Extracting accurate time domain features from vibration signals for reliable classification of bearing faults

Tahir, et al.

doi:10.21833/ijaas.2018.01.021

Cited by 7 publications

(4 citation statements)

References 30 publications

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“…This technology has been applied in different fields. In fruit recognition, researchers applied deep learning convolutional neural networks (CNN) to the visual techniques of agricultural robots (Yang et al, 2016;Bargoti and Underwood, 2017;Chen et al, 2017;Rahnemoonfar and Sheppard, 2017;Tahir and Badshah, 2018). Hou et al (2016) developed a fruit recognition algorithm based on a CNN.…”

Section: Deep Learning Methodsmentioning

confidence: 99%

Recognition and Localization Methods for Vision-Based Fruit Picking Robots: A Review

et al. 2020

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The utilization of machine vision and its associated algorithms improves the efficiency, functionality, intelligence, and remote interactivity of harvesting robots in complex agricultural environments. Machine vision and its associated emerging technology promise huge potential in advanced agricultural applications. However, machine vision and its precise positioning still have many technical difficulties, making it difficult for most harvesting robots to achieve true commercial applications. This article reports the application and research progress of harvesting robots and vision technology in fruit picking. The potential applications of vision and quantitative methods of localization, target recognition, 3D reconstruction, and fault tolerance of complex agricultural environment are focused, and fault-tolerant technology designed for utilization with machine vision and robotic systems are also explored. The two main methods used in fruit recognition and localization are reviewed, including digital image processing technology and deep learning-based algorithms. The future challenges brought about by recognition and localization success rates are identified: target recognition in the presence of illumination changes and occlusion environments; target tracking in dynamic interference-laden environments, 3D target reconstruction, and fault tolerance of the vision system for agricultural robots. In the end, several open research problems specific to recognition and localization applications for fruit harvesting robots are mentioned, and the latest development and future development trends of machine vision are described.

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Section: Deep Learning Methodsmentioning

confidence: 99%

Recognition and Localization Methods for Vision-Based Fruit Picking Robots: A Review

et al. 2020

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“…Statistical features (STF) derived from the time domain representation of signals find application in various domains [48][49][50][51], including the detection and classification of lung sounds [52,53]. Within these features, distribution features like mean and standard deviation are employed to differentiate bimodal distributed sounds like a wheeze, while high-order statistical features such as skewness and kurtosis help distinguish non-periodic sounds like a crackle [45].…”

Section: Extracted Featuresmentioning

confidence: 99%

A New Shapley-Based Feature Selection Method in a Clinical Decision Support System for the Identification of Lung Diseases

Kababulut,

Gürkan Kuntalp,

Düzyel

et al. 2023

Diagnostics

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The aim of this study is to propose a new feature selection method based on the class-based contribution of Shapley values. For this purpose, a clinical decision support system was developed to assist doctors in their diagnosis of lung diseases from lung sounds. The developed systems, which are based on the Decision Tree Algorithm (DTA), create a classification for five different cases: healthy and disease (URTI, COPD, Pneumonia, and Bronchiolitis) states. The most important reason for using a Decision Tree Classifier instead of other high-performance classifiers such as CNN and RNN is that the class contributions of Shapley values can be seen with this classifier. The systems developed consist of either a single DTA classifier or five parallel DTA classifiers each of which is optimized to make a binary classification such as healthy vs. others, COPD vs. Others, etc. Feature sets based on Power Spectral Density (PSD), Mel Frequency Cepstral Coefficients (MFCC), and statistical characteristics extracted from lung sound recordings were used in these classifications. The results indicate that employing features selected based on the class-based contribution of Shapley values, along with utilizing an ensemble (parallel) system, leads to improved classification performance compared to performances using either raw features alone or traditional use of Shapley values.

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“…The common frequencies generated in rolling element bearings are the fundamental train frequency (FTF), ball pass frequency of inner race (BPFI), ball pass frequency of outer race (BPFO), and ball spin frequency (BSF). They were calculated using the below equations [15]. The common rolling element bearings geometry consists of four components: the outer race, inner race, ball, and cage involved in producing the fault frequencies [3].…”

Section: Characteristic Defect Frequenciesmentioning

confidence: 99%

Bearings Health Monitoring Based on Frequency-Domain Vibration Signals Analysis

Jawad¹,

Jaber²

2022

ETJ

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 Fabricate test rig to simulate the state and capture information  Extraction time domain signal.  Transform time domain to frequency domain by FFT transform using sigview program.  Analysis result.Rotating machine health monitoring is critical for system safety, cost savings, and increased reliability. The need for a simple and accurate fault diagnosis method has led to the development of various monitoring techniques. They incorporate vibration, motor's current signature, and acoustic emission signals analysis in condition monitoring. So, based on using vibration signal analysis, a test rig was built for bearing fault identification. The test rig replicates and investigates various bearing problems, such as those found in the inner and outer races. An accelerometer, type ADXL335, was interfaced to a data acquisition device (DAQ USB-6215) for collecting vibration signals under various operating circumstances. In addition, a load cell was embedded with the test rig, interfaced with a digital panel meter, and used for recording the applied load on the bearings. The time-domain signal analysis technique was used after acquiring vibration signals at various bearing health states. Then, the time-domain signal was converted to the frequency domain using the fast Fourier transform, and the result was analyzed to investigate the generated fault frequencies. Finally, the obtained frequencies were compared with the theoretical values extracted from the theoretical equations, and the method proved its effectiveness in detecting the fault generated.

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Extracting accurate time domain features from vibration signals for reliable classification of bearing faults

Cited by 7 publications

References 30 publications

Recognition and Localization Methods for Vision-Based Fruit Picking Robots: A Review

Recognition and Localization Methods for Vision-Based Fruit Picking Robots: A Review

A New Shapley-Based Feature Selection Method in a Clinical Decision Support System for the Identification of Lung Diseases

Bearings Health Monitoring Based on Frequency-Domain Vibration Signals Analysis

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