Failure Mode and Effects Analysis on Control Equipment Using Fuzzy Theory

2013 ◽  
Vol 837 ◽  
pp. 16-21
Author(s):  
Nadia Belu ◽  
Daniel Constantin Anghel ◽  
Nicoleta Rachieru
Keyword(s):  
System Design ◽  
Failure Mode ◽  
Failure Modes ◽  
Fuzzy Theory ◽  
Risk Level ◽  
Risk Priority Number ◽  
Control Equipment ◽  
Wide Range ◽  
Criticality Analysis ◽  
Made In

Failure Mode and Effects Analysis is a methodology to evaluate a system, design, process, machine or service for possible ways in which failures (problems, errors, risks and concerns) can occur and it has been used in a wide range of industries. Traditional method uses a Risk Priority Number to evaluate the risk level of a component or process. This is obtained by finding the multiplication of three factors, which are the severity of the failure (S), the probability/occurrence of the failure (O), and the probability of not detecting the failure (D). There are significant efforts which have been made in FMEA literature to overcome the shortcomings of the crisp RPN calculation. Fuzzy logic appears to be a powerful tool for performing a criticality analysis on a system design and prioritizing failure identified in analisys FMEA for corrective actions. In this paper we present a parallel between the typical and the fuzzy computation of RPNs, in order to assess and rank risks associated to failure modes that could appear in the functioning of control equipment.

A New Approach for Prioritization of Failure Mode in FMECA Using Encouragement Variable Weight AHP

2013 ◽  
Vol 289 ◽  
pp. 93-98 ◽  
Author(s):  
Shu Zhong Zhang ◽  
Qin Da Zeng ◽  
Gong Zhang
Keyword(s):  
Failure Mode ◽  
Failure Modes ◽  
Risk Level ◽  
Analytic Hierarchy ◽  
New Approach ◽  
Variable Weight ◽  
Potential Applications ◽  
Criticality Analysis ◽  
Hierarchy Process

The traditional failure mode, effect, and criticality analysis (FMECA) uses risk priority number (RPN) to evaluate the risk level of a failure mode. The RPN index is calculated by multiplication of severity, occurrence and detection factors. The most critically debated disadvantage of this approach is that various combinations of these three factors may produce an identical value of RPN. This paper reviews the drawbacks in traditional FMECA and proposes a new approach to overcome these shortcomings. The proposed approach evaluates risk of failure mode by encouragement-variable-weighted analytic hierarchy process (EVW-AHP) that can prioritize failure modes even if two or more failure modes have same RPN. An example is provided to show the potential applications of the proposed approach and the detailed computational process is presented. The results based on the case study show the proposed new methodology solves the limitations of traditional FMECA approach and is feasible.

scholarly journals Cartilage Tissue Engineering by Extrusion Bioprinting: Process Analysis, Risk Evaluation, and Mitigation Strategies

Materials ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3528
Author(s):  
Mauro Petretta ◽  
Giovanna Desando ◽  
Brunella Grigolo ◽  
Livia Roseti
Keyword(s):  
Tissue Engineering ◽  
Risk Analysis ◽  
Failure Mode ◽  
Failure Modes ◽  
Process Analysis ◽  
Cartilage Tissue Engineering ◽  
Cartilage Tissue ◽  
Risk Level ◽  
Main Process ◽  
Criticality Analysis

Extrusion bioprinting is considered promising in cartilage tissue engineering since it allows the fabrication of complex, customized, and living constructs potentially suitable for clinical applications. However, clinical translation is often complicated by the variability and unknown/unsolved issues related to this technology. The aim of this study was to perform a risk analysis on a research process, consisting in the bioprinting of a stem cell-laden collagen bioink to fabricate constructs with cartilage-like properties. The method utilized was the Failure Mode and Effect Analysis/Failure Mode and Effect Criticality Analysis (FMEA/FMECA) which foresees a mapping of the process to proactively identify related risks and the mitigation actions. This proactive risk analysis allowed the identification of forty-seven possible failure modes, deriving from seventy-one potential causes. Twenty-four failure modes displayed a high-risk level according to the selected evaluation criteria and threshold (RPN > 100). The results highlighted that the main process risks are a relatively low fidelity of the fabricated structures, unsuitable parameters/material properties, the death of encapsulated cells due to the shear stress generated along the nozzle by mechanical extrusion, and possible biological contamination phenomena. The main mitigation actions involved personnel training and the implementation of dedicated procedures, system calibration, printing conditions check, and, most importantly, a thorough knowledge of selected biomaterial and cell properties that could be built either through the provided data/scientific literature or their preliminary assessment through dedicated experimental optimization phase. To conclude, highlighting issues in the early research phase and putting in place all the required actions to mitigate risks will make easier to develop a standardized process to be quickly translated to clinical use.

Fuzzy risk assessment for electro-optical target tracker

2016 ◽  
Vol 33 (6) ◽  
pp. 830-851 ◽  
Author(s):  
Soumen Kumar Roy ◽  
A K Sarkar ◽  
Biswajit Mahanty
Keyword(s):  
Failure Mode ◽  
Failure Modes ◽  
Linear Equations ◽  
Mode Analysis ◽  
Failure Behavior ◽  
Membership Functions ◽  
Design And Development ◽  
Effect Analysis ◽  
Content Type ◽  
Criticality Analysis

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.

Machine learning-based failure mode identification of RCSPSW

2021 ◽  
Author(s):  
Dongqi Jiang ◽  
Shanquan Liu ◽  
Tao Chen ◽  
Gang Bi
Keyword(s):  
Machine Learning ◽  
Failure Mode ◽  
Failure Modes ◽  
Shear Failure ◽  
Tall Buildings ◽  
Shear Walls ◽  
Superior Performance ◽  
Ann Model ◽  
Mode Recognition ◽  
Wide Range

<p>Reinforced concrete – steel plate composite shear walls (RCSPSW) have attracted great interests in the construction of tall buildings. From the perspective of life-cycle maintenance, the failure mode recognition is critical in determining the post-earthquake recovery strategies. This paper presents a comprehensive study on a wide range of existing experimental tests and develops a unique library of 17 parameters that affects RCSPSW’s failure modes. A total of 127 specimens are compiled and three types of failure modes are considered: flexure, shear and flexure-shear failure modes. Various machine learning (ML) techniques such as decision trees, random forests (RF), <i>K</i>-nearest neighbours and artificial neural network (ANN) are adopted to identify the failure mode of RCSPSW. RF and ANN algorithm show superior performance as compared to other ML approaches. In Particular, ANN model with one hidden layer and 10 neurons is sufficient for failure mode recognition of RCSPSW.</p>

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.

An Assessment of RPN Prioritization in a Failure Modes Effects and Criticality Analysis

2004 ◽  
Vol 47 (1) ◽  
pp. 51-56 ◽  
Author(s):  
John Bowles
Keyword(s):  
Failure Modes ◽  
Measurement Scale ◽  
Risk Priority Number ◽  
Probability Of Occurrence ◽  
Ordinal Ranking ◽  
Potential Failure ◽  
Different Characteristics ◽  
Criticality Analysis

The Risk Priority Number methodology for prioritizing failure modes is an integral part of the Automobile Failure Modes and Effects Analysis (FMECA) technique. This technique consists of ranking potential failures from 1 to 10 with respect to their severity, probability of occurrence, and likelihood of detection in later tests, and multiplying the numbers. The result is a numerical ranking, called the RPN, on a scale from 1 to 1000. Potential failure modes having higher RPNs are assumed to have a higher design risk than those having lower values. Although this method is well documented and easy to apply, it is seriously flawed from a technical perspective, making the interpretation of the analysis results problematic. Problems with the methodology include: use of ordinal ranking numbers as numeric quantities; lack of continuity in the RPN measurement scale; duplicate RPN values with extremely different characteristics; and varying sensitivity to small changes. Recommendations for an improved methodology are provided.

Failure Mode, Effect and Criticality Analysis of CNC Grinder

2011 ◽  
Vol 141 ◽  
pp. 284-288
Author(s):  
Hong Jun Wang ◽  
Lei Sun ◽  
Lei Shi
Keyword(s):  
Failure Mode ◽  
Production Line ◽  
Failure Modes ◽  
Mode Effect ◽  
Technical Basis ◽  
Criticality Analysis ◽  
Fault Record

CNC grinder is considered as the key equipment of camshaft production line. Its reliability directly influences the quality of the products. This article takes an analysis of fault record of 15 sets of CNC grinders of production line, gets failure positions analysis, and then reaches data of failure modes, fault reason of CNC grinder. Comprehensive analyses of criticality for the machine are done through establishing models. Therefore, this analysis provides technical basis for repair, maintenance and update of CNC grinder of camshaft production line.

scholarly journals Application of Failure Mode Effect and Criticality Analysis (FMECA) to a Computer Integrated Manufacturing (CIM) Conveyor Belt

2018 ◽  
Vol 8 (3) ◽  
pp. 3023-3027
Author(s):  
I. Elbadawi ◽  
M. A. Ashmawy ◽  
W. A. Yusmawiza ◽  
I. A. Chaudhry ◽  
N. B. Ali ◽  
...  
Keyword(s):  
Failure Mode ◽  
Failure Modes ◽  
Production Systems ◽  
Conveyor Belt ◽  
Computer Integrated Manufacturing ◽  
Formal Specifications ◽  
Production Time ◽  
Integrated Manufacturing ◽  
Mode Effect ◽  
Criticality Analysis

Fault finding and failure predicting techniques in manufacturing and production systems often involve forecasting failures, their effects, and occurrences. The majority of these techniques predict failures that may appear during the regular system production time. However, they do not estimate the failure modes and they require extensive source code instrumentation. In this study, we suggest an approach for predicting failure occurrences and modes during system production time intervals at the University of Hail (UoH). The aim of this project is to implement failure mode effect and criticality analysis (FMECA) on computer integrated manufacturing (CIM) conveyors to determine the effect of various failures on the CIM conveyor belt by ranking and prioritizing each failure according to its risk priority number (RPN). We incorporated the results of FMECA in the development of formal specifications of fail-safe CIM conveyor belt systems. The results show that the highest RPN values are for motor over current failure (450), conveyor chase of vibration (400), belt run off at the head pulley (200), accumulated dirt (180), and Bowed belt (150). The study concludes that performing FMECA is highly effective in improving CIM conveyor belt reliability and safety in the mechanical engineering workshop at UoH.

Fuzzy-FMSA: Evaluating Fault Monitoring and Detection Strategies Based on Failure Mode and Symptom Analysis and Fuzzy Logic

2020 ◽  
Vol 6 (3) ◽  
Author(s):  
Carlos Alberto Murad ◽  
Arthur Henrique de Andrade Melani ◽  
Miguel Angelo de Carvalho Michalski ◽  
Adherbal Caminada Netto ◽  
Gilberto Francisco Martha de Souza ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Failure Mode ◽  
Failure Modes ◽  
Monitoring Techniques ◽  
Knowledge Based ◽  
Diagnosis And Prognosis ◽  
Kaplan Turbine ◽  
Shaft System ◽  
Fmea Method ◽  
Criticality Analysis

Abstract Failure mode and symptoms analysis (FMSA) is a relatively new and still not very much employed variation of failure modes, effects and criticality analysis (FMECA), a technique broadly used in reliability, safety, and quality engineering. While FMECA is an extension of the well-known failure mode and effects analysis (FMEA) method, primarily used when a criticality analysis is required, FMSA focuses on the symptoms produced by each considered failure mode and the selection of the most appropriate detection and monitoring techniques and strategies, maximizing the confidence level in the diagnosis and prognosis. However, in the same way as FMECA and FMEA, FMSA inherits some deficiencies, presenting somewhat biased results and uncertainties intrinsic to its development, due to its own algorithm and the dependence on knowledge-based inputs from experts. Accordingly, this article presents a fuzzy logic application as a complement to FMSA in order to mitigate such uncertainties' effects. As a practical example, the method is applied to a Kaplan turbine shaft system. The monitoring priority number (MPN) obtained through FMSA is compared to the fuzzy monitoring priority number (FMPN) resulting from fuzzy logic application, demonstrating how the proposed method improves the evaluation of detection and monitoring techniques and strategies.

ANALYSIS OF REPLACEMENT PART TIME INTERVAL DETERMINATION OF CRITICAL IN ENGINE WATER COOLANT PUMP TYPES KRI FPB57

JOURNAL ASRO ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 32
Author(s):  
Sutrisno Sutrisno ◽  
Abdul Rahman ◽  
Bambang Suharjo ◽  
Rachmana F Antariksa
Keyword(s):  
Failure Mode ◽  
Vital Role ◽  
Time Interval ◽  
Risk Priority Number ◽  
Water Coolant ◽  
Coolant Pump ◽  
Mode Effects ◽  
Critical Components ◽  
Seal Shaft ◽  
Criticality Analysis

Maintenance management is required and has a very vital role for a KRI types FPB57, considering the type KRI is one Alutsista Navy who have a high frequency activity, as well as the broad range of operations support capabilities are varied so that the automatic machine is also high activity and in the end reliability will decrease. Methodology Failure Mode Effects and Criticality Analysis (FMECA) is a widely recognized tool for the study and analysis of the reliability of the design or process. Many authors in the field have emphasized specifically the usefulness of this method and its limitations. At this writing considering the lifetime of the machine and the elements therein specifically the components of the water coolant pump has had a lifetime of more than 20 years, because it can be said that the components have entered a critical period. Based on the steps Failure Mode Effects and Criticality Analysis (FMECA) through the calculation of Risk Priority Number (RPN), so we can determine the critical components of acquired 9 of 19 chances damage that has critical component is Angular Bearings, Cylindrical Bearings, Spacer Ring, Water Seal, shaft Seal, Seal Slip Ring, Impeller, O'Ring and shaft. These components if damaged can lead to engine breakdown. From the optimization results indicate that the component replacement Cylindrical Bearings have the fastest time, ie 98 days. While the replacement of components with the longest time, which is a component Impeller 134 days. Besides obtain the most optimal replacement time of each component, also produced the cost of replacement is effective, it is proved by the value of the optimal CBR. CBR value for all types of components is less than 1 (CBR <1).Keywords : FMECA, Risk Priority Number, Reliability, Replacement Intervals,CBR.

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