Prediction of Remaining Useful Lifetime (RUL) of turbofan engine using machine learning

Mathew, Vimala; Toby, Tom; Singh, Vikram; Rao, B. Maheswara; Kumar, Mithilesh

doi:10.1109/iccs1.2017.8326010

Cited by 83 publications

(62 citation statements)

References 12 publications

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“…It has been developed into a wide field of research over the past decades. ML can be defined as a technology by which the outcomes can be forecasted based on a model prepared and trained on past or historical input data and its output behavior [21]. According to Samuel, A.L.…”

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confidence: 99%

“…Some other authors [64][65][66][67]81,82,[84][85][86][87][88]90,116,117] studied RF technique. In the last years, use of SVM technique has received attention from authors [14,21,48,56,66,79,[93][94][95][96][97][98][99][100]102,107,111,115,121,124]. Some authors [21,56,82,112,120,124] have given attention to GBM technique.…”

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confidence: 99%

“…In the last years, use of SVM technique has received attention from authors [14,21,48,56,66,79,[93][94][95][96][97][98][99][100]102,107,111,115,121,124]. Some authors [21,56,82,112,120,124] have given attention to GBM technique. Some authors [65,66,79,109,112,117] carried out studies by use of DT technique.…”

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confidence: 99%

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

et al. 2020

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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.

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

et al. 2020

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“…., 100}. And we use the turbofan engine aging dataset provided by the NASA Prognostics Data Repository [31] as nonsensitive data. Table 3 shows the environmental parameters.…”

Section: Experimental Configurationmentioning

confidence: 99%

Protection of Sensitive Data in Industrial Internet Based on Three-Layer Local/Fog/Cloud Storage

Liu

Yuan

Lai

et al. 2020

Security and Communication Networks

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Industrial Internet technology has developed rapidly, and the security of industrial data has received much attention. At present, industrial enterprises lack a safe and professional data security system. Thus, industries urgently need a complete and effective data protection scheme. This study develops a three-layer framework with local/fog/cloud storage for protecting sensitive industrial data and defines a threat model. For real-time sensitive industrial data, we use the improved local differential privacy algorithm M-RAPPOR to perturb sensitive information. We encode the desensitized data using Reed–Solomon (RS) encoding and then store them in local equipment to realize low cost, high efficiency, and intelligent data protection. For non-real-time sensitive industrial data, we adopt a cloud-fog collaborative storage scheme based on AES-RS encoding to invisibly provide multilayer protection. We adopt the optimal solution of distributed storage in local equipment and the cloud-fog collaborative storage scheme in fog nodes and cloud nodes to alleviate the storage pressure on local equipment and to improve security and recoverability. According to the defined threat model, we conduct a security analysis and prove that the proposed scheme can provide stronger data protection for sensitive data. Compared with traditional methods, this approach strengthens the protection of sensitive information and ensures real-time continuity of open data sharing. Finally, the feasibility of our scheme is validated through experimental evaluation.

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“…In this paper, we discuss the problem area, use of a machine learning model to address the problem, selected features for the model and their criticality in detail and output of the model. The supporting idea for this research work was found in [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16] where fault detections were made in the automatic meter reading systems, to improve scalability and reliability.…”

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confidence: 99%

Predictive Maintenance and Monitoring of Industrial Machine using Machine Learning

Masani

Oza

Agrawal

2019

SCPE

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Machine learning is one of the break-through technologies of the modern digital world. It's applications are found in various research domain such as medicine, image processing, production and manufacturing, aviation and autonomics and many more. To efﬁciently run a machine, it's maintenance and its monitoring automation system play a key role. The major problem we are targetting is to overcome the lack of an automation system which can give an accuracy rate of the production machine at a given instance of time. Also, the important energy meter parameters required to make power report in an automation system for addressing the production issues, at a given interval of time, were also not recorded. Thus in this paper, we describe how machine learning techniques are used for prediction of the accuracy of running production machine. To address these issues, we have used supervised machine learning technique of Binary decision tree using CART method and for power report, while the data is fetched using RS232 to RS485 convertor via Modbus communication protocol. Using CART we have predicted the machine accuracy at a given time with speciﬁc energy meter readings as its input features. This paper discusses the problem deﬁnition identiﬁed, data analysis of energy meter data and it's fetching and at the end ML techniques applied to predict the accuracy of running production machine. In the end, we prepare various power reports of the different machines from the fetched parameters as well as produce a graphical warning of deteriorating performance of the machine at a given instance of the time.

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Prediction of Remaining Useful Lifetime (RUL) of turbofan engine using machine learning

Cited by 83 publications

References 12 publications

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

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

Protection of Sensitive Data in Industrial Internet Based on Three-Layer Local/Fog/Cloud Storage

Predictive Maintenance and Monitoring of Industrial Machine using Machine Learning

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