Application of prognostics and health management (PHM) in the field of Proton Exchange Membrane (PEM) fuel cells is emerging as an important tool in increasing the reliability and availability of these systems. Though a lot of work is currently being conducted to develop PHM systems for fuel cells, various challenges have been encountered including the self-healing effect after characterization as well as accelerated degradation due to dynamic loading, all which make RUL predictions a difficult task. In this study, a prognostic approach based on adaptive particle filter algorithm is proposed. The novelty of the proposed method lies in the introduction of a self-healing factor after each characterization and the adaption of the degradation model parameters to fit to the changing degradation trend. An ensemble of five different state models based on weighted mean is then developed. The results show that the method is effective in estimating the remaining useful life of PEM fuel cells, with majority of the predictions falling within 5% error. The method was employed in the IEEE 2014 PHM Data Challenge and led to our team emerging the winner of the RUL category of the challenge.

Renewable energies have an entirely different cost structure than fossil-fuel-based electricity generation. This is mainly due to the operation at zero marginal cost, whereas for fossil fuel plants the fuel itself is a major driver of the entire cost of energy. For a wind turbine, most of the materials and resources are spent up front. Over its lifetime, this initial capital and material investment is converted into usable energy. Therefore, it is desirable to gain the maximum benefit from the utilized materials for each individual turbine over its entire operating lifetime. Material usage is closely linked to individual damage progression of various turbine components and their respective failure modes. In this work, we present a novel approach for an optimal long-term planning of the operation of wind energy systems over their entire lifetime. It is based on a process for setting up a mathematical optimization problem that optimally distributes the available damage budget of a given failure mode over the entire lifetime. The complete process ranges from an adaptation of real-time wind turbine control to the evaluation of long-term goals and requirements. During this process, relevant deterministic external conditions and real-time controller setpoints influence the damage progression with equal importance. Finally, the selection of optimal planning strategies is based on an economic evaluation. The method is applied to an example for demonstration. It shows the high potential of the approach for an effective damage reduction in different use cases. The focus of the example is to effectively reduce power of a turbine under conditions where high loads are induced from wake-induced turbulence of neighbouring turbines. Through the optimization approach, the damage budget can be saved or spent under conditions where it pays off most in the long term. This way, it is possible to gain more energy from a given system and thus to reduce cost and ecological impact by a better usage of materials.

This study introduces DigiWind, an extensible digital twin platform specifically designed for the wind energy domain. The research aims to identify the fundamental requirements and architectural design for such a platform. Functional requirements are identified through a requirements engineering process using the use-case methodology. Existing digital twin and co-simulation platforms are reviewed, and the proposed DigiWind architecture is presented in detail. Three use cases are presented to highlight the integration of workflows and simulation models in the digital twin process. The DigiWind platform features a layered architecture with core implementations such as the template service, model assembly service, co-simulation service, and measurement data service. These components enable the automation of simulations, incorporation of historic measurement data, and data feedback and exchange. The platform supports various use cases including retrospective evaluation, performance monitoring, and scenario simulations. Additionally, a knowledge base and a versioning system ensure automation, documentation, and reproducibility of simulation results. The platform’s openness promotes collaboration among wind energy stakeholders and supports standardized models for co-simulation using the Functional Mock-up Interface. Overall, DigiWind offers a solution for developing, managing, and integrating digital twins in the wind energy sector, enhancing wind farm performance and operational efficiency.

To increase quality and reliability of copper wire bonds, self-optimization is a promising technique. For the implementation of self-optimization for ultrasonic heavy copper wire bonding machines, a model of stick-slip motion between tool and wire and between wire and substrate during the bonding process is essential. Investigations confirm that both of these contacts do indeed show stick-slip movement in each period oscillation. In a first step, this paper shows the importance of modeling the stick-slip effect by determining, monitoring and analyzing amplitudes and phase angles of tool tip, wire and substrate experimentally during bonding via laser measurements. In a second step, the paper presents a dynamic model which has been parameterized using an iterative numerical parameter identification method. This model includes Archard's wear approach in order to compute the lost volume of tool tip due to wear over the entire process time. A validation of the model by comparing measured and calculated amplitudes of tool tip and wire reveals high model quality. Then it is then possible to calculate the lifetime of the tool for different process parameters, i.e. values of normal force and ultrasonic voltage.

To implement a self-optimization technique for ultrasonic wire bonding machines, a model of the pre-deformation phase is essential. The local material characteristics change abruptly because of the cold work during deformation. Investigations confirm a significant influence on the material properties of the contact members during touchdown. In a first step this paper validates the importance of modeling the pre-deformation experimentally. In a second step, the paper presents a numerical study of the elasto-plastic deformation based on the finite element method. This model includes measured overshoots in the touchdown forces in order to achieve accurate model responses. A validation of the model with the resulting nominal contact area, surface pressure and penetration depth reveals the high model quality.

So-called reliability adaptive systems are able to adapt their system behavior based on the current reliability of the system. This allows them to react to changed operating conditions orfaults within the system that change the degradation behavior. To implement such reliability adaptation, self-optimization can be used. A self-optimizing system pursues objectives, of which the priorities can be changed at runtime, in turn changing the system behavior. When including system reliability as an objective of the system, it becomes possible to change the system based on the current reliability as well. This capability can be used to control the reliability of the system throughout its operation period in order to achieve a pre-defined or user-selectable system lifetime. This way, optimal planning of maintenance intervals is possible while also using the system capabilities to their full extent. Our proposed control system makes it possible to react to changed degradation behavior by selecting objectives of the self-optimizing system and in turn changing the operating parametersin a closed loop. A two-stage controller is designed which is used to select the currently required priorities of the objectives in order to fulfill the desired usable lifetime. Investigations using a model of an automotive clutch system serve to demonstrate the feasibility of our controller. It is shown that the desired lifetime can be achieved reliably.