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Accurate measurement of the mill level is a key factor to improve the ball mill's productive efficiency, safety and economy. Aiming at solving the critical problem of the mill level soft sensor, feature extraction of the processing parameters, a novel method based on Deep Belief Network (DBN) is proposed. DBN is one of the deep learning methods, which focuses on learning deep hierarchical models of...
The safe and stable operation of the large capacity thermal power unit must be seriously affected by the rapidly changed load when the unit participates in peak regulation. This paper takes a domestic 600MW steam turbine unit as an example, sets up a mass-spring model with lumped parameter method and calculates the torsional vibration responses of the shafts with the method combined Riccati transfer...
In view of the nonlinear and non-stationary characteristics of fault vibration signal in roller bearing, a self-adaptive fault diagnosis method known as LMD (Local mean decomposition) is proposed. Initially the original vibration signal is decomposed into several stationary PF (product function) which possessed physical meaning and a residual component by using of LMD. Subsequently, the main components...
In order to solve the problem that the excessive dimensions of feature vector will lead to probabilistic neural network (PNN) 's structure becoming complicated and recognition rate slowing down when we take the wavelet energy spectrum of the rolling bearing vibration signal as the feature vector, a novel approach based on wavelet energy spectrum, principal component analysis (PCA) and probabilistic...
Due to mechanic vibration, multi-frame coupled and gyro random drift and so on, the design and control of the aerial photography stabilized platform is a complicated process. To improve the existing problem of aerial photography stabilized platform, such as the heavy structure, the lacking of adaptability and so on, the small aerial photography stabilized platform is developed. This system consists...
Based on the constitutive equations of piezoelectric materials, the dynamic model of the piezoelectric stack actuators is derived. Subsequently, experimental setup for the dynamic characteristics measurement is established. Parameters such as voltage-displacement coefficient, stiffness and damping constant are identified from the experimental results by the principle of piezoelectric effect and classic...
Bearing vibrations of a power plant blower have random characteristics with stark noise, which make it difficult for fault feature extraction both by using vibration effective data of monitoring system and vibration analysis function of a portable measuring instrument. Made use of the vibration signals which were measured from a normal blower and a fault one, the vibration characteristics of fault...
In this paper, a fault diagnosis method based on support vector machine (SVM) is proposed for gas turbine bearing. Firstly, through analysis and processing of vibration signals, the singular value decomposition related EEMD technique is applied to extract feature vectors of the signals. The results are used as the input of SVM classifier model. Then, by using the SVM network intelligence, the turbine...
The vibration signal of rolling bearings is always the mixed fault signal which is interfered by noise or mixed aliasing by different fault source, and analysis and fault feature extraction of the signal is difficult problem. Therefore, a mixed fault detection method based independent component analysis and Teager Energy Operator (TEO) demodulation (ICA-Teager) is proposed. Firstly, make the vibration...
The purity of molten steel is a crucial factor of steel products' quality. Detection and removal of slag carryover from the molten metal is an important task in steel making procedure, especially in the continuous casting process. Researchers have applied several different slag carryover detection methods to solve the slag carry-over since 1980s, such as electromagnetic coils method of AMEPA, Infrared...
A fault detection method based on empirical likelihood is presented to deal with the incipient fault in process and equipment. The problem of incipient fault detection is studied in the view of distribution test by a moving window approach. The original fault detection problem is transformed into distribution test, and a set of empirical likelihood values is computed. Based on the likelihood values,...
As one of the most widely used parts and components of rotating machineries, fault detection of rolling bearing is of great significance. In this paper, a new method named EMD-DPCA is proposed based on Empirical Mode Decomposition (EMD) and Dynamic Principal Component Analysis (DPCA). Firstly, the vibration signals are decomposed by EMD and Intrinsic Mode Functions (IMFs) are achieved. Then DPCA model...
A new fault diagnosis method for rolling element bearing is proposed based on empirical mode decomposition (EMD) and fisher discriminant analysis (FDA). First, non-stationary vibration signals are processed by applying EMD technique, and stationary IMF components are obtained. Then, fault feature vectors with the moving time-lagged windows are composed using the absolute values of IMF components of...
The vibration suppressive control problem is a challenge of flexible-joint space manipulators, which is settled by solving the energy optimal nonlinear control problem in this paper. Based on Legendre pseudo-spectrum method, the nonlinear model of optimal control problem is converted into the corresponding nonlinear programming problem. The complex and difficult two-point boundary value problem is...
Microvibrations produced by high speed momentum wheels can highly degrade the performance of precision instruments in satellites. To suppress such microvibrations, one of the promising methods is to utilize the magnetically suspended momentum wheel (MSMW) due to the convenient and effective control strategy. However, little research is dedicated to the microvibrations caused by MSMW. This paper focuses...
The application of the multifractal theory in gearbox fault diagnosis has been studied in the paper, and the fractal characteristics of gearbox vibration signals is shown. Based on using EMD, the improved algorithm of multifractal spectrum is put forward, and is applying to extract the fault feature. At last, the fault diagnosis application in gear box takes as the example to prove the feasibility...
Rolling bearings vibration signal is complex and non-stationary signal. In order to diagnose the bearing failures accurately and quickly, propose an approach about rolling bearing fault diagnosis, which is based on LS-SVM and LMD. Firstly, decompose the original vibration signal by LMD (Local Mean Decomposition LMD) to get a series of PF(Production Function, PF); secondly, establish the AR model of...
By using finite-element analysis theory, we divided wheat into a finite number of units and assumed the displacement of joint between units as unknown, building the lodging-resistant mechanical model of wheat. Combining with the model analysis method, we calculated steady state response caused by harmonic load excitation and analyzed the maximum displacement of wheat spike at certain wind scale, thereby...
This paper utilized multi-degree of freedom vibration theory combined with considering the influence of synergy of wind load and self-weight on the wheat to build the five-degree of freedom mechanical model of wheat and calculated the lodging bending moment of wheat root under a certain wind scale, in response to lodging problem in the late period of wheat development. Because of the complicated distribution...
A nonlinear self-excited vibration model of main drive system in cold rolling is established. The mechanism of torsional vibration in main drive system stability is discussed within multiple scale method as well as Hopf bifurcation theorem. Then, the periodic solutions and condition of Hopf bifurcation are derived and the influence by subcritical/supercritical bifurcation is analyzed. Based on the...
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