A smart sensor-based monitoring system for vibration measurement and bearing fault detection

Shukla, Aman; Mahmud, Rasel; Wang, Wilson

doi:10.1088/1361-6501/ab8dfc

Cited by 19 publications

(13 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Figure 8 shows that the fitted equation of K eq and ADA is K eq = 4.136 − 25.59 ψ2 . We then substitute this value into Compared with [45][46][47][48][49][50][51][52], the novelty and significance of our measurement technique include (a) using modal analysis technique and equivalent linearization theory to identify the parameters of the ship's quasi-rolling system, the control is simpler, the experiment is more economical and efficient than the traditional tank experiment. (b) The rolling test in this experiment is convenient to adjust repeatedly according to the results of numerical simulation, which can lay a foundation for the optimization of identification results and the adjustment of mathematical model.…”

Section: Parameter Identification Of the Restoring Momentmentioning

confidence: 99%

Parameter identification of ship roll motion based on vibration tests and CFD method

Wang¹,

Xu²

2021

Meas. Sci. Technol.

View full text Add to dashboard Cite

This paper implements the parameter identification of ship roll motion based on vibration tests and the computational fluid dynamics (CFD) method. By applying sinusoidal moment excitations from a shaker, we successfully simulate the quasi-roll motion behavior of the ship model in the air. We conduct two vibration tests, including a constant-amplitude angular displacement test and a constant-amplitude angular velocity test, to identify the restoring moment and the damping moment coefficients of the quasi-roll motion. In addition, we perform the approximate equivalence of the fluid around the ship through the CFD method and the fluid-solid coupling technique. Thus, we obtain the hydrodynamic coefficients, such as the added mass moment of inertia and the equivalent damping, to accomplish the parameter identification of the ship's real roll motion. Finally, the accuracy of the identified parameters is verified by means of numerical simulations based on the Runge-Kutta method.

show abstract

Section: Parameter Identification Of the Restoring Momentmentioning

confidence: 99%

Parameter identification of ship roll motion based on vibration tests and CFD method

Wang¹,

Xu²

2021

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…In time-frequency domain analysis, the Wigner-Ville distribution, short-time Fourier transform (STFT), wavelet transform (WT), and mode decomposition methods are often used to analyze the non-stationary fault characteristics of rolling bearings [17]. Among them, mode decomposition algorithms, as adaptive analysis methods, unlike STFT, Wigner-Ville distribution, and WT, which rely on predefined functions, are attracting increasing research interest.…”

Section: Introductionmentioning

confidence: 99%

“…Many efforts have been made to improve IMF selection and signal reconfiguration. For example, Shukla et al [17], Lei and Zuo [22], and Mahmud and Wang [23] used correlation analysis for IMFs selection; Tsao et al selected IMFs by checking the IMF spectrum [24]; Osman and Wang proposed a correlation measure based on the normalized correlation measure and deficiency of mutual information (NCM/DMI) for IMFs analysis [25]; Sui et al selected the most representative IMF for fault feature extraction based on envelope correlation analysis [26]; Osman et al introduced the D'Agostino-Pearson normality test for IMFs selection and integration [9,27]; and Imaouchen et al used the product value of kurtosis, instantaneous energy, and approximate entropy to measure the distinctiveness of IMFs [28]. However, among the above methods, those relying on correlation measurements may encounter a potential issue, that is, IMFs may be selected because of their strong correlation with the interfering components in the original signal.…”

Section: Introductionmentioning

confidence: 99%

An adaptive enhanced envelope spectrum technique for bearing fault detection in conditions characterized by strong noise

Xv,

Liao,

Cao

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

Rolling bearings are widely used in rotating machinery and have a high failure rate. Regrettably, the task of ensuring dependable bearing fault detection presents a formidable challenge, especially when the bearing fault-related characteristics are non-stationary or even affected by strong noise. In response to this challenge, a novel adaptive enhanced envelope spectrum (AEES) technique is proposed in this study. Firstly, it generates representative intrinsic mode functions (IMFs) using the variational mode decomposition algorithm. Then, based on the analysis of the envelope spectrum normalized mutual information and time-domain fuzzy entropy, a new IMF selection and integration strategy combining time- and frequency-domain metrics is suggested to reconstruct the most informative analytical signal. An adaptive filter is employed to post-process the reconfigured signal to reinforce fault-related impulsive characteristics, the optimal length of which is ascertained through the proposed variable step-size search technique based on unbiased autocorrelation analysis. The efficacy of the AEES technique has been validated through a sequence of experiments conducted under diverse bearing conditions. Its robustness and distinct advantages under strong noise conditions are tested using a publicly available dataset. The validation results show that the AEES technique can effectively identify the health conditions of bearings under high noise conditions (signal-to-noise ratios between 1 dB and 3 dB). Compared with two relevant techniques in the existing literature and a classical method, the proposed AEES technique can achieve signal processing results with fewer interference components and more prominent characteristic frequency information and has a unique ability to identify fault features in some challenging situations.

show abstract

“…Recently, various diagnostic methods, including vibration [4][5][6], acoustic emission [7][8][9], stray flux monitoring [10,11], and motor current signature analysis (MCSA) [12][13][14] have been used to detect bearing faults. The vibration, acoustic emission, and stray flux analysis methods are sometimes unsuitable for practical use because the vibration and acoustic sensors detect ambient noise.…”

Section: Introductionmentioning

confidence: 99%

Bearing Scratch Fault Detection by Three‐Dimensional Features and a Support Vector Machine

Yatsugi

Pandarakone

Mizuno

et al. 2022

IEEJ Transactions Elec Engng

View full text Add to dashboard Cite

Induction motors play a crucial role in various industries owing to their high robustness. The demand for early fault detection is getting attention to avoid serious damage to the machines. Bearing fault is the most common failure in induction motors and the possibility of scratches has a higher probability among the various classes of the bearing faults. Recently, the effective diagnosis method considering the progression and orientation of the scratch fault by using a machine learning algorithm have been reported. However, the diagnosis of such scratch faults with high accuracy is still required and challenging for early fault detection. In this article, the scratch faults are diagnosed with high accuracy rate and the detailed analysis are conducted. A support vector machine algorithm is used to diagnose the faults, where sideband frequency components of the load current are used as the features. In order to obtain high accuracy rate, additional features are considered, where in this study, the rotating speed of the motor and a higher order frequency component are selected as candidates and compared. The rotating speed along with sideband components of load current shows high accuracy rate. Furthermore, the robustness of the method is tested against the multiple faults. The results show that the rotating speed is an effective feature for highly sensitive diagnoses of bearing scratch faults.

show abstract

A smart sensor-based monitoring system for vibration measurement and bearing fault detection

Cited by 19 publications

References 29 publications

Parameter identification of ship roll motion based on vibration tests and CFD method

Parameter identification of ship roll motion based on vibration tests and CFD method

An adaptive enhanced envelope spectrum technique for bearing fault detection in conditions characterized by strong noise

Bearing Scratch Fault Detection by Three‐Dimensional Features and a Support Vector Machine

Contact Info

Product

Resources

About