Fault Diagnosis of Active Magnetic Bearing–Rotor System via Vibration Images

Yan, Xunshi; Sun, Zhe; Zhao, Jingjing; Shi, Zhengang; Zhang, Chen-An

doi:10.3390/s19020244

Cited by 21 publications

(18 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Yang_2017 [161] Isham_2019 [162] Amarnath_2013 [163] Mao_2018 [164] Chen_2015 [165] Rafiq_2021 [166] Isham_2018 [167] Jegadeeshwaran_2014 [168] Cyclostationary and cyclo-non-stationary analysis [173] Sun_2020 [174] Jeon_2020 [175] Fan_2020 [176] Youcef_2020 [177] Yang_2019 [178] Xin_2018 [179] Hamadache_2018 [180] Song_2018 [181] Golbaghi_2017 [182] Li_2016c [137] Raj_2015 [183] Ocak_2001 [184] Oh_2018 [185] Tarek_2020 [186] Li_2018 [187] Hong_2017 [188] Cerrada_2015 [189] Fan_2015 [190] Yang_2018 [191] Qiang_2014 [192] Moghadam_2021 [193] He_2016 [194] Gierlak_2017 [195] Zhao_2019b [196] Unique Jablon_2021 [197] Gu_2021 [198] Mohamad_2020 [2] Yan_2019 [199] Barbini_2018 [200] Khan_2016 [201] Biswas_2013 [202] Bai_2021a [203] Mohamad_2020 [2] Hizarci_2019 [204] Medina_2019 [205] Chen_2002 [206] Chen_2002…”

Section: Stft Wavelet Wigner-ville (Wv) Distribution Hilbert-huang Transform Cohen Class Functionsmentioning

confidence: 99%

A Review on Vibration-Based Condition Monitoring of Rotating Machinery

et al. 2022

View full text Add to dashboard Cite

Monitoring vibrations in rotating machinery allows effective diagnostics, as abnormal functioning states are related to specific patterns that can be extracted from vibration signals. Extensively studied issues concern the different methodologies used for carrying out the main phases (signal measurements, pre-processing and processing, feature selection, and fault diagnosis) of a malfunction automatic diagnosis. In addition, vibration-based condition monitoring has been applied to a number of different mechanical systems or components. In this review, a systematic study of the works related to the topic was carried out. A preliminary phase involved the analysis of the publication distribution, to understand what was the interest in studying the application of the method to the various rotating machineries, to identify the interest in the investigation of the main phases of the diagnostic process, and to identify the techniques mainly used for each single phase of the process. Subsequently, the different techniques of signal processing, feature selection, and diagnosis are analyzed in detail, highlighting their effectiveness as a function of the investigated aspects and of the results obtained in the various studies. The most significant research trends, as well as the main innovations related to the various phases of vibration-based condition monitoring, emerge from the review, and the conclusions provide hints for future ideas.

show abstract

Section: Stft Wavelet Wigner-ville (Wv) Distribution Hilbert-huang Transform Cohen Class Functionsmentioning

confidence: 99%

A Review on Vibration-Based Condition Monitoring of Rotating Machinery

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Then, MB-CNN is used to extract features and deal with the fault recognition of rotating machinery. Yan et al [65] proposed a fault diagnosis method for an active magnetic bearing-rotor system using vibration images. In this method, three features, histogram of vibration image (HVI), the histogram of oriented vibration image (HOVI), and 2-D FFT of vibration image (2DFFT) are designed based on signal amplitude, signal phase, and frequency domain, respectively.…”

Section: Introductionmentioning

confidence: 99%

Connected Components-based Colour Image Representations of Vibrations for a Two-stage Fault Diagnosis of Roller Bearings Using Convolutional Neural Networks

Ahmed

Nandi

2021

Chin. J. Mech. Eng.

View full text Add to dashboard Cite

Roller bearing failure is one of the most common faults in rotating machines. Various techniques for bearing fault diagnosis based on faults feature extraction have been proposed. But feature extraction from fault signals requires expert prior information and human labour. Recently, deep learning algorithms have been applied extensively in the condition monitoring of rotating machines to learn features automatically from the input data. Given its robust performance in image recognition, the convolutional neural network (CNN) architecture has been widely used to learn automatically discriminative features from vibration images and classify health conditions. This paper proposes and evaluates a two-stage method RGBVI-CNN for roller bearings fault diagnosis. The first stage in the proposed method is to generate the RGB vibration images (RGBVIs) from the input vibration signals. To begin this process, first, the 1-D vibration signals were converted to 2-D grayscale vibration Images. Once the conversion was completed, the regions of interest (ROI) were found in the converted 2-D grayscale vibration images. Finally, to produce vibration images with more discriminative characteristics, an algorithm was applied to the 2-D grayscale vibration images to produce connected components-based RGB vibration images (RGBVIs) with sets of colours and texture features. In the second stage, with these RGBVIs a CNN-based architecture was employed to learn automatically features from the RGBVIs and to classify bearing health conditions. Two cases of fault classification of rolling element bearings are used to validate the proposed method. Experimental results of this investigation demonstrate that RGBVI-CNN can generate advantageous health condition features from bearing vibration signals and classify the health conditions under different working loads with high accuracy. Moreover, several classification models trained using RGBVI-CNN offered high performance in the testing results of the overall classification accuracy, precision, recall, and F-score.

show abstract

“…Generally, rotary machinery monitoring systems are usually based on vibration signal analysis. These signals are essential and vital fault sources and contain enough information about operating states of rotating machinery [16]. The application of magnetic suspension technology in the bearing system of high-speed machine rotors enables one to measure vibrations with the use of eddy current sensors, which are a necessary control system element.…”

Section: Introductionmentioning

confidence: 99%

Measurement System of a Magnetic Suspension System for a Jet Engine Rotor

Kurnyta-Mazurek

Kurnyta

Henzel

2020

Sensors

View full text Add to dashboard Cite

This paper presents laboratory results on the measurement system of a magnetic suspension bearing system for a jet engine rotor of an unmanned aerial vehicle (UAV). Magnetic suspension technology enables continuous diagnostics of a rotary machine and eliminates of the negative properties of classical bearings. This rotor-bearing system consists of two radial magnetic bearings and one axial (thrust) magnetic bearing. The concept of the bearing system with a magnetically suspended rotor for UAV is presented in this paper. Rotor geometric and inertial characteristics were assumed according to the parameters of a TS-21 jet engine. Preliminary studies of the measurement system of rotor engines were made on a laboratory stand with homopolar active magnetic bearings. The measurement system consisted of strain gauges, accelerometers, and contactless proximity sensors. During the research, strains were registered with the use of a wireless data acquisition (DAQ) system. Measurements were performed for different operational parameters of rotational rotor speed, control system parameters, and with the presence of disturbance signals from the control system. In this paper, obtained operational characteristics are presented and discussed.

show abstract

Fault Diagnosis of Active Magnetic Bearing–Rotor System via Vibration Images

Cited by 21 publications

References 31 publications

A Review on Vibration-Based Condition Monitoring of Rotating Machinery

A Review on Vibration-Based Condition Monitoring of Rotating Machinery

Connected Components-based Colour Image Representations of Vibrations for a Two-stage Fault Diagnosis of Roller Bearings Using Convolutional Neural Networks

Measurement System of a Magnetic Suspension System for a Jet Engine Rotor

Contact Info

Product

Resources

About