Strict-Feedback Backstepping Digital Twin and Machine Learning Solution in AE Signals for Bearing Crack Identification
| Autor: | Farzin Piltan, Rafia Nishat Toma, Dongkoo Shon, Kichang Im, Hyun-Kyun Choi, Dae-Seung Yoo, Jong-Myon Kim |
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| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
Chemical technology
strict-feedback backstepping observer bearing digital twin machine learning acoustic emission autoregressive technique Gaussian process regression Laguerre filter fuzzy logic support vector regression support vector machine crack size diagnosis crack type diagnosis TP1-1185 Biochemistry Atomic and Molecular Physics and Optics Article Analytical Chemistry Feedback Fuzzy Logic Artificial Intelligence Electrical and Electronic Engineering Instrumentation Algorithms |
| Zdroj: | Sensors, Vol 22, Iss 539, p 539 (2022) Sensors (Basel, Switzerland) Sensors; Volume 22; Issue 2; Pages: 539 |
| ISSN: | 1424-8220 |
| Popis: | Bearings are nonlinear systems that can be used in several industrial applications. In this study, the combination of a strict-feedback backstepping digital twin and machine learning algorithm was developed for bearing crack type/size diagnosis. Acoustic emission sensors were used to collect normal and abnormal data for various crack sizes and motor speeds. The proposed method has three main steps. In the first step, the strict-feedback backstepping digital twin is designed for acoustic emission signal modeling and estimation. After that, the acoustic emission residual signal is generated. Finally, a support vector machine is recommended for crack type/size classification. The proposed digital twin is presented in two steps, (a) AE signal modeling and (b) AE signal estimation. The AE signal in normal conditions is modeled using an autoregressive technique, the Laguerre algorithm, a support vector regression technique and a Gaussian process regression procedure. To design the proposed digital twin, a strict-feedback backstepping observer, an integral term, a support vector regression and a fuzzy logic algorithm are suggested for AE signal estimation. The Ulsan Industrial Artificial Intelligence (UIAI) Lab’s bearing dataset was used to test the efficiency of the combined strict-feedback backstepping digital twin and machine learning technique for bearing crack type/size diagnosis. The average accuracies of the crack type diagnosis and crack size diagnosis of acoustic emission signals for the bearings used in the proposed algorithm were 97.13% and 96.9%, respectively. |
| Databáze: | OpenAIRE |
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