Gear fault feature extraction and diagnosis method under different load excitation based on EMD, PSO-SVM and fractal box dimension

Han, Dongying; Zhao, Na; Shi, Pengfei

doi:10.1007/s12206-019-0101-z

Cited by 91 publications

(39 citation statements)

References 19 publications

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“…IMF3 to IMF7 are low-frequency components, including generator frequency and some other interference frequency. Therefore, the fault characteristics of the middle speed pinion are mainly concentrated in the three signals of IMF1, IMF2 and IMF3 [26,36,37]. Figure 7 elaborates why the frequency spectrum of the signal using EMD has performed better than PSD.…”

Section: Empirical Mode Decomposition Analysismentioning

confidence: 99%

Vibration Based Gear Fault Diagnosis under Empirical Mode Decomposition and Power Spectrum Density Analysis

Akram¹,

Khushnood²,

Tariq³

et al. 2019

Adv. Sci. Technol. Res. J.

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Rotating machinery plays a significant role in industrial applications and covers a wide range of mechanical equipment. A vibration analysis using signal processing techniques is generally conducted for condition monitoring of rotary machinery and engineering structures in order to prevent failure, reduce maintenance cost and to enhance the reliability of the system. Empirical mode decomposition (EMD) is amongst the most substantial non-linear and non-stationary signal processing techniques and it has been widely utilized for fault detection in rotary machinery. This paper presents the EMD, time waveform and power spectrum density (PSD) analysis for localized spur gear fault detection. Initially, the test model was developed for the vibration analysis of single tooth breakage of spur gear at different RPMs and then specific fault was introduced in driven gear under different damage conditions. The data, recorded by means of a wireless tri-axial accelerometer, was then analyzed using EMD and PSD techniques and the results were plotted. The results depicted that EMD algorithms are found to be more functional than the ordinarily used PSD and time waveform techniques.

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Section: Empirical Mode Decomposition Analysismentioning

confidence: 99%

Vibration Based Gear Fault Diagnosis under Empirical Mode Decomposition and Power Spectrum Density Analysis

Akram¹,

Khushnood²,

Tariq³

et al. 2019

Adv. Sci. Technol. Res. J.

View full text Add to dashboard Cite

show abstract

“…The penalty parameter and the kernel parameter have a large influence in the SVM with Gaussian kernel. The PSO method is employed to optimize the parameters as follows [14]:…”

Section: Pso-svmmentioning

confidence: 99%

Fault diagnosis of rope tension in hoisting systems based on vibration signals

Tan¹,

Xue²,

Shi³

2020

Vib. proced.

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Fault diagnosis of rope tension is of great significance for safety in hoisting systems. A novel diagnosis method based on the vibration signals of the head sheaves is proposed. First, the signal is decomposed by the ensemble empirical mode decomposition (EEMD); then the main intrinsic module functions (IMFs) are extracted by correlation analysis. Second, the energy and the permutation entropy (PE) of the main IMFs were calculated to create the feature vector. Third, a particle swarm optimization-support vector machine (PSO-SVM) is applied to classify tension states. The effectiveness and advantage of the proposed method are validated by experiments. Compared with the conventional force-sensor-based method, it has clear advantages in sensor installation, data transmission, safety, and reliability.

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“…Preliminary study has shown that the vibration energy spectrum of shore bridge has certain self-similarity and longrange correlation which indicate a fractal character of the vibration energy spectrum. In the complexity analysis method, fractal dimension is able to describe complexity quantitatively and some studies have been processed in fault feature extraction [13,14] as well as degradation feature extraction [15]. Fractal dimension calculated by mathematical morphological theory is able to overcome the shortcomings of traditional boxcounting dimension in accuracy and has some advantages of stability, accuracy, and calculating speed.…”

Section: Introductionmentioning

confidence: 99%

An Online Degradation Feature Extraction Technique for Shore Bridge Gearbox Based on Morphological Fractal Dimension and Sliding Window Weibull Fitting

Sun

Wang

Xiong

et al. 2019

Shock and Vibration

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Shore bridge and other port cranes have some working condition characters including high speed, heavy load, and large impact. In order to solve the degradation feature extraction issue of hoisting mechanism gearbox, an online degradation feature extraction technique based on morphological fractal dimension and sliding window Weibull fitting is proposed. Firstly, taking the vibration energy spectrum collecting from the gearbox as the online data source, the fractal dimension of the vibration energy spectrum during an analysis period is calculated and a fractal evolution curve is obtained. A three-parameter Weibull fitting on the fractal curve within a sliding window after setting the window’s width and step size is performed. The scale parameter of the Weibull fitting model is introduced as the performance degradation feature. The effectiveness of the technique is verified by the full-life vibration data of hoisting gearbox from Shanghai Port Group. The results show that the morphological fractal dimension is able to describe the fractal complexity of the vibration energy spectrum. The scale parameter of Weibull distribution is able to reflect the performance degradation trend of fractal curve smoothly, which lays a theoretical foundation for further solving the problem of online health assessment.

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Gear fault feature extraction and diagnosis method under different load excitation based on EMD, PSO-SVM and fractal box dimension

Cited by 91 publications

References 19 publications

Vibration Based Gear Fault Diagnosis under Empirical Mode Decomposition and Power Spectrum Density Analysis

Vibration Based Gear Fault Diagnosis under Empirical Mode Decomposition and Power Spectrum Density Analysis

Fault diagnosis of rope tension in hoisting systems based on vibration signals

An Online Degradation Feature Extraction Technique for Shore Bridge Gearbox Based on Morphological Fractal Dimension and Sliding Window Weibull Fitting

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