Functional Evaluation Using Fuzzy FMEA for a Non-Invasive Measurer for Methane and Carbone Dioxide

Abstract

This paper combines the use of two tools: Failure Mode and Effect Analysis (FMEA) and Fuzzy Logic (FL), to evaluate the functionality of a quantifier prototype of Methane gas (CH4) and Carbon Dioxide (CO2), developed specifically to measure the emissions generated by cattle. Unlike previously reported models for the same purpose, this device reduces damage to the integrity of the animal and does not interfere with the activities of livestock in their development medium. FMEA and FL are used to validate the device’s functionality, which involves identifying possible failure modes that represent a more significant impact on the operation and prevent the prototype from fulfilling the function for which it was created. As a result, this document presents the development of an intelligent fuzzy system type Mamdani, supported in the Fuzzy Inference System Toolbox of MatLabR2018b®, for generating a risk priority index. A Fuzzy FMEA model was obtained to validate the prototype for measuring Methane and Carbon Dioxide emissions, which allows considering this prototype as a reliable alternative for the reliable measurement of these gases. This study was necessary as a complementary part in the validation of the design of the prototype quantifier of CH4 and CO2 emissions. The methods used (classic FMEA and Fuzzy FMEA) to evaluate the RPN show asymmetric graphs due to data disparity. Values in the classical method are mostly lower than the Mamdani model results due to the description of the criteria with which it is evaluated.

A measurement system for enteric CH4 emissions monitoring from ruminants in livestock farming

“…• Integration in real livestock farms. In [27], a research study reporting experimental results from a real integration of a monitoring system for methane emission from ruminants in real livestock farms, is presented. The analysis is done adopting a Bayesian network which models the relationships among various factors that are related to the enteric methane emissions from cows.…”

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

“…In their study, Goksu and Arslan [15] proposed a fuzzy FMEA to evaluate risks in the naval industry and thus improve ship operating conditions. Furthermore, Cruz-Rivero et al [16] combined FMEA with fuzzy logic to evaluate the functionality of a prototype that quantifies methane gas and carbon dioxide in the livestock industry. In each study cited above, the risk assessment is carried out in an environment of linguistic uncertainty, and stochastic uncertainty is not considered, unlike in the present research, which proposes an evaluation system that integrates the two types of uncertainty to generate a robust evaluation framework.…”

Improvement of Failure Mode and Effects Analysis Using Fuzzy and Adaptive Neuro-Fuzzy Inference System

“…The following systematic review [3] of papers on this topic, produced in the period between 1998 and 2018, points to a very quick increase in annual publications on FMEA improvement in the past two decades, after the publication of the review article by the above mentioned authors in 2013. The majority of papers selected the fuzzy logic system in an attempt to eliminate the disadvantages of the traditional FMEA based on "if-then" rules formed in fuzzy inference system (FIS) and improve upon calculated fuzzy RPN and risk prioritization, for example, for milling machines [4,5], environmental waste water treatment plant [6], automotive industry [7,8] and other industries [9][10][11][12][13][14][15]. For determining risk levels and prioritizing mitigation action for the risks, some authors have taken into account additional variables, for example, costs based on economic impact [16][17][18] or impact of assets, people, environment and/or reputation [19,20].…”