Influence of uneven loading condition on the sound radiation of starved lubricated full ceramic ball bearings

Shi, Huaitao; Bai, Xiaotian; Zhang, K.; Wu, Yuhou; Yue, Guodong

doi:10.1016/j.jsv.2019.114910

Cited by 27 publications

(24 citation statements)

References 27 publications

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“…Step4: Determine S and extend the data set to the augment matrix X S . Step5: Select the appropriate order j, and calculate the corresponding X Sjk according to formula (14) Step6: Calculate the number of principal component of each X Sjk , the eigenvalue matrix jk , and the eigenvector matrix P jk .…”

Section: B the Steps Of Deep Rdpcamentioning

confidence: 99%

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Gearbox Incipient Fault Detection Based on Deep Recursive Dynamic Principal Component Analysis

et al. 2020

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As a part of the energy transmission chain, gearboxes are considered as important components in rotating machines, and the gearbox failure results in costly economic losses. Therefore, it is necessary to detect the appearance of incipient gearbox faults by implementing an appropriate detected model. The incipient failure characteristics of the gearbox are weak and hidden in a set of time-varying series signals the vibration signals, which is difficult to effectively extract under the background of strong noise. The PCA method is not effective in detecting weak fault features in time-varying signals, so this paper proposes a method based on Deep Recursive Dynamic Principal Component Analysis (Deep RDPCA) to detect incipient faults in gearboxes. The proposed approach is modeled via both the deep decomposed theorems and time-varying dynamic model based on traditional PCA to extract characteristic of time-varying and weak fault information under the background of strong noise. The proposed method could get a better real-time reflection for changed system by introducing ''Moving Window'' technologies, so that the incipient fault of gearbox could be detected accurately, too. Finally, the effect of Deep RDPCA-based fault diagnosis is compared with the results of PCA, DPCA, RDPCA, Deep PCA, and Deep DPCA methods. It is concluded that the proposed method can effectively capture the time-varying relationship of process variables and accurately extract the weak fault characteristics in the vibration signal, which effectively improves the fault detection performance. INDEX TERMS Gearbox, fault diagnosis, gear failure experiment, feature extraction, Deep RDPCA.

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Section: B the Steps Of Deep Rdpcamentioning

confidence: 99%

“…Step5: Calculate the corresponding X Stjk according to formula (14). Step6: Obtain the statistics T 2 jk and SPE jk .…”

Section: B: Online Monitoringmentioning

confidence: 99%

Gearbox Incipient Fault Detection Based on Deep Recursive Dynamic Principal Component Analysis

et al. 2020

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“…It is necessary to improve the robustness of the visual servo system in the presence of uncertainty [13][14][15][16]. Under the uncertainty of system dynamics and vision framework, Zergeroglu [17] studied the control of planar mobile devices, in order to compensate the uncertainty of the system; a robust controller was designed to ensure the final uniform boundedness of the position tracking.…”

Section: Literature Reviewmentioning

confidence: 99%

Adaptive Visual Servoing Control for Hoisting Positioning Under Disturbance Condition

et al. 2020

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This paper proposes a visual servo scheme for hoisting positioning under disturbance conditions. In actual hoisting work, disturbances such as equipment and load vibration are inevitable, which brings challenges to the development of a visual servo for hoisting positioning. The main problems are as follows: (1) the correlation between visual error and disturbance is not considered or well resolved; (2) the disturbance has a great influence on the control stability, but it is difficult to model. At present, there is no detailed research on the above problems. In this paper, the visual error is defined by the image error of the feedback signal based on dynamic equations containing disturbances. An adaptive sliding mode control algorithm is employed to decrease the influence of external disturbance, and the coefficient of the slide surface is established based on the adaptive gain. In view of the belief that it is difficult to model disturbance terms, a nonlinear disturbance observer is introduced to obtain equivalent disturbance. On this basis, an adaptive control algorithm with disturbance compensation is proposed to improve the robustness of the visual servo system. We use Lyapunov’s method to analyze the stability conditions of the system. Compared with the other state-of-the-art methods, the simulation results show that our method has superior performance in convergence, accuracy, and restraining disturbance. Finally, the proposed algorithm is applied to the hoisting platform for experimental research, which proves the effectiveness of the controller.

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“…The serious faults do not occur in an instant, but has a process of gradual deterioration [5][6][7]. Therefore, it is of great significance to study the incipient fault detection of rolling bearings, find the incipient signal characteristics of faults, and eliminate the safety hazards in time when the fault has not developed toward a serious degree [8,9].…”

Section: Introductionmentioning

confidence: 99%

Research on a Nonlinear Dynamic Incipient Fault Detection Method for Rolling Bearings

et al. 2020

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The incipient fault detection technology of rolling bearings is the key to ensure its normal operation and is of great significance for most industrial processes. However, the vibration signals of rolling bearings are a set of time series with non-linear and timing correlation, and weak incipient fault characteristics of rolling bearings bring about obstructions for the fault detection. This paper proposes a nonlinear dynamic incipient fault detection method for rolling bearings to solve these problems. The kernel function and the moving window algorithm are used to establish a non-linear dynamic model, and the real-time characteristics of the system are obtained. At the same time, the deep decomposition method is used to extract weak fault characteristics under the strong noise, and the incipient failures of rolling bearings are detected. Finally, the validity and feasibility of the scheme are verified by two simulation experiments. Experimental results show that the fault detection rate based on the proposed method is higher than 85% for incipient fault of rolling bearings, and the detection delay is almost zero. Compared with the detection performance of traditional methods, the proposed nonlinear dynamic incipient fault detection method is of better accuracy and applicability.

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Influence of uneven loading condition on the sound radiation of starved lubricated full ceramic ball bearings

Cited by 27 publications

References 27 publications

Gearbox Incipient Fault Detection Based on Deep Recursive Dynamic Principal Component Analysis

Gearbox Incipient Fault Detection Based on Deep Recursive Dynamic Principal Component Analysis

Adaptive Visual Servoing Control for Hoisting Positioning Under Disturbance Condition

Research on a Nonlinear Dynamic Incipient Fault Detection Method for Rolling Bearings

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