A New Concordance Coefficients-Based Approach to Compare Improved FMECA Methods

2021 ◽  
Author(s):  
Andrés Zúñiga ◽  
Paulo Branco ◽  
João F. P. Fernandes
Keyword(s):  
Failure Modes ◽  
Fuzzy Inference ◽  
Comparative Approach ◽  
Additional Parameter ◽  
Type I ◽  
Type Ii ◽  
Qualitative Comparison ◽  
Inference System ◽  
Inference Systems

This work introduces the application of Cohen’s kappa concordance coefficient as part of a comparative approach between different methods used to improve the FMECA analysis. The proposed approach considers the concordance assessment between different methodologies used in FMECA (Risk Isosurface function, VIKOR, ITWH, FWGM, Type-I and Type-II Fuzzy Inference System) when applied to the same problem and regarding an FMECA ranking selected as the reference one. The analyzed problem is a blood transfusion case study consisting of eleven failure modes widely used for benchmarking. Results show that Type-II fuzzy inference systems achieve the highest agreement regarding the reference FMECA ranking; one possible explanation for this result is that Type-II FIS considers uncertainty as an additional parameter. This approach proves effective to compare statistically different FMECA methods instead of the classical qualitative comparison between rankings.

A New Concordance Coefficients-Based Approach to Compare Improved FMECA Methods

2021 ◽  
Author(s):  
Andrés Zúñiga ◽  
Paulo Branco ◽  
João F. P. Fernandes
Keyword(s):  
Failure Modes ◽  
Fuzzy Inference ◽  
Comparative Approach ◽  
Additional Parameter ◽  
Type I ◽  
Type Ii ◽  
Qualitative Comparison ◽  
Inference System ◽  
Inference Systems

This work introduces the application of Cohen’s kappa concordance coefficient as part of a comparative approach between different methods used to improve the FMECA analysis. The proposed approach considers the concordance assessment between different methodologies used in FMECA (Risk Isosurface function, VIKOR, ITWH, FWGM, Type-I and Type-II Fuzzy Inference System) when applied to the same problem and regarding an FMECA ranking selected as the reference one. The analyzed problem is a blood transfusion case study consisting of eleven failure modes widely used for benchmarking. Results show that Type-II fuzzy inference systems achieve the highest agreement regarding the reference FMECA ranking; one possible explanation for this result is that Type-II FIS considers uncertainty as an additional parameter. This approach proves effective to compare statistically different FMECA methods instead of the classical qualitative comparison between rankings.

Interval type-2 fuzzy inference-based failure mode and effect analysis model in a group decision-making setting

Kybernetes ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
İlker Gölcük
Keyword(s):  
Failure Modes ◽  
Fuzzy Inference ◽  
Real Life ◽  
Effect Analysis ◽  
Inference System ◽  
Content Type ◽  
Proposed Model ◽  
Interval Type

PurposeThis paper proposes an integrated IT2F-FMEA model under a group decision-making setting. In risk assessment models, experts' evaluations are often aggregated beforehand, and necessary computations are performed, which in turn, may cause a loss of information and valuable individual opinions. The proposed integrated IT2F-FMEA model aims to calculate risk priority numbers from the experts' evaluations and then fuse experts' judgments using a novel integrated model.Design/methodology/approachThis paper presents a novel failure mode and effect analysis (FMEA) model by integrating the fuzzy inference system, best-worst method (BWM) and weighted aggregated sum-product assessment (WASPAS) methods under interval type-2 fuzzy (IT2F) environment. The proposed FMEA approach utilizes the Mamdani-type IT2F inference system to calculate risk priority numbers. The individual FMEA results are combined by using integrated IT2F-BWM and IT2F-WASPAS methods.FindingsThe proposed model is implemented in a real-life case study in the furniture industry. According to the case study, fifteen failure modes are considered, and the proposed integrated method is used to prioritize the failure modes.Originality/valueMamdani-type singleton IT2F inference model is employed in the FMEA. Additionally, the proposed model allows experts to construct their membership functions and fuzzy rules to capitalize on the experience and knowledge of the experts. The proposed group FMEA model aggregates experts' judgments by using IT2F-BWM and IT2F-WASPAS methods. The proposed model is implemented in a real-life case study in the furniture company.

Investigating the potential of redundant manipulators in narrow channels

2020 ◽  
pp. 095440622096451
Author(s):  
Ashish Singla ◽  
Jyotindra Narayan ◽  
Himanshu Arora
Keyword(s):  
Case Studies ◽  
Fuzzy Inference ◽  
Redundant Manipulators ◽  
Redundancy Resolution ◽  
Inference System ◽  
Narrow Channels ◽  
First Case ◽  
Task Priority ◽  
Patient Side

In this paper, an attempt has been made to investigate the potential of redundant manipulators, while tracking trajectories in narrow channels. The behavior of redundant manipulators is important in many challenging applications like under-water welding in narrow tanks, checking the blockage in sewerage pipes, performing a laparoscopy operation etc. To demonstrate this snake-like behavior, redundancy resolution scheme is utilized using two different approaches. The first approach is based on the concept of task priority, where a given task is split and prioritize into several subtasks like singularity avoidance, obstacle avoidance, torque minimization, and position preference over orientation etc. The second approach is based on Adaptive Neuro Fuzzy Inference System (ANFIS), where the training is provided through given datasets and the results are back-propagated using augmentation of neural networks with fuzzy logics. Three case studies are considered in this work to demonstrate the redundancy resolution of serial manipulators. The first case study of 3-link manipulator is attempted with both the approaches, where the objective is to track the desired trajectory while avoiding multiple obstacles. The second case study of 7-link manipulator, tracking trajectory in a narrow channel, is investigated using the concept of task priority. The realistic application of minimum-invasive surgery (MIS) based trajectory tracking is considered as the third case study, which is attempted using ANFIS approach. The 5-link spatial redundant manipulator, also known as a patient-side manipulator being developed at CSIR-CSIO, Chandigarh is used to track the desired surgical cuts. Through the three case studies, it is well demonstrated that both the approaches are giving satisfactory results.

Sustainable management of water demand using fuzzy inference system: a case study of Kenyir Lake, Malaysia

2021 ◽  
Author(s):  
Nor Najwa Irina Mohd Azlan ◽  
Marlinda Abdul Malek ◽  
Maslina Zolkepli ◽  
Jamilah Mohd Salim ◽  
Ali Najah Ahmed
Keyword(s):  
Fuzzy Inference System ◽  
Water Demand ◽  
Sustainable Management ◽  
Fuzzy Inference ◽  
Inference System ◽  
System A

Failure mode, effects and criticality analysis improvement by using new criticality assessment and prioritization based approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ammar Chakhrit ◽  
Mohammed Chennoufi
Keyword(s):  
Failure Mode ◽  
Conventional Method ◽  
Fuzzy Inference System ◽  
Risk Evaluation ◽  
Failure Modes ◽  
Fuzzy Inference ◽  
Decision Makers ◽  
Inference System ◽  
Content Type ◽  
Criticality Analysis

Purpose This paper aims to enable the analysts of reliability and safety system to assess the criticality and prioritize failure modes perfectly to prefer actions for controlling the risks of undesirable scenarios. Design/methodology/approach To resolve the challenge of uncertainty and ambiguous related to the parameters, frequency, non-detection and severity considered in the traditional approach failure mode effect and criticality analysis (FMECA) for risk evaluation, the authors used fuzzy logic where these parameters are shown as members of a fuzzy set, which fuzzified by using appropriate membership functions. The adaptive neuro-fuzzy inference system process is suggested as a dynamic, intelligently chosen model to ameliorate and validate the results obtained by the fuzzy inference system and effectively predict the criticality evaluation of failure modes. A new hybrid model is proposed that combines the grey relational approach and fuzzy analytic hierarchy process to improve the exploitation of the FMECA conventional method. Findings This research project aims to reflect the real case study of the gas turbine system. Using this analysis allows evaluating the criticality effectively and provides an alternate prioritizing to that obtained by the conventional method. The obtained results show that the integration of two multi-criteria decision methods and incorporating their results enable to instill confidence in decision-makers regarding the criticality prioritizations of failure modes and the shortcoming concerning the lack of established rules of inference system which necessitate a lot of experience and shows the weightage or importance to the three parameters severity, detection and frequency, which are considered to have equal importance in the traditional method. Originality/value This paper is providing encouraging results regarding the risk evaluation and prioritizing failures mode and decision-makers guidance to refine the relevance of decision-making to reduce the probability of occurrence and the severity of the undesirable scenarios with handling different forms of ambiguity, uncertainty and divergent judgments of experts.

scholarly journals Utilization of Current Data for Geospatial Analysis of the Appropriateness of Apple Plantation Land Based on Fuzzy Inference Systems

2019 ◽  
Vol 4 (1) ◽  
pp. 64
Author(s):  
Prayudi Lestantyo
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Geospatial Analysis ◽  
Current Data ◽  
Inference System ◽  
Apple Industry ◽  
Accuracy Test ◽  
Spatial Factor ◽  
Apple Production ◽  
Inference Systems

Apple is a high-value import fruit in Indonesia. One of the Apple production centers in Indonesia is Batu City, but the results tend to be declining in every year. To fulfill the demand of domestic apple industry, it is than a must to open new plantation land by observing the spatial factor. Expert and direct field review are needed to perform the analysis of land suitability, so that it will takes a lot of time and effort. Therefore, a smart system that can conduct geospatial analysis by using fuzzy inference system is developed. The data was obtained by using satellite imagery, data interpolation, and digitized and then analyzed into information. The analysis was performed on each pixel with six variable inputs including altitude, rainfall, humidity, air temperature, soil type and sun shine intensity. Besides that, the five-clustering output makes the results more accurate. From the results of the accuracy test, it is obtained a 92,86% accuracy, by comparing the results of the spatial analysis using fuzzy inference system with direct review on the field.

Fuzzy Logic-Based Inference Systems

2015 ◽  
pp. 209-239
Keyword(s):  
Fuzzy Logic ◽  
Knowledge Representation ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Mathematical Formulation ◽  
Cognitive Map ◽  
Fuzzy Cognitive Map ◽  
Adaptive Networks ◽  
Inference System ◽  
Inference Systems

This chapter presents the mathematical formulation of the fuzzy logic-based inference systems, used as means to infer about the response of ill-conditioned systems, based on the field knowledge representation in the fuzzy world. Particular approaches are explored, e.g., Fuzzy Inference System (FIS), Adaptive Networks-based FIS (ANFIS), Intuitionistic FIS (IFIS) and Fuzzy Cognitive Map (FCM), surfacing their potentialities in modeling applications, such as those in the field of learning, examined in the chapters of Part III that follow.

An Integrated Data Mining and Simulation Solution

2010 ◽  
pp. 929-948
Author(s):  
Mouhib Alnoukari ◽  
Asim El Sheikh ◽  
Zaidoun Alzoabi
Keyword(s):  
Data Mining ◽  
Fuzzy Inference ◽  
Decision Support Tools ◽  
Market Demand ◽  
Inference System ◽  
Car Market ◽  
Neuro Fuzzy ◽  
Support Tools ◽  
Quality In Higher Education

Simulation and data mining can provide managers with decision support tools. However, the heart of data mining is knowledge discovery; as it enables skilled practitioners with the power to discover relevant objects and the relationships that exist between these objects, while simulation provides a vehicle to represent those objects and their relationships. In this chapter, the authors will propose an intelligent DSS framework based on data mining and simulation integration. The main output of this framework is the increase of knowledge. Two case studies will be presented, the first one on car market demand simulation. The simulation model was built using neural networks to get the first set of prediction results. Data mining methodology used named ANFIS (Adaptive Neuro-Fuzzy Inference System). The second case study will demonstrate how applying data mining and simulation in assuring quality in higher education

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