Large wind farms are gaining prominence due to increasing dependence on renewable energy. In order to operate these wind farms reliably and efficiently, advanced maintenance strategies such as ...condition based maintenance are necessary. However, wind turbines pose unique challenges in terms of irregular load patterns, intermittent operation and harsh weather conditions, which have deterring effects on life of rotating machinery. This paper reviews the state-of-the-art in the area of diagnostics and prognostics pertaining to two critical failure prone components of wind turbines, namely, low-speed bearings and planetary gearboxes. The survey evaluates those methods that are applicable to wind turbine farm-level health management and compares these methods on criteria such as reliability, accuracy and implementation aspects. It concludes with a brief discussion of the challenges and future trends in health assessment for wind farms.
IntroductionPatients with neurological or neurosurgical disease can suffer from impaired cough, which may result in life-threatening retention of tracheobronchial secretions, atelectasis, pneumonia ...and finally death. Due to a lack of alternatives and pathophysiological plausibility, the application of mechanical insufflation-exsufflation (MI-E) has already become international standard care in neuromuscular disease and spinal cord injury although a lack of evidence for efficacy. High-quality studies to support the use of MI-E in neurological and neurosurgical patients during weaning from mechanical ventilation are missing. The goal of this exploratory study is to display the effect size of MI-E intervention on the duration of mechanical ventilation and additional outcomes.Methods and analysisOne hundred adult patients with a cough deficiency or retention of secretion admitted to a neurological intensive care unit (ICU) are planned to be recruited for this randomised controlled trial. Patients are randomised 1:1 to receive either MI-E or best standard care. Observation will take place until discharge from the hospital, death or end of the study period. The primary endpoint of this trial is the duration of mechanical ventilation from randomisation until successful weaning. The outcome will be analysed with Kaplan-Meier estimation and competing risks analyses. Secondary endpoint is the proportion of patients with successful weaning. Further outcomes will include the incidence of hospital-acquired pneumonia, mortality, decannulation rate, length of stay on the ICU and the total score of the Glasgow Coma Scale.Ethics and disseminationThe study was approved by the Medical Ethics Committee of the University of Oldenburg. The findings of this study will be submitted for publication in a peer-reviewed journal.Trial registration numberDRKS00020981.
This paper proposes a method for calculating a health indicator (HI) for low-speed axial rolling element bearing (REB) health assessment by utilizing the latent representation obtained by variational ...inference using Variational Autoencoders (VAEs), trained on each speed reference in the dataset. Further, versatility is added by conditioning on the speed, extending the VAE to a conditional VAE (CVAE), thereby incorporating all speeds in a single model. Within the framework, the coefficients of autoregressive (AR) models are used as features. The dimensionality reduction inherent in the proposed method lowers the need of expert knowledge to design good condition indicators. Moreover, the suggested methodology allows for setting the probability of false alarms when encoding new data points to the latent variable space using the trained model. The effectiveness of the proposed method is validated based on two different datasets: from a workshop test of an offshore drilling machine and from an in-house test rig for axial bearings. In both datasets, the HI is exceeding the warning and alarm levels with a probability of false alarm (PFA) of 10 -6 , and the method is most effective at lower shaft speeds.
Agile tools such as Git are widely used in the industry for source control, collaboration and documentation. Such tools have been implemented in a mechatronic product development course to allow for ...easier collaboration between students. The course content is mainly provided using a GitLab Pages webpage which hosts software documentation and scripts. This course was first changed in 2019 to include the development of an autonomous strawberry picker. However, the use of standard learning management system and lecture slides provided a cumbersome experience for the students. Therefore, these agile tools were presented in 2020 version to improve the course. In this paper, the course content is detailed, and student feedback from both years are discussed to reveal the outcome of the changes.
Engineering projects affect many of the UN’s sustainability goals. The development and design of new products and systems using a circular economy perspective is an important challenge towards a more ...sustainable society. Product development projects have common process characteristics, but methodologies, tools and methods differ for developing hardware (mechanical and electrical) or developing software. In Mechatronics such methods need to be combined, and product development and project-based learning is well suited for teaching and learning within Mechatronics, in addition to technical specialization subjects within both mechanical, electrical and software engineering. It is also suited for developing consciousness for sustainability. At UiA project-based learning is a part of several courses. This article draw out some experiences from teaching two different project-based product development courses the last two years, one at Bachelor and one at Master level. The courses are viewed as products, and product development is used as methodology for developing teaching and learning as well. The aim is continuous improvement of the learning outcome of these courses. This is in accordance with the SoTL-approach, Scholarship of teaching and learning.
The industry is moving towards maintenance strategies that consider component health, which require extensive collection and analysis of data. Condition monitoring methods that require manual feature ...extraction and analysis, become infeasible on an industrial scale. Machine learning algorithms can be used to automatically detect and classify faults, however, obtaining sufficient data for training is required for deep learning and other data-driven classification approaches. Data from healthy machine operation is generally available in abundance, while data from representative fault- and operating conditions is limited. This limits both development and deployment of deep learning-based CM systems on an industrial scale. This paper addresses both the challenges of automated analysis and lack of training data. A deep learning classifier architecture utilizing 1-dimensional dilated convolutions is proposed. Dilation of the convolution kernel allows for analysis of raw vibration signals while simultaneously maintaining the receptive field of the classifier enough to capture temporal patterns. The proposed method performs classification in time domain on signal segments of 1 second or shorter. With knowledge of the bearing specification, artificial vibration signals with similar characteristics as an actual bearing fault can be created. In this work, generated fault signals are combined with healthy operational data to obtaintraining data for a deep classifier. Parameters of the vibration model is chosen as distributions rather than fixed values. By using a range parameters in the vibration model, the classifier learns to recognize temporal features from the training data that generalize to unseen data. The effectiveness of the proposed method is demonstrated by training classifiers on generated data and testing on real signals from faulty bearingsat both low and high speed. One dataset containing seeded faults and three run-to-failure tests are used for the demonstration.
•New processing method for low-speed bearing fault diagnosis using vibration signals.•Combines pre-whitening and cross-correlation to highlight bearing fault vibration.•Cross-correlation of vibration ...signal and envelope amplifies bearing fault.•Requires no historic data or experimental settings to function properly.•Its use is illustrated for the bearing fault diagnosis at 20 rpm.
Rolling-element bearings are crucial components in all rotating machinery, and their failure will initially degrade the machine performance, and later cause complete shutdown. The period between an initial crack and complete failure is short due to crack propagation. Therefore, early fault detection is important to avoid unexpected machine shutdown and to aid in maintenance scheduling. Bearing condition monitoring has been applied for several decades to detect incipient faults at an early stage. However, low-speed conditions pose a challenge for bearing fault diagnosis due to low fault impact energy. To reliably detect bearing faults at an early stage, a new method termed Whitened Cross-correlation Spectrum (WCCS) is proposed. The method computes the cross-correlation between the whitened vibration signal and its envelope. In this paper, it is detailed how this correlation can improve the fault diagnosis compared to analyzing the envelope spectrum alone. Compared to other methods reported in the literature, the WCCS provides accurate fault detection without involving experimentally tuned settings or bandpass-filtering. Vibration data at 20 rpm rotational speed from an accelerated life-time test of a 40 mm bore size bearing is used to verify the performance of the proposed method. An additional case study using the WCCS on a difficult dataset from the Case Western Reserve University database is also presented to verify the performance.
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