scholarly journals Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0

2020 ◽  
Vol 12 (19) ◽  
pp. 8211 ◽  
Author(s):  
Zeki Murat Çınar ◽  
Abubakar Abdussalam Nuhu ◽  
Qasim Zeeshan ◽  
Orhan Korhan ◽  
Mohammed Asmael ◽  
...  
Keyword(s):  
Machine Learning ◽  
Communication Networks ◽  
Industry 4.0 ◽  
Fault Detection And Diagnosis ◽  
Predictive Maintenance ◽  
Smart Manufacturing ◽  
Utilization Rate ◽  
Smart Systems ◽  
Promising Tool ◽  
Detection And Diagnosis

Recently, with the emergence of Industry 4.0 (I4.0), smart systems, machine learning (ML) within artificial intelligence (AI), predictive maintenance (PdM) approaches have been extensively applied in industries for handling the health status of industrial equipment. Due to digital transformation towards I4.0, information techniques, computerized control, and communication networks, it is possible to collect massive amounts of operational and processes conditions data generated form several pieces of equipment and harvest data for making an automated fault detection and diagnosis with the aim to minimize downtime and increase utilization rate of the components and increase their remaining useful lives. PdM is inevitable for sustainable smart manufacturing in I4.0. Machine learning (ML) techniques have emerged as a promising tool in PdM applications for smart manufacturing in I4.0, thus it has increased attraction of authors during recent years. This paper aims to provide a comprehensive review of the recent advancements of ML techniques widely applied to PdM for smart manufacturing in I4.0 by classifying the research according to the ML algorithms, ML category, machinery, and equipment used, device used in data acquisition, classification of data, size and type, and highlight the key contributions of the researchers, and thus offers guidelines and foundation for further research.

scholarly journals Machine Learning Approach to Predictive Maintenance in Manufacturing Industry - A Comparative Study

2021 ◽  
Vol 2 (4) ◽  
pp. 246-255
Author(s):  
Karrupusamy P
Keyword(s):  
Machine Learning ◽  
Communication Networks ◽  
Asset Management ◽  
Information Acquisition ◽  
Manufacturing Industry ◽  
Predictive Maintenance ◽  
Utilization Rate ◽  
Accuracy And Precision ◽  
Machine Learning Approach ◽  
Maintenance Process

Predictive maintenance is the way to improve asset management in every manufacturing industry. While handling advance costlier machinery in the industry, the predictive maintenance knowledge will be essential to protect the machinery before gets degradation performance. Recently, the emergence of business in manufacturing industry deals with good systems, regular intervals maintenance process, predictive maintenance (PdM), machine learning (ML) approaches are extensively applied for handling the health standing of business instrumentation. Now the digital transformation towards I4.0, data techniques, processed management and communication networks; it’s doable to gather huge amounts of operational and processes conditions information generated type many items of kit and harvest information for creating an automatic fault detection and diagnosing with the aim to attenuate period of time and increase utilization rate of the parts and increase their remaining helpful lives. The predictive maintenance is inevitable for property good producing in I40. This paper aims to provide a comprehensive review of the recent advancements of metric capacity unit techniques wide applied to PdM for good producing in I4.0 by classifying the analysis consistent with metric capacity unit algorithms, ML class, machinery and instrumentation used device employed in information acquisition, classification of knowledge size and kind, and highlight the key contributions of the researchers and so offers pointers and foundation for additional analysis. In this research paper we constructed a Random Forest model to predict the failure of the various machine in manufacturing industry. It compares the prediction result with Decision Tree (DT) algorithm and proves its superiority in accuracy and precision.

scholarly journals Intelligent Steam Power Plant Boiler Waterwall Tube Leakage Detection via Machine Learning-Based Optimal Sensor Selection

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6356
Author(s):  
Salman Khalid ◽  
Woocheol Lim ◽  
Heung Soo Kim ◽  
Yeong Tak Oh ◽  
Byeng D. Youn ◽  
...  
Keyword(s):  
Machine Learning ◽  
Power Plants ◽  
Detection System ◽  
Fault Detection And Diagnosis ◽  
Sensor Selection ◽  
Test Case ◽  
Steam Power ◽  
Selection Scheme ◽  
Waterwall Tube ◽  
Detection And Diagnosis

Boiler waterwall tube leakage is the most probable cause of failure in steam power plants (SPPs). The development of an intelligent tube leak detection system can increase the efficiency and reliability of modern power plants. The idea of e-maintenance based on multivariate algorithms was recently introduced for intelligent fault detection and diagnosis in SPPs. However, these multivariate algorithms are highly dependent on the number of input process variables (sensors). Therefore, this work proposes a machine learning-based model integrated with an optimal sensor selection scheme to analyze boiler waterwall tube leakage. Finally, a real SPP test case is employed to validate the proposed model’s effectiveness. The results indicate that the proposed model can successfully detect waterwall tube leakage with improved accuracy vs. other comparable models.

Machine learning techniques applied to industrial engineering: a multi criteria approach

2019 ◽  
Author(s):  
Fabio De Felice ◽  
Marta Travaglioni ◽  
Giuseppina Piscitelli ◽  
Raffaele Cioffi ◽  
Antonella Petrillo
Keyword(s):  
Machine Learning ◽  
Industry 4.0 ◽  
Technological Development ◽  
Predictive Maintenance ◽  
Industrial Engineering ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
The World ◽  
The Subject ◽  
To Receive

With the Industry 4.0 (I4.0) beginning, the world is witnessing an important technological development. The success of I4.0 is linked to the implementation of enabling technologies, including Machine Learning, which focuses on the machines’ ability to receive a series of data and learn on their own. The present research aims to systematically analyze the existing literature on the subject in various aspects, including publication year, authors, scientific sector, country, institution and keywords. Understanding and analyzing the existing literature on Machine Learning applied to predictive maintenance is preparatory to recommend policy on the subject.

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 Review and Perspectives of Machine Learning Methods for Wind Turbine Fault Diagnosis

2021 ◽  
Vol 9 ◽  
Author(s):  
Mingzhu Tang ◽  
Qi Zhao ◽  
Huawei Wu ◽  
Ziming Wang ◽  
Caihua Meng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Control System ◽  
Fault Diagnosis ◽  
System Integration ◽  
Fault Detection And Diagnosis ◽  
System Control ◽  
Learning Methods ◽  
Pitch System ◽  
Detection And Diagnosis ◽  
Financial Losses

Wind turbines (WTs) generally comprise several complex and interconnected systems, such as hub, converter, gearbox, generator, yaw system, pitch system, hydraulic system control system,integration control system, and auxiliary system. Moreover, fault diagnosis plays an important role in ensuring WT safety. In the past decades, machine learning (ML) has showed a powerful capability in fault detection and diagnosis of WTs, thereby remarkably reducing equipment downtime and minimizing financial losses. This study provides a comprehensive review of recent studies on ML methods and techniques for WT fault diagnosis. These studies are classified as supervised, unsupervised, and semi-supervised learning methods. Existing state-of-the-art methods are analyzed and characteristics are discussed. Perspectives on challenges and further directions are also provided.

Sign in / Sign up

Export Citation Format

Share Document