Fuzzy Risk Priority Numbers Research Articles

Enhanced Condition Monitoring of Gearboxes using Fuzzy Comprehensive Evaluation and Group Decision-making: A Case Study

In this study, we introduce a novel methodology for the condition monitoring of gearboxes by integrating fuzzy comprehensive evaluation and group decision-making. Traditional risk assessment methods often suffer from subjective biases and uncertainties inherent in expert evaluations. Our approach mitigates these limitations by aggregating expert opinions using fuzzy membership functions and assigning weights based on the similarity of individual evaluations to the group consensus. This converts qualitative judgments into quantitative measures, resulting in more precise and objective Risk Priority Numbers (RPNs). We validate the efficacy of our methodology through a case study involving a gearbox. The primary failure modes identified include gear tooth wear, misalignment, bearing failure, lubrication failure, and thermal overload. Our results indicate a significant improvement in condition monitoring accuracy, with calculated fuzzy RPN values closely aligning with historical data and expert feedback. Comparative analysis highlights the advantages of our methodology over conventional RPN calculations, particularly in reducing subjective biases and enhancing the reliability of risk assessments. Our findings demonstrate that this methodology can be effectively applied in various industrial settings, establishing a robust framework for mechanical system condition monitoring. Future research should explore integrating advanced data analytics and machine learning techniques to enhance the methodology's accuracy and efficiency.

A quantitative safety assessment for offshore equipment evaluation using fuzzy FMECA: A case study of the hydraulic submersible pump system

The Hydraulic submersible pump system (HSPS) is integral to the offloading of oil on offshore platforms and oil tankers. Due to the inflammable and explosive properties of the oil, the working process of HSPS is of high risk. Therefore, this study focused on the safety assessment of HSPS, key potential failure modes of HSPS were identified. A Fuzzy Failure Mode Effects and Criticality Analysis (FFMECA) model was proposed based on fuzzy logic theory and failure mode effects and criticality analysis (FMECA) method. Fuzzy linguistic terms were used to assess the Occurrence (O), Severity (S), and Detection (D) of the identified failure modes. The weights for O, S, and D were calculated based on experts’ ratings and entropy theory. A case study was carried out and the Fuzzy Risk Priority Numbers (FRPNs) for the failure modes were obtained. The results highlighted that 5 failure modes, namely wear of bearings, failure of mechanical seal, failure of rotary seal, zero pressure on the outlet and influence of excessive temperatures, were of critical level and required immediate preventive action. Risk control measures were proposed to ensure the safety of HSPS in practical application and to improve the reliability of system operation.

Automated prioritization of construction project requirements using machine learning and fuzzy Failure Mode and Effects Analysis (FMEA)

Due to the emergence of risk-based verification practices, requirement prioritization has gained importance as a task in project requirements management. However, it is challenging due to the voluminous paper-based requirements, the reliance on manual content analysis and interviews, the limited focus on specific requirement types, and the neglect of the non-conformance detectability factor. This paper leverages historical data of risk priority numbers (RPN) for many written requirements to develop a novel machine learning model for requirement prioritization. The training data includes requirement statements manually annotated with professionals' ratings for three risk factors severity, probability, and non-detectability. The overall fuzzy RPN data was aggregated using fuzzy logic to tackle uncertainty in subjective ratings. The model was trained using a Convolutional Neural Network (CNN). The study is anticipated to greatly aid professionals in prioritizing extensive textual project requirements essential for effective resource allocation that can eventually eliminate costly consequences of high-risk non-compliance.

Study on intelligent fuzzy assessment of failure effects for marine power plants

Due to its advantages like zero carbon dioxide emissions, high power density, and long life cycle, power devices show great application prospects in civil cargo ships and icebreakers. The equipment composition and structure configuration of power plants are complex, with harsh operating conditions, which will yield to increased failure rates and serious security threats. Given that the relative importance relying on precise data, an intelligent assessment method of failure effects based on fuzzy logic is proposed. Marine power plant hierarchy division is descripted, suitable for the failure effect assessment process. The fuzzy sets and fuzzy numbers are used to get the fuzzy evaluations and relative weights. The alpha-level sets of fuzzy risk priority numbers (RPNs) are calculated with the proposed benchmark adjustment search strategy. Meanwhile, using the synthesized alpha-level set and centroid defuzzification, the fuzzy RPNs are defuzzied. The comparative analysis of examples reveals that the suggested assessment method can improve the confidence level of risk prioritization for marine power plants.

Fuzzy FMEA risk assessment approach for IFF system in military helicopters using Matlab R2022A

In this article, the author reviews the fuzzy Failure Mode and Effect Analysis (fuzzy FMEA) as one of the effective methods of risk assess- ment. Using the fuzzy logic toolbox of the MATLAB R2022a software, the Identification Friend or Foe (IFF) system of military helicopters is analysed in a case study, during which the fuzzy risk priority numbers (F-RPN) for this system of military helicopters is created. Finally, the author of this paper summarizes the results obtained.

Fuzzy failure mode and effect analysis application to reduce risk level in a ready-mixed concrete plant: A fuzzy rule based system modelling approach

In this study, failure mode and effect analysis were applied to evaluate and eliminate potential failure modes in a Ready-Mixed Concrete Plant using a fuzzy-rule-base system. The questionnaires were specially prepared for each sub-department such as production plant, workshop and maintenance, dumping grounds, materials transportation and storage, utilities, administrative office, social facility, quality control laboratory and wastewater pool and recycling facilities. The questions were answered by the workers in each section. Risk Priority Numbers (RPNs) and Fuzzy Risk Priority Numbers (FRPN), which measure potential failure modes, were calculated using the risk parameters. High-risk areas were identified, and some suggestions were made to reduce accident risk at the Ready-Mixed Concrete Plant. Three questionnaires were prepared, based on these suggestions, and distributed to workers to determine whether the suggestions would reduce the risk or not. Based on conditions at the time the recommendations were implemented and improvement rates were calculated. The results showed that the fuzzy failure mode and effect analysis methodology were effective in identifying and eliminating potential failure modes at the Ready-Mixed Concrete Plant. The results can also be used by other ready-mixed concrete manufacturers who want to improve the safety of their operations.

Reliability analysis of the starting and landing system of UAV by FMECA and FTA

ABSTRACTFailure mode and effects analysis (FMEA), it is difficult to incorporate interdependencies between various failure modes with uncertain and inaccurate information into the failure analysis. In this paper, the Fuzzy Weighted Geometric Mean (FWGM) algorithm, Benchmark Adjustment Search Algorithm (BASA), Minimum Cut Sets (MCSS) and Boolean operations are used to effectively combine multiple failure modes, and the new Fuzzy risk priority numbers (FRPN) is used to evaluate the system, which solves the possible indicators of the traditional RPN assessment method. To evaluate the inaccuracy problem, and use the copula function to model the reliability of the relevant multiple failure modes, and obtain the failure probability of each MCSS, and use it as the probability weight to propose the Fuzzy Weighted Geometric Mean probability weighted risk priority number (PWGMRPN) algorithm. Finally, the above method is applied to the reliability risk assessment of the unmanned aerial vehicle take-off and landing system.

An Improved Assessment Method for FMEA for a Shipboard Integrated Electric Propulsion System Using Fuzzy Logic and DEMATEL Theory

Shipboard integrated electric propulsion systems (IEPSs) are prone to suffer from system failures and security threats because of their complex functional structures and poor operational environments. An improved assessment method for failure mode and effects analysis (FMEA), integrating fuzzy logic and decision–making trial and evaluation laboratory (DEMATEL) theory, is proposed to enhance the system’s reliability and handle the correlation effects between failure modes and causes. In this method, information entropy and qualitative analysis are synthesized to determine the credibility weights of domain experts. Each risk factor and its relative importance are evaluated by linguistic terms and fuzzy ratings. The benchmark adjustment search algorithm is designed to obtain the alpha-level sets of fuzzy risk priority numbers (RPNs) for defuzzification. The defuzzified RPNs are regarded as the inputs for the DEMATEL technique to investigate the causal degrees of failure modes and causes. Accordingly, the risk levels of the failure modes are prioritized with respect to the causal degrees. The practical application to the typical failure modes of the propulsion subsystem is provided. The assessment results show that this system contributes to risk priority decision-making and disastrous accident prevention.

An intutionistic approach based on failure mode and effect analysis for prioritizing corrective and preventive strategies

AbstractAlthough Failure Mode and Effect Analysis (FMEA) is a prominent approach that has been used with convenience as the most popular risk definition and evaluation tool related to a system, a product, or a service, it has several deficiencies. This study addresses these deficiencies and proposes a new intuitionistic approach, which combines FMEA and Weighted Aggregated Sum Product Assessment (WASPAS) by implementing a new intuitionistic scale. Intuitionistic FMEA‐WASPAS can handle uncertainty, vagueness, and hesitancy of the risk evaluation process and provides flexibility for risk assessment. In this study, rankings of corrective‐preventive strategies for failure modes (FMs) are obtained by the proposed approach. To compute Intuitionistic Fuzzy Risk Priority Numbers, occurrence, severity, detection, cost, duration of exposure, and system safety factors are used. A numerical example is also illustrated to present the practicality and effectiveness of the Intuitionistic FMEA‐WASPAS approach.

Fuzzy risk assessment for electro-optical target tracker

Purpose – The purpose of this paper is to evolve a guideline for scientists and development engineers to the failure behavior of electro-optical target tracker system (EOTTS) using fuzzy methodology leading to success of short-range homing guided missile (SRHGM) in which this critical subsystems is exploited. Design/methodology/approach – Technology index (TI) and fuzzy failure mode effect analysis (FMEA) are used to build an integrated framework to facilitate the system technology assessment and failure modes. Failure mode analysis is carried out for the system using data gathered from technical experts involved in design and realization of the EOTTS. In order to circumvent the limitations of the traditional failure mode effects and criticality analysis (FMECA), fuzzy FMCEA is adopted for the prioritization of the risks. FMEA parameters – severity, occurrence and detection are fuzzifed with suitable membership functions. These membership functions are used to define failure modes. Open source linear programming solver is used to solve linear equations. Findings – It is found that EOTTS has the highest TI among the major technologies used in the SRHGM. Fuzzy risk priority numbers (FRPN) for all important failure modes of the EOTTS are calculated and the failure modes are ranked to arrive at important monitoring points during design and development of the weapon system. Originality/value – This paper integrates the use of TI, fuzzy logic and experts’ database with FMEA toward assisting the scientists and engineers while conducting failure mode and effect analysis to prioritize failures toward taking corrective measure during the design and development of EOTTS.

Fuzzy and grey theories in failure mode and effect analysis for tanker equipment failure prediction

Failure Mode and Effects Analysis (FMEA) has been extensively used for examining potential failures in many industries. Unlike the traditional FMEA, the risk factors of occurrence (O), severity (S) and detection (D) of each failure mode are evaluated by linguistic terms and fuzzy ratings in this research, and their relative weights are considered, which make the results more useful and practical. Both grey theory and fuzzy theory are applied in FMEA, in which fuzzy set theory is used to calculate the fuzzy risk priority numbers (FRPNs), and grey theory is applied to calculate the grey relational coefficient. The rankings of the failure modes are both determined by FRPNs and the grey theory. An example of oil tanker equipment failure is provided, and the results show that the evaluation of failure modes by both fuzzy theory and grey theory are quite similar. The practical application of fuzzy and grey theories proposed in this paper helps to enhance the reliability of the prediction, and the predicted ranking of equipment failure can be used for better decision-making concerning inspection and maintenance, which in turn will make tanker shipping more safe and reliable.

A fuzzy linear programming model for risk evaluation in failure mode and effects analysis

In traditional approach, failure mode and effects analysis determines the risk priories of failure modes through the risk priority number which is determined by multiplication of three risk factors namely, failure occurrence (O), failure severity (S) and failure detection ability (D). In this approach, different weights of risk factors were not taken into consideration so that the three risk factors were assumed to have the same weight. This may not be realistic in real applications. In this paper we treat the risk factors as fuzzy variables and evaluate them using fuzzy linguistic terms and fuzzy ratings. As a result, fuzzy risk priority numbers (FRPNs) are proposed for prioritization of failure modes. The FRPNs are defined as fuzzy geometric means of the fuzzy ratings for O, S and D and can be computed using alpha-level sets and linear programming models. A numerical example is provided to examine the results of this model.

Risk Priority Analysis for Engine Based on Fuzzy Weighted Geometric Mean

Fuzzy risk priority numbers (FRPNs) were proposed for assessment of the probabilistic risk by synthetically using fuzzy set theory, failure mode and effect analysis. The priority rank of failure modes is evaluated by fuzzy risk priority number in the fuzzy failure mode and effect analysis, where occurrence probability ranking, effect severity ranking and detection difficulty ranking are all fuzzy weighted geometric mean, and the FRPN can be computed using alpha-level set and optimization model. An engine example was provided to illustrate the proposed fuzzy FMEA and the detailed computational process of the FRPNs.

Risk prioritization in failure mode and effects analysis under uncertainty

Failure mode and effects analysis (FMEA) is a powerful tool for identifying and assessing potential failures. The tool has become increasingly important in new product development, manufacture or engineering applications. Generally, risk assessment in FMEA is carried out by using risk priority numbers (RPNs) which can be determined by evaluating three factors: occurrence (O), severity (S) and detection (D). Due to the vagueness and uncertainty existing in the evaluating process, crisp numbers representing RPNs in the traditional FMEA might be improper or insufficient in contrast to fuzzy numbers. Currently, the fuzzy methods and linear programming method have been proposed as an effective solution for the calculations of fuzzy RPNs. However, considering the fact that fuzzy RPNs are determined on a multidimensional scale spanning O, S and D along with their interactions under a fuzzy environment, several gaps should be bridged in the evaluation, calculation, and ranking of fuzzy RPNs. First, decision makers tend to use multi-granularity linguistic term sets for expressing their assessments because of their different backgrounds and preferences. Second, numerical compensation may be existed among O, S and D that can derive different RPNs in the engineering applications. Third, the complete ranking results for fuzzy RPNs may be easily changed by the effects of uncertain factors. In this study, a fuzzy-RPNs-based method integrating weighted least square method, the method of imprecision and partial ranking method is proposed to generate more accurate fuzzy RPNs and ensure to be robust against the uncertainty. A design example of new horizontal directional drilling machine is used for illustrating the application of the proposed approach.

An integrated approach for performance enhancement in automobile repair shops

Performance management has been a topic of interest for academicians and practitioners. This paper aims at integrating fuzzy logic, data envelopment analysis (DEA) and fuzzy failure mode and effects analysis (fuzzy FMEA) for enhancing the performance in automobile (car) repair shops. A questionnaire with linguistic terms has been used to collect the data related to customer's perceived service quality and the perceptions are quantified using triangular fuzzy numbers. The weights for the service quality parameters considered are gained through analytical hierarchy process (AHP). Fuzzy perceived quality score is then calculated by combining the fuzzy numbers of criteria with the corresponding weights. Then the service performance is measured using DEA by considering both quantitative and qualitative measures. The results from DEA model provide the relative efficiency measure and efficient input/output targets for each repair shop. For further improvement of the service process, fuzzy FMEA is used to prioritise the potential failure modes. Based on the calculated fuzzy risk priority numbers (FRPN) remedial actions can be initiated.

Risk evaluation in failure mode and effects analysis using fuzzy weighted geometric mean

Failure mode and effects analysis (FMEA) has been extensively used for examining potential failures in products, processes, designs and services. An important issue of FMEA is the determination of risk priorities of the failure modes that have been identified. The traditional FMEA determines the risk priorities of failure modes using the so-called risk priority numbers (RPNs), which require the risk factors like the occurrence (O), severity (S) and detection (D) of each failure mode to be precisely evaluated. This may not be realistic in real applications. In this paper we treat the risk factors O, S and D as fuzzy variables and evaluate them using fuzzy linguistic terms and fuzzy ratings. As a result, fuzzy risk priority numbers (FRPNs) are proposed for prioritization of failure modes. The FRPNs are defined as fuzzy weighted geometric means of the fuzzy ratings for O, S and D, and can be computed using alpha-level sets and linear programming models. For ranking purpose, the FRPNs are defuzzified using centroid defuzzification method, in which a new centroid defuzzification formula based on alpha-level sets is derived. A numerical example is provided to illustrate the potential applications of the proposed fuzzy FMEA and the detailed computational process of the FRPNs.