A Machine Learning Approach for Wear Monitoring of End Mill by Self-Powering Wireless Sensor Nodes

Ostaševičius, Vytautas; Karpavicius, Paulius; Paulauskaitė-Tarasevičienė, Agnė; Jurenas, Vytautas; Mystkowski, Arkadiusz; Česnavičius, Ramūnas; Kizauskiene, Laura

doi:10.3390/s21093137

Cited by 7 publications

(4 citation statements)

References 34 publications

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“…For the data collected from Internet data sources [19], the method similar to the statistical analysis of department data is used to analyze and evaluate the quality of data sources. The evaluation results are shown in Figure 3.…”

Section: Internet Big Data Supplements the List Of Basic Unitsmentioning

confidence: 99%

Census and Inventory Method of Pollution Sources Based on Big Data Technology under Machine Learning

Liu

Sun

Jing

et al. 2022

Wireless Communications and Mobile Computing

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In order to strengthen the supervision and management of environmental pollution and timely understand and record the basic information of potential environmental pollution of enterprises and institutions, this paper proposes a general survey and inventory method of pollution sources based on big data technology under machine learning. Firstly, this paper evaluates and screens the data provided by government departments, constructs a machine learning classification model, and uses a variety of classification algorithms to compare and analyze according to the basic idea of machine learning to deal with practical problems. Then, the calibration data set constructed is used as the training set to predict and classify the national industrial and commercial data, provincial industrial and commercial data, and municipal industrial and commercial data provided by government departments. The experimental results show that the naive Bayesian classification algorithm is the best algorithm, and the F1 values of each data set are increased by 32.92%, 21.42%, and 14.91%, respectively. The classified prediction of the screened Internet data shows that the accuracy of the final Internet supplementary data is 17.26%, which is similar to the industrial and commercial data of the city. The availability of the machine learning model established in this paper is proven.

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Section: Internet Big Data Supplements the List Of Basic Unitsmentioning

confidence: 99%

Census and Inventory Method of Pollution Sources Based on Big Data Technology under Machine Learning

Liu

Sun

Jing

et al. 2022

Wireless Communications and Mobile Computing

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“…Numerous assessment measures, including as the confusion matrix, accuracy, precision, recall, and F-score, have been used in order to assess the efficacy of this methodology [29][30][31][32]. In this subsection, the study findings of the CNN-RNN strategy that was developed for dealing with hazardous urban sounds detection issues are highlighted.…”

Section: Evaluation Parametersmentioning

confidence: 99%

A Novel Deep CNN-RNN Approach for Real-time Impulsive Sound Detection to Detect Dangerous Events

Смайлов¹,

Dosbayev²,

Omarov³

et al. 2023

IJACSA

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In this research paper, we presented a novel approach to detect impulsive sounds in real-time using a combination of Deep CNN and RNN architectures. The proposed approach was evaluated using our collected dataset of impulsive sounds, and the results showed that it outperformed traditional audio signal processing methods in terms of accuracy and F1score. The proposed approach has several advantages over traditional methods, including the ability to handle complex audio patterns, detect impulsive sounds in real-time, and improve its performance with a large dataset of labeled impulsive sounds. However, there are some limitations to the proposed approach, including the requirement for a large amount of labeled data to train effectively, environmental factors that may impact the accuracy of the detection, and high computational requirements. Overall, the proposed approach demonstrates the effectiveness of using a combination of Deep CNN and RNN architectures for impulsive sound detection, with potential applications in various fields such as public safety, industrial settings, and home security systems. The proposed approach is a significant step towards developing automated systems for detecting dangerous events and improving public safety.

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“…To reduce the needed battery sizes and prolong its cycle life, energy harvesting techniques have been utilized to continuously charge the utilized battery based on mechanical vibration energy [ 128 , 129 , 130 ], electromagnetic energy [ 120 ], or utilizing an inductive power transmission system [ 84 , 107 ]. However, the application of such systems is restricted.…”

Section: Sensing and Data Acquisitionmentioning

confidence: 99%

Tool Condition Monitoring for High-Performance Machining Systems—A Review

Mohamed

Hassan

M’Saoubi³

et al. 2022

Sensors

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In the era of the “Industry 4.0” revolution, self-adjusting and unmanned machining systems have gained considerable interest in high-value manufacturing industries to cope with the growing demand for high productivity, standardized part quality, and reduced cost. Tool condition monitoring (TCM) systems pave the way for automated machining through monitoring the state of the cutting tool, including the occurrences of wear, cracks, chipping, and breakage, with the aim of improving the efficiency and economics of the machining process. This article reviews the state-of-the-art TCM system components, namely, means of sensing, data acquisition, signal conditioning and processing, and monitoring models, found in the recent open literature. Special attention is given to analyzing the advantages and limitations of current practices in developing wireless tool-embedded sensor nodes, which enable seamless implementation and Industrial Internet of Things (IIOT) readiness of TCM systems. Additionally, a comprehensive review of the selection of dimensionality reduction techniques is provided due to the lack of clear recommendations and shortcomings of various techniques developed in the literature. Recent attempts for TCM systems’ generalization and enhancement are discussed, along with recommendations for possible future research avenues to improve TCM systems accuracy, reliability, functionality, and integration.

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A Machine Learning Approach for Wear Monitoring of End Mill by Self-Powering Wireless Sensor Nodes

Cited by 7 publications

References 34 publications

Census and Inventory Method of Pollution Sources Based on Big Data Technology under Machine Learning

Census and Inventory Method of Pollution Sources Based on Big Data Technology under Machine Learning

A Novel Deep CNN-RNN Approach for Real-time Impulsive Sound Detection to Detect Dangerous Events

Tool Condition Monitoring for High-Performance Machining Systems—A Review

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