Balluff Blog - automation

Digitalization & IIoT Industrial Internet of Things

Bringing machine learning models (uncontaminated) into production

We must give consideration to the languages used in model training and model deployment – and we should do this before any model work begins. It is better to consider the two environments (and sometimes the two teams) as a whole and then work to a common interface.

Automation Digitalization & IIoT Industrial Internet of Things Industry 4.0 Metalworking

Sensor-based monitoring of punching machines

Quality problems in the production of stamped and bent metal parts require data for analysis and sensors that provide it. Condition monitoring captures many data points that allow analysis and thus faster detection of the root causes of defects.

Automation Digitalization & IIoT Industrial Identification Industrial Internet of Things Industrial RFID Systems Industry 4.0

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.

Automotive Inductive Proximity Sensors Sensors

Factor 1 sensors make automotive production more flexible

Read in this blog post what Factor 1 proximity sensors are and what advantages they offer for automotive production

person holding blue ballpoint pen on white notebook

Digitalization & IIoT Industrial Internet of Things

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

Digitalization & IIoT Industrial Internet of Things

Improving black-box process efficiency using Bayesian Optimization

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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.

Machine & Plant Engineering Machine Vision & Optical Identification Photoelectronic Sensors

Using MicroSpot LEDs for Precise Evaluations in Life Science

Handling microfluidics and evaluating samples based on light is a precise science. And that precision comes from the light source, not the actual detection method

Digitalization & IIoT Industrial Internet of Things

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.

Digitalization & IIoT Industrial Internet of Things

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.