A Recommendation Mechanism of Selecting Machine Learning Models for Fault Diagnosis

Faults of a machine tool generally lead to a suspension of a production line when the defeated parts need a long lead time. The prevention of such suspension depends on the health condition of machine tools in a factory. Hence, monitoring the health conditions of machine tools with modern Machine Learning (ML) technologies is one of the highlights of industry evolution 4.0. Though researchers presented several methods and mechanisms to solve the fault detection and prediction of machine tools, the current works usually focus on deploying one ML algorithm to one specific machine tool and generating a well-trained model for fault diagnosis and detection for that machine tool, which are impractical since a factory typically runs a variety of machine tools. This paper presents an Automatic Fault Diagnosis Mechanism (AFDM), taking historical data provided by an administrator and then recommending a machine-learning algorithm for fault diagnosis. AFDM can handle different types of data, diagnose faults for different machine tools, and provide a friendly interface for a factory administrator to select a proper analytical model for the specified type of machine tools. We design a series of experiments to prove the diversity, feasibility, and stability of AFDM.