Amalgamated Criticality Analysis Methodology

2013 ◽  
Vol 679 ◽  
pp. 101-106
Author(s):  
Ming Li Liao ◽  
Yung Tsan Jou ◽  
Cheng Shih Liaw

Failure mode effects and criticality analysis (FMECA) is a widely useful design tool for enhancing product quality, safety and reliability. Most of the current FMECA procedure is in accordance with MIL-STD-1629A by which to conduct FMECA and criticality analysis, which is able to prioritize the failure modes and undertake limited corrective actions toward eliminating product risks. However, the criticality analysis calculation and its interpretation for a failure mode have some mathematical difficulties and erroneous omissions. To resolve these problems, this study proposes a new amalgamated criticality analysis methodology, which is knowledge-based to obtain the four different factor criteria and then using the maximal entropy ordered weighted geometric averaging (ME-OWGA) approach to compute the criticality numbers for a system. This study evaluates criticality analysis in the context of a communication system; the experimental results demonstrate that the proposed method is both accurate and provides discriminating analysis information that helps decision making in product design processes.

Author(s):  
Carlos Alberto Murad ◽  
Arthur Henrique de Andrade Melani ◽  
Miguel Angelo de Carvalho Michalski ◽  
Adherbal Caminada Netto ◽  
Gilberto Francisco Martha de Souza ◽  
...  

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.


Author(s):  
Mohammad Reza Abedini ◽  
Mostafa Abedi

This paper proposes a robust fault-tolerant control algorithm for a three-axis satellite. In this regard, an adaptive sliding attitude control algorithm is suggested, which has the capability of fault estimation in the satellite actuators and correction of their effects. For this, the disturbances due to environmental effects and actuator failures and also the satellite unknown parameters are estimated by the adaptive updating law; the sliding mode algorithm compensates the errors due to estimation process. In the suggested design process, the sliding surface is selected so that the unwinding and singularity problems are solved, and also a compensator part is included to remove unstable equilibrium points. In this paper, the failure mode effects criticality analysis have been done to classify different failure modes of reaction wheel according to their severity and probability of occurrence. Accordingly, the critical failure modes and their effects at the control system level are derived. It is shown that the derived critical failures lead to small or severe variations in the generated torques of reaction wheels for which a supervision level will be proposed to correct their effects. Finally, different simulations are conducted to validate expected performance of the suggested algorithms.


2013 ◽  
Vol 655-657 ◽  
pp. 2409-2413
Author(s):  
Xiao Li Xu ◽  
Qing Liu ◽  
Bo Qiang Zhu

Since human error accounts for more than 70% in the causes of maritime accidents, the analysis of human error mode will benefit the scientific analysis and evaluation of maritime traffic safety management, to get the nature security finally. With the application of Failure mode, effects, and criticality analysis (FMECA), this paper sums up ten specific human failure modes. After the analysis of the modes based on materials from British Maritime Commission, it uses the Criticality Analysis (CA) to analyze the major mode of high hazard. The result has important guiding significance and value on refining the main hazards of maritime accidents, and the nature of maritime management.


Author(s):  
Shan Guan ◽  
Knut Erik Knutsen ◽  
Øystein Åsheim Alnes

Condition monitoring technique has been widely applied in Maritime to ensure safe operation and minimise unscheduled downtime. However, in practice, ship operators need to assure that a failure mode is indeed monitored by the sensor intended for it, and the sensor has sufficient accuracy and precision for its purpose. Additionally, for a reliable condition monitoring technique, issues such as sensors degradation or drift that will reduce the data quality over time must be addressed. All these require that ship owners to select a monitoring system with the best suitable sensors technology while is economically viable. In this paper, tunnel thruster was used as a case study to demonstrate the basic approach to develop a reliable condition monitoring technique through Failure Mode, Effects and Criticality Analysis (FMECA). Based on failure modes, four types of condition monitoring techniques were identified including Vibration Monitoring, Acoustic Emission Monitoring, Wear Debris /Water in Oil Monitoring, and Thermal Monitoring, where vibration monitoring is discussed in detail as an example for defining the sensor specification. For a reliable condition monitoring technique, prediction of sensor reliability will be especially useful in the situation where sensors systems can degrade over time in service. Using temperature sensors as an example, a Bayesian network (BN) modeling approach has been carried out for assessing sensor reliability affected by aging.


Author(s):  
ABDELKADER BOUTI ◽  
DAOUD AIT KADI

The Failure Mode and Effects Analysis (FMEA) documents single failures of a system, by identifying the failure modes, and the causes and effects of each potential failure mode on system service and defining appropriate detection procedures and corrective actions. When extended by Criticality Analysis procedure (CA) for failure modes classification, it is known as Failure Mode Effects and Criticality Analysis (FMECA). The present paper presents a literature review of FME(C)A, covering the following aspects: description and review of the basic principles of FME(C)A, types, enhancement of the method, automation and available computer codes, combination with other techniques and specific applications. We conclude with a discussion of various issues raised as a result of the review.


Entropy ◽  
2019 ◽  
Vol 21 (12) ◽  
pp. 1230 ◽  
Author(s):  
Lixiang Wang ◽  
Wei Dai ◽  
Guixiu Luo ◽  
Yu Zhao

Failure Mode, Effects and Criticality Analysis (FMECA) is a method which involves quantitative failure analysis. It systematically examines potential failure modes in a system, as well as the components of the system, to determine the impact of a failure. In addition, it is one of the most powerful techniques used for risk assessment and maintenance management. However, various drawbacks are inherent to the classical FMECA method, especially in ranking failure modes. This paper proposes a novel approach that uses complex networks theory to support FMECA. Firstly, the failure modes and their causes and effects are defined as nodes, and according to the logical relationship between failure modes, and their causes and effects, a weighted graph is established. Secondly, we use complex network theory to analyze the weighted graph, and the entropy centrality approach is applied to identify influential nodes. Finally, a real-world case is presented to illustrate and verify the proposed method.


2014 ◽  
Vol 17 (2) ◽  
pp. 193-210 ◽  
Author(s):  
Hwee Hwang ◽  
Kevin Lansey ◽  
Daniel R. Quintanar

An alternative risk assessment method, known as failure mode effects and criticality analysis (FMECA), is demonstrated on the regional water supply systems (RWSS) in Tucson, AZ, USA that combines delivery of potable and reclaimed water and conveyance of wastewater to a developing area within the Tucson RWSS. The goal of FMECA is to examine the volumetric severity of a component failure on the overall system function by modeling the system under alternative failure modes. Within FMECA, the Risk Priority Number (RPN) is applied to compare the risk criticality between components' failures. To complete FMECA, the Tucson RWSS is represented in a network flow model that optimally allocates flows between sources and demand points to minimize operational costs. Potential failure mode consequences are evaluated from the flow model as the volume of water not delivered to users if the component is unavailable. The volumetric severity of the failure event is converted to an ordinal value using stakeholder judgment. Likelihood of each failure mode is similarly defined by stakeholders on a 1–10 scale. The RPN is then computed as the product of the severity and likelihood. RPN values for all failure modes are then ranked to assess the most critical elements. Alternative system configurations are examined to assess the value of redundancies on the Tucson RWSS resilience.


Author(s):  
Srikesh G. Arunajadai ◽  
Robert B. Stone ◽  
Irem Y. Tumer

Knowledge of potential failure modes during design is critical for prevention of failures. Currently industries use procedures such as Failure Modes and Effects Analysis (FMEA), Fault Tree analysis, or Failure Modes, Effects and Criticality analysis (FMECA), as well as knowledge and experience, to determine potential failure modes. When new products are being developed there is often a lack of sufficient knowledge of potential failure mode and/or a lack of sufficient experience to identify all failure modes. This gives rise to a situation in which engineers are unable to extract maximum benefits from the above procedures. In this work we report on a new failure identification scheme and integrate it with a function-based failure identification methodology, which would act as a storehouse of information and experience, providing useful information about the potential failure modes for the design under consideration, as well as enhancing the usefulness of procedures like FMEA. As an example, the method is applied to 41 products and the benefits are illustrated.


Author(s):  
Inggit Marodiyah ◽  
Indung Sudarso

Manajemen kualitas digunakan untuk mengevaluasi kualitas bangunan, sedangkan manajemen risiko digunakan untuk mengetahui risiko yang berpengaruh terhadap kualitas bangunan. Seperti sumber daya manusia yang tidak memakai alat pelindung diri (APD) saat melakukan pekerjaan dikarenakan dengan alasan pekerjaan semakin lama serta ribet dikerjakan. Sehingga dengan adanya beberapa proses yang belum terstandar, maka dapat berisiko mempengaruhi kualitas pembangunan yang tidak sesuai harapan. Tujuan dilakukan penelitian ini adalah untuk mengetahui tingkat risiko yang berpengaruh terhadap kualitas pembangunan dan menentukan mitigasi untuk peningkatan kualitas pembangunan. Metode yang di gunakan adalah Quality Risk Management (QRM) yang berfungsi mengevaluasi indikator yang mempengaruhi peningkatan kualitas dan Failure Modes Effects and Criticality Analysis (FMECA) untuk mengevaluasi dampak potensial dari setiap kegagalan dengan memberi skala prioritas demi mengetahui tingkat risiko. Hasil yang diperoleh dari pendekatan QRM dan FMECA yaitu pekerjaan rangka atap dengan RPN sebesar 42 dan plesteran dinding luar dengan RPN sebesar 28 yang tergolong risiko tinggi (Critical High ). Sehingga mitigasi risiko yang diberikan yaitu pemakaian APD seperti safety belt safety shoes, helmet serta konsentrasi agar dapat membantu mencegah atau mengatasi apabila risiko yang tidak di inginkan dapat mempengaruhi kualitas bangunan.  


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