229 research outputs found
Online Condition Monitoring of Electric Powertrains using Machine Learning and Data Fusion
Safe and reliable operations of industrial machines are highly prioritized in industry. Typical industrial machines are complex systems, including electric motors, gearboxes and loads. A fault in critical industrial machines may lead to catastrophic failures, service interruptions and productivity losses, thus condition monitoring systems are necessary in such machines. The conventional condition monitoring or fault diagnosis systems using signal processing, time and frequency domain analysis of vibration or current signals are widely used in industry, requiring expensive and professional fault analysis team. Further, the traditional diagnosis methods mainly focus on single components in steady-state operations. Under dynamic operating conditions, the measured quantities are non-stationary, thus those methods cannot provide reliable diagnosis results for complex gearbox based powertrains, especially in multiple fault contexts.
In this dissertation, four main research topics or problems in condition monitoring of gearboxes and powertrains have been identified, and novel solutions are provided based on data-driven approach. The first research problem focuses on bearing fault diagnosis at early stages and dynamic working conditions. The second problem is to increase the robustness of gearbox mixed fault diagnosis under noise conditions. Mixed fault diagnosis in variable speeds and loads has been considered as third problem. Finally, the limitation of labelled training or historical failure data in industry is identified as the main challenge for implementing data-driven algorithms. To address mentioned problems, this study aims to propose data-driven fault diagnosis schemes based on order tracking, unsupervised and supervised machine learning, and data fusion. All the proposed fault diagnosis schemes are tested with experimental data, and key features of the proposed solutions are highlighted with comparative studies.publishedVersio
Battery-Electric Powertrain System Design for the HorizonUAM Multirotor Air Taxi Concept
The work presented herein has been conducted within the DLR internal research
project HorizonUAM, which encompasses research within numerous areas related to
urban air mobility. One of the project goals was to develop a safe and
certifiable onboard system concept. This paper aims to present the conceptual
propulsion system architecture design for an all-electric battery-powered
multirotor electric Vertical Takeoff and Landing (eVTOL) vehicle. Therefore, a
conceptual design method was developed that provides a structured approach for
designing the safe multirotor propulsion architecture. Based on the concept of
operation the powertrain system was initially predefined, iteratively refined
based on the safety assessment and validated through component sizing and
simulations. The analysis was conducted within three system groups that were
developed in parallel: the drivetrain, the energy supply and the thermal
management system. The design process indicated that a pure quadcopter
propulsion system can merely be designed reasonably for meeting the European
Union Aviation Safety Agency (EASA) reliability specifications. By adding two
push propellers and implementing numerous safety as well as passivation
measures the reliability specifications defined by EASA could finally be
fulfilled. The subsequent system simulations also verified that the system
architecture is capable of meeting the requirements of the vehicle concept of
operations. However, further work is required to extend the safety analysis to
additional system components as the thermal management system or the battery
management system and to reduce propulsion system weight.Comment: 38 pages, 27 figures, CEAS Aeronautical Journal Special Issue
"HorizonUAM - Opportunities and Challenges of Urban Air Mobility
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