A new shaft multi-objective optimization dynamic balancing method based on differential search algorithm

Combined with the influence coefficient methods and the Holo-balancing theory of shaft system, a new shaft multi-objective optimization dynamic balancing method including energy, uniformity and maximum of the residual vibration is proposed by building a multi-objective fuzzy evaluation function and application of Differential Search (DS) algorithm. The advantage of DS algorithm is studied by comparing with four other optimization algorithms. And the principle of balancing weight optimization of DS algorithm is studied to realize the shaft dynamic balancing. Finally, the validity and effectiveness of the proposed method is verified through a field power generator set balancing case

A new time synchronous average method for variable speed operating condition gearbox

Gearbox is a widely used component for power transmission and speed change. Time synchronous average (TSA) is one of the most effective methods for vibration monitoring and diagnosis of gearboxes. Traditional TSA technique requires key-phase signal and constant operating speed. So the application of TSA is difficult in many situations such as in the case of gearboxes used in wind power generators and automobiles. A new method to implement TSA without key-phase signal for variable speed condition gearbox is proposed in the paper. The reported method is based on the estimation of instantaneous speed with time-frequency domain filtering and equal angular interval re-sampling of vibration signal. Experimental investigation performed in a variable speed gearbox test rig indicates that the proposed method can eliminate the influence of large speed fluctuation of gearboxes and provide satisfactory TSA results

A new transient field balancing method of a rotor system based on empirical mode decomposition

Effective reduction of the vibration in rotor and stator at critical speed is important for steady operation of rotor systems. A new transient field balancing method is proposed in this paper. The empirical mode decomposition (EMD) method coupled with holospectral technique is used to extract rotating frequency information including precise frequency, amplitude and phase nearby the critical speed from the run-up vibration signals. Reasonable trial weights are selected through estimating the unbalance masses and position. Moreover, the correction masses and position are obtained by holo-balancing method. Compared with the traditional dynamic balancing method, this method does not need obtain steady-state vibration signals, and the rotor can pass through the critical speed smoothly. The principle and detailed procedures of this method are described in this paper, and the effectiveness of the new method was validated by field balancing of rotor kit system

An efficient optimized independent component analysis method based on genetic algorithm

Three simulation experiments are designed to evaluate and compare the performance of three common independent component analysis implementation algorithms – FastICA, JADE, and extended-Infomax. Experiment results show that the above three algorithms can’t separate the mixtures of super-Gaussian and sub-Gaussian precisely, and FastICA fails in recovering weak source signals from mixed signals. In this case an independent component analysis algorithm, which applies genetic algorithm to minimize the difference between joint probability and product of marginal probabilities of separated signals, is proposed. The computation procedure, especially the fitness evaluation when signals are in discrete form, is discussed in detail. The validity of the proposed algorithm is proved by simulation tests. Moreover, the results indicate that the proposed algorithm outperforms the above three common algorithms significantly. Finally the proposed algorithm is applied to separate the mixture of rolling bearing sound signal and electromotor signal, and the results are satisfied

Theoretical study of small signal modulation behavior of Fabry-Perot Germanium-on-Silicon lasers

This work investigated the small signal performance of Fabry-Perot Ge-on-Si lasers by modeling and simulations. The 3dB bandwidth dependence on the structure parameters such as poly-Si cladding thickness, Ge cavity width and thickness, and minority carrier lifetime were studied. A 3dB bandwidth of 33.94 GHz at a biasing current of 270.5 mA is predicted after Ge laser structure optimization with a defect limited carrier lifetime of 1 ns

Fault diagnosis of mechanical drives under non-stationary conditions based on manifold learning of kernel mapping

For the detection of mechanical faults under the operating conditions of varying speeds and loads (such as wind turbines, excavators or helicopters, etc.), a new method for extracting the low-dimensional embedding of vibration data sets of mechanical drives under variable operation conditions is proposed. The hypothesis is that the space spanned by a set of vibration signals can be captured in a varying condition, to a close approximation, by a low-dimensional, nonlinear manifold. This paper presents a method to learn such a low-dimensional manifold from a given data set. The embedding manifold generated by vibration signals can be constructed from the feature set of parameters. Taking the variable operation condition into consideration, the kernel mapping is also introduced to improve the identification of submanifolds in terms of the projection distance. With the kernel mapping, the manifold coordinates can accurately capture the differences of the varying operation conditions. Experimental vibration signals obtained from normal and chipped tooth fault of gearbox in varying operation conditions are analyzed in this study. Results show that the proposed method is superior in identifying fault patterns and effective for gearbox condition monitoring

Genetic algorithm for Lagrangian support vector machine optimization and its application in diagnostic practice

In this article a genetic algorithm optimized Lagrangian support vector machine algorithm and its application in rolling bearing fault diagnosis is introduced. As an effective global optimization method, genetic algorithm is applied to find the optimum multiplier of Lagrangian support vector machine. Synthetic numerical experiments revealed the effectiveness of this genetic algorithm optimized Lagrangian support vector machine as a classifier. Then this classifier is applied to recognize faulty bearings from normal ones. Its performance is compared with that of backpropagation neural network and standard Lagrangian support vector machine. Experimental results show that the classification ability of our classifier is higher and the computing time required to find the separating plane is relative shorter

EEMD Independent Extraction for Mixing Features of Rotating Machinery Reconstructed in Phase Space

Empirical Mode Decomposition (EMD), due to its adaptive decomposition property for the non-linear and non-stationary signals, has been widely used in vibration analyses for rotating machinery. However, EMD suffers from mode mixing, which is difficult to extract features independently. Although the improved EMD, well known as the ensemble EMD (EEMD), has been proposed, mode mixing is alleviated only to a certain degree. Moreover, EEMD needs to determine the amplitude of added noise. In this paper, we propose Phase Space Ensemble Empirical Mode Decomposition (PSEEMD) integrating Phase Space Reconstruction (PSR) and Manifold Learning (ML) for modifying EEMD. We also provide the principle and detailed procedure of PSEEMD, and the analyses on a simulation signal and an actual vibration signal derived from a rubbing rotor are performed. The results show that PSEEMD is more efficient and convenient than EEMD in extracting the mixing features from the investigated signal and in optimizing the amplitude of the necessary added noise. Additionally PSEEMD can extract the weak features interfered with a certain amount of noise

A New Modelling and Feature Extraction Method Based on Complex Network and Its Application in Machine Fault Diagnosis

The application of the existing complex network in fault diagnosis is usually modelled based on the time domain, resulting in the loss of sign frequency-domain features, and the extracted topology features of network are too macroscopic and insensitive to local changes within the network. This paper proposes a new method of local feature extraction based on frequency complex network (FCN) decomposition and builds a new complex network structure feature on this basis, namely, subnetwork average degree. The variation law of signals in frequency domain is obtained with the aid of the structural features of complex network. The local features that are sensitive to local changes of the network are applied to characterize the whole network, with flexible application and without limitation in mechanism. The average degree of subnetwork could be regarded as feature parameters for rolling bearing fault diagnosis and degradation state recognition. Analysis on the experimental data and bearing life cycle data shows that the method proposed in this paper is effective, revealing that the extracted features have effective separability and high accuracy in fault recognition and the degradation detection of the life cycle of rolling bearings combined with neural networks. Moreover, the proposed method has reference value for the processing and recognition of other nonstationary signals