Multi-Dimensional Mathematical Wear Models of Vibration Generated by Rolling Ball Bearings Made of AISI 52100 Bearing Steel

Zmarzły, Paweł

doi:10.3390/ma13235440

Cited by 22 publications

(22 citation statements)

References 26 publications

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“…In particular, as for FDM technology, where the thickness of a single layer could be 0.2 mm, the surface quality should be examined for irregularities with longer wavelengths, i.e., surface waviness [ 4 ]. Additionally, the surface waviness is important, because it affects the vibration of mechanical components [ 26 , 27 , 28 ]. It should be mentioned that the influence of the 3D printing parameters on the quality of the surface layer may be more visible for the parameters of the primary profile and waviness than for the surface roughness [ 29 ].…”

Section: Introductionmentioning

confidence: 99%

Quality of Surface Texture and Mechanical Properties of PLA and PA-Based Material Reinforced with Carbon Fibers Manufactured by FDM and CFF 3D Printing Technologies

et al. 2021

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The paper presents the results of mechanical tests of models manufactured with two 3D printing technologies, FDM and CFF. Both technologies use PLA or PA-based materials reinforced with carbon fibers. The work includes both uniaxial tensile tests of the tested materials and metrological measurements of surfaces produced with two 3D printing technologies. The test results showed a significant influence of the type of technology on the strength of the models built and on the quality of the technological surface layer. After the analysis of the parameters of the primary profile, roughness and waviness, it can be clearly stated that the quality of the technological surface layer is much better for the models made with the CFF technology compared to the FDM technology. Furthermore, the tensile strength of the models manufactured of carbon fiber-enriched material is much higher for samples made with CFF technology compared to FDM.

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Section: Introductionmentioning

confidence: 99%

Quality of Surface Texture and Mechanical Properties of PLA and PA-Based Material Reinforced with Carbon Fibers Manufactured by FDM and CFF 3D Printing Technologies

et al. 2021

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“…Fault diagnosis uses various methods to ensure safe operation and reduce losses for rotating machinery, which is very important [ 1 , 2 ]. Traditionally, fault diagnosis contains signal acquisition, feature extraction, and fault classification that can detect most intrinsic mechanical information and identify fault features [ 3 , 4 , 5 ]. There are a variety of fault diagnosis methods, including time-frequency analysis, that can analyze the characteristic frequency of a vibration signal in the time domain; however, more time is required for data consumption.…”

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Deep Learning Method for Fault Diagnosis of Rotating Machinery Based on Extended WDCNN and Long Short-Term Memory

Gao

Kim

2021

Sensors

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Deep learning (DL) plays a very important role in the fault diagnosis of rotating machinery. To enhance the self-learning capacity and improve the intelligent diagnosis accuracy of DL for rotating machinery, a novel hybrid deep learning method (NHDLM) based on Extended Deep Convolutional Neural Networks with Wide First-layer Kernels (EWDCNN) and long short-term memory (LSTM) is proposed for complex environments. First, the EWDCNN method is presented by extending the convolution layer of WDCNN, which can further improve automatic feature extraction. The LSTM then changes the geometric architecture of the EWDCNN to produce a novel hybrid method (NHDLM), which further improves the performance for feature classification. Compared with CNN, WDCNN, and EWDCNN, the proposed NHDLM method has the greatest performance and identification accuracy for the fault diagnosis of rotating machinery.

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“…The complexity of the modeling, especially in nonlinear systems, and the reduction of accuracy in unknown conditions are the most important challenges of the modeling-based technique [ 10 ]. Moreover, the multi-dimensional mathematical models can be used for evaluating the impact of selected factors on the vibration rolling ball bearings [ 11 ]. System identification-based methods are very close to data-driven algorithms.…”

Section: Introductionmentioning

confidence: 99%

Crack Size Identification for Bearings Using an Adaptive Digital Twin

Piltan

Kim

2021

Sensors

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In this research, the aim is to investigate an adaptive digital twin algorithm for fault diagnosis and crack size identification in bearings. The main contribution of this research is to design an adaptive digital twin (ADT). The design of the ADT technique is based on two principles: normal signal modeling and estimation of signals. A combination of mathematical and data-driven techniques will be used to model the normal vibration signal. Therefore, in the first step, the normal vibration signal is modeled to increase the reliability of the modeling algorithm in the ADT. Then, to help challenge the complexity and uncertainty, the data-driven method will solve the problems of the mathematically based algorithm. Thus, first, Gaussian process regression is selected, and then, in two steps, we improve its resistance and accuracy by a Laguerre filter and fuzzy logic algorithm. After modeling the vibration signal, the second step is to design the data estimation for ADT. These signals are estimated by an adaptive observer. Therefore, a proportional-integral observer is then combined with the proposed technique for signal modeling. Then, in two stages, its robustness and reliability are strengthened using the Lyapunov-based algorithm and adaptive technique, respectively. After designing the ADT, the residual signals that are the difference between original and estimated signals are obtained. After that, the residual signals are resampled, and the root means square (RMS) signals are extracted from the residual signals. A support vector machine (SVM) is recommended for fault classification and crack size identification. The strength of the proposed technique is tested using the Case Western Reserve University Bearing Dataset (CWRUBD) under diverse torque loads, various motor speeds, and different crack sizes. In terms of fault diagnosis, the average detection accuracy in the proposed scheme is 95.75%. In terms of crack size identification for the roller, inner, and outer faults, the proposed scheme has average detection accuracies of 97.33%, 98.33%, and 98.33%, respectively.

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Multi-Dimensional Mathematical Wear Models of Vibration Generated by Rolling Ball Bearings Made of AISI 52100 Bearing Steel

Cited by 22 publications

References 26 publications

Quality of Surface Texture and Mechanical Properties of PLA and PA-Based Material Reinforced with Carbon Fibers Manufactured by FDM and CFF 3D Printing Technologies

Quality of Surface Texture and Mechanical Properties of PLA and PA-Based Material Reinforced with Carbon Fibers Manufactured by FDM and CFF 3D Printing Technologies

A Novel Hybrid Deep Learning Method for Fault Diagnosis of Rotating Machinery Based on Extended WDCNN and Long Short-Term Memory

Crack Size Identification for Bearings Using an Adaptive Digital Twin

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