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Vibration parameter of water pump is studied by power flow theory, water pump isolation vibration system can be simplified a single-deck isolation system model, by using electric-machine (E-F) analog method, the curves of foundation speed amplitude and power flow are drawn, the results show that the inertia block has clearly reduced the vibration power flow and speed amplitude, increasing the mass of inertia block can decrease the first peak point and make the first peak point moving toward the low frequency direction, increasing surface density of the basement can decrease input power flow and the vibration of basement.

Blind source separation (BSS) is a technique for recovering a set of source signals without priori information on the transformation matrix or the probability distributions of the source signals. In the previous works of BSS, the choice of the learning rate would reflect a trade-off between the stability and the speed of convergence. In this paper, a particle swarm optimization (PSO)-based learning rate adjustment method is proposed for BSS. In the simulations, three source signals are mixed and separated and the results are compared with natural gradient algorithm. The proposed approach exhibits rapid convergence, and produces more efficient and more stable independent component analysis algorithms than other related approaches.

The problem about pump piping system vibration isolation that is hard to be solved at the present stage in the practice engineering was studied in this paper. Damping measures which is the necessary measure is proposed in this study. Theoretical analysis is given to the main parameter (thickness) of the influences of the damping. Based on the free damping measure, constrained damping measures was proposed and verified by the experiments. The results show that the damping effect was improved with the increasing of the thickness. Under the same conditions, the effect of the constrained damping measures was better than the free damping measures. The results have significant guidance in the isolation vibration of the pump system.

Aim at the problem of wind turbine gearbox fault diagnosis , a method of Hilbert space feature entropy is proposed. Using the information entropy theory, the faults information obtained in the HHT spectrum quantify to the value of Hilbert space feature entropy, thus achieving quantitative analysis of the failure of the wind turbine gearbox. The method was applied to fault diagnosis of the wind turbine gearboxes, which proved that the method is efficacious.

The fault diagnosis of wind turbines is difficult because of the wind speed changing. To solve this problem, a method designated as Hilbert space feature entropy is proposed. The information entropy based on HHT is used to describe the wind turbine bearing vibration signal under different wind speeds. Using this method to analysis the fault of the D70 type wind turbine bearings, which proved that this method can effectively identify the fault of wind turbine bearing under different wind speed . Thus the method can be used as index and basis of the fault diagnosis of wind turbine bearing.

This paper presents blind source separation of rolling bearing based on particle swarm optimization. The algorithm combines the advantages of both blind source separation and particle swarm optimization. Through the experiment it is shown that the algorithm can separate the signals collected from rolling bearing and gearbox effectively, which can provide a new method for fault diagnosis and signal processing of machinery equipment.

With the actual case of isolation system design and noise control of roots blower room of a pharmaceutical factory in Shenyang taken into consideration, the overall computation was done on the basis of multi-vibration noise source. The vibration energy of equipments and noise propagation were analyzed both qualitatively and quantitatively with the spectral characteristics of vibration and wideband noise taken into account. Furthermore, the acoustic environment in the blower room was improved and the noise emission of the factory got down to the standard after the vibration isolation and noise reduction measures were applied. This work has important guiding significance and reference value for the projects on vibration isolation and noise control in the future.

To extract the AE signal feature effectively, an automatically operating blind source separation method is presented. The blind deconvolution algorithm is discussed for the non-stationary AE signals to eliminate the noise through minimizing the mutual information to get the separating matrix. Finally, the experimental result is indicated that the mixing noise of the AE signals in the collecting process can be eliminated by this method, and the individual extensional and flexural wave modes can be separated, which can provides the theory analysis basis for the crack recognition of wind turbine blade.

Isolation system of the heat water pumps can be simplified as a double sources exciting and double output double-deck vibration isolation system model, expressions of transmitted power flow and vibration speed to the basement are deduced based on the double sources exciting and double output double-deck vibration isolation system electric-force(E-F) analog picture, the curves of power flow and vibration speed transmitted to basement how the upper deck vibration isolation and intermediate mass effect are drawn by using mat lab program. The results show that the adjusting the upper deck vibration isolation stiffness parameters has little effect on the amplitude of vibration power flow, increasing intermediate mass can move first peak to the low frequency, increasing intermediate mass can obviously reduce t transmitted power flow and transmitted vibration speed amplitude to the basement.

It is difficult to obtain the obvious fault features of wind turbine, because the vibration signal of them are non-linear and non-stationary. To solve the problem, a multifractal analysis based on wavelet is presented in this research. The real signals of 1.5 MW wind turbine are studied by multifractal theory. The incipient fault features are extracted from the original signal. Using the Wavelet Transform Modulo Maxima Method, the multifractal was obtained. The results show that fault features of high rotational frequency of wind turbine are different from low rotational frequency, and the complexity of the vibration signals increases with the rotational frequency. These demonstrate the multifractal analysis is effective to extract the fault features of wind turbine generator.

The presence of irregularity in periodical vibration signals usually indicates the occurrence of wind turbine gearbox faults. Unfortunately, detecting the incipient faults is a difficult job because they are rather weak and often interfered by heavy noise and higher level macro-structural vibrations. Therefore, a proper signal processing method is necessary. We used the wavelet-based multifractal method to extract the impulsive features buried in noisy vibration signals. We first calculated the wavelet transform modulo maxima lines from the real vibration signals, then, obtained the singularity spectrum from the lines. The analysis results of the real signals showed that the proposed method can effectively extract weak fault features.

Noise detection and vibration analysis have been made in the enterprise where screw compressor unit has been running with problems for a long term. According to the characteristics of noise and vibration spectrum, ammonia refrigeration operation fault and noise source have been analyzed, and a noise reduction scheme is made. Based on this, through the optimization of compound sound absorbing structure parameters design and manufacturing device of sound insulation, thereby hidden safety trouble can be eliminated and the working environment of workers can be improved. Besides, mechanical properties of the compressor group are more stable. In practical engineering applications, good effect of noise reduction has been achieved.

The oscillating or cyclic time behavior can characterize many processes. This holds for rotating machines. The vibration signals of them are periodic or contain periodic parts, which can be used for fault diagnosis for the rotating machines. The periodic time series of the superposition of two oscillations were investigated by the multifractal theory in this study. The wavelet transform modulus maxima method was used for the singularity spectrum computations. From the results, we can find that when the amplitude, frequency or the phase difference changed, the width and the peak position of the singularity spectrum changed significantly. Therefore, a new measure - the width and the peak position of the singularity spectrum – can be used for the periodic signals.

Because the vibration signals of faulty wind turbine are non-linear and non-stationary, to obtain the obvious fault features become difficult. In this study, the incipient fault of the main bearing used in large scale wind turbine is studied by using a multifractal method based on the Wavelet Modulus Maxima (WTMM) method. The real vibration signals from the main bearings are analyzed using the multifractal spectrum. The spectrum of the vibration signals is quantified by spectral characteristics including its range and the Hölder exponent corresponding to the maximum dimension. The results show that the range of Hölder exponent of the main bearing which worked normally is much narrower. While the ranges of the vibration signals of the main bearing with incipient fault are wider. We also found that the fault features are different at various wind turbine rotational frequencies. Those demonstrate that the incipient fault features of main bearing of large scale wind turbine can be extract effectively using the multifractal spectrum obtained from WTMM method.

In this work an efficient and simplified method for crack identification in wind turbine blade has been developed based on fractal dimension. Firstly, the algorithm is studied on the calculation of the correlation dimension of acoustic emission signals, and an analysis of these equations makes it possible to identify cracks. Then it turns out that the complexity could vary with different crack expansion conditions, i.e. reduction and augmentation of the correlation dimension due to the occurrence of a crack by the fatigue experiment. Finally, the proposed detection methodology is compared to wavelet analysis. It is testified that the method exploits both the typical steady expansion of the crack and the appearance phenomenon due to the presence of crack.

The relationship between stress and crack growth was studied. Applying the software ANSYS, the method of calculating stress intensity factor of composite mode cracks was built. At the same time, the stress intensity factor of mode I crack was calculated and the influence of crack size and loading on the stress intensity factor was studied. The results show that the results of ANSYS are almost the same to analytic results, which proves that the method is feasible. When the crack is quite big or quite small, the result of ANSYS’ error is comparatively obvious, which is due to the problem of the refinement of meshing. When the crack is too big, the dense of meshing is inadequate.

Spring vibration isolators and rubber damping vibration isolators are often used in active isolation engineering of water source heat pump units. The method is inefficient in shock and low frequency vibration isolation. Because the stiffness of equipments reduces with the isolation system the vibration severity itself becomes too big for working safely. Particle damping materials can absorb vibration energy to reduce vibration. The paper testifies that successful application of particle damping materials in vibration reduction of water source heat pump units is a practical simple way with low cost.

A new method based on finite element analysis and wavelet analysis for the fault diagnosis of wind turbine main bearings is presented in the paper. Firstly, take example for low speed bearing, the three-dimension models of the good and fault bearings are set up, which was discussed with using finite element method. The stress, strain and contact stress distribution were computed by this method. Then the wavelet transform of data signals collected in experiment was carried out with db6 wavelet and 4 scales on the basis of the stress wave analysis. The frequency D3 and D4 signals were reconstructed and characteristic frequencies of stress waves with the simulative faults were extracted. It has been shown that the method applying finite element analysis and wavelet analysis is an efficient method for the fault diagnosis of wind turbine main bearings.

The monitoring about key components of large wind turbine is important, especially blade. Damages in the material of blade in large wind turbine will cause strain and thus can produce acoustic emission. And this study is presently a new research area in china. This paper refers to the preliminary exploration of common fracture characteristic. Acoustic emission can be used to identify and diagnose the damage. Analyzing the acoustic emission characteristics of the damage in the wind turbine system is emphasized by the experiment. At first, the acoustic emission signals of two different damages and vibration signal of those are compared and analyzed to demonstrate the superiority of the time domain of acoustic emission signals based method for the damage identification. Then, the wavelet is introduced and its characteristics are illustrated. Finally, the wavelet is used to analyze and discuss the time-frequency features of the damage acoustic emission. Some frequency band of wavelets is recomposed, and the results can efficiently extract the features of acoustic emission signal and identify the damage. It offers a valuable reference to monitor the damage about blade of large wind turbine.

The properties of the SW and its transmission law in the low-speed rolling bearing were studied in this paper. Firstly, the three-dimension contact model of the bearing was set up. The defects which occurred on the outer race were simulated in the model. According to the model, the stress, the strain and the contact stress distribution was computed. Then, the stress and strain distribution law and the contact stress distribution law of the interface on good and fault bearing were compared. In the real-world testing, fault signals were acquired using stress wave transducer. Fault characteristic parameters were extracted and the background noise was reduced using wavelet analysis. Both simulation and real-world testing result obtained indicate that SW and wavelet transform can be the effective method in the fault diagnosis of low-speed bearing.