Predictive maintenance system for production lines in manufacturing: A machine learning approach using IoT data in real-time

Ayvaz, Serkan; Alpay, Koray

doi:10.1016/j.eswa.2021.114598

Cited by 264 publications

(101 citation statements)

References 44 publications

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“…The proposed automated ML provided the ability to recognize various patterns from IoT data and determine the best analytic process for different data to achieve the goal. Furthermore, ML can be used for prediction, big data analytics, and enabling IoT devices to learn from the dataset collected from the environments [ 68 , 69 ]. For example, ML can analyze and predict drone mobility for delivering services to any specific area and for rescue and relief tasks during a disaster [ 2 , 70 ].…”

Section: Machine Learning For Iotmentioning

confidence: 99%

“…e proposed automated ML provided the ability to recognize various patterns from IoT data and determine the best analytic process for different data to Computational Intelligence and Neuroscience achieve the goal. Furthermore, ML can be used for prediction, big data analytics, and enabling IoT devices to learn from the dataset collected from the environments [68,69].…”

Section: Machine Learning For Iotmentioning

confidence: 99%

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Machine Learning for Smart Environments in B5G Networks: Connectivity and QoS

Alsamhi

Almalki

Al-Dois

et al. 2021

Computational Intelligence and Neuroscience

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The number of Internet of Things (IoT) devices to be connected via the Internet is overgrowing. The heterogeneity and complexity of the IoT in terms of dynamism and uncertainty complicate this landscape dramatically and introduce vulnerabilities. Intelligent management of IoT is required to maintain connectivity, improve Quality of Service (QoS), and reduce energy consumption in real time within dynamic environments. Machine Learning (ML) plays a pivotal role in QoS enhancement, connectivity, and provisioning of smart applications. Therefore, this survey focuses on the use of ML for enhancing IoT applications. We also provide an in-depth overview of the variety of IoT applications that can be enhanced using ML, such as smart cities, smart homes, and smart healthcare. For each application, we introduce the advantages of using ML. Finally, we shed light on ML challenges for future IoT research, and we review the current literature based on existing works.

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Section: Machine Learning For Iotmentioning

confidence: 99%

Section: Machine Learning For Iotmentioning

confidence: 99%

Machine Learning for Smart Environments in B5G Networks: Connectivity and QoS

Alsamhi

Almalki

Al-Dois

et al. 2021

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

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“…Ref. [21] uses random forests and XGBoost in a predictive maintenance system for production lines. In [22], the authors propose an innovative maintenance policy.…”

Section: Literature Reviewmentioning

confidence: 99%

Identifying Benchmarks for Failure Prediction in Industry 4.0

et al. 2021

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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.

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“…al., 2021;SILVA et. al., 2019) becomes a necessary method since it is possible to prevent the stoppage of machines in a production process through indicators offered by monitoring systems, identify the small irregularities that can evolve to large failures early and thus allow for correction (AYVAZ and ALPAY, 2021;TIAN, LIU and SHU, 2021). Some methods for performing equipment-monitoring diagnostics are used in the literature (SCHWENDEMANN, AMJAD AND SIKORA, 2021;MANHERTZ and BERECZKY, 2021;LUGHOFER and SAYED-MOUCHAWEH, 2019), such as:…”

Section: Predictive Maintenancementioning

confidence: 99%

Bayesian Regularizers of Artificial Neural Networks applied to the reliability forecast of internal combustion machines in the short-term

Silva

Nascimento

Júnior

et al. 2021

Int J Innov Educ Res

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Predictive as well as preventive maintenance are tools of maintenance programs that aim to increase or maintain the life expectancy of an equipment through computational techniques and tools. Bearing in mind that the power generation industry has a high maintenance rate with machines and / or electric generators stopped, this research aims to develop a computational model for predicting the Reliability Key Performance Indicator (KPI) to identify how available the equipment will be in a time span of 22 days, for this the methodology to be used will be based on analyzes and tests of artificial neural network (ANN) architectures using the Bayesian Regularizers training algorithm, alternating the transfer functions in the layers hidden to find the best state of convergence and the minimum Root Mean Square Error (RMSE) value calculated between the real and simulated outputs. According to the results obtained by the training, validation and test steps, the algorithm presented a RMSE rate of 0.0000104202 and a 99.9% correlation between the real and simulated values, thus the model is able to identify which machine will have the greatest efficiency and less efficiency within the defined time span.

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Predictive maintenance system for production lines in manufacturing: A machine learning approach using IoT data in real-time

Cited by 264 publications

References 44 publications

Machine Learning for Smart Environments in B5G Networks: Connectivity and QoS

Machine Learning for Smart Environments in B5G Networks: Connectivity and QoS

Identifying Benchmarks for Failure Prediction in Industry 4.0

Bayesian Regularizers of Artificial Neural Networks applied to the reliability forecast of internal combustion machines in the short-term

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