**Enhancing Failure Mode and E** ff**ects Analysis Using Auto Machine Learning: A Case Study of the Agricultural Machinery Industry**

#### **Sami Sader 1,\*, István Husti 2 and Miklós Daróczi 2**

1 Doctoral School of Mechanical Engineering, Szent Istvan University, 2100 Godollo, Hungary


Received:9January2020;Accepted:12 February2020;Published:14February2020

**Abstract:** In this paper, multiclass classification is used to develop a novel approach to enhance failure mode and e ffects analysis and the generation of risk priority number. This is done by developing four machine learning models using auto machine learning. Failure mode and e ffects analysis is a technique that is used in industry to identify possible failures that may occur and the e ffects of these failures on the system. Meanwhile, risk priority number is a numeric value that is calculated by multiplying three associated parameters namely severity, occurrence and detectability. The value of risk priority number determines the next actions to be made. A dataset that includes a one-year registry of 1532 failures with their description, severity, occurrence, and detectability is used to develop four models to predict the values of severity, occurrence, and detectability. Meanwhile, the resulted models are evaluated using 10% of the dataset. Evaluation results show that the proposed models have high accuracy whereas the average value of precision, recall, and F1 score are in the range of 86.6–93.2%, 67.9–87.9%, 0.892–0.765% respectively. The proposed work helps in carrying out failure mode and e ffects analysis in a more e fficient way as compared to the conventional techniques.

**Keywords:** Industry 4.0; auto machine learning; failure mode e ffects analysis; risk priority number
