An illustration of new methods in machine condition monitoring, Part I: stochastic resonance

12th International Conference on Damage Assessment of Structures

Worden, K.^1 ; Antoniadou, I.^1 ; Marchesiello, S.^2 ; Mba, C.^2 ; Garibaldi, L.^2

Dynamics Research Group, Department of Mechanical Engineering, University of Sheffield, Mappin Street, Sheffield

S1 3JD, United Kingdom^1

Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Torino

10129, Italy^2

关键词: Damage information;    Damage-sensitive features;    Discrete dynamical systems;    Machine condition monitoring;    Novelty detection;    State-of-the-art procedures;    Stochastic resonances;    Two-step procedure;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/842/1/012058/pdf
DOI  :  10.1088/1742-6596/842/1/012058

来源: IOP

【 摘 要 】

There have been many recent developments in the application of data-based methods to machine condition monitoring. A powerful methodology based on machine learning has emerged, where diagnostics are based on a two-step procedure: extraction of damage-sensitive features, followed by unsupervised learning (novelty detection) or supervised learning (classification). The objective of the current pair of papers is simply to illustrate one state-of-the-art procedure for each step, using synthetic data representative of reality in terms of size and complexity. The first paper in the pair will deal with feature extraction. Although some papers have appeared in the recent past considering stochastic resonance as a means of amplifying damage information in signals, they have largely relied on ad hoc specifications of the resonator used. In contrast, the current paper will adopt a principled optimisation-based approach to the resonator design. The paper will also show that a discrete dynamical system can provide all the benefits of a continuous system, but also provide a considerable speed-up in terms of simulation time in order to facilitate the optimisation approach.