Bibliographic Details
| Title: |
Evaluation of operational reliability and life cycle of key components of automotive equipment under conditions of intensive operation. |
| Authors: |
Saken, Pernebekov, Aitmukhamed, Junusbekov, Umirzhan, Kokayev, Antonina, Kartashova, Almira, Madiyarova |
| Source: |
Periodicals of Engineering & Natural Sciences (PEN); Sep2026, Vol. 14 Issue 3, p55-64, 10p |
| Subject Terms: |
Weibull distribution, Reliability in engineering, Failure mode & effects analysis, Automobile parts, Fatigue cracks, Fatigue life, Condition-based maintenance |
| Abstract: |
Herein, the reliability analysis of several critical automotive components operating under intensive conditions have been reported. Understanding failure mechanisms under high-stress environments compared to nomal operating conditions has been addressed. Real-world data failures have been collected. Weibull distribution of the failure data of several components where some are considered as wear sensitive components (like brake pads, tires and pistons) and fatigue-sensitive components like transmission and shock absorbers were modelled. Reliability quantification was based on incorporating Weibull parameters for shape (β) and characteristic life (η). The results showed a significant difference between normal and intensive regimes. Under intensive operational conditions, the characteristic life parameter eta decreased by 46% for brake pads, 44 % for tires, and 99% for piston engines. The failure modes classification indicated that wear was responsible of 52% of the failure cases. The main causes of failure were originating from intensive service, poor lubrication, and the non-respect of maintenance protocols, amplifying fatigue accumulation in key automotive components. These findings suggest incorporating predictive maintenance strategies based on Weibull parameters to mitigate such failures. [ABSTRACT FROM AUTHOR] |
|
Copyright of Periodicals of Engineering & Natural Sciences (PEN) is the property of International University of Sarajevo and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| Database: |
Complementary Index |