Quality Assurance through In-line Failure Detection by Vibration Analysis

Abstract: The production of faulty parts poses significant challenges for production facilities, as it leads to increased inventory levels, operating costs, and impedes overall productivity. Despite its fundamental nature, this issue remains prevalent in manufacturing operations. To effectively reduce the rate of faulty parts, it is crucial to have a thorough understanding of the manufacturing process and exercise control by monitoring various parameters.  The aim of this study is to investigate the right prerequisites which enable quality assurance through in-line failure detection by vibration analysis. The research questions formulated for this thesis are as follows:  RQ1: What are the essential prerequisites for quality assurance through in-line failure detection by vibration analysis in the machining of splines? RQ2: How suitable is the use of vibration measurements in identifying and sorting out poor quality in the specific machining process of splines? The study was conducted through a literature review and a single case study of a gear hobbing process in an industrial manufacturing company.  The collection of data was acquired via interviews, observations, and vibration measurements during the spline manufacturing process. To analyse the collected data several tools got used. Python was used as the tool for performing several operations on the dataset, such as FFT of the vibration signals. To later visualize the results which facilitated the analysis of the entire dataset.  The results of the study indicate several similarities between the documented fault progression in gear systems and the manufacturing of splines. However, further research is needed to identify the core differences between these two fault progressions.  Furthermore, the study identified the essential prerequisites for implementing vibration analysis as an in-line failure detection method in spline manufacturing operations. Additionally, it concluded on the suitability of vibration analysis for identifying faults in this context.