Mechatronics applications of condition monitoring using a statistical change detection method

M. Mazzoleni, M. Scandella, L. Maurelli, F. Previdi

Abstract

In this paper, we propose the use of a change detection strategy to perform condition monitoring of mechanical components. The method looks for statistical changes in the distribution of features extracted from raw measurements, such as Root Mean Square or Crest Factor indicators. The proposed method works in a batch fashion, comparing data from one experiment to another. When these distributions differ by a specified amount, a degradation score is increased. The approach is tested on three experimental applications: (i) an ElectroMechanical Actuator (EMA) employed in flight applications, where the focus of the monitoring is on the ballscrew transmission; (ii) a CNC workbench, where the focus is on the vertical shaft bearing, (iii) an industrial EMA with focus on the ballscrew bearing. All components have undergone a severe experimental degradation process, that ultimately led to their failure. Results show how the proposed method is able to assess component degradation prior to their failure. [Paper]

Reference

M. Mazzoleni, M. Scandella, L. Maurelli, F. Previdi, "Mechatronics applications of condition monitoring using a statistical change detection method," in 21st IFAC World Congress, vol. 53, no. 2, pp. 92-97, Jul. 2020, doi: 10.1016/j.ifacol.2020.12.100 , ISSN: 2405-8963.

Bibtex

@article{MAZZOLENI202092,
title = {Mechatronics applications of condition monitoring using a statistical change detection method},
journal = {IFAC-PapersOnLine},
volume = {53},
number = {2},
pages = {92-97},
year = {2020},
note = {21th IFAC World Congress},
issn = {2405-8963},
doi = {10.1016/j.ifacol.2020.12.100},
author = {M. Mazzoleni and M. Scandella and L. Maurelli and F. Previdi},
keywords = {Predictive maintenance, condition monitoring, actuators, bearings},
}