A two-stage blind deconvolution strategy for bearing fault vibration signals

Had, Anas; Sabri, Khalid

doi:10.1016/j.ymssp.2019.106307

Cited by 34 publications

(19 citation statements)

References 32 publications

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“…They are capable of acquiring data from more than one channel at a time, but with a unit maximum sampling frequency limit of 50 kHz, which means using one single channel for analyzing certain components such as bearings. Nevertheless, a lower sampling frequency can be used to record more components such as can be seen in [ 13 ]; however this involves sacrificing masked high frequency information.…”

Section: Introductionmentioning

confidence: 99%

Low-Cost, High-Frequency, Data Acquisition System for Condition Monitoring of Rotating Machinery through Vibration Analysis-Case Study

Soto-Ocampo

Mera

Cano-Moreno

et al. 2020

Sensors

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Data acquisition is a crucial stage in the execution of condition monitoring (CM) of rotating machinery, by means of vibration analysis. However, the major challenge in the execution of this technique lies in the features of the recording equipment (accuracy, resolution, sampling frequency and number of channels) and the cost they represent. The present work proposes a low-cost data acquisition system, based on Raspberry-Pi, with a high sampling frequency capacity in the recording of up to three channels. To demonstrate the effectiveness of the proposed data acquisition system, a case study is presented in which the vibrations registered in a bearing are analyzed for four degrees of failure.

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Section: Introductionmentioning

confidence: 99%

Low-Cost, High-Frequency, Data Acquisition System for Condition Monitoring of Rotating Machinery through Vibration Analysis-Case Study

Soto-Ocampo

Mera

Cano-Moreno

et al. 2020

Sensors

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show abstract

“…Struktur mesin biasanya terdiri poros yang dihubungkan oleh kopling dan motor listrik. Mesin tersebut sering mengalami kegagalan yang dapat menyebabkan kerusakan karena struktur mekaniknya yang rumit dan lingkungan kerja yang buruk membuatnya mudah rusak [1,2]. Komponen utama seperti roda gigi, bearing, kopling dan poros akan mudah mengalami berbagai kerusakan yang dapat membahayakan kinerjanya dan bahkan menyebabkan masalah serius jika tidak terdeteksi dan ditangani [3,4].…”

Section: Pendahuluanunclassified

Analisis Ketidaksesumbuan Poros (Misalignment) pada Rotordinamik Berdasarkan Sinyal Suara

Suryadi

Febriyanto

Fitrilina

2021

JRM

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This research aims to identify misalignment of the rotor dynamics based on sound spectrum characteristic. In this study, rotor dynamics consist of motor, shaft, coupling and bearings. Three types of misalignment were considered, namely parallel, angular, and combination misalignment. In order to obtain the best signal, microphones were used as sensors to capture sound signal placed on coupling and each bearing. The signal obtained was in time series. The sound signal in the time domain is then filtered to remove noise signals, which are then transferred to be signals in the frequency domain using Fast Fourier Transform (FFT). From the test results, it is found that in the case of parallel misalignment, the sound frequency spectrum is obtained with a peak amplitude at 2x rpm. The case of angular misalignment obtained a sound spectrum with a peak amplitude value and is dominant at 1x rpm than 2x rpm. Meanwhile, in the case of a combination of parallel and angular misalignment, a peak amplitude sound spectrum appears at 1x rpm and 2x rpm with relatively close spacing between the peaks of the sound spectrum. The result shows that sound signal can be used for identification of misalignment of the rotor dynamics.

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“…In recent years, multiple signal analysis methods have been introduced to bearing fault diagnosis to solve the difficulties in fault characteristics extraction, including empirical mode decomposition (EMD) [7], ensemble empirical mode decomposition (EEMD) [8,9], variational mode decomposition (VMD) [10,11], blind source separation [12] and so on. FREI brought forward ITD self-adaptive decomposition algorithm in 2007 [13], which was firstly applied to the field of electrical signal.…”

Section: Introductionmentioning

confidence: 99%

A study on the extraction of characteristics of compound faults of rolling bearings based on ITD-AF-CAF

Yu¹,

Ge²,

Qiao³

et al. 2021

J. vibroeng.

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In view of the cyclostationary characteristics of vibration signals from aero-engine, the combination of cyclic autocorrelation function and intrinsic timescale decomposition (ITD) has been proposed. According to the proposed method, vibration signals are decomposed by ITD algorithm to obtain the autocorrelation function of proper rotation components (PRC), based on which characteristic extraction and identification of compound faults of rolling bearings is made possible. To validate the effectiveness of method, an analysis has been given to the vibration signals of rolling bearings collected by sensors of different positions in different compound fault modes. As shown by results, the method combining ITD and cyclostationary theory can precisely and effectively extract the characteristic frequency relative to the type of faults and identify the compound faults.

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A two-stage blind deconvolution strategy for bearing fault vibration signals

Cited by 34 publications

References 32 publications

Low-Cost, High-Frequency, Data Acquisition System for Condition Monitoring of Rotating Machinery through Vibration Analysis-Case Study

Low-Cost, High-Frequency, Data Acquisition System for Condition Monitoring of Rotating Machinery through Vibration Analysis-Case Study

Analisis Ketidaksesumbuan Poros (Misalignment) pada Rotordinamik Berdasarkan Sinyal Suara

A study on the extraction of characteristics of compound faults of rolling bearings based on ITD-AF-CAF

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