Time-Dependent Probabilistic Approach of Failure Mode and Effect Analysis

Jang, Hyeonae; Min, Seungsik

doi:10.3390/app9224939

Cited by 3 publications

(3 citation statements)

References 29 publications

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“…In a differentiated way, our approach integrates both types of uncertainty but respects the stages of a Mamdani-type fuzzy model where probabilistic concepts are introduced based on historical data. Furthermore, Jang [23] developed an essential proposal to overcome the limitations of FMEA by constructing a time-dependent probabilistic model. This study considered that the longer the time that had passed since the failure occurred, the greater the loss it caused in the process or system.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Fuzzy System for Evaluation and Classification in Failure Mode and Effect Analysis

Cardiel-Ortega,

Baeza-Serrato

2024

Processes

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Failure Mode and Effect Analysis (FMEA) is an essential risk analysis tool that is widely applicable in various industrial sectors. This structured technique allows us to identify and assign priority levels to potential failures that violate the reliability of a system or process. Failure evaluation occurs in a decision-making environment with uncertainty. This study proposes a probabilistic fuzzy system that integrates linguistic and stochastic uncertainty based on a Mamdani-type model to strengthen the FMEA technique. The system is based on analyzing the frequency of failures and obtaining the parameters to determine the probability of occurrence through the Poisson distribution. In addition, the severity and detection criteria were evaluated by the experts and modeled using the Binomial distribution. The evaluation result is a discrete value analogous to the process of obtaining the success or failure of the expert generating the evaluation of 10 Bernoulli experiments. Three fuzzy inference expert systems were developed to combine multiple experts’ opinions and reduce linguistic subjectivity. The case study was implemented in the knitting area of a textile company in the south of Guanajuato to validate the proposed approach. The potential failure of the knitting machinery, which compromises the top tension subsystem’s performance and the product’s quality, was analyzed. The proposed system, which is based on a robust mathematical model, allows for reliable fault evaluation with a simple scale. The classification performed by the system and the one performed by the experts has similar behavior. The results show that the proposed approach supports decision-making by prioritizing failure modes.

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Section: Introductionmentioning

confidence: 99%

Probabilistic Fuzzy System for Evaluation and Classification in Failure Mode and Effect Analysis

Cardiel-Ortega,

Baeza-Serrato

2024

Processes

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“…RPN evaluation of a potential failure requires evaluating three risk factors, i.e., O, S and D, via the 1-to-10 scale. The higher the RPN of a failure, the greater is the associated risk concerning system/product reliability [10].…”

Section: Introductionmentioning

confidence: 99%

“…Others included an integration-based approach [21,22,26,27] and probability-based methodology [19,36]. Apart from these approaches, in our previous study [10,36], we proposed an economic expected loss model in consideration of the time between the occurrence of the cause of failure and the time it takes to be detected when targeting a step-tier system and a multi-tier system. The proposed model is a timedependent FMEA model, which has the advantage of being able to intuitively assess the size of risk, unlike RPN, which is a traditional FMEA risk assessment index, RPN.…”

Section: Introductionmentioning

confidence: 99%

Case Study of Expected Loss Failure Mode and Effect Analysis Model Based on Maintenance Data

Min

Jang

2021

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 during the product design and manufacturing phases. Risk priority number (RPN), a risk assessment indicator used in FMEA, is widely used in the field due to its simple calculation process, but its limitations as an absolute risk assessment indicator have been pointed out. There has also been criticism of the unstructured nature and lack of systematicity in the FMEA procedures. This work proposes an expected loss-FMEA (EL-FMEA) model that organizes FMEA procedures and structures quantitative risk assessment metrics. In the EL-FMEA model, collectible maintenance record data is defined and based on this, the failure rate of components and systems and downtime and uptime of the system are calculated. Moreover, based on these calculated values, the expected economic loss is computed considering the failure detection time. It also provides an alternative coefficient to evaluate whether or not a detection system is installed to improve the expected loss of failure. Finally, a case study was conducted based on the maintenance record data, and the application procedure of the EL-FMEA model was presented in detail, and the practicality of this model was verified through the results.

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