Vibration Data Analysis for Fault Detection in Manufacturing Systems - A Systematic Literature Review

Lorenz, Aske; Siewertsen, Bende; Clemmensen, Victor Kyhe; Petersen, Josephine Blaamann; Friederich, Jonas; Lazarova-Molnar, Sanja

doi:10.1109/iciea54703.2022.10006127

Cited by 1 publication

(1 citation statement)

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“…Recent years have experienced noticeable growth in the use of wavelet frameworks for noise reduction, wavelet transformation, and wavelet time scattering (WTS) [27]. In the feature extraction stage, wavelet transform and WTS are useful tools [28]. The continuous wavelet scalogram can also function as the visual input for feature extraction in a convolution layer [29].…”

Section: Introductionmentioning

confidence: 99%

Classification of Unbalanced and Bowed Rotors under Uncertainty Using Wavelet Time Scattering, LSTM, and SVM

et al. 2023

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A growing interest in intelligent fault detection may sometimes lead to practical issues when existing malfunctions reveal analogous indications and the number of observations is limited. This article addresses the classification problem of two identical malfunctions, i.e., unbalancing and shaft bow in rotary machines, where only 56 observations were utilized for the training. The faulty systems are modeled in ABAQUS/CAE; a data set for each fault is created by simulation under various physical and operational conditions employing the uncertainty concept. The wavelet time scattering (WTS) technique extracts low-variance presentations from signals. With respect to the classification procedure of the faulted rotor systems, two models are examined with the extracted features from WTS as the input. Initially, a long short-term memory (LSTM) network is trained and tested, and then, the capability of a support vector machine (SVM) model is inquired. Ultimately, the classification models are trained and tested using the raw time series data and the extracted features to compare the effectiveness of the suggested methods, i.e., WTS. The employed approach for feature extraction demonstrated remarkable effectiveness in addressing a potential hurdle in identifying faults in rotating systems: the ability to differentiate between unbalanced and bowed rotors, irrespective of the classification model utilized.

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Section: Introductionmentioning

confidence: 99%