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:
- 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)
- Selection of proper validation requirements regarding the deep learning application.
- Obtain a database with acoustic signals of assembled axial bearing components.
- Preprocess and label the samples in the dataset.
- Development, training, optimization, and comparison of different deep learning models and architectures for clearance testing of the axial needle-bearing components after the assembly.
- Evaluation of the applicability of the deep learning testing method in serial production.
Literature
- H. Wittel, D. Jannasch, J. Voßiek, C. Spura. „Roloff/Matek Maschinenelemente“. 23. Auflage. Wiesbaden, Deutschland: Springer Vieweg, 2017.
- Schaeffler. „Axial-Nadellager“. Schaeffler. Viewed 03. July 2023: https://medias.schaeffler.de/de/axial-needle-roller-bearings.
- 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.