In this study, a data driven predictive maintenance system was developed for production lines in manufacturing. By utilizing the data generated from IoT sensors in real-time, the system aims to detect signals for potential failures before they occur by using machine learning methods. Consequently, it helps address the issues by notifying operators early such that preventive actions can be taken prior to a production stop. In current study, the effectiveness of the system was also assessed using real-world manufacturing system IoT data. The evaluation results indicated that the predictive maintenance system was successful in identifying the indicators of potential failures and it can help prevent some production stops from happening. The findings of comparative evaluations of machine learning algorithms indicated that models of Random Forest, a bagging ensemble algorithm, and XGBoost, a boosting method, appeared to outperform the individual algorithms in the assessment. The best performing machine learning models in this study have been integrated into the production system in the factory.

Predictive maintenance system for production lines in manufacturing: A machine learning approach using IoT data in real-time

Employee acceptance of wearable technology in the workplace.

Deep learning models for predictive maintenance: a survey, comparison, challenges and prospects

The traditional paradigm for developing machine prognostics usually relies on generalization from data acquired in experiments under controlled conditions prior to deployment of the equipment. Detecting or predicting failures and estimating machine health in this way assumes that future field data will have a very similar distribution to the experiment data. However, many complex machines operate under dynamic environmental conditions and are used in many different ways. This makes collecting comprehensive data very challenging, and the assumption that pre-deployment data and post-deployment data follow very similar distributions is unlikely to hold. Transfer Learning (TL) refers to methods for transferring knowledge learned in one setting (the source domain) to another setting (the target domain). In this work, we present a TL method for predicting Remaining Useful Life (RUL) of equipment, under the assumption that labels are available only for the source domain and not the target domain. This setting corresponds to generalizing from a limited number of run-to-failure experiments performed prior to deployment into making prognostics with data coming from deployed equipment that is being used under multiple new operating conditions and experiencing previously unseen faults. We employ a deviation detection method, Consensus Self-Organizing Models (COSMO), to create transferable features for building the RUL regression model. These features capture how different target equipment is in comparison to its peers. The efficiency of the proposed TL method is demonstrated using the NASA Turbofan Engine Degradation Simulation Data Set. Models using the COSMO transferable features show better performance than other methods on predicting RUL when the target domain is more complex than the source domain.

Transfer learning for Remaining Useful Life Prediction Based on Consensus Self-Organizing Models

As internet of things and other technologies are widely used in industrial manufacturing, automation and intelligence have witnessed rapid developments, resulting in the proposal of the industrial internet of things (IIoT). However, the IIoT still faces various network security threats; hence, data integrity, confidentiality, and anonymity need to be ensured. The use of cloud and edge servers as semitrusted third parties often results in the leaking of privacy sensitive user data. Meanwhile, existing security schemes treat the cloud and edge as fully trusted entities, which is not always valid. Considering edge servers as semitrusted entities, we propose a novel message authentication scheme that leverages group signature technology and proxy reencryption technology to ensure data integrity, confidentiality, and anonymity. Through theoretical analysis and performance comparison, we prove the security of our scheme. In addition, we implement our scheme on a real publish/subscribe system, and the experimental results show the feasibility of our scheme.

Anonymous Message Authentication Scheme for Semitrusted Edge-Enabled IIoT

Today employees in IT related areas are expected to know the newest technologies and to be able to apply it independently in their projects. This requires a deeper understanding and additional competences that are not taught in lectures or addressed by Project Based Learning (PBL) approaches. In this paper, an approach to teach software engineering concepts using the Scrum framework in real life projects is presented. It extends the PBL concept with respect to competences. Feedback from students, customers as well as our own experience confirm the benefits of the presented approach.

Teaching agile principles and software engineering concepts through real-life projects

Despite the vast economic potential of wearable technologies, up to now there is only little scientific research on the acceptance determinants of the wearable computing phenomenon. Therefore, the overall aim of this study is to explain psychographic factors that lead to either acceptance or resistance of wearable computing. This paper synthesises a cause and effect model of wearable computing adoption in the European market. The basis of the proposed conceptual framework builds an explorative study consisting of expert interviews and a subsequent qualitative content analysis to identify salient acceptance factors. The results indicate that the strongest factor that supports the acceptance of wearable technologies is perceived usefulness, whilst the main reason for resistance towards these technologies are perceived IT security risks. Keywords—wearable computing; technology acceptance model; perceived pervasiveness; perceived IT security; Big Five personality traits

Acceptance Factors of Wearable Computing: An Empirical Investigation.

Marken stellen den bedeutendsten immateriellen Vermogensgegenstand von Unternehmen in modernen Volkswirtschaften dar und tragen masgeblich zum Erfolg von Unternehmen bei. In diesem Punkt besteht weitgehend Einigkeit zwischen Wissenschaftlern und Managern. Der vorliegende Beitrag skizziert den vielseitigen Nutzen, den Marken Unternehmen stiften und zeigt die Grundlagen einer identitatsorientierten Markenfuhrung auf, wobei explizit auf die Markenfuhrung mit Medien, mit anderen Worten die Nutzung von Medien zum Markenaufbau, und Markenfuhrung von Medien, d. h. den Aufbau und die Pflege von Medienmarken, eingegangen wird. Des Weiteren werden aktuelle Trends auf den Gebieten des Markenmanagements und Mediensektors vorgestellt, bevor abschliesend ein kurzes Fazit zum Status quo von Marken und Medien gezogen wird.

Grundlagen zur Marke und einigen relevanten Trends der Markenführung

Marken und Medien

Marken haben auch im Bereich von Umweltschutz-NGOs eine wichtige Bedeutung. Dabei kann der Erfolg von Marken unterschiedlich gemessen werden. Eine Moglichkeit, den Erfolg zu messen, ist die Operationalisierung mittels eines verhaltenswissenschaftlichen Konstruktes, der Markenstarke bzw. der Markenpersonlichkeit als Teil der Markenstarke, eine andere Moglichkeit ist die Eruierung der Mehrzahlungsbereitschaften, des so genannten Preispremiums. Eine geeignete Methode, dieses zu berechnen, ist die Conjoint-Analyse. Im vorliegenden Beitrag werden neben den theoretischen Grundlagen zu Marke, Markenstarke und Markenwert den Forschungsfragen nachgegangen, welche Markenstarke unterschiedliche NGOs besitzen und wie hoch die Spendenbereitschaften resp. das Preispremium fur die unterschiedlichen NGOs ist. Die Daten dazu wurden mittels einer Online-Befragung und Online-Conjoint-Analyse erhoben. Es konnte gezeigt werden, dass die Markenpersonlichkeit der Umweltschutz-NGOs deutlich unterschiedlich eingeschatzt werden und dass daruber hinaus sehr unterschiedliche Spendenbereitschaften bzw. Preispremien fur die NGOs existieren.

Die Markenstärke von Umweltschutz-NGOs in Deutschland

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