Failure modes, mechanisms and effect analysis on temperature redundant sensor stage

Catelani, Marcantonio; Ciani, Lorenzo; Venzi, Matteo

doi:10.1016/j.ress.2018.08.013

Cited by 57 publications

(21 citation statements)

References 11 publications

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“…Taking the carburizing process design of the secondlevel process in the heat treatment stage as an example, the factor set is all the first-level processes involved in this second-level process, namely, = { 5 5 , 6 5 , . .…”

Section: Conduct the First-level And Second-levelmentioning

confidence: 99%

“…Each risk factor generally takes a discrete value in the range [0,10]. The FMECA analysis is widely used in different fields, M. Catelani et al combined FMECA and FMMEA (Failure Modes, Mechanisms and Effect Analysis) to perform failure analysis in a redundant architecture based on temperature sensors included in a Safety Instrumented System for Oil & Gas application [5]. Hu-Chen Liu et al reviewed 75 FMEA papers published between 1992 and 2012 in the international journals and categorized them according to the approaches used to overcome the limitations of the conventional RPN method and, then gave a review on the risk evaluation approaches in FMECA [6].…”

Section: Introductionmentioning

confidence: 99%

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Research Methods and Applications of Gear Manufacturing Process Optimization

Xie

Song

et al. 2019

Mathematical Problems in Engineering

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This paper presents an optimization method for gear processes; through this method, the most worth optimized processes can be obtained and optimized, thus improving the reliability and supportability of gear products. Firstly, the POPN (Process Optimization Priority Number) analysis method considering the current process level and the process improvement cost is proposed with reference to the RPN (Risk Priority Number) analysis method to obtain the most worth optimized processes. Due to the fact that the unreasonable weight distribution of importance (I), changing difficulty (C), rationality (R), and detective difficulty (D) still exists in the POPN analysis, then combining the POPN analysis method and the AHP (Analytic Hierarchy Process)-fuzzy comprehensive evaluation method to evaluate and rank the FPOPN (fuzzy POPN) level of each process, the higher the FPOPN level is, the more the process should be optimized. Finally, according to the evaluation results, some processes with high FPOPN level are optimized and the optimal parameters’ combination of these processes is obtained through the tests based on the Taguchi method. A detailed optimization example is also given from the beginning to end in accordance with the above methods, and compared with the original process gears, the optimized gears have a big increase in gear performance through the above optimization method.

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Section: Conduct the First-level And Second-levelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research Methods and Applications of Gear Manufacturing Process Optimization

Xie

Song

et al. 2019

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…However, this traditional FMEA risk-evaluation approach has often been extensively criticized in extant literature for a variety of reasons [6]. The major shortcomings of the traditional RPN-based approach are as follows: (i) The relative importance among O, S, and D is not considered [7][8][9][10]; (ii) the three risk factors are difficult to precisely evaluate [4,[11][12][13][14][15][16][17]; (iii) interdependencies among various failure modes and effects are not considered [1,5,18,19]; (iv) the method depends on experts' intuition and experience rather than the scientific method to estimate the three risk components [20,21]; and (v) there is no consideration of possible hierarchical relationships among failures [6,22,23].…”

Section: Introductionmentioning

confidence: 99%

“…Zammori and Gabbrielli [31] tried to identify possible interactions among the principal causes of failure by integrating ANP and a multi-criteria decision-making technique. Some researchers have proposed an integrated FMEA technique combining the various methods, i.e., the decision-making method [18,20]; ordered weighted geometric averaging (OWGA) [8]; the linguistic distribution assessment method [16]; Dempster-Shafer theory (DST) [15]; fault-tree analysis (FTA) [32]; robust data-envelopment analysis (RDEA) [21]; and failure modes, mechanisms, and effect analysis (FMMEA) [17]. Parracho Sant'Anna [14] suggested a method based on treating the numerical initial measurements as estimates of the location parameters of probability distributions, which enables objective consideration of the uncertainty inherent in such measurements and computation of the probabilities of each potential failure being the most important according to each criterion.…”

Section: Introductionmentioning

confidence: 99%

Time-Dependent Probabilistic Model for Hierarchical Structure in Failure Mode and Effect Analysis

Jang

Min

2019

Applied Sciences

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Failure mode and effect analysis (FMEA) is a structured technique for identifying risks that may occur during a given stage of a system’s life cycle. However, the use of the risk priority number (RPN) in traditional FMEA results in difficulties with regard to quantification of the degree of risk in the hierarchical failure structure. This study proposes the use of a hierarchical time-dependent FMEA approach to overcome the limitations encountered during the implementation of traditional FMEA approaches. In place of the RPN, a probabilistic loss model is developed under a hierarchical structure considering the elapsed time from the failure-cause (FC) to the system failure. By assuming exponential and case functions for each occurrence and detection time instant, the expected loss corresponding to each FC can be evaluated. As a result of the practical application of the time-dependent probabilistic model through the numerical example, we could reasonably evaluate the risk from the cause of failure in the hierarchical structure in terms of economic loss.

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“…However, this traditional FMEA risk-evaluation approach has often been extensively criticized in extant literature for a variety of reasons [2]. Noteworthy drawbacks of the traditional RPN-based approach include (i) no consideration of relative importance among O, S, and D parameters [7][8][9][10]; (ii) difficulties involved in precise evaluation of the three risk factors [5,[11][12][13][14][15][16][17]; (iii) no consideration of interdependencies among different failure-causes and the corresponding effects [6,[18][19][20]; and (iv) over-dependence on expert intuition and experience instead of scientific methods for evaluation of the three risk components [21,22].…”

Section: Introductionmentioning

confidence: 99%

Time-Dependent Probabilistic Approach of Failure Mode and Effect Analysis

Jang

Min

2019

Applied Sciences

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Failure mode and effect analysis (FMEA) is one of the most widely employed pre-evaluation techniques to avoid risks that may occur during product design and manufacturing phases. However, use of the risk priority number (RPN) in traditional FMEA results in difficulties being encountered with regard to quantification of the degree of risk involved. This study proposes the use of a probabilistic time-dependent FMEA (TD-FMEA) approach to overcome limitations encountered during implementation of traditional FMEA approaches. To this end, the proposed method defines the risk priority metric (RPM) as a priority decision value. RPM corresponds to the product of the expected loss and occurrence rate of the failure-cause. By assuming exponential and case functions for each occurrence and detection time instant, the expected loss corresponding to each failure-cause can be evaluated. Utility of the proposed approach has been described in the light of results obtained via its implementation during an automotive-manufacturing case study performed for the purpose of illustration.

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Failure modes, mechanisms and effect analysis on temperature redundant sensor stage

Cited by 57 publications

References 11 publications

Research Methods and Applications of Gear Manufacturing Process Optimization

Research Methods and Applications of Gear Manufacturing Process Optimization

Time-Dependent Probabilistic Model for Hierarchical Structure in Failure Mode and Effect Analysis

Time-Dependent Probabilistic Approach of Failure Mode and Effect Analysis

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