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Prediction of Rolling Bearing Performance Degradation Trend Based on IBA-SVR
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Ya-zhou HUANG1, Meng SHAO1, Hao WU2, Dong AN1, *, Hao-long ZHANG1, Zhi-qiang CUI1
Science Technology and Engineering | 2025, 25(6) : 2428 - 2434
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Science Technology and Engineering | 2025, 25(6): 2428-2434
• Papers·Automation and Computational Technology •
Prediction of Rolling Bearing Performance Degradation Trend Based on IBA-SVR
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Ya-zhou HUANG1, Meng SHAO1, Hao WU2, Dong AN1, *, Hao-long ZHANG1, Zhi-qiang CUI1
Affiliations
  • 1 School of Mechanical Engineering, Shenyang Jianzhu University, Shenyang 110168, China
  • 2 China National Automobile Research Institute, Jinan 250102, China
Published: 2025-02-28 doi: 10.12404/j.issn.1671-1815.2309601
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Establishing an accurate rolling bearing performance degradation prediction model plays a crucial role in subsequent processing such as bearing fault classification and life prediction. In order to solve the problem of inaccurate prediction of bearing performance degradation model, an IBA(improved bat algorithm) was proposed to improve the accuracy of degradation model prediction. Firstly, Cat chaotic mapping was applied to the initial position of the population to enhance the traversability of the population and improve the quality of the initial solution. Secondly, an inverse tangent-like control factor was added in the iterative process to improve the algorithm’s accuracy in finding the optimum. Finally, the position updating strategy was improved to prevent from falling into the local optimum. By comparing the results with those obtained from SVR(support vector regression machine) optimized by BA(bat algorithm), SVR optimized by particle swarm optimization algorithm, and SVR optimized by gray wolf optimization algorithm, the results show that the absolute mean error of the prediction model optimized by the IBA decreases by 70.60%, 67.19%, 55.56%, and the root-mean-square error decreases by 76.64%, 76.12%, and 76.12%, respectively. 76.64%, 76.12%, and 30.29%, respectively, further proving the accuracy of the improved prediction model.

bat algorithm  /  rolling bearings  /  degradation trend prediction  /  support vector regression machine
Ya-zhou HUANG, Meng SHAO, Hao WU, Dong AN, Hao-long ZHANG, Zhi-qiang CUI. Prediction of Rolling Bearing Performance Degradation Trend Based on IBA-SVR[J]. Science Technology and Engineering, 2025 , 25 (6) : 2428 -2434 . DOI: 10.12404/j.issn.1671-1815.2309601
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Article Info
doi: 10.12404/j.issn.1671-1815.2309601
  • Receive Date：2023-12-05
  • Online Date：2025-07-27
  • Published：2025-02-28
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  • Received：2023-12-05
  • Revised：2024-11-28
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    1 School of Mechanical Engineering, Shenyang Jianzhu University, Shenyang 110168, China
    2 China National Automobile Research Institute, Jinan 250102, China
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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