Deduction of time-dependent machine tool characteristics by fuzzy-clustering

Abstract

With the onset of ICT and big data capabilities, the physical asset and data computation is integrated in manufacturing through Cyber Physical Systems (CPS). This strategy also denoted as Industry 4.0 will improve any kind of monitoring for maintenance and production planning purposes. So-called bigdata approaches try to use the extensive amounts of diffuse and distributed data in production systems for monitoring based on artificial neural networks (ANN). These machine learning approaches are robust and accurate if the data base for a given process is sufficient and the scope of the target functions is curtailed. However, a considerable proportion of high-performance manufacturing is characterized by permanently changing process, workpiece and machine configuration conditions, e.g. machining of large workpieces is often performed in batch sizes of one or of a few parts. Therefore, it is not possible to implement a robust condition monitoring based on ANN without structured data-analyses considering different machine states-e.g. a certain machining operation for a certain machine configuration. Fuzzy-clustering of machine states over time creates a stable pool representing different typical machine configuration clusters. The time-depending adjustment and automatized creation of clusters enables monitoring and interpretation of machine tool characteristics independently of single machine states and pre-defined processes.

Entropy-MAUT integrated approach supported by Fuzzy K-means: a robust tool for determining critical components for maintenance monitoring and a case study of Kaplan hydro generator unit

“…Other authors use clustering and other techniques: an example is the combined use of artificial neural networks and k-nearest neighbors to diagnose the failures of induction motors (Drakaki et al, 2020) and predict them to reduce the maintenance costs (Abdelhadi et al, 2015). In Goh et al (2012) and Frieß et al (2018Frieß et al ( , 2019, k-means and fuzzy k-means are applied with artificial neural networks for fault detection and condition monitoring, while another application regards the introduction of k-means clustering and threshold correction for predicting the remaining useful life (Wang et al, 2020). In Langone et al (2015), the least squares support vector machine technique is applied based on spectral clustering and regression to anticipate the need for maintenance during the normal functioning of industrial machines.…”