Fault Detection in Rotating Machinery Based on Sound Signal Using Edge Machine Learning

Shubita, Rashad R.; Alsadeh, Ahmad; Khater, Ismail M.

doi:10.1109/access.2023.3237074

Cited by 19 publications

(5 citation statements)

References 12 publications

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“…where x(t) denotes the time-series data, f denotes frequency, and SP(t, f ) denotes the energy density at time t and frequency f . SC is a type of wavelet transform that presents changes in frequency components over time in time-series data as a 2D image [30,31]. Similar to SP, SC also employs time and scale (inversely related to frequency) as its two axes, using color to depict energy density at specific locations.…”

Section: Image Encoding Methodsmentioning

confidence: 99%

“…Recent research has compared traditional 1D time-series data approaches with image encoding techniques, presenting findings that substantiate the superior efficacy of image encoding methods [ 17 , 18 ]. Representative image encoding techniques include recurrence plot (RP) [ 19 , 20 , 21 , 22 , 23 ], Gramian angular field (GAF) [ 14 , 24 , 25 , 26 , 27 ], Markov transition field (MTF) [ 28 , 29 , 30 ], spectrogram (SP) [ 31 , 32 ], and scalogram (SC) [ 33 , 34 ]. These image encoding techniques have recently been applied in research that converts time-series data from vibration and current signals, collected for diagnosing faults in robots and various machinery (such as bearings, gearboxes, rotating machinery, complex distribution networks, ventilation, and air conditioning systems), into images for various convolutional neural network (CNN) models.…”

Section: Introductionmentioning

confidence: 99%

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Fault Diagnosis Method for Human Coexistence Robots Based on Convolutional Neural Networks Using Time-Series Data Generation and Image Encoding

Choi,

Park,

et al. 2023

Sensors

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This paper proposes fault diagnosis methods aimed at proactively preventing potential safety issues in robot systems, particularly human coexistence robots (HCRs) used in industrial environments. The data were collected from durability tests of the driving module for HCRs, gathering time-series vibration data until the module failed. In this study, to apply classification methods in the absence of post-failure data, the initial 50% of the collected data were designated as the normal section, and the data from the 10 h immediately preceding the failure were selected as the fault section. To generate additional data for the limited fault dataset, the Wasserstein generative adversarial networks with gradient penalty (WGAN-GP) model was utilized and residual connections were added to the generator to maintain the basic structure while preventing the loss of key features of the data. Considering that the performance of image encoding techniques varies depending on the dataset type, this study applied and compared five image encoding methods and four CNN models to facilitate the selection of the most suitable algorithm. The time-series data were converted into image data using image encoding techniques including recurrence plot, Gramian angular field, Markov transition field, spectrogram, and scalogram. These images were then applied to CNN models, including VGGNet, GoogleNet, ResNet, and DenseNet, to calculate the accuracy of fault diagnosis and compare the performance of each model. The experimental results demonstrated significant improvements in diagnostic accuracy when employing the WGAN-GP model to generate fault data, and among the image encoding techniques and convolutional neural network models, spectrogram and DenseNet exhibited superior performance, respectively.

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Section: Image Encoding Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis Method for Human Coexistence Robots Based on Convolutional Neural Networks Using Time-Series Data Generation and Image Encoding

Choi,

Park,

et al. 2023

Sensors

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“…The results show that GoogleNet achieves in the range of 89.66%-98.23% accuracy in fault classification. A fault diagnosis system using acoustic emission and machine learning techniques was presented in the paper [9]. The fault diagnosis of commercial drill tool CT10128 was presented.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Fault diagnosis of electrical faults of three phase induction motors using acoustic analysis

Lucas,

Lui,

Brumercik

et al. 2024

Bulletin of the Polish Academy of Sciences Technical Sciences

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Fault diagnosis techniques of electrical motors can prevent unplanned downtime and loss of money, production, and health. Various parts of the induction motor can be diagnosed: Rotor, stator, rolling bearings, fan, insulation damage, and shaft. Acoustic analysis is non-invasive. Acoustic sensors are low-cost. Changes in the acoustic signal are often observed for faults in induction motors. In this paper, the authors present a fault diagnosis technique for three-phase induction motors (TPIM) using acoustic analysis. The authors analyzed acoustic signals for three conditions of the TPIM: healthy TPIM, TPIM with twobroken bars, and TPIM with a faulty ring of the squirrel cage. Acoustic analysis was performed using Fast Fourier Transform(FFT), a new feature extraction method called MoD-7 (Maxima of differences between the conditions), and deep neural networks: GoogLeNet, and ResNet-50. The results of the analysis of acoustic signals were equal to 100% for the three analyzed conditions. The proposed technique is excellent for acoustic signals. The described technique can be used for electric motor fault diagnosis applications.

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“…The paper considers the long short-term memory (LSTM) model, whose results reach 90.67% and 100% accuracy [60] 2023 This article presents a process for building an Acoustic Emission fault detection system using Machine Learning methods…”

Section: Paper Year Main Idea Conclusionmentioning

confidence: 99%

Enhancing Fault Detection in Wireless Sensor Networks Through Support Vector Machines: A Comprehensive Study

Mardenov,

Adamova,

Zhukabayeva

et al. 2023

Journal of Robotics and Control

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The Wireless Sensor Network (WSN) consists of many sensors that are distributed in a specific area for the purpose of monitoring physical conditions. Factors such as hardware limitations, limited resources, unfavourable WSN deployment environment, and the presence of various attacks on nodes can lead to the presence of Faulty Nodes in a WSN. This raises the problem of detecting Faulty Nodes and avoiding Data loss. Detecting Faulty Nodes in real-world scenarios will improve the quality of a WSN. The research was aimed at developing an algorithm to determine the location of Faulty Nodes in a WSN. The algorithm uses characteristics of Machine Learning and Support Vector Machines (SVM), which use the classification of Data into true and false. A Mathematical Model for determining Faulty Nodes using the SVM is considered. A methodology for detecting a Faulty Node is demonstrated, which consists of Data Collection, Feature Extraction, Training, and Testing the Model. The Results of simulated experiments that were conducted with different numbers of nodes from 50 to 320 are shown. The Model is tested on Data very similar to real-world sensing Data to evaluate the ability of the Model to detect failed nodes and calculate training and testing errors. As a result, the training error is 4.6667%, the accuracy of detecting faulty nodes was 97%. The simulation results demonstrate the high stability of the proposed algorithm and are suitable for network environments with irregular node distribution or coverage gaps. In real scenarios, it can provide a high level of uninterrupted operation of the WSN and lossless data transmission. Shortcomings and prospects in research on fault detection in WSN, such as studying real-world hardware issues and its security, were presented.

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Fault Detection in Rotating Machinery Based on Sound Signal Using Edge Machine Learning

Cited by 19 publications

References 12 publications

Fault Diagnosis Method for Human Coexistence Robots Based on Convolutional Neural Networks Using Time-Series Data Generation and Image Encoding

Fault Diagnosis Method for Human Coexistence Robots Based on Convolutional Neural Networks Using Time-Series Data Generation and Image Encoding

Fault diagnosis of electrical faults of three phase induction motors using acoustic analysis

Enhancing Fault Detection in Wireless Sensor Networks Through Support Vector Machines: A Comprehensive Study

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