Modification optimization-based fatigue life analysis and improvement of EMU gear

• Published in: International journal of structural integrity 2021-10, Vol.12 (5), p.760-772
• Main Authors: Li, Yong-Hua, Zhang, Chi, Yin, Hao, Cao, Yang, Bai, Xiaoning
• Format: Article
• Language: English
• Subjects: Algorithms; Crack initiation; Crack propagation; Cumulative damage; Design analysis; Design defects; Design modifications; Efficiency; Electric railroads; Fatigue failure; Fatigue life; Life prediction; Load; Optimization; Railcars; Response surface methodology; Simulation; Stress cycles; Tooth root; Velocity
• ISSN: 1757-9864; 1757-9872
• DOI: 10.1108/IJSI-07-2021-0072

PurposeThis paper proposes an improved fatigue life analysis method for optimal design of electric multiple units (EMU) gear, which aims at defects of traditional Miner fatigue cumulative damage theory.Design/methodology/approachA fatigue life analysis method by modifying S–N curve and considering material difference is presented, which improves the fatigue life of EMU gear based on shape modification optimization. A corrected method for stress amplitude, average stress and S–N curve is proposed, which considers low stress cycle, material difference and other factors. The fatigue life prediction of EMU gear is carried out by corrected S–N curve and transient dynamic analysis. Moreover, the gear modification technology combined with intelligent optimization method is adopted to investigate the approach of fatigue life analysis and improvement.FindingsThe results show that it is more corresponded to engineering practice by using the improved fatigue life analysis method than the traditional method. The function of stress and modification amount established by response surface method meets the requirement of precision. The fatigue life of EMU gear based on the intelligent algorithm for seeking the optimal modification amount is significantly improved compared with that before the modification.Originality/valueThe traditional fatigue life analysis method does not consider the influence of working condition and material. The life prediction results by using the method proposed in this paper are more accurate and ensure the safety of the people in the EMU. At the same time, the combination of intelligent algorithm and gear modification can improve the fatigue life of gear on the basis of accurate prediction, which is of great significance to the portability of EMU maintenance.