Anomaly Detections for Manufacturing Systems Based on Sensor Data—Insights into Two Challenging Real-World Production Settings

To build, run, and maintain reliable manufacturing machines, the condition of their components has to be continuously monitored. When following a fine-grained monitoring of these machines, challenges emerge pertaining to the (1) feeding procedure of large amounts of sensor data to downstream processing components and the (2) meaningful analysis of the produced data. Regarding the latter aspect, manifold purposes are addressed by practitioners and researchers. Two analyses of real-world datasets that were generated in production settings are discussed in this paper. More specifically, the analyses had the goals (1) to detect sensor data anomalies for further analyses of a pharma packaging scenario and (2) to predict unfavorable temperature values of a 3D printing machine environment. Based on the results of the analyses, it will be shown that a proper management of machines and their components in industrial manufacturing environments can be efficiently supported by the detection of anomalies. The latter shall help to support the technical evangelists of the production companies more properly

Corona Health -- A Study- and Sensor-based Mobile App Platform Exploring Aspects of the COVID-19 Pandemic

Physical and mental well-being during the COVID-19 pandemic is typically assessed via surveys, which might make it difficult to conduct longitudinal studies and might lead to data suffering from recall bias. Ecological momentary assessment (EMA) driven smartphone apps can help alleviate such issues, allowing for in situ recordings. Implementing such an app is not trivial, necessitates strict regulatory and legal requirements, and requires short development cycles to appropriately react to abrupt changes in the pandemic. Based on an existing app framework, we developed Corona Health, an app that serves as a platform for deploying questionnaire-based studies in combination with recordings of mobile sensors. In this paper, we present the technical details of Corona Health and provide first insights into the collected data. Through collaborative efforts from experts from public health, medicine, psychology, and computer science, we released Corona Health publicly on Google Play and the Apple App Store (in July, 2020) in 8 languages and attracted 7,290 installations so far. Currently, five studies related to physical and mental well-being are deployed and 17,241 questionnaires have been filled out. Corona Health proves to be a viable tool for conducting research related to the COVID-19 pandemic and can serve as a blueprint for future EMA-based studies. The data we collected will substantially improve our knowledge on mental and physical health states, traits and trajectories as well as its risk and protective factors over the course of the COVID-19 pandemic and its diverse prevention measures

Machine learning under concept drift for industrial data using Python

Künstliche Intelligenz und Machine Learning sind Begriffe, welche in den letzten Jahren nicht zuletzt aufgrund rasant wachsender Rechnerleistungen immer mehr in den Fokus von Industrie und Forschung gerückt sind. Dabei ist für die Industrie vor allem der Informationsgewinn aus Daten von Interesse. Die Validität der Analyse aus historischen Daten ist jedoch in einer sich immer schneller wandelnden Welt fraglich. Die Vorhersagen von Maschinen aus nicht aktuellen Daten können obsolet sein, weil sich deren Kontext geändert hat. Diese Arbeit befasst sich daher mit dem maschinellen Lernen unter Concept Drift. Die Analyse wird mit zwei realen Datensätzen aus der Industrie, unter Simulation eines verteilten Systems, durchgeführt. Es werden dazu verschiedene Regressoren (Polynomregression, Decision Trees, Random Forests und Neuronale Netze) implementiert und die Vorhersagegenauigkeit untereinander verglichen. Bei den Regressoren werden die Einstellungsparameter sowie die Vorhersage- und Trainingszeit variiert. Das Ergebnis zeigt, dass die polynomialen Regressoren den Random Forests, Regression Trees und neuronalen Netzen in der Vorhersagegenauigkeit und Agilität unterlegen sind. Die Vorhersagegenauigkeit nimmt für alle Regressoren ab, wenn die Vorhersagedauer erhöht wird. Random Forests sind gegenüber Regression Trees weniger ausreißerempfindlich. Es lässt sich aus den Ergebnissen nicht erschlieÿen, dass ein Forest genauere Vorhersagen macht als ein Regression Tree, obwohl der Forest als Ensemble agiert. Machine Learning erfährt in den vergangenen Jahren zurecht vermehrt Beachtung in der Forschung und Industrie. Random Forests sind ein effizientes Instrument zur Erfassung von Daten mit unbekannter Verteilung und zur Schätzung von unbekannten Parametern und somit eine echte Alternative zu klassischen Regressionen und neuronalen Netzen

Microscopic Dynamics of Polyethylene Glycol Chains Interacting with Silica Nanoparticles

We present high resolution neutron spectroscopic investigations of polyethylene glycol matrices interacting attractively with neat SiO2 nanoparticles. We observe a very rich dynamical picture that significantly contradicts earlier conclusions on such systems. Investigating a short chain matrix we realized that a fraction of chains is attached at the nanoparticle surface suppressing completely its translational diffusion. Nevertheless these attached chains undergo an unchanged segmental dynamics seemingly forming a micellelike corona of chains attached with their OH end groups. Changing to methyl-terminated chains the picture changes drastically, now showing a tightly adsorbed layer that however is not glassy as often assumed but undergoes fast picosecond local dynamics. With the singular importance of end groups, mean field approaches are not applicable and future simulations should be redirected to model such unexpected phenomena. © 2013 American Physical Society

Prediction of tinnitus perception based on daily life mHealth data using country origin and season

Tinnitus is an auditory phantom perception without external sound stimuli. This chronic perception can severely affect quality of life. Because tinnitus symptoms are highly heterogeneous, multimodal data analyses are increasingly used to gain new insights. MHealth data sources, with their particular focus on country- and season-specific differences, can provide a promising avenue for new insights. Therefore, we examined data from the TrackYourTinnitus (TYT) mHealth platform to create symptom profiles of TYT users. We used gradient boosting engines to classify momentary tinnitus and regress tinnitus loudness, using country of origin and season as features. At the daily assessment level, tinnitus loudness can be regressed with a mean absolute error rate of 7.9% points. In turn, momentary tinnitus can be classified with an F1 score of 93.79%. Both results indicate differences in the tinnitus of TYT users with respect to season and country of origin. The significance of the features was evaluated using statistical and explainable machine learning methods. It was further shown that tinnitus varies with temperature in certain countries. The results presented show that season and country of origin appear to be valuable features when combined with longitudinal mHealth data at the level of daily assessment

Predicting the Gender of Individuals with Tinnitus based on Daily Life Data of the TrackYourTinnitus mHealth Platform

Tinnitus is an auditory phantom perception in the absence of an external sound stimulation. People with tinnitus often report severe constraints in their daily life. Interestingly, indications exist on gender differences between women and men both in the symptom profile as well as in the response to specific tinnitus treatments. In this paper, data of the TrackYourTinnitus platform (TYT) were analyzed to investigate whether the gender of users can be predicted. In general, the TYT mobile Health crowdsensing platform was developed to demystify the daily and momentary variations of tinnitus symptoms over time. The goal of the presented investigation is a better understanding of gender-related differences in the symptom profiles of users from TYT. Based on two questionnaires of TYT, four machine learning based classifiers were trained and analyzed. With respect to the provided daily answers, the gender of TYT users can be predicted with an accuracy of 81.7%. In this context, worries, difficulties in concentration, and irritability towards the family are the three most important characteristics for predicting the gender. Note that in contrast to existing studies on TYT, daily answers to the worst symptom question were firstly investigated in more detail. It was found that results of this question significantly contribute to the prediction of the gender of TYT users. Overall, our findings indicate gender-related differences in tinnitus and tinnitus-related symptoms. Based on evidence that gender impacts the development of tinnitus, the gathered insights can be considered relevant and justify further investigations in this direction

Anomaly Detections for Manufacturing Systems Based on Sensor Data—Insights into Two Challenging Real-World Production Settings

o build, run, and maintain reliable manufacturing machines, the condition of their components has to be continuously monitored. When following a fine-grained monitoring of these machines, challenges emerge pertaining to the (1) feeding procedure of large amounts of sensor data to downstream processing components and the (2) meaningful analysis of the produced data. Regarding the latter aspect, manifold purposes are addressed by practitioners and researchers. Two analyses of real-world datasets that were generated in production settings are discussed in this paper. More specifically, the analyses had the goals (1) to detect sensor data anomalies for further analyses of a pharma packaging scenario and (2) to predict unfavorable temperature values of a 3D printing machine environment. Based on the results of the analyses, it will be shown that a proper management of machines and their components in industrial manufacturing environments can be efficiently supported by the detection of anomalies. The latter shall help to support the technical evangelists of the production companies more properly