Research on fault diagnosis method of rolling bearing based on AMD and LabVIEW

Aiming at the problem of rolling bearing fault diagnosis, a fault diagnosis method of rolling bearing is proposed based on analytical mode decomposition (AMD) and LabVIEW. For the fault feature frequency of rolling bearing is predictable, the AMD method can be used to extract the signal in the frequency band of fault characteristic frequency in rolling bearing signal, and seek frequency spectrum of vibration signal. If the spectrum contains fault characteristic frequency, then the rolling bearing fault can be diagnosed by vibration signal. A rolling bearing fault diagnosis system is developed based on LabVIEW and AMD, and the application of AMD algorithm is realized. The validity of the method is proved by the analysis of actual fault signal of rolling bearing

Nonlinear dynamic model and vibration response of faulty outer and inner race rolling element bearings

By considering the deep groove ball bearing as the research object, and using Hertzian contact theory and elastic mechanics and geometry, a nonlinear dynamic model is established herein for the study of vibration response of deep groove ball bearings having single defect on surfaces of inner and outer races. The outer race and inner race defect size parameters are introduced into this nonlinear dynamic model, and dynamic models of localized fault on outer race, inner race of rolling element bearing are simulated and analyzed by using Runge-Kutta method. Both simulated and experimental localized fault signals (acceleration signals) are subjected to the same diagnostic techniques; namely time domain waveform comparisons and envelope analysis. Then, the impact characteristic that reflects the fault severity in rolling element bearings is obtained from the time interval between two impact points. The simulating results are in accordance with experimental results, which proves the accuracy and practicality of the models used in engineering application. The characteristic defect frequencies and related harmonics are broadly investigated and presented herein. This proposed model provides theoretical basis for monitoring and fault diagnosis of rolling bears

Nonlinear Torsional Vibration Dynamics Behaviors of Rolling Mill’s Multi-DOF Main Drive System under Parametric Excitation

Considering the influence caused by joint angle, nonlinear damping, and nonlinear rigidity, the nonlinear torsional vibration dynamical modeling of the multi-DOF rolling mill’s main drive system is established. To analyze the coupled equations by analytic method, the equations are decoupled by transforming them into principal coordinates. The amplitude-frequency characteristic equations are obtained by multiscale method. Furthermore, numerical example based on the 1780 rolling mill’s main drive system of some Steel Co. is given to illustrate the effects of the resonance on the response of the system. The relationship between amplitude and frequency varies according to the parameters changes of nonlinear stiffness, nonlinear friction damping, torque disturbance, and joint angle. During the rolling process, the limited joint angles range is obtained and the variation rules of the joint angle caused by the nonlinear damping, nonlinear stiffness, and the disturbance torque are gained. The results present that the rolling mill can work more stably with the joint angle at a range from 2° to 8° by controlling the value of parameters. The research results provide a theoretical basis and reference for analyzing torsional vibration of rolling mill’s transmission system caused by joint angle

A weak fault diagnosis method for rotating machinery based on compressed sensing and stochastic resonance

Vibration signals used for rotating machinery fault diagnosis often constitute large amount of data. It is a big challenge to extract faults feature information from these data. Recently, a new sampling framework called compressed sensing has been proposed, which enables the recovery from a small set of measured data if the signals are sparse or compressible. In reality, the sparseness of the signals is not very well due to noise, so it is difficult and unavailing to recover the whole signal. Thus, a new mechanical fault diagnosis method is proposed in this paper. First, the machine fault vibration signals are pretreated by stochastic resonance. By this way, the fault signal drowned by noise is amplified and the sparseness of the signals is enhanced, which make it possible to apply compressed sensing. Second, fault features are extracted directly from the compressed data without recovering completely, which reduces the dimensionality of the measurement data and the complexity of algorithm. Finally, the effectiveness of the proposed method is proved by the experiments

A novel intelligent fault diagnosis method of rotating machinery based on deep learning and PSO-SVM

A novel intelligent fault diagnosis method based on deep learning and particle swarm optimization support vectors machine (PSO-SVM) is proposed. The method uses deep learning neural network (DNN) to extract fault features automatically, and then uses support vector machine to classify diagnose faults based on extracted features. DNN consists of a stack of denoising autoencoders. Through pre-training and fine-tuning of DNN, features of input parameters can be extracted automatically. This paper uses particle swarm optimization algorithm to select the best parameters for SVM. The extracted features from multiple hidden layers of DNN are used as the input of PSO-SVM. Experimental data is derived from the data of rolling bearing test platform of West University. The results demonstrate that deep learning can automatically extract fault feature, which removes the need for manual feature selection, various signal processing technologies and diagnosis experience, and improves the efficiency of fault feature extraction. Under the condition of small sample size, combining the features of the multiple hidden layers as the input into the PSO-SVM can significantly increase the accuracy of fault diagnosis

Purification and Characterization of Two New Allergens from the Venom of Vespa magnifica

Due to poor diagnostic facilities and a lack of medical alertness, allergy to Vespa wasps may be underestimated. Few allergens have been identified from Vespa wasps

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Fault Diagnosis of Rotating Machinery Based on Adaptive Stochastic Resonance and AMD-EEMD

An adaptive stochastic resonance and analytical mode decomposition-ensemble empirical mode decomposition (AMD-EEMD) method is proposed for fault diagnosis of rotating machinery in this paper. Firstly, the stochastic resonance system is optimized by particle swarm optimization (PSO), and the best structure parameters are obtained. Then, the signal with noise is put into the stochastic resonance system and denoising and enhancing the signal. Secondly, the signal output from the stochastic resonance system is extracted by analytical mode decomposition (AMD) method. Finally, the signal is decomposed by ensemble empirical mode decomposition (EEMD) method. The simulation results show that the optimal stochastic resonance system can effectively improve the signal-to-noise ratio, and the number of effective components of EEMD decomposition is significantly reduced after using AMD, thus improving the decomposition results of EEMD and enhancing the amplitude of components frequency. Through the extraction of the rolling bearing fault signal feature proved that the method has a good effect