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

Saeed Na’Amnh,István Husti,Muath Bani Salim,Miklós Daróczi

doi:10.3390/pr9081444

Saeed Na’Amnh, István Husti + Show 2 more

Open Access

https://doi.org/10.3390/pr9081444

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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.

Highlights

As the world’s economy is developing rapidly, many companies started focusing their efforts on innovation technology so that they can obtain competitive privilege in the future
Failure modes and effects analysis (FMEA) is an analytical tool for detecting, defining, and lessening the potential failures that may occur for the product and process systematically by identifying the root causes, potential occurrence, and consequences [5]
This paper proposed a solution to attain consistency in risk evaluation by using machine learning techniques to analyze failures Figure 1 illustrates the research framework in the proposed approach

Summary

Introduction

As the world’s economy is developing rapidly, many companies started focusing their efforts on innovation technology so that they can obtain competitive privilege in the future These innovations aim to mitigate the development cycle of products and provide customers distinguished products at high quality and low cost [1]. One of the popular methods is the failure modes and effects analysis (FMEA), which is considered an efficient method and is vastly used in the process of service and production [3,4]. Failure modes and effects analysis (FMEA) is an analytical tool for detecting, defining, and lessening the potential failures that may occur for the product and process systematically by identifying the root causes, potential occurrence, and consequences [5]. RPN is the final result after multiplication of the three parameters: Severity (S), Occurrence (O), and Detectability (D)

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Results

Conclusion