scholarly journals Cloud-Based Analytics Module for Predictive Maintenance of the Textile Manufacturing Process

2021 ◽  
Vol 11 (21) ◽  
pp. 9945
Author(s):  
Ray-I Chang ◽  
Chia-Yun Lee ◽  
Yu-Hsin Hung
Keyword(s):  
Quality Management ◽  
Industry 4.0 ◽  
Mean Squared Error ◽  
Production Quality ◽  
Machine Learning Algorithms ◽  
Predictive Maintenance ◽  
Third Party ◽  
Manufacturing Processes ◽  
Textile Manufacturing ◽  
Iot Applications

Industry 4.0 has remarkably transformed many industries. Supervisory control and data acquisition (SCADA) architecture is important to enable an intelligent and connected manufacturing factory. SCADA is extensively used in many Internet of Things (IoT) applications, including data analytics and data visualization. Product quality management is important across most manufacturing industries. In this study, we extensively used SCADA to develop a cloud-based analytics module for production quality predictive maintenance (PdM) in Industry 4.0, thus targeting textile manufacturing processes. The proposed module incorporates a complete knowledge discovery in database process. Machine learning algorithms were employed to analyze preprocessed data and provide predictive suggestions for production quality management. Equipment data were analyzed using the proposed system with an average mean-squared error of ~0.0005. The trained module was implemented as an application programming interface for use in IoT applications and third-party systems. This study provides a basis for improving production quality by predicting optimized equipment settings in manufacturing processes in the textile industry.

scholarly journals Improved Ensemble-Learning Algorithm for Predictive Maintenance in the Manufacturing Process

2021 ◽  
Vol 11 (15) ◽  
pp. 6832
Author(s):  
Yu-Hsin Hung
Keyword(s):  
Quality Management ◽  
Ensemble Learning ◽  
Manufacturing Process ◽  
Semiconductor Manufacturing ◽  
Data Communication ◽  
Predictive Maintenance ◽  
Learning Performance ◽  
Smart Manufacturing ◽  
Manufacturing Processes ◽  
Total Quality

Industrial Internet of Things (IIoT) technologies comprise sensors, devices, networks, and applications from the edge to the cloud. Recent advances in data communication and application using IIoT have streamlined predictive maintenance (PdM) for equipment maintenance and quality management in manufacturing processes. PdM is useful in fields such as device, facility, and total quality management. PdM based on cloud or edge computing has revolutionized smart manufacturing processes. To address quality management problems, herein, we develop a new calculation method that improves ensemble-learning algorithms with adaptive learning to make a boosted decision tree more intelligent. The algorithm predicts main PdM issues, such as product failure or unqualified manufacturing equipment, in advance, thus improving the machine-learning performance. Herein, semiconductor and blister packing machine data are used separately in manufacturing data analytics. The former data help in predicting yield failure in a semiconductor manufacturing process. The blister packing machine data are used to predict the product packaging quality. Experimental results indicate that the proposed method is accurate, with an area under a receiver operating characteristic curve exceeding 96%. Thus, the proposed method provides a practical approach for PDM in semiconductor manufacturing processes and blister packing machines.

scholarly journals Identifying Benchmarks for Failure Prediction in Industry 4.0

Informatics ◽  
2021 ◽  
Vol 8 (4) ◽  
pp. 68
Author(s):  
Mouhamadou Saliou Diallo ◽  
Sid Ahmed Mokeddem ◽  
Agnès Braud ◽  
Gabriel Frey ◽  
Nicolas Lachiche
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Prediction Model ◽  
Supervised Learning ◽  
Industry 4.0 ◽  
Learning Algorithms ◽  
Failure Prediction ◽  
Machine Learning Algorithms ◽  
Predictive Maintenance ◽  
Selection Of

Industry 4.0 is characterized by the availability of sensors to operate the so-called intelligent factory. Predictive maintenance, in particular, failure prediction, is an important issue to cut the costs associated with production breaks. We studied more than 40 publications on predictive maintenance. We point out that they focus on various machine learning algorithms rather than on the selection of suitable datasets. In fact, most publications consider a single, usually non-public, benchmark. More benchmarks are needed to design and test the generality of the proposed approaches. This paper is the first to define the requirements on these benchmarks. It highlights that there are only two benchmarks that can be used for supervised learning among the six publicly available ones we found in the literature. We also illustrate how such a benchmark can be used with deep learning to successfully train and evaluate a failure prediction model. We raise several perspectives for research.

scholarly journals Predictive Maintenance Studies Applied to an Industrial Press Machine Using Machine Learning

2020 ◽  
pp. 57-63
Author(s):  
Erkut Yigit ◽  
Mehmet Zeki Bilgin ◽  
Ahmet Erdem Oner
Keyword(s):  
Machine Learning ◽  
Industry 4.0 ◽  
Prediction Models ◽  
Machine Learning Algorithms ◽  
Predictive Maintenance ◽  
Production Costs ◽  
Training Data ◽  
Accurate Information ◽  
Production Environment ◽  
Production Chain

The main purpose of Industry 4.0 applications is to provide maximum uptime throughout the production chain, to reduce production costs and to increase productivity. Thanks to Big Data, Internet of Things (IoT) and Machine Learning (ML), which are among the Industry 4.0 technologies, Predictive Maintenance (PdM) studies have gained speed. Implementing Predictive Maintenance in the industry reduces the number of breakdowns with long maintenance and repair times, and minimizes production losses and costs. With the use of machine learning, equipment malfunctions and equipment maintenance needs can be predicted for unknown reasons. A large amount of data is needed to train the machine learning algorithm, as well as adequate analytical method selection suitable for the problem. The important thing is to get the valuable signal by cleaning the data from noise with data processing. In order to create prediction models with machine learning, it is necessary to collect accurate information and to use many data from different systems. The existence of large amounts of data related to predictive maintenance and the need to monitor this data in real time, delays in data collection, network and server problems are major difficulties in this process. Another important issue concerns the use of artificial intelligence. For example, obtaining training data, dealing with variable environmental conditions, choosing the ML algorithm better suited to a specific scenario, necessity of information sensitive to operational conditions and production environment are of great importance for analysis. In this study, predictive maintenance studies for the transfer press machine used in the automotive industry, which can predict the maintenance need time and give warning messages to the relevant people when abnormal situations approach, are examined. First of all, various sensors have been placed in the machine for the detection of past malfunctions and it has been determined which data will be collected from these sensors. Then, machine learning algorithms used to detect anomalies with the collected data and model past failures were created and an application was made in a factory that produces automotive parts.

scholarly journals The Role of Industry 4.0 and BPMN in the Arise of Condition-Based and Predictive Maintenance: A Case Study in the Automotive Industry

2021 ◽  
Vol 11 (8) ◽  
pp. 3438
Author(s):  
Jorge Fernandes ◽  
João Reis ◽  
Nuno Melão ◽  
Leonor Teixeira ◽  
Marlene Amorim
Keyword(s):  
Business Process ◽  
Automotive Industry ◽  
Industry 4.0 ◽  
Process Model ◽  
Business Process Management ◽  
Process Management ◽  
Predictive Maintenance ◽  
The Evolution Of Industry

This article addresses the evolution of Industry 4.0 (I4.0) in the automotive industry, exploring its contribution to a shift in the maintenance paradigm. To this end, we firstly present the concepts of predictive maintenance (PdM), condition-based maintenance (CBM), and their applications to increase awareness of why and how these concepts are revolutionizing the automotive industry. Then, we introduce the business process management (BPM) and business process model and notation (BPMN) methodologies, as well as their relationship with maintenance. Finally, we present the case study of the Renault Cacia, which is developing and implementing the concepts mentioned above.

Sign in / Sign up

Export Citation Format

Share Document