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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.

Because of non-satisfactory results obtained at the iron rods steel mill, during the last period of work and to collect relevant data to facilitate rapid and efficient decision-making, the purpose of this study is to describe a global analysis method of risks encountered by companies resulting from their own effects or by their environment to integrate prevention as soon as the conception of work place. From this point of view, it seems that it is time for companies that did learn to master direct costs to learn how to control indirect costs, which means controlling the risks and learning to face them once they are detected.

The method of work used is hazard identification and risk assessment (HIRA), which is based on Kinney evaluation. The HIRA allowed the authors to collect and analyze different dangers and risks at work posts and consider different corrective measures to prevent damages. The HIRA techniques are based on identifying the different hazardous situations and the risk related to each working station; the risks are identified and are classified and mitigated accordingly. Using unique criteria, the objective of the risk assessment part is to assign to each risk a numerical value. The criteria used for classifying and ordering the risk importance depend greatly on the selected method.

Risks are identified, classified and mitigated accordingly using Kinney method criteria depending on three factors: frequency of exposure (NE), probability of occurrence (NP) and severity level (NG). The level of risk mitigation (NM) is obtained as the product of the three factors (NP, NG and NE). Using the Pareto plot (20-80 per cent), the authors highlighted the priorities to be taken in the order of importance of the most important causes to take targeted measures.

This work is an opportunity for the steel mill complex to start a global approach in prevention of risks and improve working conditions. This can be reached by taking into consideration the technical, organizational and human solutions; It would not be a better method to search for ways and means to reach better prevention of industrial and technological risks, but it will preserve health of workers and get to a global higher level of security. Also, the steel mill complex can be engaged in a continuous improvement process. Therefore, management of risks will be a relevant way to decrease undesirable effects due to the diverse activities of the company.

The present paper aims to present a numerical modeling of a train bogie following a railway accident in the Algerian railway which clearly showed the presence of cracks at the level of the bogie axles.

Our approach consists of making a numerical modeling in dynamic under variable load taking into account the reality of several parameters.

The aim is to present a predictive model allowing the identification of the probable causes that are the cause of this premature deterioration. We have determined the lifetime and the influence of frequency on railway axle. Also, the influence of overload on railway axle. The numerical model will be directed by Ansys software.

Our study allowed us to understand the mechanical behavior of a railway axle under several loadings, and to determine the critical value of the final failure to avoid damage.

This paper aims to enable the analysts of reliability and safety systems to evaluate the risk and prioritize failure modes ideally to prefer measures for reducing the risk of undesired events.

To address the constraints considered in the conventional failure mode and effects analysis (FMEA) method for criticality assessment, the authors propose a new hybrid model combining different multi-criteria decision-making (MCDM) methods. The analytical hierarchy process (AHP) is used to construct a criticality matrix and calculate the weights of different criteria based on five criticalities: personnel, equipment, time, cost and quality. In addition, a preference ranking organization method for enrichment evaluation (PROMETHEE) method is used to improve the prioritization of the failure modes. A comparative work in which the robust data envelopment analysis (RDEA)-FMEA approach was used to evaluate the validity and effectiveness of the suggested approach and simplify the comparative analysis.

This work aims to highlight the real case study of the automotive parts industry. Using this analysis enables assessing the risk efficiently and gives an alternative ranking to that acquired by the traditional FMEA method. The obtained findings offer that combining of two multi-criteria decision approaches and integrating their outcomes allow for instilling confidence in decision-makers concerning the risk assessment and the ranking of the different failure modes.

This research gives encouraging outcomes concerning the risk assessment and failure modes ranking in order to reduce the frequency of occurrence and gravity of the undesired events by handling different forms of uncertainty and divergent judgments of experts.