Table of Contents
International Journal of Quality, Statistics, and Reliability
Volume 2010 (2010), Article ID 816407, 8 pages
Research Article

Evaluation of Fatigue Life Reliability of Steering Knuckle Using Pearson Parametric Distribution Model

1Malaysia France Institute, Universiti Kuala Lumpur, Bandar Baru Bangi, 43650 Selangor, Malaysia
2Faculty of Mechanical Engineering, Universiti Teknikal Malaysia Melaka, 76100 Melaka, Malaysia
3Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600 Bangi Selangor, Malaysia

Received 8 May 2010; Revised 17 October 2010; Accepted 10 December 2010

Academic Editor: Ratna Babu Chinnam

Copyright © 2010 E. A. Azrulhisham et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Steering module is a part of automotive suspension system which provides a means for an accurate vehicle placement and stability control. Components such as steering knuckle are subjected to fatigue failures due to cyclic loads arising from various driving conditions. This paper intends to give a description of a method used in the fatigue life reliability evaluation of the knuckle used in a passenger car steering system. An accurate representation of Belgian pave service loads in terms of response-time history signal was obtained from accredited test track using road load data acquisition. The acquired service load data was replicated on durability test rig and the SN method was used to estimate the fatigue life. A Pearson system was developed to evaluate the predicted fatigue life reliability by considering the variations in material properties. Considering random loads experiences by the steering knuckle, it is found that shortest life appears to be in the vertical load direction with the lowest fatigue life reliability between 14000–16000 cycles. Taking into account the inconsistency of the material properties, the proposed method is capable of providing the probability of failure of mass-produced parts.