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 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.

Effective Predictive Maintenance to Overcome System Failures—A Machine Learning Approach

2021 ◽  
pp. 341-357
Author(s):  
Sai Kumar Chilukuri ◽  
Nagendra Panini Challa ◽  
J. S. Shyam Mohan ◽  
S. Gokulakrishnan ◽  
R. Vasanth Kumar Mehta ◽  
...  
Keyword(s):  
Machine Learning ◽  
Predictive Maintenance ◽  
Learning Approach ◽  
System Failures ◽  
Machine Learning Approach

scholarly journals PrimaVera: Synergising Predictive Maintenance

2020 ◽  
Vol 10 (23) ◽  
pp. 8348
Author(s):  
Bram Ton ◽  
Rob Basten ◽  
John Bolte ◽  
Jan Braaksma ◽  
Alessandro Di Bucchianico ◽  
...  
Keyword(s):  
Asset Management ◽  
Process Model ◽  
Building Blocks ◽  
Predictive Maintenance ◽  
Full Potential ◽  
Support Tools ◽  
Maintenance Cycle ◽  
Optimal Maintenance ◽  
Organisational Factors ◽  
Maintenance Process

The full potential of predictive maintenance has not yet been utilised. Current solutions focus on individual steps of the predictive maintenance cycle and only work for very specific settings. The overarching challenge of predictive maintenance is to leverage these individual building blocks to obtain a framework that supports optimal maintenance and asset management. The PrimaVera project has identified four obstacles to tackle in order to utilise predictive maintenance at its full potential: lack of orchestration and automation of the predictive maintenance workflow, inaccurate or incomplete data and the role of human and organisational factors in data-driven decision support tools. Furthermore, an intuitive generic applicable predictive maintenance process model is presented in this paper to provide a structured way of deploying predictive maintenance solutions.

Improving rail network velocity: A machine learning approach to predictive maintenance

2014 ◽  
Vol 45 ◽  
pp. 17-26 ◽  
Author(s):  
Hongfei Li ◽  
Dhaivat Parikh ◽  
Qing He ◽  
Buyue Qian ◽  
Zhiguo Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Predictive Maintenance ◽  
Learning Approach ◽  
Rail Network ◽  
Machine Learning Approach

scholarly journals Role of Machine Learning in Manufacturing Sector

2019 ◽  
Vol 8 (4) ◽  
pp. 2320-2328
Keyword(s):  
Machine Learning ◽  
Manufacturing Industry ◽  
Manufacturing Sector ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Applications Of Machine Learning ◽  
Relationship Of ◽  
The Impact ◽  
Maintenance Process

This paper discusses the basic concept Machine learning and its techniques, algorithms as well the impact of Machine Learning in Manufacturing processes and Industrial Production. There has been an unprecedented increase in the data available in the last couple of decades. This has enabled machine learning to be applied in various fields. Machine learning is field of study which enables the computer system to learn automatically as well as improve from experience and perform various tasks without explicit instructions. The primary aim of machine learning is to allow the computers learn automatically without human assistance or intervention and adjust actions accordingly It is being employed in many fields. Manufacturing one area where machine learning is very useful. This paper discusses the various areas where machine learning can improve the process of manufacturing like predictive maintenance, process optimisation, quality control, scheduling of resources among others. This can be done by employing various machine learning techniques and algorithms using concepts such as deep learning, neural networks, supervised, unsupervised and reinforcement learning. The relationship of how machines and humans can co-exist and work together to improve the efficiency of production is also discussed. Industry 4.0 or fourth revolution that has occurred in manufacturing which deals with advent of automation in manufacturing industry and its significance is discussed. We take a look at the various benefits and applications of machine learning in the field of manufacturing engineering. This paper also discusses the various challenges and future scope of employing machine learning in the manufacturing

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