scholarly journals Linear Bearing Fault Detection in Operational Condition Using Artificial Neural Network

2019 ◽  
Vol 24 ◽  
pp. 01004
Author(s):  
Siwanu Lawbootsa ◽  
Prathan Chommaungpuck ◽  
Jiraphon Srisertpol
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fault Detection ◽  
Ball Bearing ◽  
Fault Detection And Isolation ◽  
Bearing Faults ◽  
Bearing Fault ◽  
Bearing Fault Detection ◽  
Artificial Neural ◽  
Bearing Damage

Nowadays, Factors of a competition of Hard Disk Drive (HDD) industry have reduced the cost of manufacturing process via increasing the rate of productivity and reliability of the automation machine. This paper aims to increase the efficacy of Condition-Based Maintenance (CBM) of linear bearing in Auto Core Adhesion Mounting machine (ACAM). The linear bearing faults considered in three causes such as healthy bearing, one ball bearing damage and one ball bearing damage with starved lubricant. The Fast Fourier Transform spectrum (FFT spectrum) can be detected for linear bearing faults and Artificial Neural Network (ANN) method used to analyze the cause of linear bearing faults in operational condition. The experimental results show the potential application of ANN and FFT spectrum technique as Fault Detection and Isolation (FDI) tool for linear bearing fault detection performance. The accuracy and decision making of ANN is enough to develop the diagnostic method for automation machine in operational condition.

Temperature Rise Prediction of Oil-Air Lubricated Angular Contact Ball Bearings Using Artificial Neural Network

2019 ◽  
Vol 12 (3) ◽  
pp. 248-261
Author(s):  
Baomin Wang ◽  
Xiao Chang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Temperature Rise ◽  
High Speed ◽  
Ball Bearing ◽  
Influence Factors ◽  
Ball Bearings ◽  
Ann Model ◽  
Angular Contact Ball Bearing ◽  
Artificial Neural

Background: Angular contact ball bearing is an important component of many high-speed rotating mechanical systems. Oil-air lubrication makes it possible for angular contact ball bearing to operate at high speed. So the lubrication state of angular contact ball bearing directly affects the performance of the mechanical systems. However, as bearing rotation speed increases, the temperature rise is still the dominant limiting factor for improving the performance and service life of angular contact ball bearings. Therefore, it is very necessary to predict the temperature rise of angular contact ball bearings lubricated with oil-air. Objective: The purpose of this study is to provide an overview of temperature calculation of bearing from many studies and patents, and propose a new prediction method for temperature rise of angular contact ball bearing. Methods: Based on the artificial neural network and genetic algorithm, a new prediction methodology for bearings temperature rise was proposed which capitalizes on the notion that the temperature rise of oil-air lubricated angular contact ball bearing is generally coupling. The influence factors of temperature rise in high-speed angular contact ball bearings were analyzed through grey relational analysis, and the key influence factors are determined. Combined with Genetic Algorithm (GA), the Artificial Neural Network (ANN) model based on these key influence factors was built up, two groups of experimental data were used to train and validate the ANN model. Results: Compared with the ANN model, the ANN-GA model has shorter training time, higher accuracy and better stability, the output of ANN-GA model shows a good agreement with the experimental data, above 92% of bearing temperature rise under varying conditions can be predicted using the ANNGA model. Conclusion: A new method was proposed to predict the temperature rise of oil-air lubricated angular contact ball bearings based on the artificial neural network and genetic algorithm. The results show that the prediction model has good accuracy, stability and robustness.

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