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This paper aims to present the fatigue life behaviour of upper arm suspension. The main objectives are to predict the fatigue life of the component and to identify the critical location. In this analysis, three aluminium alloys were used for the suspension, and their fatigue life was compared to select the suitable material for the suspension arm.

CAD model was prepared using Solid Works software, and finite element analysis was done using ANSYS 14.0 software by importing the Parasolid file to ANSYS. The model is subjected to loading and boundary conditions; the authors consider a vertical force with constant amplitude applied at the bushing that connected to the tire, the others two bushing that connected to the body of the car are constraint. Tetrahedral elements given enhanced results as compared to other types of elements; therefore, the elements (TET 10) are used. The maximum principal stress was considered in the linear static analysis, and fatigue analysis was done using strain life approach.

Life and damage are evaluated and the critical location was considered at node 63,754. From the fatigue analysis, aluminium alloys 7175-T73 (Al 90%-Zn 5.6%-Mg 2.5% -… …) and 2014-T6 (Al 93.5%-Cu 4.4%-Mg 0.5%… …) present a similar behaviour as compared to 6061-T6 (Al 97.9%-Mg 1.0%-Si 0.6%… … .); in this case of study, these lather are considered to be the materials of choice to manufacture the suspension arms; but 7175-T73 aluminium alloys remain the material with a better resistance to fatigue.

By the finite element analysis method and assistance of ANSYS software, it is able to analyse the different car components from varied aspects such as fatigue, and consequently save time and cost. For further research, the experimental works under controlled laboratory conditions should be done to determine the validation of the result from the software analysis.

Complex repairable systems (CRSs) are generally modeled by stochastic processes called “point processes.” These are generally summed up in the nonhomogeneous Poisson process (NHPP) and the renewal process (RP), which represent the minimum and maximum repair, respectively. However, the industrial environment affects systems in some way. This is why the main objective of this work is to model the CRS with a concept reflecting the real state of the system by incorporating an indicator in the form of covariate. This type of model, known as the proportional intensity model (PIM), will be analyzed with simulated failure data to understand the behavior of the failure process, and then it will be tested for real data from a petroleum company to evaluate the effectiveness of corrective actions carried out.

To solve the partial repair modeling problem, the PIM was used by introducing, on the basis of the NHPP model, a multiplicative scaling factor, which reflects the degree of efficiency after each maintenance action. Several values of this multiplicative factor will be considered to generate data. Then, based on the reliability and maintenance history of 12-year pump's operation obtained from the SONATRACH Company (south industrial center (CIS), Hassi Messaoud, Algeria), the performance of the PIM will be judged and compared with the model of NHPP and RP in order to demonstrate its flexibility in modeling CRS. Using the maximum likelihood approach and relying on the Matlab software, the best fitting model should have the largest likelihood value.

The use of the PIM allows a better understanding of the physical situation of the system by allowing easy modeling to apply in practice. This is expressed by the value which, in this case, represents an improvement in the behavior of the system provided by a good quality of the corrective maintenance performed. This result is based on the hypothesis that modeling with the PIM can provide more clarification on the behavior of the system. It can indicate the effectiveness of the maintenance crew and guide managers to confirm or revise their maintenance policy.

The work intends to reflect the real situation in which the system operates. The originality of the work is to allow the consideration of covariates influencing the behavior of the system during its lifetime. The authors focused on modeling the degree of repair after each corrective maintenance performed on an oil pump. Since PIM does not require a specific reliability distribution to apply it, it allows a wide range of applications in the various industrial environments. Given the importance of this study, the PIM can be generalized for more covariates and working conditions.