Using Artificial Neural Network and Fuzzy Inference System Based Prediction to Improve Failure Mode and Effects Analysis: A Case Study of the Busbars Production

Abstract

Nowadays, Busbars have been extensively used in electrical vehicle industry. Therefore, improving the risk assessment for the production could help to screen the associated failure and take necessary actions to minimize the risk. In this research, a fuzzy inference system (FIS) and artificial neural network (ANN) were used to avoid the shortcomings of the classical method by creating new models for risk assessment with higher accuracy. A dataset includes 58 samples are used to create the models. Mamdani fuzzy model and ANN model were developed using MATLAB software. The results showed that the proposed models give a higher level of accuracy compared to the classical method. Furthermore, a fuzzy model reveals that it is more precise and reliable than the ANN and classical models, especially in case of decision making.

Reflection on methodology of the correlation between electromagnetic interference and safety in high-speed railways

“…Specific methodologies include AHP (Santarremigia, Molero, Poveda-Reyes, & Aguilar-Herrando, 2018), FAHP (Peng et al. , 2021), gray relational analysis (GRA) (Wang, Wang, He, Bhamra, & Yang, 2021), artificial neural networks (ANN) (Na’amnh, Salim, Husti, & Daróczi, 2021), technique for order preference by similarity to an ideal solution (TOPSIS) (Seker, 2022), among others, each method presenting its own strengths and weaknesses. As outlined in Section 2.3, within the railway signal safety domain, widely applied methodologies like AHP (especially hierarchical analysis based on expert scoring) and its derivatives exhibit significant subjectivity in factor weighting, potentially leading to biases in computational outcomes.…”

Aggregation of Risk Management and Non-Parametric Models to Rank Failure Modes of Radio Frequency Identification Systems

“…This was carried out by simulating the psychological behavior characteristics of the team members and considering the correlations among them, and the weights of risk indicators were calculated by considering their interaction [14]. Machine learning uses an artificial neural network (ANN) model and a fuzzy inference system (FIS) to assess the failures of busbars used in the electrical vehicle industry [15]. An integrated-approach WLSM-MOI-partial-ranking method that incorporated the imprecision approach with a nonlinear programming model for the calculation of FRPNs was presented in [16].…”

Integrating large language models for improved failure mode and effects analysis (FMEA): a framework and case study

“…Liu et al (2019) highlighted how multi-criteria decision making methods can support risk assessment in FMEA, while Soltanali et al (2023) proposed a smart FMEA platform with hybrid FMEA models that combines uncertainty quantification, machine learning techniques and multi-criteria decision making. Na'amnh et al (2021) present improved risk assessment models using fuzzy inference and neural networks that outperform classical methods, with the fuzzy model proving superior for decision making. Furthermore, researchers have explored data-driven approaches using machine learning to continuously update and predict risk priority numbers (RPNs) for new failure modes (Peddi et al, 2023).…”