Index

AI for IT Operations (AIOps): Leveraging Large Language Models as Support for Process Management in Mainframes

AI-based generation of 2D vehicle geometries through Natural Language

Word Embeddings Applied to Alzheimer’s Disease

Development of a deep learning approach to detect faulty axial bearing components after assembly using acoustic signals

Bearings are used to support and guide relatively movable parts, especially those capable of rotational movement, in machines and devices, and to absorb and transmit external forces to a housing or other components [1]. In general, bearings are clustered into radial and axial bearings. Axial bearings have a disc-shaped flat design, which is particularly advantageous for pure axial loads, not radial loads. Axial bearings with needle-shaped rolling elements have a compact size and feature smooth operations for applications with high speeds and forces either too high for an axial ball bearing or too low for an axial roller bearing [2]. Axial needle bearings are primarily used in vehicle transmissions and allow a smooth and precise rotation of axles and shafts with axial impact. Axial needle bearings come (1) with an inner or an outer washer and a cage with rolling elements and (2) with both inner and outer washers and a cage with rolling elements. In the serial production line of axial needle bearings, these two or three individual components are assembled automatically in a rotary indexing machine.

Due to the geometry of the components the bearing is joined together by a rotational motion during the assembly. This can lead to deformation of one or more components, which hinders the freedom of movement of the components (clearance). In addition to deformed parts, components within the end of the tolerance limits can also cause problems with the clearance of the bearing. Currently, the clearance is tested using a “friction torque test station,” where the counter torque occurring during rotation of the bearing is measured with a torque sensor. Due to the lack of an established test procedure concerning the calibration of the testing station and insufficient resolution of the sensor for the measurement task, pseudo-defective goods are detected and ditched.

In this thesis, a new testing approach is considered to use deep learning methods for the classification of sound and defective axial needle bearings concerning the clearance of the components and other errors, like pressure marks, that cause acoustic abnormalities using acoustic or vibration data during the rotary motion of the bearing. Therefore, the signal can be converted to an image representation which can be processed by a convolutional neural network (CNN) architecture. Suitable pre-trained CNN architectures could be GoogleLeNet, ResNet-50, SqueezeNet, and Inception-Resnet v2, which should also be considered for faster development of a working neural network. By transfer learning, it is possible to use a pre-trained model that is already well suited for 2-D images and retrain it on a new target. The recorded signal data of the axial needle bearing during rotation can be converted to a 2-D time-frequency image, from which the CNN can automatically detect patterns to decide regarding the classification [3].
The main goal of this work is to implement a testing method using acoustic signals for detecting assembly errors in axial needle-bearing components. Different deep learning methods will be implemented and evaluated to achieve a high-performing, reliable, low cost and low-maintenance testing station. To achieve this goal, the following steps will be performed:

1. Selection of an adequate test procedure concerning movement and signal recording regarding the clearance and noise development of the axial needle bearing (e.g., rotational movement of the components and proper acoustic or vibration recording)
2. Selection of proper validation requirements regarding the deep learning application.
3. Obtain a database with acoustic signals of assembled axial bearing components.
4. Preprocess and label the samples in the dataset.
5. Development, training, optimization, and comparison of different deep learning models and architectures for clearance testing of the axial needle-bearing components after the assembly.
6. Evaluation of the applicability of the deep learning testing method in serial production.

Literature

1. H. Wittel, D. Jannasch, J. Voßiek, C. Spura. „Roloff/Matek Maschinenelemente“. 23. Auflage. Wiesbaden, Deutschland: Springer Vieweg, 2017.
2. Schaeffler. „Axial-Nadellager“. Schaeffler. Viewed 03. July 2023: https://medias.schaeffler.de/de/axial-needle-roller-bearings.
3. B. U. Deveci, M. Celtikoglu, O. Albayrak, P. Unal, P. Kirci. “Transfer Learning Enable Bearing Fault Detection Methods Based on Image Representations of Single-Dimensional Signals”. In: Information Systems Frontiers, 2023. https://doi.org/10.1007/s10796-023-10371-z.

Photovoltaic Plant Inspection: Identifying Modules and their Defects in Electroluminesence Imagery

Deep learning-based detection of early biomarker in age-related macular degeneration in volume-merged high resolution optical coherence tomography

Eye diseases such as age-related macular degeneration (AMD) can cause significant visual impairment
and even vision loss, impacting patients’ quality of life. Early detection and diagnosis are crucial for
improving treatment outcomes. Optical coherence tomography (OCT) has become a vital medical
imaging technique for this purpose.

A promising biomarker for early AMD detection is the presence of basal laminar and basal linear
deposits in the outer retina1. However, conventional OCT devices often fail to detect these changes,
making them visible only in advanced stages or histological examinations.

In this thesis, an OCT prototype, which enables ultra-high-resolution imaging, is utilized to visualize
these deposits2. Moreover, motion correction and volume merging techniques were used to generate
consistent high-quality volumetric OCT data3. For accurate segmentation and thickness measurement
of basal laminar and basal linear deposits, deep learning algorithms will be investigated to provide a
data-driven automated approach with particular focus on the use of volumetric data and high-precision
results. For this purpose, a literature review of state-of-the-art approaches for retinal layer segmentation
will be conducted, followed by the development, training, and evaluation of a new problem-specific
designed deep learning approach and a comparison to existing baseline methods. A workflow utilizing
a high-performance computing cluster will be designed for efficient data-driven training.

The developed approach should be a step towards correlation analysis between the thickness and characteristics
of these deposits and the early stages of AMD solely using OCT images. A difficulty is the
particularly small size of this structure and its varying visibility, so that these characteristics must be
taken into account when working out the concept of the approach. The thesis aims to fully automate
the analysis of deposit thickness and visibility. Ultimately, such analysis could lead to better understanding
of the pathogenesis in AMD and in the long run improve treatment outcomes for patients.

1Sura, A. A., Chen, L., Messinger, J. D., Swain, T. A., McGwin, G., Freund, K. B., & Curcio, C. A. (2020). Measuring
the contributions of basal laminar deposit and Bruch’s membrane in age-related macular degeneration. Investigative
ophthalmology & visual science, 61(13), 19.
2Chen, S., Abu-Qamar, O., Kar, D., Messinger, J. D., Hwang, Y., Moult, E. M., … & Fujimoto, J. G. (2023).
Ultrahigh Resolution OCT Markers of Normal Aging and Early Age-related Macular Degeneration. Ophthalmology
Science, 3(3), 100277.
3Ploner, S., Chen, S., Won, J., Husvogt, L., Breininger, K., Schottenhamml, J., … & Maier, A. (2022, September). A
spatiotemporal model for precise and efficient fully-automatic 3D motion correction in OCT. In International Conference
on Medical Image Computing and Computer-Assisted Intervention (pp. 517-527). Cham: Springer Nature Switzerland.

Exploring Stylistic Invariance in Self-Supervised Pretraining for Feature Extraction

Service-Oriented Preprocessing for Cost-Effective and Efficient Deep Learning Training

Uncertainty analysis for automated misannotation detection in medical segmentation datasets

Automated Wood Identification Using Micro-Computed Tomography on a Cellular Level: A Study with Maple and Pine Wood Samples

Wood identification is of paramount importance in various fields, including forensic verification of timber origin and detecting illegally logged tropical timber at ports [1]. While DNA analysis has been widely used for this purpose, the cellular-level structure of wood also offers valuable information for species discrimination. This research explores the potential of micro-computed tomography (µCT) systems, with resolutions down to 1µm, to reveal distinct features in wood samples for automatic identification and quantification [2, 4, 5].

In this Master’s thesis, we focus on investigating wood identification through micro-CT using ten exemplary samples of maple and pine wood. All wood samples underwent µCT scanning, and the primary objective is to semantically segment the wood volumes, preferably in 3D, to extract different cell types. An optimal labeling tool and strategy will be selected to facilitate the segmentation process. Considering that data from µCT scans are inherently noisy, an essential aspect of this research is to determine an optimal denoising strategy [3].

In the subsequent step, we aim to identify specific structures within the wood samples that allow for accurate assignment to a particular tree species or genus. Due to the limited number of CT volumes available, techniques will be employed to virtually increase the dataset’s size. Furthermore, considering the distinct anatomical characteristics of wood in different cutting directions (axial, lateral, and radial), the orientation of the wood samples will be considered and detected for more accurate identification.

Some of the salient features that can be identified through µCT systems include the distinction between scattered-pored woods (e.g., beech, birch, maple) and ring-pored woods (e.g., oak, elm, ash) based on pore structure, as well as the structure and number of resin canals in conifers. Additionally, other features, such as the shape of the tracheid and the number of rays, contribute to the clear identification of wood samples.

Through this research, we aim to establish a robust framework for wood identification using micro-CT, paving the way for future applications in identifying tropical timber from µCT scans of timber samples. The potential of this method lies in its ability to complement DNA-based wood identification and offer a comprehensive approach to verify the origin of timber and combat illegal logging effectively.

[1] Jiao, L., Lu, Y., He, T., Guo, J., & Yin, Y. (2020). DNA barcoding for wood identification: global review of the last decade and future perspective, IAWA Journal, 41(4), 620-643.

[2] Steppe, K., Cnudde, V., Girard, C., Lemeur, R., Cnudde, J. P., & Jacobs, P. (2004). Use of X-ray computed microtomography for non-invasive determination of wood anatomical characteristics. Journal of structural biology, 148(1), 11–21.

[3] Ghani, M. U., Ren, L., Wong, M., Li, Y., Zheng, B., Rong, X. J., Yang, K., & Liu, H. (2017). Noise Power Characteristics of a Micro-Computed Tomography System. Journal of computer assisted tomography, 41(1), 82–89.

[4] Haag, V., Dremel, K., & Zabler, S. (2022). Volumetric imaging by micro computed tomography: a suitable tool for wood identification of charcoal, IAWA Journal, 44(2), 210-224.

[5] Applications in the scope of anatomical wood identification using sub-µCt based volumetric images. In: IUFRO Div 5 Conference: The forest treasure chest: delivering outcomes for everyone; 4-8 June 2023, Cairns, Australia.