Sound Detection Monitoring Tool in CNC Milling Sounds by K-Means Clustering Algorithm

Peng, Cheng-Yu; Raihany, Ully; Kuo, Shu‐Wei; Chen, Yen-Zuo

doi:10.3390/s21134288

Cited by 19 publications

(13 citation statements)

References 37 publications

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“…One study [ 58 ] reported that the noise signal acquisition system based on LabVIEW virtual technology can better complete the functions of noise signal acquisition and storage, noise signal filtering, time-domain analysis, and spectrum analysis. Peng et al [ 59 ] used LabVIEW virtual instruments to collect noise signals during milling. The signal and its power spectrum can be observed simultaneously during the acquisition process, and the collected signal can be stored in real-time.…”

Section: Noise Signal Acquisition and Numerical Analysis And Noise Co...mentioning

confidence: 99%

Research Progress of Noise in High-Speed Cutting Machining

Wei

Shang

Peng

et al. 2022

Sensors

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High-speed cutting technology has become a development trend in the material processing industry. However, high-intensity noise generated during high-speed cutting exerts a potential effect on the processing efficiency, processing accuracy, and product quality of the workpiece; it may even cause hidden safety hazards. To conduct an in-depth study of noise in high-speed cutting machining, this work reviews noise sources, noise collection and numerical recognition, noise control, and condition monitoring based on acoustic signals. First, this article introduces noise sources, noise signal acquisition equipment, and analysis software. It is pointed out that how to accurately classify and recognize the target signal in the complex high-speed machining environment is one of the focuses of scholars’ research. Then, it points out that a computer achieves high accuracy and practicability in signal analysis, processing, and result display. Second, in the aspect of noise signal processing, the characteristics of noise signals are analyzed. It is pointed out that accurately analyzing the characteristics of different noise source signals and adopting appropriate methods for identification and processing are the necessary conditions for effectively controlling and reducing the noise in the process of high-speed cutting. The advantages and applicable fields of artificial intelligence algorithms in processing mixed noise source signals with different frequency characteristics are compared, providing ideas for studying the mechanism of noise generation and the identification of noise sources. Third, in terms of noise control, a detailed overview is provided from the aspects of the treatment of the noise source that contributes the most to the overall noise, the improvement of the tool structure, the optimization of cutting parameters, and the analysis of contact factors between the tool and the workpiece. It provides an effective way for noise control in the process of high-speed cutting. In addition, the application of acoustic signals to condition monitoring is also thoroughly analyzed. The practical application value of condition monitoring based on acoustic signals in high-speed machining is highlighted. Finally, this paper summarizes the positive significance of noise research in high-speed machining and identifies key problems and possible research methods that require further study in the future.

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Section: Noise Signal Acquisition and Numerical Analysis And Noise Co...mentioning

confidence: 99%

Research Progress of Noise in High-Speed Cutting Machining

Wei

Shang

Peng

et al. 2022

Sensors

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“…This strategy, known as Maintenance 4.0, may therefore better address issues that develop, including those that are not known ahead of time. Predictive maintenance (PdM) [19] is one of the key components of Maintenance 4.0, while one of the crucial parts of PdM is anomaly detection, which can be applied, for example, on the temperature characteristic of the technological process measured in real-time and analyzed using a neural network [20], or by monitoring the sounds produced by the milling process using spectral analysis and K-means clustering algorithms [21]. When applied in an unsupervised way, the approach can be used for predicting the remaining useful life in the absence of available run-to-failure data, as was done in [22] using the autoencoder based methodology to analyze the vibrations of a robotic arm.…”

Section: Analysis Of Industrial Machinery Data For Predictive Maintenancementioning

confidence: 99%

Acoustic Anomaly Detection of Mechanical Failures in Noisy Real-Life Factory Environments

2021

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Anomaly detection without employing dedicated sensors for each industrial machine is recognized as one of the essential techniques for preventive maintenance and is especially important for factories with low automatization levels, a number of which remain much larger than autonomous manufacturing lines. We have based our research on the hypothesis that real-life sound data from working industrial machines can be used for machine diagnostics. However, the sound data can be contaminated and drowned out by typical factory environmental sound, making the application of sound data-based anomaly detection an overly complicated process and, thus, the main problem we are solving with our approach. In this paper, we present a noise-tolerant deep learning-based methodology for real-life sound-data-based anomaly detection within real-world industrial machinery sound data. The main element of the proposed methodology is a generative adversarial network (GAN) used for the reconstruction of sound signal reconstruction and the detection of anomalies. The experimental results obtained in the Malfunctioning Industrial Machine Investigation and Inspection (MIMII) show the superiority of the proposed methodology over baseline approaches based on the One-Class Support Vector Machine (OC-SVM) and the Autoencoder–Decoder neural network. The proposed schematics using the unscented Kalman Filter (UKF) and the mean square error (MSE) loss function with the L2 regularization term showed an improvement of the Area Under Curve (AUC) for the noisy pump data of the pump.

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“…The functionalities of this tier in addition to the database server are depicted in Figure 8: newPro-ductEntry, openBox, sensorAlert, handleProduct, emptyPackageExit, sendToBlockchain, prediction, correlation and patterDetection. A sequence diagram in Figure 9 illustrates interactions between tier 2 functions, along with their relationships with other modules, including the occurrence of events, their blockchains recording, and the activation of desired machine learning functions (i.e., prediction [48], classification [49] and clusterization [50]). Tier 2 has the following security properties: (a) every table has a hash field; (b) there is a shadow table…”

Section: Tier 2-data Processing and Machine Learningmentioning

confidence: 99%

APTM: A Model for Pervasive Traceability of Agrochemicals

et al. 2021

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As the world population increases and the need for food monoculture farms are using more and more agrochemicals, there is also an increase in the possibility of theft, misuse, environmental damage, piracy of products, and health problems. This article addresses these issues by introducing the agrochemical pervasive traceability model (APTM), which integrates machine learning, sensors, microcontrollers, gamification, and two blockchains. It contributes in two dimensions: (I) the study of the environmental, product piracy and regulatory of agrochemical control; (II) the technological dimension: application of an adequate set of sensors collecting multiple data; modeling and implementation of a system via machine learning for analyzing and predicting the behavior and use of agrochemicals; development of a scoring system via gamification for reverse use of agrochemicals; and presenting a record of transactions in a consortium of two blockchains, simultaneously. Its main advantage is to be a flexible, adaptable, and expansive model. Results indicated that the model has positive aspects, from detecting the agrochemical, its handling, and disposal, recording of transactions, and data visualization along the reverse supply chain. This study obtained a round trip time of 0.510 ms on average; data transfers between layer one and its persistence in the database were between 4 to 5 s. Thus, blockchain nodes consumed only 34 to 38% of CPU and recorded transactions between 2 to 4 s. These results point to a horizon of applicability in real situations within agricultural farms.

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Sound Detection Monitoring Tool in CNC Milling Sounds by K-Means Clustering Algorithm

Cited by 19 publications

References 37 publications

Research Progress of Noise in High-Speed Cutting Machining

Research Progress of Noise in High-Speed Cutting Machining

Acoustic Anomaly Detection of Mechanical Failures in Noisy Real-Life Factory Environments

APTM: A Model for Pervasive Traceability of Agrochemicals

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