Vibration Feature Extraction Using Audio Spectrum Analyzer Based Machine Learning

Liang, Jyun-Shun; Wang, Kerwin

doi:10.1109/icice.2017.8479273

Cited by 5 publications

(5 citation statements)

References 2 publications

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“…Both the approach of using the complete spectral magnitude data as input for a machine learning model, as well as using feature extraction (e.g., a combination of spectral moments, mel frequency cepstrum coefficients (MFCCs) and other audio features) beforehand have been explored in prior work. Liang and Wang describe the application of condition monitoring to a Desktop CNC 3D engraver machine [39]. They extracted vibrational features using a piezoelectric sensor and calculating spectral features in the range of human hearing by using a spectrum analyzer with seven frequency banks.…”

Section: Machine Learning Approaches For Process Classification In Production Environmentsmentioning

confidence: 99%

Auditory augmented process monitoring for cyber physical production systems

Iber

Lechner

Jandl

et al. 2020

Pers Ubiquit Comput

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We describe two proof-of-concept approaches on the sonification of estimated operation states and conditions focusing on two scenarios: a laboratory setup of a manipulated 3D printer and an industrial setup focusing on the operations of a punching machine. The results of these studies form the basis for the development of an “intelligent” noise protection headphone as part of Cyber Physical Production Systems which provides auditorily augmented information to machine operators and enables radio communication between them. Further application areas are implementations in control rooms (equipped with multi-channel loudspeaker systems) and utilization for training purposes. As a first proof-of-concept, the data stream of error probability estimations regarding partly manipulated 3D printing processes were mapped to three sonification models, providing evidence about momentary operation states. The neural network applied indicates a high accuracy (> 93%) of the error estimation distinguishing between normal and manipulated operation states. None of the manipulated states could be identified by listening. An auditory augmentation, or sonification of these error estimations, provides a considerable benefit to process monitoring. For a second proof-of-concept, setup operations of a punching machine were recorded. Since all operations were apparently flawlessly executed, and there were no errors to be reported, we focused on the identification of operation phases. Each phase of a punching process could be algorithmically distinguished at an estimated probability rate of > 94%. In the auditory display, these phases were represented by different instrumentations of a musical piece in order to allow users to differentiate between operations auditorily.

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Section: Machine Learning Approaches For Process Classification In Production Environmentsmentioning

confidence: 99%

Auditory augmented process monitoring for cyber physical production systems

Iber

Lechner

Jandl

et al. 2020

Pers Ubiquit Comput

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“…where is the digital output of voltage after ADC the , is reference voltage, and N is a number of bits in ADC converter. The and N was set to 5 and 12 as shown in [27], so we can get Equation 3and Equation (5). In order to reduce the complexity of the model, the data set was downsampled and trained.…”

Section: Dataset Collectionmentioning

confidence: 99%

“…In reference [4], the data from the gas sensor was converted to the frequency samples, then these samples were fed to ANN to be classified to different gases, such as C 2 H 2 and CO 2 and CO 2 . In addition, the frequency spectral data of audio is classified to several machine states by k-nearest neighbor (KNN), support vector machine (SVM) and random forest (RF) [5]. T. N. Sainath extracted of speech features by mel-frequency cepstral coefficients (MFCC) can enhance the human ear sensitivity of frequency.…”

Section: Introductionmentioning

confidence: 99%

Design of a Chamfering Tool Diagnosis System Using Autoencoder Learning Method

Hung

Mao

et al. 2019

Energies

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. In this paper, the autoencoder learning method is proposed for the system diagnosis of chamfering tool equipment. The autoencoder uses unsupervised learning architecture. The training dataset that requires only a positive sample is quite suitable for industrial production lines. The abnormal tool can be diagnosed by comparing the output and input of the autoencoder neural network. The adjustable threshold can effectively improve accuracy. This method can effectively adapt to the current environment when the data contain multiple signals. In the experimental setup, the main diagnostic signal is the current of the motor. The current reflects the torque change when the tool is abnormal. Four-step conversions are developed to process the current signal, including (1) current-to-voltage conversion, (2) analog-digital conversion, (3) downsampling rate, and (4) discrete Fourier transform. The dataset is used to find the best autoencoder parameters by grid search. In training results, the testing accuracy, true positive rate, and precision approach are 87.5%, 83.33%, and 90.91%, respectively. The best model of the autoencoder is evaluated by online testing. The online test means loading the diagnosis model in the production line and evaluating the model. It is shown that the proposed tool can effectively detect abnormal conditions. The online assessment accuracy, true positive rate, and precision are 75%, 90%, and 69.23% in the original threshold, respectively. The accuracy can be up to 90% after adjusting the threshold, and the true positive rate and precision are up to 80% and 100%, respectively.

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“…Similar approaches also derived in the study of [8]- [12], where different ML approaches are used to classify the audio spectrum data. It is also observed that out of different approaches, NN-based learning approaches have been widely studied in audio signal attributes to deal with various synthesis and processing parameters.…”

Section: Introductionmentioning

confidence: 99%

A computationally efficient learning model to classify audio signal attributes

Prasad¹,

Kumar²

2022

IJECE

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<p>The era of machine learning has opened up groundbreaking realities and opportunities in the field of medical diagnosis. However, it is also observed that faster and proper diagnosis of any diseases/medical conditions require proper analysis and classification of digital signal data. It indicates the proper identification of tumors in the brain. Brain magnetic resonance imaging (MRI) data has to be appropriately classified, and similarly, pulse signal analysis is required to evaluate the human heart operating condition. Several studies have used machine learning (ML) modeling to classify speech signals, but very few studies have explored the classification of audio signal attributes in the context of intelligent healthcare monitoring. The study thereby aims to introduce novel mathematical modeling to analyze and classify synthetic pulse audio signal attributes with cost-effective computation. The numerical modeling is composed of several functional blocks where deep neural network-based learning (DNNL) plays a crucial role during the training phase, and also it is further combined with a recurrent structure of long-short term memory (R-LSTM) feedback connections (FCs). The design approaches further experiment in a numerical computing environment in terms of accuracy and computational aspects. The classification outcome of the proposed approach shows that it attains approximately 85% accuracy, which is comparable to the baseline approaches and execution time.</p>

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Vibration Feature Extraction Using Audio Spectrum Analyzer Based Machine Learning

Cited by 5 publications

References 2 publications

Auditory augmented process monitoring for cyber physical production systems

Auditory augmented process monitoring for cyber physical production systems

Design of a Chamfering Tool Diagnosis System Using Autoencoder Learning Method

A computationally efficient learning model to classify audio signal attributes

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