Severity evaluation of the transverse crack in a cylindrical part using a PZT wafer based on an interval energy approach

Xiao, Han; Zheng, Jiajia; Song, Gangbing

doi:10.1088/0964-1726/25/3/035021

Cited by 18 publications

(11 citation statements)

References 18 publications

(21 reference statements)

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“…When the failure of a rolling bearing occurs, the change of the dynamic characteristics can be reflected from collected vibration signals [ 1 , 2 , 3 , 4 ], however, in most cases, the early fault of the rolling bearing is difficult to identify during the initial phase of the fault. The dynamic characteristics generated by early fault are usually submerged in strong background noise, thus, the weak fault is difficult to extract, and the exact time when the fault starts to happen is difficult to identify [ 5 , 6 , 7 , 8 ]. In most cases, the fault of the rolling bearing can only be identified when it has developed to a certain degree.…”

Section: Introductionmentioning

confidence: 99%

Health Degradation Monitoring and Early Fault Diagnosis of a Rolling Bearing Based on CEEMDAN and Improved MMSE

Yuan

Wang

et al. 2018

Materials

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Rolling bearings play a crucial role in rotary machinery systems, and their operating state affects the entire mechanical system. In most cases, the fault of a rolling bearing can only be identified when it has developed to a certain degree. At that moment, there is already not much time for maintenance, and could cause serious damage to the entire mechanical system. This paper proposes a novel approach to health degradation monitoring and early fault diagnosis of rolling bearings based on a complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and improved multivariate multiscale sample entropy (MMSE). The smoothed coarse graining process was proposed to improve the conventional MMSE. Numerical simulation results indicate that CEEMDAN can alleviate the mode mixing problem and enable accurate intrinsic mode functions (IMFs), and improved MMSE can reflect intrinsic dynamic characteristics of the rolling bearing more accurately. During application studies, rolling bearing signals are decomposed by CEEMDAN to obtain IMFs. Then improved MMSE values of effective IMFs are computed to accomplish health degradation monitoring of rolling bearings, aiming at identifying the early weak fault phase. Afterwards, CEEMDAN is performed to extract the fault characteristic frequency during the early weak fault phase. The experimental results indicate the proposed method can obtain a better performance than other techniques in objective analysis, which demonstrates the effectiveness of the proposed method in practical application. The theoretical derivations, numerical simulations, and application studies all confirmed that the proposed health degradation monitoring and early fault diagnosis approach is promising in the field of prognostic and fault diagnosis of rolling bearings.

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Section: Introductionmentioning

confidence: 99%

Health Degradation Monitoring and Early Fault Diagnosis of a Rolling Bearing Based on CEEMDAN and Improved MMSE

Yuan

Wang

et al. 2018

Materials

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“…Structural health monitoring (SHM) has made significant advances in the past decades [1][2][3][4][5][6][7][8]. Monitoring of refractories, which are widely used in furnace, iron, and steel industries due to their ability to gain strength rapidly and to withstand aggressive environments and high temperature [9], receives increasing attention [10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Damage Detection of Refractory Based on Principle Component Analysis and Gaussian Mixture Model

et al. 2018

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Acoustic emission (AE) technique is a common approach to identify the damage of the refractories; however, there is a complex problem since there are as many as fifteen involved parameters, which calls for effective data processing and classification algorithms to reduce the level of complexity. In this paper, experiments involving three-point bending tests of refractories were conducted and AE signals were collected. A new data processing method of merging the similar parameters in the description of the damage and reducing the dimension was developed. By means of the principle component analysis (PCA) for dimension reduction, the fifteen related parameters can be reduced to two parameters. The parameters were the linear combinations of the fifteen original parameters and taken as the indexes for damage classification. Based on the proposed approach, the Gaussian mixture model was integrated with the Bayesian information criterion to group the AE signals into two damage categories, which accounted for 99% of all damage. Electronic microscope scanning of the refractories verified the two types of damage.

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“…Heavy gearboxes are widely used in manufacture, metallurgic, and marine fields for their advantages of strong load-bearing capacity, compact structure, and large transmission ratio. e high-speed gearbox usually works in the harsh operating conditions, such as inevitable impact and complex alternating loads which cause the gear to be difficult to avoid crack, pitting, scratch, and spalling [1,2]. Moreover, gear is a critical component of gearbox, and gear faults occupy a significant proportion of all faults in gearbox, which can cause the unit damage as well as high cost of maintenance.…”

Section: Introductionmentioning

confidence: 99%

A Novel Detrended Fluctuation Analysis Method for Gear Fault Diagnosis Based on Variational Mode Decomposition

Xiao

2018

Shock and Vibration

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The vibration signal of heavy gearbox has the nonlinear and nonstationary characteristic, which makes the gear fault diagnosis difficult. Moreover, the useful fault information is mainly focused on the high-frequency components of the raw signal, which also affects the fault feature extraction from vibration signal. For this reason, a novel signal processing method based on variational mode decomposition (VMD) and detrended fluctuation analysis (DFA) is proposed to diagnose the gear faults of heavy gearbox. Since high-frequency component contains more fault information, the raw vibration signal is decomposed several mode components by VMD, which can remove the low-frequency component to retain the high-frequency component. Moreover, the most sensitive mode component is selected in these high-frequency components by a maximal indicator, which is composed of kurtosis and correlation coefficient. The most sensitive mode component is calculated by DFA to obtain bi-logarithmic map, and the sliding windowing algorithm is employed to capture turning point of the bi-logarithmic map, thus extracting the fault feature of small time scale to identify gear faults. The effectiveness of the proposed method for fault diagnosis is validated by experimental data analysis, and the comparison results demonstrate that the recognition rate of gear faults condition have marked improvement by proposed method than the DFA of small time scale (STS-DFA) and EMD-DFA.

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Severity evaluation of the transverse crack in a cylindrical part using a PZT wafer based on an interval energy approach

Cited by 18 publications

References 18 publications

Health Degradation Monitoring and Early Fault Diagnosis of a Rolling Bearing Based on CEEMDAN and Improved MMSE

Health Degradation Monitoring and Early Fault Diagnosis of a Rolling Bearing Based on CEEMDAN and Improved MMSE

Damage Detection of Refractory Based on Principle Component Analysis and Gaussian Mixture Model

A Novel Detrended Fluctuation Analysis Method for Gear Fault Diagnosis Based on Variational Mode Decomposition

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