A Novel Hybrid Approach for Risk Evaluation of Vehicle Failure Modes

Abstract

This paper addresses the problem of evaluating vehicle failure modes efficiently during the driving process. Generally, the most critical factors for preventing risk in potential failure modes are identified by the experience of experts through the widely used failure mode and effect analysis (FMEA). However, it has previously been difficult to evaluate the vehicle failure mode with crisp values. In this paper, we propose a novel hybrid scheme based on a cost-based FMEA, fuzzy analytic hierarchy process (FAHP), and extended fuzzy multi-objective optimization by ratio analysis plus full multiplicative form (EFMULTIMOORA) to evaluate vehicle failure modes efficiently. Specifically, vehicle failure modes are first screened out by cost-based FMEA according to maintenance information, and then the weights of the three criteria of maintenance time (T), maintenance cost (C), and maintenance benefit (B) are calculated using FAHP and the rankings of failure modes are determined by EFMULTIMOORA. Different from existing schemes, the EFMULTIMOORA in our proposed hybrid scheme calculates the ranking of vehicle failure modes based on three new risk factors (T, C, and B) through fuzzy linguistic terms for order preference. Furthermore, the applicability of the proposed hybrid scheme is presented by conducting a case study involving vehicle failure modes of one common vehicle type (Hyundai), and a sensitivity analysis and comparisons are conducted to validate the effectiveness of the obtained results. In summary, our numerical analyses indicate that the proposed method can effectively help enterprises and researchers in the risk evaluation and the identification of critical vehicle failure modes.

Test Plan for the Verification of the Robustness of Sensors and Automotive Electronic Products Using Scenario-Based Noise Deployment (SND)

“…From this viewpoint, the designers should focus on the critical product functions. Making the design insensitive to unavoidable variations is called robust design that can be supported by Variation Mode and Effect Analysis (VMEA) [ 18 ], which technique is similar to Failure Mode and Effect Analysis (FMEA), which is widely used in sensor development [ 19 ] and risk evaluation of vehicle failure modes [ 20 ]. Robustness was similarly defined as “the variance of product performance due to environmental noise” [ 21 ], and a similar approach pointed out the basis of the concept of Reliable Design Space (RDS), within which any design satisfies the reliability requirements [ 22 ].…”

Highly Redundant Safety Scheme and Its Experimental Validation for the Electronic-Controlled Pneumatic Braking System of Commercial Vehicle for Autonomous Driving

“…They utilized Failure Mode and Effects Analysis (FMEA) to evaluate 42 potential failures that could occur within automotive leaf springs. They determined high-potential failures based on four scoring criteria: severity, occurrence rate, detectability, and Risk Priority Number (RPN).Zhou W [6] focused on proposing a novel hybrid approach to effectively assess failure modes in vehicles during operation. Xu X [7] explored the complex field-circuit coupling system of permanent magnet synchronous motors in electric vehicles, discussing fault forms, diagnostic methods, and the current state of research.…”

Improvement of Failure Mode and Effects Analysis Using Fuzzy and Adaptive Neuro-Fuzzy Inference System

“…The following systematic review [3] of papers on this topic, produced in the period between 1998 and 2018, points to a very quick increase in annual publications on FMEA improvement in the past two decades, after the publication of the review article by the above mentioned authors in 2013. The majority of papers selected the fuzzy logic system in an attempt to eliminate the disadvantages of the traditional FMEA based on "if-then" rules formed in fuzzy inference system (FIS) and improve upon calculated fuzzy RPN and risk prioritization, for example, for milling machines [4,5], environmental waste water treatment plant [6], automotive industry [7,8] and other industries [9][10][11][12][13][14][15]. For determining risk levels and prioritizing mitigation action for the risks, some authors have taken into account additional variables, for example, costs based on economic impact [16][17][18] or impact of assets, people, environment and/or reputation [19,20].…”

“…For determining risk levels and prioritizing mitigation action for the risks, some authors have taken into account additional variables, for example, costs based on economic impact [16][17][18] or impact of assets, people, environment and/or reputation [19,20]. Research by W. Zhou et al [7] presents three new risk factors; maintenance time, maintenance cost, and maintenance benefit through fuzzy logic terms for ranking vehicle failure mode and performance order. The study of authors Fabis-Domagala et al proposes a modification of the conventional RPN number, where severity and occurrence are valued based on subfactors while the detection number remains unchanged.…”