scholarly journals A Modified Model of Failure Mode and Effects Analysis Based on Generalized Evidence Theory

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Deyun Zhou ◽  
Yongchuan Tang ◽  
Wen Jiang
Keyword(s):  
Risk Factors ◽  
Failure Mode ◽  
Evidence Theory ◽  
Turbine Rotor ◽  
Rotor Blades ◽  
Modified Model ◽  
Open World ◽  
Open Issue ◽  
Conventional Risk Factors

Due to the incomplete knowledge, how to handle the uncertain risk factors in failure mode and effects analysis (FMEA) is still an open issue. This paper proposes a new generalized evidential FMEA (GEFMEA) model to handle the uncertain risk factor, which may not be included in the conventional FMEA model. In GEFMEA, not only the conventional risk factors, the occurrence, severity, and detectability of the failure mode, but also the other incomplete risk factors are taken into consideration. In addition, the relative importance among all these risk factors is well addressed in the proposed method. GEFMEA is based on the generalized evidence theory, which is efficient in handling incomplete information in the open world. The efficiency and some merit of the proposed method are verified by the numerical example and a real case study on aircraft turbine rotor blades.

scholarly journals Modeling and Fusing the Uncertainty of FMEA Experts Using an Entropy-Like Measure with an Application in Fault Evaluation of Aircraft Turbine Rotor Blades

Entropy ◽  
2018 ◽  
Vol 20 (11) ◽  
pp. 864 ◽  
Author(s):  
Xuelian Zhou ◽  
Yongchuan Tang
Keyword(s):  
Evidence Theory ◽  
Turbine Rotor ◽  
Rotor Blades ◽  
Information Modeling ◽  
Subjective Assessments ◽  
Prediction And Prevention ◽  
Practical Engineering ◽  
New Perspective ◽  
Fmea Method

As a typical tool of risk analysis in practical engineering, failure mode and effects analysis (FMEA) theory is a well known method for risk prediction and prevention. However, how to quantify the uncertainty of the subjective assessments from FMEA experts and aggregate the corresponding uncertainty to the classical FMEA approach still needs further study. In this paper, we argue that the subjective assessments of FMEA experts can be adopted to model the weight of each FMEA expert, which can be regarded as a data-driven method for ambiguity information modeling in FMEA method. Based on this new perspective, a modified FMEA approach is proposed, where the subjective uncertainty of FMEA experts is handled in the framework of Dempster–Shafer evidence theory (DST). In the improved FMEA approach, the ambiguity measure (AM) which is an entropy-like uncertainty measure in DST framework is applied to quantify the uncertainty degree of each FMEA expert. Then, the classical risk priority number (RPN) model is improved by aggregating an AM-based weight factor into the RPN function. A case study based on the new RPN model in aircraft turbine rotor blades verifies the applicable and useful of the proposed FMEA approach.

scholarly journals Deng Entropy Weighted Risk Priority Number Model for Failure Mode and Effects Analysis

Entropy ◽  
2020 ◽  
Vol 22 (3) ◽  
pp. 280 ◽  
Author(s):  
Haixia Zheng ◽  
Yongchuan Tang
Keyword(s):  
Risk Factors ◽  
Risk Factor ◽  
Failure Mode ◽  
Evidence Theory ◽  
Relative Weight ◽  
Relative Importance ◽  
Risk Priority Number ◽  
Domain Experts ◽  
Entropy Weighted ◽  
Deng Entropy

Failure mode and effects analysis (FMEA), as a commonly used risk management method, has been extensively applied to the engineering domain. A vital parameter in FMEA is the risk priority number (RPN), which is the product of occurrence (O), severity (S), and detection (D) of a failure mode. To deal with the uncertainty in the assessments given by domain experts, a novel Deng entropy weighted risk priority number (DEWRPN) for FMEA is proposed in the framework of Dempster–Shafer evidence theory (DST). DEWRPN takes into consideration the relative importance in both risk factors and FMEA experts. The uncertain degree of objective assessments coming from experts are measured by the Deng entropy. An expert’s weight is comprised of the three risk factors’ weights obtained independently from expert’s assessments. In DEWRPN, the strategy of assigning weight for each expert is flexible and compatible to the real decision-making situation. The entropy-based relative weight symbolizes the relative importance. In detail, the higher the uncertain degree of a risk factor from an expert is, the lower the weight of the corresponding risk factor will be and vice versa. We utilize Deng entropy to construct the exponential weight of each risk factor as well as an expert’s relative importance on an FMEA item in a state-of-the-art way. A case study is adopted to verify the practicability and effectiveness of the proposed model.

scholarly journals A Method to Determine Generalized Basic Probability Assignment in the Open World

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Wen Jiang ◽  
Jun Zhan ◽  
Deyun Zhou ◽  
Xin Li
Keyword(s):  
Information Fusion ◽  
Evidence Theory ◽  
Triangular Fuzzy Number ◽  
Probability Assignment ◽  
Basic Probability Assignment ◽  
Open World ◽  
Differentiation Degree ◽  
Basic Probability ◽  
Open Issue ◽  
The Given

Dempster-Shafer evidence theory (D-S theory) has been widely used in many information fusion systems since it was proposed by Dempster and extended by Shafer. However, how to determine the basic probability assignment (BPA), which is the main and first step in D-S theory, is still an open issue, especially when the given environment is in an open world, which means the frame of discernment is incomplete. In this paper, a method to determine generalized basic probability assignment in an open world is proposed. Frame of discernment in an open world is established first, and then the triangular fuzzy number models to identify target in the proposed frame of discernment are established. Pessimistic strategy based on the differentiation degree between model and sample is defined to yield the BPAs for known targets. If the sum of all the BPAs of known targets is over one, then they will be normalized and the BPA of unknown target is assigned to0; otherwise the BPA of unknown target is equal to1minus the sum of all the known targets BPAs. IRIS classification examples illustrated the effectiveness of the proposed method.

Case Study of Electro-Optical Probing Signal with High Signal-Noise Ratio

2019 ◽  
Author(s):  
Ryan Xiao ◽  
William Wang ◽  
Ang Li ◽  
Shengqiu Xu ◽  
Binghai Liu
Keyword(s):  
Failure Mode ◽  
High Signal ◽  
Signal Noise ◽  
Electrical Failure ◽  
Current Path ◽  
Probing Signal ◽  
Optical Probing ◽  
Noise Ratio ◽  
Case Collection

Abstract With the development of semiconductor technology and the increment quantity of metal layers in past few years, backside EFA (Electrical Failure Analysis) technology has become the dominant method. In this paper, abnormally high Signal Noise Ratio (SNR) signal captured by Electro-Optical Probing (EOP)/Laser Voltage Probing (LVP) from backside is shown and the cause of these phenomena are studied. Based on the real case collection, two kinds of failure mode are summarized, and simulated experiments are performed. The results indicate that when a current path from power to ground is formed, the high SNR signal can be captured at the transistor which was on this current path. It is helpful of this consequence for FA to identify the failure mode by high SNR signal.

Case Study and Fault Modeling for Wrong Redundancy Evaluation on DRAM Devices

2007 ◽  
Author(s):  
Martin Versen ◽  
Dorina Diaconescu ◽  
Jerome Touzel
Keyword(s):  
Failure Mode ◽  
Failure Modes ◽  
Fault Modeling ◽  
Mode Analysis ◽  
Root Cause ◽  
Failure Mode Analysis

Abstract The characterization of failure modes of DRAM is often straight forward if array related hard failures with specific addresses for localization are concerned. The paper presents a case study of a bitline oriented failure mode connected to a redundancy evaluation in the DRAM periphery. The failure mode analysis and fault modeling focus both on the root-cause and on the test aspects of the problem.

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