Risk Prioritization for Failure Modes in Mining Railcars

Abstract

Railway transportation systems are generally used to transport minerals from large-scale mines. Any failure in the railcar components may cause delays or even catastrophic derailment accidents. Failure mode and effect analysis (FMEA) is an effective tool for the risk assessment of mechanical systems. This method is an appropriate approach to identify the critical failure modes and provide proper control measures to reduce the level of risk. This research aims to propose an approach to identify and prioritize the failure modes based on their importance degree. To achieve this, the analytical hierarchy process (AHP) is used along with the FMEA. To compensate for the scarcities of the conventional FMEA in using the linguistic variables, the proposed approach is developed under the fuzzy environment. The proposed approach was applied in a case study, a rolling stock operated in an iron ore mine located in Sweden. The results of this study are helpful to identify not only the most important failure modes but also the most serious and critical ones.

WITHDRAWN: A reliability analysis method for industrial robots based on double hierarchy hesitant fuzzy linguistic term set and TODIM

“…FMEA is a simple method to solve the complex failure mode evaluation process [4]. Because of its simple and convenient operation, it is widely used in transportation [5], energy [6], medical treatment and supply chain [7][8].The traditional FMEA process involves team members evaluating failure modes of collected products or systems, scoring the Severity (S), Occurrence (O) and Detection (D) of failure modes according to their expertise, and finally calculating and sorting the scores of each failure mode [9]. Although FMEA is widely used, it also has some inherent defects.…”

Fuzzy Approach for the Safety Risk Assessment in Dimension Stone Mining

“…The complete membership and non-membership are represented by one and zero, respectively and values between one and zero indicate the intermediate membership degrees. The fuzzy set is characterized by a membership (or characteristic) function which assigns to each object a grade of membership between zero and one (Rahimdel and Ghodrati, 2021).…”

“…In this paper, triangular fuzzy numbers (TFNs) are used because TFNs have the characteristics of being constructive and easy to calculate in comparison to trapezoid and Gaussian fuzzy numbers (Rahim, 2017;Tsai et al, 2022). TFNs expressed with M̃ = (l,m,u), where l ≤ m ≤ u, in which the parameter l indicates the smallest possible value and m and u, respectively, represent the most promising and the largest possible value with the membership function as (Rahimdel and Ghodrati, 2021)…”

Reliability Analysis of Intelligent Manufacturing Systems Based on Improved FMEA Combined with Machine Learning

“…Identification and evaluation of the risks of failures are essential to improve the maintenance strategy and management. It obtains more importance in complex engineering systems [5]. To achieve this, several techniques such as failure mode and effects analysis (FMEA), fault tree analysis (FTA), reliability block diagram (RBD), Monte-Carlo simulation (MCS), Markov analysis (MA), and Bayesian networks (BN) have been developed and applied [6].…”