Classical machine elements have been around for centuries, even millennia. However, the current advancement in Structural Health Monitoring (SHM), together with Condition Monitoring (CM), requires that machine elements should be upgraded from a not-simple object to an intelligent object, able to provide information about its working conditions to its surroundings, especially its health. However, the integration of electronics in a mechanical component may lead to a reduction in its load capacity since the component may need to be modified in order to accommodate them. This paper describes a case study, where, differently from other cases present in the literature, sensor integration has been developed under the gear teeth of an actual case-hardened helical gear pair to be used within an actual gearbox. This article has two different purposes. On the one hand, it aims to investigate the effect that component-level SHM/CM has on the gear load carrying capacity. On the other hand, it also aims to be of inspiration to the reader who wants to undertake the challenges of designing a sensor-integrated gear.
Gear flank changes caused by wear do not only affect the dynamic behavior of gear systems, but they can also compromise the load-carrying capacity of gear teeth up to critical failure. To help avoid unintended consequences like downtime or safety risks, a condition monitoring system needs to be able to estimate the current wear during operation based on available sensor measurements. While many condition monitoring approaches in research rely on vibrational analysis with manual feature engineering, gearboxes running at slow speed do not reveal much excitation information for this purpose. We therefore introduce an approach for slow-speed gear wear monitoring that is based on the dynamic gear transmission error and that contains an automated feature selection process. For this purpose, we extract a large set of features from the preprocessed transmission error samples. Applying combined filter and embedded feature selection methods enables us to automatically identify and remove features with low relevance. The selection process consists of filtering features with no statistical dependence on the target wear value, removing redundant features with a correlation analysis and a recursive feature elimination process with cross-validation based on a random forest regressor. The remaining relevant set of features is the basis for model training and subsequent wear estimation. For this, the present research employed two independent ensemble models, random forest regression and gradient boosted regression trees. To train and test the proposed approach, we conducted slow-speed gear experiments with developing gear wear on a single-stage spur gear test rig setup. The results of both models show good gear wear estimation performance compared to the actual wear mass loss, even for small quantities. Hence, the proposed transmission error-based approach with automated feature selection is able to quantify the degree of slow-speed wear and offers a possible way for condition monitoring and fault diagnosis.
Due to the growing need for gearboxes to be as lightweight and efficient as possible, it is most important that the gear mesh’s potential is utilized as well as possible. One way of doing that is to define a flank modification that optimally distributes the load over the flank. Best practice for defining a flank modification is to manually check out the load distribution and to define a value of the flank modification. In general, this is an iterative method to get an optimally distributed load. This method can also be automated. To do this, the deformations of the gearbox (shafts, bearings, gear mesh) are calculated. With those results, a modification proposal is calculated and applied to the calculation model. As soon as the values for the next additional modification proposal drop under a certain limit, the iteration is finished. This method consumes time and computing power. Additionally, since it is an iteration, it does not always converge. A new method for calculating the lead flank modification for all gear stages in the gearbox to be calculated is presented in this paper. The method shown in this paper uses additional degrees of freedom and equations, which are integrated into the linear equation system of the gearbox model. Those degrees of freedom and the equations apply the boundary condition to the model of a constant load distribution. By introducing additional factors in the equations, it is possible to calculate a lead flank modification for an arbitrary load distribution. By integrating these additional degrees of freedom and the equations, only one additional calculation is needed to get a modification proposal. Examples throughout this paper show the results of this method.
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