Mechanical model development of rolling bearing-rotor systems: A review

Complete fault sample is essential to activate artificial intelligent (AI) models. A novel fault detection scheme is proposed to build a bridge between AI and real-world running mechanical systems. First, the finite element method simulation is used to simulate samples with different faults to overcome the shortcoming of missing fault samples. Second, to enlarge datasets, new samples similar to the simulation and measurement fault samples are generated by generative adversarial networks and further combined with the original simulation and measurement samples to obtain synthetic samples. Finally, the synthetic and unknown fault samples are severed as the training and test samples, respectively, to the classifiers of AI models, and the unknown fault types will be finally determined. A public datasets of bearings have been used to verify the effectiveness of the proposed scheme. It is expected that the proposed scheme can be extended to complex mechanical systems.

FEM Simulation-Based Generative Adversarial Networks to Detect Bearing Faults

For the past several decades, significant efforts have been devoted to modeling and analysis of rolling-element bearings to aid in their design and application. This paper reviewed the modeling and analysis of rolling-element bearings with emphasis on single-row ball and roller bearings. The application of bearing models was reviewed as well along with illustrative results.

Rolling-element bearing modeling: A review

Nonlinear thermal effects on high-speed spindle bearings subjected to preload

Classification of faults in mechanical components using machine learning is a hot topic in the field of science and engineering. Generally, every real-world running mechanical system exhibits personalized vibration behaviors that can be measured with acceleration sensors. However, faulty samples of such systems are difficult to obtain. Therefore, machine learning methods, such as support vector machine (SVM), neural network (NNs), etc., fail to obtain agreeable fault detection results through smart sensors. A personalized diagnosis fault method is proposed to activate the smart sensor networks using finite element method (FEM) simulations. The method includes three steps. Firstly, the cosine similarity updated FEM models with faults are constructed to obtain simulation signals (fault samples). Secondly, every simulation signal is separated into sub-signals to solve the time-domain indexes to generate the faulty training samples. Finally, the measured signals of unknown samples (testing samples) are inserted into the trained SVM to classify faults. The personalized diagnosis method is applied to detect bearing faults of a public bearing dataset. The classification accuracy ratios of six types of faults are 90% and 92.5%, 87.5% and 87.5%, 85%, and 82.5%, respectively. It confirms that the present personalized diagnosis method is effectiveness to detect faults in the absence of fault samples.

A Personalized Diagnosis Method to Detect Faults in a Bearing Based on Acceleration Sensors and an FEM Simulation Driving Support Vector Machine.

Analysis on the load characteristics and coefficient of friction of angular contact ball bearing at high speed

Velocity Field Analysis of Bonding Interface on Cold-Rolled Copper/Aluminum Composite Plate

Effect of Cryogenic Treatment Prior to Rolling on Microstructure and Mechanical Properties of AZ31 Magnesium Alloy

The hydraulic rolling shear whose hydraulic system is researched in this article is designed based on the theory of rolling shear. This article builds the transfer function of the unsymmetrical val...

Huang Qingxue

Papers

Analysis on the load characteristics and coefficient of friction of angular contact ball bearing at high speed

Velocity Field Analysis of Bonding Interface on Cold-Rolled Copper/Aluminum Composite Plate

Effect of Cryogenic Treatment Prior to Rolling on Microstructure and Mechanical Properties of AZ31 Magnesium Alloy

The analysis of new hydraulic rolling shear servo system dynamic characteristics

Spread Model for TC4 Alloy Rod during the Three-Roll Tandem Rolling Process