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A framework for current signal based bearing fault detection of permanent magnet synchronous motors
(2023)
Permanently excited synchronous motors are the driving components in countless systems and applications. The most common cause of motor failures are the bearings. Data-driven approaches have been used for predictive defect detections since many years, to prevent motors from an unexpected breakdown. In this way, downtime costs can be reduced and maintenance intervals based on actual wear can be realized.
Existing approaches are usually based on structure-borne sound sensors that have to be attached externally to the motors. The resulting costs reduce the economic attractiveness and scalability of the solution. Therefore, the focus of this dissertation is on fault detection based on internal motor current signals. Hurdles, arising from the choice of this signal sources, are to be tackled by the developed fault detection framework. By this, an adequate alternative to the use of external sensors is achieved. The core of the framework is the development of a fault detection pipeline, which is to be applicable under expected conditions of real-world applications.
The main pillars are data transformation methods derived from expert knowledge of different domains. These are concatenated and parameterized in an automated manner to reduce the human induced bias on the solution generation process.
Starting with a review of the state of research, existing research gaps are identified. From this, the research hypothesis and concrete research questions are derived and the general relevance of research is motivated. Subsequently, a conceptual description of the developed framework is given. In contrast to related work, the proposed approach focuses on the abstraction of the motors operating parameters from the pipeline hyperparameters uniquely at training time. This makes reparameterizations in the course of varied motor parameters obsolete, which increases the robustness with respect to real-world use cases.
The data used for the validation of the framework was acquired under real-world operating conditions to enable extensive stress tests of the developed pipelines. The results confirm the suitability of the framework in terms of general current based bearing fault detection as well as the intended use cases, regarding the working condition transfers.
The aim of this project is to prepare a nutrition guidebook for early childhood active stakeholders that are applicable across Europe and Turkey. The developed nutrition guidebook is the result of two-year collaboration between academics from different professions (nutritionists, home economists, paediatricians, education scientists, health psychologists) across five countries.
The aim of this paper is to examine the causes of food waste and potential prevention strategies from a grocery retail store owner’s perspective. We therefore conducted a case study in a German region through semi-structured expert interviews with grocery retail store owners. From the collected responses, we applied a qualitative content analysis. The results indicated that store owners try to avoid food waste as this incurs a financial loss for them that directly affects them personally, as opposed to store managers of supermarket chains who receive a fixed salary. The main causes of food waste in the grocery retail stores in the region surveyed are expiration dates, spoilage, consumer purchasing behavior, and over-ordering of food products. The most appropriate food waste prevention strategies developed by store owners are those based on store owners’ experience and their own management style, such as the optimization of sales and management strategies, including precise planning, accurate ordering, and timely price reductions on soon-to-be-expiring food products. The redistribution of food surpluses as donations to food banks, employees, and as animal feed further helps to reduce the amount of food waste, but not the financial loss. This study enhances the literature by revealing that grocery retail store owners have the ability and are willing to successfully implement and enforce food prevention strategies in their stores.
Can Buddhism be called a stronghold of free thinking? What relevance might Buddhism have for social developments in the twenty-first century, and where will it position itself in these processes? Free thinking has been emphasized and celebrated as an outstanding accomplishment of the human mind. This anthology might inspire the reader to look at some questions of global concern from a new angle and provide a stimulus for developing a freethinking attitude. It is the outcome of international and even transcontinental cooperation involving expert authors from Asia, Australia, Europe and the U.S.A. Contributions have been made by Bhikkhu Anâlayo, Karl-Heinz Brodbeck, Ashby Butnor, Silja Graupe, Guang Xing, Barbara Kameniar, Sallie B. King, and Charles S. Prebish.
The research papers published in this reader were presented to an audience of academicians and practitioners at several international business research conferences. All of the submitted articles and presentations abstracts were subject to a review by the Editorial Board of the conference, comprised of the following persons: Prof. Dr. Klaus Kellner (Universitiy of Applied Science Augsburg, Germany), Prof. em. Dr. Johannes Lachhammer (Augsburg University, Germany) and Prof. Guenther Kress, PhD, California State University. The Editorial Board also reviewed and approved the submitted full papers for publication in this reader. This reader intends to sustainably stimulate the discussion concerning recent developments in Business Management Research among scholars and practitioners. Each and every feedback, also and particularly from students, is most welcome.
Background: In 2021, a significant proportion of adult deaths in Germany, comprising of 447,473 individuals, occurred within hospital settings, representing nearly half of the total deaths in the country, which numbered 1,023,687 (statistika 2021). Consequently, nurses play a pivotal role as primary caregivers for this patient group, necessitating comprehensive education to address their specific needs. Existing literature suggests that nursing students often lack the adequate preparation to provide care for this group, with factors such as insufficient theoretical knowledge and suboptimal mentoring during clinical placements (Bloomfield et al. 2015; Gillan et al. 2014; Leighton 2009, Leighton/Dubas 2009). The use of simulation has proven effective in bridging the theory-practice gap, particularly in the context of End-of-Life Care. The objective of this study was to assess nursing students' perceptions of the use of simulation in learning about End-of-Life Care (Gillan et al. 2014; Moreland et al. 2012).
Methods: Over a three-year period, three cohorts of third-year nursing students at Fulda University of Applied Sciences engaged in simulated experiences involving a dying patient and one or more family members. The authors created three different scenarios in which the students had to perform oral care, break bad news to family members and administer palliative pain medication. During the simulations, the family member(s) confronted the students with questions concerning spiritual care and improving the quality of life at this stage. This project utilized a qualitative design. After the simulation and debriefing sessions, semi-structured interviews and group discussion were conducted. After transcribing, the interviews were analyzed using open and axial coding, after the Glaser and Strauss approach to Grounded Theory.
Results: The process of theoretical coding yielded five results: Simulation revealed to be a good tool to learn about End-of-Life Care (1), simulation focused on communication (2), the importance of spiritual care (3), the aspect of realism (4) and a lack of theoretical knowledge (5).
Conclusion: Simulation-based learning seems to be a valuable tool in the teaching of End-of-Life-Care especially with a focus on communication.
This guideline is a result of the project CHANCE, funded by the EU-programme GRUNDTVIG / “Lifelong Learning Programme” conducted from December 2007 to November 2009.
The project focuses on the approach of “CommunityBuilding“, which is beyond counselling and education campaigns designed for the social and environmental circumstances and aims to initiate the build-up of networks and local communities.
The manual is based on the interdisciplinary view of health (holistic according to the WHO), community and social environment (promotion of personal and structural potential).
After the introduction with regard to the subject matter, the manual presents 13 fundamental guidelines and illustrates project examples from the participating countries.
In our paper we investigate the role of civil society organisations (CSOs) in the provision of services and in forming advocacy coalitions for illegalized migrants in Bern and Vienna. We analyse the variety of CSOs which actively challenge policies of exclusion at the urban level. We examine the political and social practices of CSOs in local welfare arrangements and their organizational structures, the way they build up solidarity relations, networks and alliances, and their relations to municipality and urban authorities. By focusing on varieties of practices and strategies of CSOs, we shed light on civil society’s crucial role concerning the construction of urban infrastructure of solidarity and aim to show how local arrangements for illegalized migrants are co-produced and negotiated by a variety of actors within urban settings.
CHANCE is a project funded by the EU-programme GRUNDTVIG/ “Lifelong Learning Programme” conducted from December 2007 to November 2009. Partners from the participating countries presented their individual project results at the 2nd international meeting on June 12th 2009, in Fulda, Germany. CHANCE describes new pathways to enhance and support people in the long term to be well-informed and to take responsibility for their own health. The focus of the project was based on the following questions: - What resources are offered by the community to live healthy or healthier and what are the barriers that need to be resolved? - Are there cultural differences in health behaviours and in the perception of health information? - What health information is perceived in general and by whom? - What information and health interventions are required? CHANCE shows how people in different European cities and communities live, perceive information with regard to health and process it. The inhabitants of the communities were motivated to participate actively in the improvement of local interventions with regard to consumer education in health. The community approach aims to reach socially, culturally or economically disadvantaged groups such as elderly people, migrants and single parents.
Abstract knowledge is deeply grounded in many computer-based applications. An important research area of Artificial Intelligence (AI) deals with the automatic derivation of knowledge from data. Machine learning offers the according algorithms. One area of research focuses on the development of biologically inspired learning algorithms. The respective machine learning methods are based on neurological concepts so that they can systematically derive knowledge from data and store it. One type of machine learning algorithms that can be categorized as "deep learning" model is referred to as Deep Neural Networks (DNNs). DNNs consist of multiple artificial neurons arranged in layers that are trained by using the backpropagation algorithm. These deep learning methods exhibit amazing capabilities for inferring and storing complex knowledge from high-dimensional data.
However, DNNs are affected by a problem that prevents new knowledge from being added to an existing base. The ability to continuously accumulate knowledge is an important factor that contributed to evolution and is therefore a prerequisite for the development of strong AIs. The so-called "catastrophic forgetting" (CF) effect causes DNNs to immediately loose already derived knowledge after a few training iterations on a new data distribution. Only an energetically expensive retraining with the joint data distribution of past and new data enables the abstraction of the entire new set of knowledge. In order to counteract the effect, various techniques have been and are still being developed with the goal to mitigate or even solve the CF problem. These published CF avoidance studies usually imply the effectiveness of their approaches for various continual learning tasks.
This dissertation is set in the context of continual machine learning with deep learning methods. The first part deals with the development of an application-oriented real-world evaluation protocol which can be used to investigate different machine learning models with regard to the suppression of the CF effect. In the second part, a comprehensive study indicates that under the application-oriented requirements none of the investigated models can exhibit satisfactory continual learning results. In the third part, a novel deep learning model is presented which is referred to as Deep Convolutional Gaussian Mixture Models (DCGMMs). DCGMMs build upon the unsupervised approach of Gaussian Mixture Models (GMMs). GMMs cannot be considered as deep learning method and they have to be initialized in a data-driven manner before training. These aspects limit the use of GMMs in continual learning scenarios.
The training procedure proposed in this work enables the training of GMMs by using Stochastic Gradient Descent (SGD) (as applied to DNNs). The integrated annealing scheme solves the problem of a data-driven initialization, which has been a prerequisite for GMM training. It is experimentally proven that the novel training method enables equivalent results compared to conventional methods without iterating their disadvantages. Another innovation is the arrangement of GMMs in form of layers, which is similar to DNNs. The transformation of GMMs into layers enables the combination with existing layer types and thus the construction of deep architectures, which can derive more complex knowledge with less resources.
In the final part of this work, the DCGMM model is examined with regard to its continual learning capabilities. In this context, a replay approach referred to as Gaussian Mixture Replay (GMR) is introduced. GMR describes the generation and replay of data samples by utilizing the DCGMM functionalities. Comparisons with existing CF avoidance models show that similar continual learning results can be achieved by using GMR under application-oriented conditions. All in all, the presented work implies that the identified application-oriented requirements are still an open issue with respect to "applied" continual learning research approaches. In addition, the novel deep learning model provides an interesting starting point for many other research areas.