Fuzzy Failure Modes Effect and Criticality Analysis of the Procurement Process of Artificial Intelligent Systems/Services

Alshehhi, Khalid; Cheaitou, Ali; Rashid, Hamad

doi:10.14569/ijacsa.2023.0141060

Cited by 2 publications

(1 citation statement)

References 43 publications

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“…Pacana and Siwiec [20] developed a valuable proposal incorporating a qualitative environmental indicator to a diffuse FMEA to analyze the risks in the quality of the product and the natural environment. The study of Alshehhi et al [21] proposes a Mamdani-type FIS in the criticality analysis and FMEA to classify the risks associated with acquiring Artificial Intelligence systems for public sectors. The works cited above are characterized by evaluating the occurrence criterion using a comparative table with established categories, where the table also contains a column of assigned probabilities as a reference base to assign the category.…”

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%