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An Optimized Parameter Estimation Method for the Mixed Weibull Distribution Based on a Novel B&R-SSA Algorithm
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Minqing ZHAO1, 4, Wei JIANG2, 3, Zilong HUANG2, 3, Deming XIONG4, Chunhui GONG4, Xiaoqiang CHENG4
Chinese Journal of Automotive Engineering | 2025, 15(3) : 385 - 394
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Chinese Journal of Automotive Engineering | 2025, 15(3): 385-394
• Other Technologies •
An Optimized Parameter Estimation Method for the Mixed Weibull Distribution Based on a Novel B&R-SSA Algorithm
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Minqing ZHAO1, 4, Wei JIANG2, 3, Zilong HUANG2, 3, Deming XIONG4, Chunhui GONG4, Xiaoqiang CHENG4
Affiliations
  • 1 School of Mechanical and Electrical Engineering，Xi'an Technological University，Xi'an 710021，China
  • 2 China Automotive Engineering Research Institute Co.，Ltd.，Chongqing 401122，China
  • 3 Intelligent and Connected Vehicle Testing Center of China Automotive Engineering Research Institute Co.，Ltd.（Hunan），Changsha 410000，China
  • 4 Product Development Technology Center，Jiangling Motors Corporation Limited，Nanchang 330000，China
Published: 2025-05-20 doi: 10.3969/j.issn.2095–1469.2025.03.11
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The mixed Weibull distribution is widely used for modeling failure distributions and predicting durability. In practical engineering development, accurate parameter estimation for the model is critically important. Therefore, improving the estimation accuracy of the mixed Weibull distribution has become an urgent and challenging issue in the field. Based on the original mixed Weibull distribution, this paper proposes an optimized parameter estimation approach using a novel B&R-SSA algorithm. Firstly, this method establishes an iterative optimization model to estimate the location, scale, and shape parameters based on the method of successive approximation. To address the low efficiency and tendency of the original Salp Swarm Algorithm (SSA) to become trapped in local optima, a novel B&R-SSA algorithm is proposed by introducing a “betrayal” behavior mechanism and an adaptive inertia weight strategy. This improved algorithm is then applied to solve the iterative model. Finally, Monte Carlo simulations and engineering case studies are conducted. Both the simulation and experimental results demonstrate that the proposed method achieves good accuracy and computational efficiency in estimating the parameters of the mixed Weibull distribution.

reliability  /  mixed weibull distribution  /  salp swarm algorithm  /  parameter estimation  /  Monte Carlo simulation
Minqing ZHAO, Wei JIANG, Zilong HUANG, Deming XIONG, Chunhui GONG, Xiaoqiang CHENG. An Optimized Parameter Estimation Method for the Mixed Weibull Distribution Based on a Novel B&R-SSA Algorithm[J]. Chinese Journal of Automotive Engineering, 2025 , 15 (3) : 385 -394 . DOI: 10.3969/j.issn.2095–1469.2025.03.11
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Year 2025 volume 15 Issue 3
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Article Info
doi: 10.3969/j.issn.2095–1469.2025.03.11
  • Receive Date：2024-02-19
  • Online Date：2025-07-18
  • Published：2025-05-20
Article Data
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History
  • Received：2024-02-19
  • Revised：2024-05-12
Funding
Affiliations
    1 School of Mechanical and Electrical Engineering，Xi'an Technological University，Xi'an 710021，China
    2 China Automotive Engineering Research Institute Co.，Ltd.，Chongqing 401122，China
    3 Intelligent and Connected Vehicle Testing Center of China Automotive Engineering Research Institute Co.，Ltd.（Hunan），Changsha 410000，China
    4 Product Development Technology Center，Jiangling Motors Corporation Limited，Nanchang 330000，China
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https://castjournals.cast.org.cn/joweb/qcgcxb/EN/10.3969/j.issn.2095–1469.2025.03.11
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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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