Reliability analysis by mapping probabilistic importance factors into bayesian belief networks for making decision in water deluge system

Bayesian network approach for reliability analysis of mining trucks

Having a safe and efficient system for mineral transportation is a top priority for all mining operations. Trucks are the most widely used material transportation systems that are applied in both surface and underground mines. Any truck failure disrupts the mineral transportation process and consequently decreases the overall output. Therefore, the reliability analysis of such equipment plays a critical role in increasing the efficiency and productivity of a mining operation. This paper proposes a novel method for analyzing the reliability of a fleet of mining trucks based on the Bayesian Network modeling. Considering the reliability block diagram, the fault tree of trucks was developed according to the logical relationship between the units. Then, a dynamic Bayesian network was constructed according to the conditional probability analysis. Moreover, the relative contributions of each truck's component to the occurrence of the fleet failure were studied by using critical analysis. The results of this paper show that the successful operation of the fleet of trucks is most sensitive to truck no. 5, which has the highest reliability level in all time intervals. The reliability of the fleet of trucks reaches 0.881 at 20 h, and the fuel injection system of the truck's engine is the main leading cause of the trucks failure. A proper preventive maintenance strategy should be paid more attention to improve the reliability and availability of the engine system.

Fuzzy multi-criteria approach for criticality assessment and optimization of decision making

Failure mode, effects, and criticality analysis (FMECA) is a proactive quality tool that allows the identification and prevention of the potential failure modes of a process or product. In a conventional FMECA, for each failure mode, three risk parameters, namely frequency, non-detection, and severity are evaluated and a risk priority number (RPN) is calculated by multiplying these parameters to assess one signal criticality. However, in many cases, it suffers from some shortcomings regarding the decision-making and the situation where the information provided is ambiguous or uncertain. This paper describes a new fuzzy multi-criticality approach for improving the use of FMECA by treating FMECA as a fuzzy multi-criteria optimization model. The new approach bases on replacing the calculation of a single criticality with a fuzzy inference system for improving the criticality evaluations which offers five partial criticalities that efficiently and separately calculate the impact of a failure on the environment, personnel, production, equipment, and management. In addition, an analytical hierarchy method (AHP) is used to calculate the priorities weights for each partial criticality and construct a criticality matrix in order to improve the relevance of decision-making. Furthermore, a real case of LPG storage system for ZCINA Hassi Messaoud in Algeria is provided to illustrate the practical implementation of the suggested approach and extremely shows the pertinence of the suggested fuzzy model as decision-making tools in preventing industrial risks with providing encouraging results regarding the criticality estimation and improve decision-making by prioritizing “preventive –corrective actions” and determine the efficient action for each partial criticality to control the risk effectively.