Maximilian Benker

Selected publications

1. Condition Monitoring of Ball Screw Feed Drives Using Convolutional Neural Networks

   Maximilian Benker, and Michael F. Zaeh

   CIRP Annals 2022

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   Ball screw feed drives are widely used in machine tools and significantly determine the manufacturing quality and efficiency. With their degradation, machining accuracy and economic efficiency decrease. Therefore, monitoring the condition of ball screws is of great interest. Past investigations showed that condition monitoring of ball screws is possible. Nevertheless, practical applications of a condition monitoring system for ball screw drives are rare, as it is unclear how well they perform on unseen components. In this paper a data-driven approach is presented, which can assess the condition of unseen ball screws with an accuracy of up to 98%.

2. A Gaussian Process Based Method for Data-Efficient Remaining Useful Life Estimation

   Maximilian Benker, Artem Bliznyuk, and Michael F. Zaeh

   IEEE Access 2021

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   The task of remaining useful life (RUL) estimation is a major challenge within the field of prognostics and health management (PHM). The quality of the RUL estimates determines the economical feasibility of the application of predictive maintenance strategies, that rely on accurate predictions. Hence, many effective methods for RUL estimation have been developed in the recent years. Especially deep learning methods have been among the best performing ones setting new record accuracies on bench mark data sets. However, those approaches often rely on numerous and representative run-to-failure sequences of the components under investigation. In real-world use cases, this kind of data (i.e. run-to-failure sequences and RUL labels) is hardly ever present. Therefore, this paper proposes a new, data-efficient method, which is based on Gaussian process classification to derive abstract health indicator (HI) values in a first step, and warped, monotonic Gaussian process regression for indirect RUL estimation in a second step. The proposed approach does neither rely on entire run-to-failure sequences nor on any RUL labels and was tested on the benchmark C-MAPSS turbo fan and FEMTO bearing data sets, achieving comparable results to the state-of-the art whilst using only a small fraction of the available training data. Hence, the proposed approach allows RUL estimation in use cases, in which gathering enough failure data for the application of deep learning models is infeasible.

3. Utilizing Uncertainty Information in Remaining Useful Life Estimation via Bayesian Neural Networks and Hamiltonian Monte Carlo

   Maximilian Benker, Lukas Furtner, Thomas Semm, and 1 more author

   Journal of Manufacturing Systems 2021

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   The estimation of remaining useful life (RUL) of machinery is a major task in prognostics and health management (PHM). Recently, prognostic performance has been enhanced significantly due to the applicationof deep learning (DL) models. However, only few authors assess the uncertainty of the applied DL models and therefore can state how certain the model is about the predicted RUL values. This is especially critical in applications, in which unplanned failures lead to high costs or even to human harm. Therefore, the determination of the uncertainty associated with the RUL estimate is important for the applicability of DL models in practice. In this article, Bayesian DL models, that naturally quantify uncertainty, were applied to the task of RUL estimation of simulated turbo fan engines. Inference is carried out via Hamiltonian Monte Carlo (HMC) and variational inference (VI). The experiments show, that the performance of Bayesian DL models is similar and in many cases even beneficial compared to classical DL models. Furthermore, an approach for utilizing the uncertainty information generated by Bayesian DL models is presented. The approach was applied and showed how to further enhance the predictive performance.