Calculation of Joint Return Period for Connected Edge Data

Liu, Guilin; Chen, Bai-yu; Gao, Zhen; Fu, Hanliang; Jiang, Song; Wang, Liping; Kou, Yi

doi:10.3390/w11020300

Cited by 20 publications

(8 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Extreme phenomena affecting Mexico, such as the occurrence of two hurricanes at the same time (Ingrid and Manuel in 2013), should be studied with mixed probability distributions of two or more populations [53]. Recent studies have shown that when using mixed distributions, such as Mixed Gumbel, Gumbel-Logistic and Gumbel-Hougaard Copula, the main problem is the adaptation of extreme values [54].…”

Section: Discussionmentioning

confidence: 99%

Optimization of Parameters in the Generalized Extreme-Value Distribution Type 1 for Three Populations Using Harmonic Search

et al. 2019

View full text Add to dashboard Cite

Due to its geographical position, Mexico is exposed annually to cold fronts and tropical cyclones, registering extremely high values that are atypical in the series of maximum annual flows. Univariate mixed probability distribution functions have been developed based on the theory of extreme values, which require techniques to determine their parameters. Therefore, this paper explores a function that considers three populations to analyze maximum annual flows. According to the structure of the Generalized Extreme-Value Distribution (GEV), the simultaneous definition of nine parameters is required: three of location, three of scale, and three of probability of occurrence. Thus, the use of a meta-heuristic technique was proposed (harmonic search). The precision of the adjustment was increased through the optimization of the parameters, and with it came a reduction in the uncertainty of the forecast, particularly for cyclonic events. It is concluded that the use of an extreme value distribution (Type I) structured with three populations and accompanied by the technique of harmonic search improves the performance in respect to classic techniques for the determination of its parameters.

show abstract

Section: Discussionmentioning

confidence: 99%

Optimization of Parameters in the Generalized Extreme-Value Distribution Type 1 for Three Populations Using Harmonic Search

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Wang et al [61] proposed an adaptive parameter optimized VMD. The other signal decomposition methods have also been proposed in recent years [62][63][64][65][66][67][68][69][70][71][72][73][74][75][76].…”

Section: Vmdmentioning

confidence: 99%

Study on a Novel Fault Diagnosis Method Based on VMD and BLM

et al. 2019

View full text Add to dashboard Cite

The bearing system of an alternating current (AC) motor is a nonlinear dynamics system. The working state of rolling bearings directly determines whether the machine is in reliable operation. Therefore, it is very meaningful to study the fault diagnosis and prediction of rolling bearings. In this paper, a new fault diagnosis method based on variational mode decomposition (VMD), Hilbert transform (HT), and broad learning model (BLM), called VHBLFD is proposed for rolling bearings. In the VHBLFD method, the VMD is used to decompose the vibration signals to obtain intrinsic mode functions (IMFs). The HT is used to process the IMFs to obtain Hilbert envelope spectra, which are transformed into the mapped features and the enhancement nodes of BLM according to the complexity of the modeling tasks, and the nonlinear transformation mean according to the characteristics of input data. The BLM is used to classify faults of the rolling bearings of the AC motor. Next, the pseudo-inverse operation is used to obtain the fault diagnosis results. Finally, the VHBLFD is validated by actual vibration data. The experiment results show that the BLM can quickly and accurately be trained. The VHBLFD method can achieve higher identification accuracy for multi-states of rolling bearings and takes on fast operation speed and strong generalization ability.

show abstract

“…It plays the role of supporting the main shaft and transmitting torque. Its operating state directly affects the working state of the motor [1][2][3][4][5]. According to statistics, when a rotating machine occurs fails, more than 30% of the faults are caused by bearings [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis for a Bearing Rolling Element Using Improved VMD and HT

Liu

Yuan

et al. 2019

Applied Sciences

View full text Add to dashboard Cite

The variational mode decomposition (VMD) method for signal decomposition is severely affected by the number of components of the VMD method. In order to determine the decomposition modal number, K, in the VMD method, a new center frequency method of the multi-threshold is proposed in this paper. Then, an improved VMD (MTCFVMD) algorithm based on the center frequency method of the multi-threshold is obtained to decompose the vibration signal into a series of intrinsic modal functions (IMFs). The Hilbert transformation is used to calculate the envelope signal of each IMF component, and the maximum frequency value of the power spectral density is obtained in order to effectively and accurately extract the fault characteristic frequency and realize the fault diagnosis. The rolling element vibration data of the motor bearing is used to test the effectiveness of proposed methods. The experiment results show that the center frequency method of the multi-threshold can effectively determine the number, K, of decomposed modes. The proposed fault diagnosis method based on MTCFVMD and Hilbert transformation can effectively and accurately extract the fault characteristic frequency, rotation frequency, and frequency doubling, and can obtain higher diagnostic accuracy.

show abstract

Calculation of Joint Return Period for Connected Edge Data

Cited by 20 publications

References 29 publications

Optimization of Parameters in the Generalized Extreme-Value Distribution Type 1 for Three Populations Using Harmonic Search

Optimization of Parameters in the Generalized Extreme-Value Distribution Type 1 for Three Populations Using Harmonic Search

Study on a Novel Fault Diagnosis Method Based on VMD and BLM

Fault Diagnosis for a Bearing Rolling Element Using Improved VMD and HT

Contact Info

Product

Resources

About