x ffe gyratory crusher

gyratory crushers - flsmidth dorr-oliver eimco - pdf catalogs | technical documentation | brochure

gyratory crushers - flsmidth dorr-oliver eimco - pdf catalogs | technical documentation | brochure

Our experience with Mining Industries (Minerals, Cement, and Aggregate) has resulted in a complete line of Gyratory Crushers to satisfy the requirements of a wide variety of our customers' applications. FLSmidth holds a leading position in crushing technology with thousands of crushers installed since the inception of the Traylor brand. These gyratory crushers have been operating successfully in some of the world's harshest conditions for 50+ years. This long operating life can be attributed to the robust design that FLSmidth still utilizes to this day. The basic concept behind the Gyratory...

The TC model gave Fuller Traylor Engineering a reputation throughout the worldwide mining industry as a provider of reliable, high quality equipment. After acquiring Fuller Company, FLSmidth has taken this original TC design and expanded on it to develop the NT line. The Type "NT" incorporates all of the "TC's" Fleavy-Duty design features (Fleavy Cast-Steel Shell Sections, Forged Main Shaft and Counter Shaft, Robust Gearing, Generous Fubrication, Long-Life Bronze Components) and focuses on coupling these historical characteristics with updated maintenance- friendly features. These design...

The "Top-Service" (TS) Line is the newest generation of Fuller-Traylor Gyratory Crushers from FLSmidth. This entire Gyratory Crusher is engineered from the ground up with Safety and Maintenance in mind. The feature that distinguishes the "TS" design from other Gyratory r Crushers is * that the "TS" machine is designed to be Serviced & Maintained from an Overhead Crane. The Eccentric & Flydraulic Cylinder Assemblies are removed through the crusher feed opening instead of the discharge. The Safety advantage of the Top Service is that maintenance personnel can access the Eccentric and...

Customer engineered solutions: FLSmidth offers custom engineered solutions as an answer to our customers unique requests. In past projects, FLSmidth has supplied custom-engineered equipment, accessories, and solutions all stemming from customer requests. FLSmidths solution to a unique crushing application in Canada was a custom designed 72x 89 Gyratory Crusher. In addition to this crusher, multiple dual pinion drive crushers were designed and successfully installed for the Minnesota (USA) Iron Ore Range. Numerous Hard-Rock applications that required oversized motors prompted FLSmidth to...

Value Added: FLSmidth offers Engineering & Maintenance Services, Custom Designs, and Parts Options to improve the operational efficiency and scheduled maintenance practices of your Gyratory Crusher. Specialty Tools Concave Installation & Removal Tool Main Shaft Servicing Stand Dump Pocket Access Tools Crusher Maintenance Carts Operations and Maintenance Services Installation Services Equipment & Maintenance Seminars OEM Spare/Emergency Parts Automation Packages Laboratory Testing Services Entire Plant Systems Process Optimization Quality & Reliable OEM Parts...

*Ultra Duty (UD) versions are available in the 1370 x 1950 (54 x 77) & 1525 x 2790 (60 x 113) Copyright 2015 FLSmidth A/S. ALL RIGHTS RESERVED. FLSmidth is a (registered) trademark of FLSmidth A/S. This brochure makes no offers, representations or warranties (express or implied), and information and data contained in this brochure are for general reference only and may change at any time. ^V^V^Va flsmidth.com Minerals Processing Technology Center FLSmidth Salt Lake City, Inc. 7158 S. FLSmidth Dr. Midvale, UT 84047-5559 USA Tel: +1 801 871 7000 Fax: +1 801 871 7001 E-mail:...

flsmidth compression crusher technology for mining

flsmidth compression crusher technology for mining

By using high intensity compressive forces, your materials can be broken down into much smaller particles. However, the process is incredibly wear intensive, and can easily take its toll on your equipment and your operation. High-quality compression crusher technology and products can not only extend life of your equipment, but also improve throughput and increase overall revenue.

Invest in a compression crusher solution you can trust. FLSmidth has over a century of experience in crushing. Our crushing equipment has its origins dating back to the Fuller Company, based in Bethlehem, Pennsylvania, USA. They have been a leading supplier of crushers under the Traylor brand name since 1905. As a world class supplier of crushing equipment for the mining, cement and aggregate industries, we offer crushing products and technology with proven names like Fuller-Traylor, ABON and Buffalo and and are supported by one of the worlds largest crusher reference lists, which includes many world firsts.

Maximise your performance and monitor and protect your equipment with our Automated Control Systems, designed to give you better results while reducing maintenance and repair costs. Incorporate our engineering expertise into any and all of your crushing systems, no matter the size, up to and including systems for total plant.

FLSmidth provides sustainable productivity to the global mining and cement industries. We deliver market-leading engineering, equipment and service solutions that enable our customers to improve performance, drive down costs and reduce environmental impact. Our operations span the globe and we are close to 10,200 employees, present in more than 60 countries. In 2020, FLSmidth generated revenue of DKK 16.4 billion. MissionZero is our sustainability ambition towards zero emissions in mining and cement by 2030.

gyratory crushers - sandrock mining

gyratory crushers - sandrock mining

Sandrock Mining Gyratory crushers are frequently used in the primary crushing stage and a little less often in the secondary stage.Gyratory crushers have an oscillating shaft. The material is reduced in a crushing cavity, between an external fixed element (bowl liner) and an internal moving element (mantle) mounted on the oscillating shaft assembly.The fragmentation of the material results from the continuous compression that takes place between the liners around the chamber. An additional crushing effect occurs between the compressed particles, resulting in less wear of the liners.The gyratory crushers are equipped with a hydraulic setting adjustment system, which makes it possible to regulate the gradation of the crushed material.

Sandrock Mining is a growing company with years of experience in produce and supply of wear parts such as the Bowl Liner, Mantle, Jaw Plate, Cheek Plate, Hammer, Frame, Linerand spare parts such as Bushing, Shafts, Gears, and so on.

gyratory crusher liners | flsmidth

gyratory crusher liners | flsmidth

Naturally, you need to protect your equipment to keep it running smoothly and continuously. In order to accomplish that, you demand liners with superior wear life and proven production. Thats precisely what you get with our Gyratory Crusher Liners. You may need something more than simply a Gyratory Crusher Liner. At FLSmidth, we are capable of helping you find the very best solution regardless of your needs. We do so by reviewing your entire process and machine setup.

Crusher operating parameters, liner selection, material selection, plant process review, and customer goals all go into providing our customers with the ideal solutions. If more than one option is available, we offer cost-benefit options to make the decision-making process easier for you.

FLSmidth Gyratory Crusher Liners solutions help enhance the efficiency of your operation and lower your operating expenses. We offer many grades of Gyratory Crusher Liners, enabling you to find the ideal solution.

Using our knowledge as an Original Equipment Manufacturer (OEM), we ensure that the supplied product is correct for your equipment and application. We offer Gyratory Crusher Liners tailored to your needs and manufactured for increased productivity. Here is what sets our Gyratory Crusher Liners apart:

FLSmidth provides sustainable productivity to the global mining and cement industries. We deliver market-leading engineering, equipment and service solutions that enable our customers to improve performance, drive down costs and reduce environmental impact. Our operations span the globe and we are close to 10,200 employees, present in more than 60 countries. In 2020, FLSmidth generated revenue of DKK 16.4 billion. MissionZero is our sustainability ambition towards zero emissions in mining and cement by 2030.

liner wear and performance investigation of primary gyratory crushers - sciencedirect

liner wear and performance investigation of primary gyratory crushers - sciencedirect

The successful integration between mine operations, primary crushing and milling has been shown to be a key factor for the success of open-pit mines. This paper presents work done in a collaborative research project between the University of British Columbia and Highland Valley Copper. The research was aimed at understanding gyratory crusher liner wear in the overall context of the crushing process. Wear measurements were taken for in-service crushers during the research period using a novel laser profile measurement device. Data from the wear measurements was correlated with crusher production information such as current draw and throughput. This work resulted in enhanced knowledge of liner wear and its link to crusher performance.

fault diagnosis of the gyratory crusher based on fast entropy multilevel variational mode decomposition

fault diagnosis of the gyratory crusher based on fast entropy multilevel variational mode decomposition

Fengbiao Wu, Lifeng Ma, Qianqian Zhang, Guanghui Zhao, Pengtao Liu, "Fault Diagnosis of the Gyratory Crusher Based on Fast Entropy Multilevel Variational Mode Decomposition", Shock and Vibration, vol. 2021, Article ID 5704271, 10 pages, 2021. https://doi.org/10.1155/2021/5704271

Gyratory crusher is a kind of commonly used mining machinery. Because of its heavy workload and complex working environment, it is prone to failure and low reliability. In order to solve this problem, this paper proposes a fault diagnosis method of the gyratory crusher based on fast entropy multistage VMD, which is used to quickly and accurately find the possible fault problems of the gyratory crusher. This method mainly extracts the vibration signal by combining fast entropy and variational mode decomposition, so as to analyze the components of the vibration signal. Among them, fast entropy is used to quickly determine the number of modes in the signal spectrum and the bandwidth occupied by the modes. The extracted parameters can be converted into the input parameters of VMD. VMD can accurately extract the modal components in the signal by inputting the number of modes and related parameters. Due to the differences between modes, using the same parameters to extract the modes often leads to inaccurate results. Therefore, the concept of multilevel VMD is proposed. The parameters of different modes are determined by fast entropy. The modes in the signals are separated and extracted with different parameters so that different signal modes can be accurately extracted. In order to verify the accuracy of the method, this paper uses the data collected from the rotary crusher to test, and the results show that the proposed FE method can quickly and effectively extract the fault components in the vibration signal.

Gear and rolling bearing are widely used parts in various rotating machinery, and they are important structures carrying transmission and force transmission in rotating machinery. Therefore, in recent years, the research on safety and reliability of rotating parts has been highly concerned, and the related research fields include mechanical fault diagnosis, life prediction, and health operation and maintenance [1]. In all kinds of mechanical faults, the mechanical fault accidents caused by gear components or rolling bearings account for more than 70%. Therefore, it is of great significance to study and find the fault problems existing in the operation of gear components or rolling bearings in time for improving the safety and reliability of the mechanical system [2]. There are many methods to study the faults of rotating mechanical parts. The most widely used methods in academic circles are to collect the vibration signals generated by the components under stable working conditions. The fault conditions of mechanical components are evaluated by analyzing the composition of vibration signals [3, 4]. At present, there are two main methods to analyze vibration signals: one is to analyze the components of signals through the time-frequency information of signals [5], and the other is to decompose the signals into different modal components by the decomposition algorithm [6, 7]. Among them, the methods to obtain the frequency band components by analyzing the spectrum include the fast kurtosis spectrum proposed by Antoni [8] and the fast entropy method proposed by Zhang et al. [9]. Signal mode decomposition is also a common method type in the field of fault diagnosis. For example, Fei used the combination of the wavelet transform and relevance vector machine to verify the role of wavelet transform in signal analysis [10] and also used the improved algorithm based on empirical mode decomposition for rolling bearing fault diagnosis [11, 12]. At present, the most commonly used and ideal mode decomposition method is variational mode decomposition. There are many applications of variational mode decomposition. He et al. combined the variational mode decomposition method with the neural network for intelligent diagnosis of the wind turbine rotating fault [13], and Zhao et al. proposed combining variational mode decomposition and signal spectrum entropy to determine the weak fault component of rotating machinery vibration signal methods [14].

The above methods have advantages in various applications, but for the rotating machinery with complex working conditions, the time-frequency analysis method has many characteristic parameters, so it is often unable to extract the main fault features of the signal effectively [15]. For the method of variational mode decomposition, because the components of the vibration signal are complex, using this kind of method for component analysis easily causes the problems of signal mode aliasing and mode underdecomposition or overdecomposition [16]. In order to solve the problem of fault diagnosis of rotating machinery, such as mining machinery or high-strength machinery assembly line, this paper proposes a multilevel VMD method based on fast entropy by combining the entropy spectrum and variational mode decomposition. For the vibration signal with complex components, the direct use of variational mode decomposition is unable to effectively extract various fault components in the signal [17]. Fast entropy can quickly extract the principal components of the signal spectrum and provide calculation parameters for each level of VMD. Multilevel VMD solves the problem of single-parameter mismatch through multiple-mode extraction of the signal. At the same time, multilevel VMD can further extract the weak impact components of the signal. The proposed fast entropy multilevel VMD method has high efficiency of feature extraction. It has the advantage of accurate feature data. Therefore, firstly, the entropy spectrum method is used to determine the modal components in the signal as VMD method extraction parameters [18]. In order to prevent the loss of effective components, the decomposed modal components are separated from the signal, and the remaining signals are extracted again until the decomposition termination parameters calculated by the entropy spectrum are met; thus, the effective components in the signal are decomposed [19] to solve the problem that the signal component is not easy to determine under complex conditions. In order to further verify the application ability of the proposed method, it is necessary to classify the extracted components to judge the ability of the proposed method in the fault diagnosis of rotating machinery [20]. Modal components can be classified by various classification methods. The common fault classifier methods include SVM (support vector machine) linear classifier, data-driven, convolutional neural network, and decision tree model [2124]. In this paper, a classification method of decision tree model, XGBoost, is used as a classifier. XGBoost has the characteristics of fast discrimination speed and good classification performance for the two-dimensional data, so it is suitable for the classification of modal components in this paper [25].

In the second part of this paper, the theory of fast entropy and variational mode decomposition is introduced. In the third part, the principle of the improved method combining fast entropy and VMD is introduced in detail. In the experimental part, the proposed method is verified by the actual vibration signal collected from the experimental platform, and the method is analyzed and summarized at the end of the section.

The improved method proposed in this paper mainly involves two kinds of vibration signal analysis methods, which are variational mode decomposition (VMD) and fast entropy spectrum (FE). Among them, the variational mode decomposition method is a classical signal decomposition method which iteratively decomposes the vibration signal into several eigenmodes [26], and the fast entropy spectrum is a method used to determine the number of signal modes through the signal spectrum [7]. These two methods are described in detail in this section.

Variational mode decomposition is an adaptive mode decomposition method, which is widely used in the modal decomposition of vibration signals. Given the number of modes, VMD can obtain the best eigenmode and band center frequency through iterative optimization. Compared with the traditional EMD, wavelet transform, and other analysis methods, VMD has higher efficiency and decomposition accuracy, which is a very important method in the field of signal analysis. The variational mode decomposition is to extract the vibration modes by iteratively solving the vibration signals as variational functions:

In formulas (1) and (2), is the number of modes, is the th modal component after decomposition, is the center frequency of the corresponding modal component, is the Dirac function, and is the convolution operator.

In the formula, the quadratic penalty term is introduced, which can be used to reduce the noise interference in the signal, then the alternating direction multiplier iterative algorithm (ADMI) is used to optimize the overlapping band, and the optimal mode and its center frequency are obtained.

In equation (6), noise tolerance is introduced for signal fidelity. The variable mode decomposition sets the maximum iteration times and the mode output conditions given the number of modes and the secondary penalty term and finally decomposes the signal into eigenmodes which reflect the main information of the vibration signal.

Entropy reflects the internal energy transformation of the signal. The impact components in the signal spectrum can be judged by entropy. Envelope entropy can screen the impact components in the signal spectrum, but its accuracy is not high, and it is greatly affected by noise in the actual complex working conditions. Fast entropy predicts the change of the impact components in the spectrum. The noise factor can be screened out to a certain extent. Fast entropy spectrum method is an improved method of fast kurtosis spectrum. Kurtosis spectrum is a computing tool used to detect nonstationary factors in signals. Kurtosis, as a parameter reflecting the change of the signal, can be used to detect the abnormal components in the stationary vibration signal, but this method has poor effect in the case of strong noise. In order to overcome this defect, the concept of kurtosis spectrum is proposed to overcome the difficulty of using kurtosis to determine the components of the strong noise signal. The spectrum is divided into equal scales until the signal spectrum is divided into two parts. The frequency band of each mode is included to determine the modal component of the signal. Fast kurtosis spectrum is a method that combines the kurtosis spectrum with the FIR filter. Fast entropy is a method that divides the signal spectrum through the trend spectrum on the basis of the fast kurtosis spectrum, so as to more accurately divide the signal mode. The basic principle of fast entropy is as follows.

In the fast entropy spectrum method, after the signal is divided through the trend spectrum, the components of the divided spectrum are extracted through the frequency slice function:where , , and are the observation time, observation frequency, and evaluation rate, scale factor , parameter is a constant, represents the frequency slice function, and is conjugate. The time domain of the frequency-sliced wavelet transform can be expressed as

When and take special values, the frequency slice wavelet transform will be transformed into the traditional short-time Fourier transform, so it shows that the method is feasible in the generalized range.

is the frequency window width of the frequency wavelet. Considering the bandwidth-frequency ratio of the frequency slice function, the frequency resolution of the frequency slice wavelet transform is set as . The frequency resolution of the signal is set to . In general, . Therefore, can be achieved by adjusting . If satisfies , the components of the original signal can be reconstructed by the following formula:

Gradient surge decision tree (XGBoost) is a kind of decision tree method which can realize the rapid classification of samples. The process of constructing the XGBoost model is as follows: firstly, the modal components obtained by decomposition are used as samples.

is the prediction result of the decision tree, denotes a sample with characteristic classes of , and is the tree model of the th tree. Through XGBoost training, each tree can obtain the corresponding weight value and the tree structure parameter according to feature learning. In addition to the weight of the tree model obtained through training, this method increases the weight value of feature to modify the results of each tree model, so as to improve the accuracy of the results of the model.

The objective function of the XGBoost decision tree iswhere is the loss function of the model tree, which is used to reduce the error between the predicted value and the real value and form the basic tree model structure, and is the regular term of the model tree, which is used to control the complexity of the tree model so that the learner can avoid overfitting as much as possible.

Because the decomposition performance of VMD depends on the selection of parameters, the number of modes and the selection of secondary penalty factors have great influence on the decomposition results. In the previous research of the VMD method, most scholars improved the performance of VMD mainly reflected in the optimization of parameters. However, in the actual signal, the number of modes in the signal is not easy to determine due to the existence of noise and environmental noise generated by the mechanical system. In addition, the frequency band width of different modes in the spectrum is not consistent due to the difference of spectrum characteristics of different modes. At present, most of the signal decomposition methods do not notice this key point. Therefore, this paper proposes a method of multistage variational mode decomposition (FE-MVMD) based on fast entropy. By using fast entropy, the parameters required by different series of VMD are obtained continuously to adapt the corresponding parameters of different modes of extraction, aiming to further improve the effect of the decomposition.

Firstly, the number of modes in the original signal is determined by the trend spectrum calculation method in fast entropy, and the frequency spectrum of the initial signal is obtained by short-time Fourier transform:

The trend spectrum can show the modal components in the signal. The modal components in the signal can be preliminarily determined by extracting the spectral peaks in the trend spectrum, so as to provide the modal number parameters for VMD. The number of qualified modes in the trend spectrum can be obtained by a high-pass filter function:

It can be seen from Figure 2 that the threshold value of the filter corresponding to the signal can be determined by fast entropy, that is, the threshold value of the high-pass filter. It can be seen from Figure 2 that the selected threshold value passes through several peaks of the trend spectrum, and the corresponding number of passes can be used as the number of modes used in the multilevel VMD method.

Figure 2 shows the spectrum of the signal, the trend spectrum of the spectrum, and the high-pass filter obtained by fast entropy calculation. The spectrum is the Fourier spectrum of the signal, and the trend spectrum is used to determine the possible modal components in the spectrum. The modal components in the trend spectrum can be screened by calculating the fast entropy of the spectrum, so as to provide the modal parameter K for the calculation of VMD.

Then, another key input parameter of VMD, the quadratic penalty term , is determined. By analyzing the decomposition results of the signal under different parameters, it is found that the value of is related to the frequency band size and the center frequency of the extracted modal components, and different values of correspond to different frequency band sizes. Therefore, the size of the penalty factor determines whether the different frequency bands of the complex signal can be extracted correctly. Therefore, it is necessary to extract the modal components corresponding to different penalty coefficients. The size of the frequency band components can be determined by the slice function in the fast entropy method, and the corresponding penalty coefficients can be obtained:

After obtaining the parameters of VMD, the signal is decomposed into modes. Corresponding to different frequency bands, different parameters are input for iteration, and VMD is performed for many times to obtain the high-precision mode, which is helpful for further analysis of signal components and finding the fault.

Gyratory crusher is a kind of rotating machinery commonly used in crushing life line and sand-making life line. Its faults are mostly caused by rotating parts, such as gear and rolling bearing faults. If the rotating faults are unable to be found and eliminated in time, it easily causes safety accidents. In order to solve this problem, it is necessary to analyze the faults of the gyratory crusher. In this paper, the crusher gearbox mechanism is used for data acquisition, and the experimental platform is shown in Figure 3.

Four groups of different types of faults are set in the experiment, which are gear fault, rolling bearing fault, compound fault, and no fault. The bearing type used in the experiment is 33116 bearing, and the gear type is 2TJ06 gear. The sampling frequency used in the experiment is 2048Hz. Figure 4 shows the time-frequency diagram of the experimental data.

Through the decomposition results in Figure 5, the ability of the proposed FE-MVMD method to extract signal modes under complex conditions can be verified. Secondly, by comparing the spectrum of the signal before and after decomposition, it can be found that the components of the signal spectrum are effectively put forward. Through the comparison between the decomposition results, different modes existing in different faults can be obviously compared. Based on different central frequencies and frequency bands of different modes, the signal components can be judged. In addition, due to the use of fast entropy as the parameter determination method, the optimized parameters are also conducive to filtering out the noise components in the signal, thus improving the reliability of the extraction results of the FE-MVMD method.

In order to further verify the performance of the proposed method, empirical mode decomposition (EMD) and variational mode decomposition (VMD) are used to compare the performance of FE-MVMD. In Figure 6, the decomposition results of VMD are compared with those of FE-MVMD. The number of modes of the VMD method is consistent with that of the MVMD method, so as to highlight the performance of the proposed method. In addition, in order to verify the function of the extracted modal components in fault diagnosis, 200 groups of signals under the same fault condition are used as samples to extract the modal components, and the modal components extracted by different methods are trained. XGBoost is used as a classifier, and empirical mode decomposition and variational mode decomposition are used as a comparison to achieve high-accuracy results; the advantages of the FE-MVMD method in fault diagnosis were verified.

The experimental signal is the vibration signal generated by the transmission mechanism of the rotary crusher under the actual working condition, and the experimental data contain complex environmental noise. Therefore, the experimental verification using this group of signals can illustrate the antinoise performance of the proposed method. Comparing the decomposition results of FE-MVMD and VMD, it can be found that even if the same number of modes is used as the input, the VMD method is unable to completely separate the modes in the signal. Through Figure 5, it can be seen that the decomposition mode of VMD is concentrated in the low-frequency part, which indicates that the VMD method is unable to find the best band adaptively, so the decomposition effect is not ideal, and the separation of FE-MVMD and EMD is unable to achieve. Comparing the decomposition results of FE-MVMD and the WT method, we can see that the WT method can hardly effectively extract the components in the spectrum. The results of the solution are compared. EMD adaptively decomposes the signal into several modes and a residual. In order to compare the actual effect, the first K modes (K is the modal quantity parameter adopted by FE-MVMD) are superimposed and compared with the results of FE-MVMD. Through Figure 6, it is observed that the difference between the mode obtained by EMD and FE-MVMD is relatively small, which indicates that the EMD has poor noise resistance and low decomposition efficiency. In summary, the FE-MVMD method proposed to extract the mode is not ideal. The speed and accuracy are better than EMD and VMD algorithms.

In order to verify the actual effect of the FE-MVMD mode in fault diagnosis, it is necessary to use the extracted mode for the training test to verify the classification effect of the extracted mode. In this paper, the XGBoost classifier is used to verify the effect of modal extraction. 50 groups of vibration signals of different fault types are sampled as the training set, and the eigenmode is extracted to form the discrimination basis. In order to verify the classification performance of the proposed method and the effectiveness of modal classification, 250 groups of test sets are used to extract the mode. The results are shown in Table 1.

In order to verify the accuracy and effectiveness of XGBoost, RF (random forest) classification algorithm is added to classify the extracted signal features, which verifies the effectiveness of the proposed method (Table 2).

The classification results show that the classification accuracy of FE-MVMD is better than that of EMD and VMD in different fault conditions, which verifies that FE-MVMD is a kind of signal analysis method suitable for rotating machinery fault diagnosis. The experimental results also show that the classification accuracy of the FE-MVMD method decreases when the modal components are complex, so it is necessary to perform further research to improve the decomposition performance of the FE-MVMD method.

In this paper, a fast entropy-based multilevel variational mode decomposition (FE-MVMD) method is proposed by combining the basic principles of fast entropy and variational mode decomposition. In this paper, the rotating machinery structure of the gyratory crusher with complex working conditions is taken as the research object, the vibration signal of the mechanical system collected is taken as the analysis sample and compared with other modal extraction methods, and the superior performance of FE-MVMD in the signal modal extraction method is verified. In addition, the extracted modal components are used as samples for further fault classification test. XGBoost is used as a classifier to verify the advanced nature of the proposed method in rotating machinery fault diagnosis. In addition, through the experimental process, some conclusions are obtained:(1)The empirical VMD method usually uses the same parameters to extract the components of the signal, but the actual extraction effect is not ideal.(2)Through the results of modal extraction, it is found that the influence of noise on the extraction of modal components is very important. Therefore, a reasonable signal denoising method will be beneficial to the effect of modal component extraction.(3)Based on the process of changing parameters in the proposed MVMD method, a method suitable for local component extraction can be considered, which can greatly improve the efficiency of fault diagnosis.

Fengbiao Wu and Lifeng Ma conceived and designed the experiments. Guanghui Zhao and Pengtao Liu performed the experiments. Fengbiao Wu and Qianqian Zhang wrote the paper. All authors read and approved the final manuscript.

Copyright 2021 Fengbiao Wu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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