A generic data analytics system for manufacturing production

Hao Zhang,Hongzhi Wang,Jianzhong Li,Hong Gao

doi:10.26599/bdma.2018.9020016

Hao Zhang, Hongzhi Wang + Show 2 more

Open Access

https://doi.org/10.26599/bdma.2018.9020016

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Abstract

The increase in the amount of manufacturing information available means that big data can be collected and, with appropriate deep analysis, could be of great value to manufacturers. However, most small manufacturers cannot afford the overhead of a professional data analytics team. To address this problem, in this paper a generic data analytics system, Generic Manufacturing Data Analytics system (GMDA), is proposed. This system can perform most manufacturing data analytics tasks and users can easily carry out data analysis even if they have no prior knowledge or experience of data analytics. To establish such a system, we designed an abstract language, GMDL, to describe the manufacturing data analytics tasks. Aimed at factory data analytics, several algorithms were selected, tuned, optimized, and finally integrated into the system. Some noteworthy techniques were developed in GMDA such as proper algorithm selection strategy and an optimal parameter determination algorithm. Case studies show the practicability and reliability of the system.

Highlights

Knowledge is always the most valuable asset in a manufacturing enterprise[1]
Most existing data analytics techniques used in manufacturing are aimed at specific scenarios[3,4,5,6,7,8,9,10]
The complexity and diversity of manufacturing processes means that it is extremely difficult to make the whole data analytics process generic and suitable for a variety of manufacturing processes and problems[14]. At this point in the study we focus on the modeling phase and propose a data analytics system named Generic Manufacturing Data Analytics system (GMDA) (Generic Manufacturing Data Analysis system), which has the following three major advantages: Hao Zhang et al.: A Generic Data Analytics System for Manufacturing Production

Summary

Introduction

Knowledge is always the most valuable asset in a manufacturing enterprise[1]. a new Industrial Revolution, named Industry 4.0, promoting smart manufacturing is emerging in an increasing number of manufacturing enterprises[2]. Large amounts of data are generated and collected Analysis of such big data could bring great opportunities for innovation, lower costs, better response to customer needs, optimal solutions, intelligent systems, etc.[2] Most existing data analytics techniques used in manufacturing are aimed at specific scenarios[3,4,5,6,7,8,9,10]. This type of data analytics can achieve a satisfactory result, but is not practical because it is not universal and requires a team of data analysis experts. We first discuss related research on manufacturing data analysis, introduce three use cases to fully describe GMDA

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