Strict-Feedback Backstepping Digital Twin and Machine Learning Solution in AE Signals for Bearing Crack Identification

Abstract

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.

A Gaussian Process State Space Model Fusion Physical Model and Residual Analysis for Fatigue Evaluation

“…Furthermore, a GP can make the model very flexible through the design of different kernel functions, and the appropriate kernel function can be selected according to the actual physical process. This method is widely used in mechanical performance evaluation, life prediction, financial stock trends, and other fields [ 31 , 32 , 33 , 34 ].…”

Bearing Crack Diagnosis Using a Smooth Sliding Digital Twin to Overcome Fluctuations Arising in Unknown Conditions

“…Based on this figure, a FAG NJ206-E-TVP2 bearing is attached in the shaft. Moreover, a wideband frequency AE sensor (PAC WSα) was used to acquire data when placed on the top of nondrive end shaft bearing at a 21.48 mm displacement under normal and abnormal conditions when the sampling rate is 250 kHz [9]. To simulate the faulty conditions, a bearing with 3-mm and 6-mm crack sizes in length and 0.3-mm crack size in depth and 0.35 mm in width is used [25][26][27].…”

“…In addition, various techniques can be introduced to design digital twins, such as model-based techniques, signal-based approaches, artificial intelligence-based algorithms, and hybrid schemes [8]. In signalbased approaches, signal processing algorithms can be used for anomaly diagnosis [8,9]. The application of a signal-based algorithm for anomaly detection and identification was suggested in [10].…”

“…The application of a signal-based algorithm for anomaly detection and identification was suggested in [10]. The application of artificial intelligence-based approaches for anomaly identification was proposed in [9]. These techniques have been used in several applications, such as bearing fault diagnosis and pipeline crack diagnosis [9].…”

“…The application of artificial intelligence-based approaches for anomaly identification was proposed in [9]. These techniques have been used in several applications, such as bearing fault diagnosis and pipeline crack diagnosis [9]. In model-based approaches, mathematical-based fault modeling algorithms can be used for reliable and stable fault detection and identification.…”