Refine
Publication Type
- Conference Proceeding (12)
- Article (10)
- Part of a Book (2)
- Report (1)
Keywords
- Analysis (1)
- Battery State Estimation (1)
- Deep Learning (1)
- Development and implementation (1)
- Diamond (1)
- End of charge (1)
- GNSS (1)
- Highly Sensitive (1)
- Hochintegrierte Mikro- und Nanosysteme (1)
- Interval analysis · Particle filtering · Kalman filtering · Bayesian filtering · Sequential Monte Carlo simulation · Bounded-error estimation (1)
Faculty
Die Beiträge, der Konferenz:
• Nanotechnologie –Von Wunder-Materialien und solchen die es werden wollen
• Mikrotechnik –to improve the qualityof live
• Quantum Sensing for Industrial Applications
• Geordnete Defekte in Graphit: Ein Fahrplan in Richtung Raumtemperatur Supraleitung
• Funktionale mikro-und nanostrukturierte Folien als Bestandteil hochintegrierter Systeme
Piston pumps play a key role in factory automation and their availability is very critical for the smooth running of production processes. Modern installations, such as production plants and machines, are becoming increasingly complex. Therefore, the probability of a complete system failure due to a single critical component also increases. Maintenance processes with intelligent devices are therefore very important to achieve maximum economic efficiency and safety. Periodic or continuous monitoring of system components provides key information about the current physical state of the system, enabling early detection of emerging failures. Knowledge of future failures makes it possible to move from the concept of preventive maintenance to intelligent predictive maintenance. In this way, consequential damage and complete system failure can be avoided, maximizing system availability and safety. This paper reflects the development and implementation of a neural network system for abnormal state prediction of piston pumps. After a short introduction into piston pumps and their potential abnormal states, statistical and periodical analysis are presented. Then the design and implementation of suitable neural networks are discussed. Finally, a conclusion is drawn and the observed accuracies as well as potential next steps are discussed.
This work describes the setup of an ultrawideband (UWB)
realtime localization system (RTLS) for tracking of particles.We describe
how the RTLS obtains distances and positions through radio waves and
the setup and evaluation of a real world system is stated in detail. In
the proposed system the particles track a subtrates surface
ow inside a
biogas plant for verication of agitation processes.
Quantum magnetometry based on optically detected magnetic resonance (ODMR) of nitrogen vacancy centers in nano- or micro-diamonds is a promising technology for precise magnetic-field sensors. Here, we propose a new, low-cost and stand-alone sensor setup that employs machine learning on an embedded device, so-called edge machine learning. We train an artificial neural network with data acquired from a continuous-wave ODMR setup and subsequently use this pre-trained network on the sensor device to deduce the magnitude of the magnetic field from recorded ODMR spectra. In our proposed sensor setup, a low-cost and low-power ESP32 microcontroller development board is employed to control data recording and perform inference of the network. In a proof-of-concept study, we show that the setup is capable of measuring magnetic fields with high precision and has the potential to enable robust and accessible sensor applications with a wide measuring range.
An improvement on a concept for all optical mag- netometry using nitrogen vacancies in diamond is presented. The concept is based on the fluorescence attenuation of optically pumped nitrogen vacancies by magnetic fields up to ≈ 50 mT. The attenuation is registered by modulating the pumping power to generate a constant signal at a photodetector. A sensitivity of 2.6μT/√Hz at a sampling frequency of 500 Hz is achieved.
Magnetometry with nitrogen–vacancy (NV) defects in diamond has been extensively stud-ied in the past [1]. While most approaches in-clude the use of microwaves (MW) for the de-tection of electron spin resonance, only few investigate the sensitivity of the photolumines-cence (PL) from NV centers to an external magnetic field without MW [2, 3, 4]. This work aims to utilize this effect to build a highly sensi-tive and compact room temperature magne-tometer. The avoidance of MW serves the re-duction of production costs and allows a com-mercialization at the current patent situation.
When developing new battery technologies, fundamental research means assembling new batteries by hand since a production line is not worthwhile for building and testing individual cells. This causes high production tolerances to occur because manual manufacturing is not as precise as machine-made. When putting these prototypes into operation, problems can arise due to the varying parameters. One of the most important exercise is finding a criterion of a full battery. This can be challenging when parameters like the capacity or the end of charge voltage are not precisely known due to the tolerances. Furthermore, new battery types do not necessarily rely on the same stopping criteria. For example zinc-air secondary batteries do not offer an end of charging voltage. Its charging current is not going to decrease when the battery is full and the charging voltage is held at a fixed value. But instead of de-oxidising zinc oxide, hydrogen is produced. In the majority of cases overcharging should be avoided as it harms the battery. Another even more dangerous consequence is the possibility of an explosion. Especially lithium based batteries are known for their need of compatible ambient and charging parameters. This paper proposes a new criterion for detecting the end of charge that is based on the rate of change of electrochemical impedance spectra of the examined batteries. Device parameter fluctuations influence every measurement. Therefore, using the rate of change offers the possibility to not depend on these fluctuations.