new technologies including safety communications.

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