Titlebook: Machine Learning for Cyber Physical Systems; Selected papers from Jürgen Beyerer,Christian Kühnert,Oliver Niggemann Conference proceedings‘

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A Process Model for Enhancing Digital Assistance in Knowledge-Based Maintenance,vide certain level of intelligence and ultimately could transform diagnostic maintenance capabilities into predictive and prescriptive maintenance. This paper proposes a process-based model to facilitate the selection of suitable DAS for supporting maintenance operations in manufacturing industries.

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Machine Learning for Enhanced Waste Quantity Reduction: Insights from the MONSOON Industry 4.0 Projrial sector. Driven by the high-tech strategy of the federal government in Germany, many manufacturers across all industry segments are accelerating the adoption of cyber-physical system and Internet of Things technologies to manage and ultimately improve their industrial production processes. In th

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Deduction of time-dependent machine tool characteristics by fuzzy-clustering, (CPS). This strategy also denoted as Industry 4.0 will improve any kind of monitoring for maintenance and production planning purposes. So-called bigdata approaches try to use the extensive amounts of diffuse and distributed data in production systems for monitoring based on artificial neural netwo

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Unsupervised Anomaly Detection in Production Lines,need for automatic data analysis in industrial production lines has increased drastically. One relevant application scenario is the usage of intelligent approaches to anticipate upcoming failures for maintenance. In this paper, we present a novel approach for anomaly detection regarding predictive m

TERRA

A Random Forest Based Classifier for Error Prediction of Highly Individualized Products,onment. Within the course of this paper, some data set and application features are highlighted that make the underlying classification problem rather complex and hinders the usage of machine learning algorithms straight out-of-the-box. The findings regarding these features and how to treat the conc

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Web-based Machine Learning Platform for Condition- Monitoring,ption for data analysis. However, currently ML algorithms are not frequently used in real-world applications. One reason is the costly and time-consuming integration and maintenance of ML algorithms by data scientists. To overcome this challenge, this paper proposes a generic, adaptable platform for

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Which deep artifical neural network architecture to use for anomaly detection in Mobile Robots kinever, even such small irregularities can have a tremendous effect on the robot’s stability and the path quality. As a basis to develop anomaly detection algorithms, kinematics data is collected exemplarily for an overrun of a cable channel and a bulb plate. A recurrent neuronal network (RNN), based o

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Condescending

Process Control in a Press Hardening Production Line with Numerous Process Variables and Quality Crsion with least squares optimization to adjust the process parameters of this process for quality improvement. The FE simulation program AutoForm was used to model the production line concerned and various process and quality parameters were measured. The proposed system is capable of automatically