Review of Machine Learning Approaches In Fault Diagnosis applied to IoT System

International audienceWith increasing complex systems, low production costs, and changing technologies, for this reason, the automatic fault diagnosis using artificial intelligence (AI) techniques is more in more applied. In addition, with the emergence of the use of reconfigurable systems, AI can assist in self-maintenance of complex systems. The purpose of this article is to summarize the diagnosis research of systems using AI approaches and examine their application particularly in the field of diagnosis of complex systems. It covers articles published from 2002 to 2018 using Machine Learning tools for fault diagnosis in industrial systems

Technology and quality management: a review of concepts and opportunities in the digital transformation

Purpose - The Digital Transformation brings change to organizations, their processes, and their production systems. Nevertheless, most efforts observed in its context tend to be technology-driven, and it is often argued that Quality Management is inadequately integrated into the discussion. Design/methodology/approach - Surveying the literature, this work reviews, list, and organizes the different technological concepts and integration opportunities that have been explored in the scope of Quality Management in the Digital Transformation. Findings - Findings include the expanded capacity of quality tools and methods for managerial purposes; the reinforced importance of Data Quality; the increased automation and augment resources for Quality control; and the increased process optimization and integration of systems and between organizational areas. Originality/value - It is demonstrated that although scattered in the literature, there are already a number of works exploring the impacts of technology in the management of Quality in the scope of the Digital Transformation. Three main areas for integration arise: (a) Digital Quality Management (application of industry 4.0 technologies to Quality Management itself, its tools, methods, and systems), (b) the management of the Quality of digital products and services, and (c) the management of the Quality of digital product development and production processes.(undefined

A framework for designing data pipelines for manufacturing systems

Data pipelines describe the path through which big data is transmitted, stored, processed and analyzed. Designing an appropriate data pipeline for a specific data driven manufacturing project can be challenging, whereas there is a paucity of frameworks to guide one in the design. In this research we develop a framework for designing data pipelines for manufacturing systems. The framework consists of a template for selecting key layers and components that make up big data pipelines in manufacturing systems. A use case is presented to provide an illustrative guideline for its application. Benefits of the framework and future directions are discusse

Development of an open source-based manufacturing execution system (MES): industry 4.0 enabling technology for small and medium-sized enterprises

The companies’ production systems have been transformed by industry 4.0 technologies, therefore increasing their sophistication. Vertical integration, one of industry 4.0 pillars, is filling the gap between lean manufacturing and industry 4.0 through manufacturing execution systems (MES), that aid in process control and improvement through data analysis. As a contribution to enterprises migration towards industry 4.0, this work aims to develop a MES based on open source technologies and functional characteristics similar to those systems available on the market, although the system developed aims at small and medium enterprises. In order to do so, the technologies and software available on the market were assessed against characteristics defined as important in the evaluation of similar systems. The MES developed through the use of open source technologies resulted in 76.22% of characteristics similar to other systems, besides having a sale price up to 55.3% lower than other commercial systems. OpMES, the developed system, was successfully implemented in a company whose production process is complex due to the high level of customization of the product

Development of a supervisory internet of things (IoT) system for factories of the future

Big data is of great importance to stakeholders, including manufacturers, business partners, consumers, government. It leads to many benefits, including improving productivity and reducing the cost of products by using digitalised automation equipment and manufacturing information systems. Some other benefits include using social media to build the agile cooperation between suppliers and retailers, product designers and production engineers, timely tracking customers’ feedbacks, reducing environmental impacts by using Internet of Things (IoT) sensors to monitor energy consumption and noise level. However, manufacturing big data integration has been neglected. Many open-source big data software provides complicated capabilities to manage big data software for various data-driven applications for manufacturing. In this research, a manufacturing big data integration system, named as Data Control Module (DCM) has been designed and developed. The system can securely integrate data silos from various manufacturing systems and control the data for different manufacturing applications. Firstly, the architecture of manufacturing big data system has been proposed, including three parts: manufacturing data source, manufacturing big data ecosystem and manufacturing applications. Secondly, nine essential components have been identified in the big data ecosystem to build various manufacturing big data solutions. Thirdly, a conceptual framework is proposed based on the big data ecosystem for the aim of DCM. Moreover, the DCM has been designed and developed with the selected big data software to integrate all the three varieties of manufacturing data, including non-structured, semi-structured and structured. The DCM has been validated on three general manufacturing domains, including product design and development, production and business. The DCM cannot only be used for the legacy manufacturing software but may also be used in emerging areas such as digital twin and digital thread. The limitations of DCM have been analysed, and further research directions have also been discussed