Advanced Materials Research Vols. 139-141

Abstract: Based on a Jeffcott rotor system with rigid support, the math models of flexural and torsional vibration were built, and their vibration characteristics were concluded. A simulation experiment in this paper was designed about the flexural and torsional vibration of shafts on a simulated turbine generator during network impacts. By analysis, flexural and torsional vibration characteristics were obtained, and the results showed the shafts flexural vibration and torsional vibration were interacted with each other. It was provided that torsional vibration could inhibit the complex shafts exercise. A new idea was suggested that the characteristics of torsion vibration should be considered as the symptom of faults diagnosis

Abstract: Simulated four different fault signals in the lab, the authors then used wavelet scalogram and amplitude spectrum to make analysis on the above four fault signals and abstract each spectrum characteristics. Wavelet scalogram was able to extract the characteristic’s frequency, show the impact components caused by rub-impact, show the beat phenomenon caused by oil whip and show the irreducible high frequency components as well as the complex low-frequency components. Amplitude spectrum was able to show the energy size distribution at various frequency bands and able to analyze and calculate the relationship between various frequency components. Thus they express the relationship between various frequency banks from a quantitative manner. Therefore, combining the wavelet scalogram and amplitude spectrum when making analysis, as they complement and verify each other, it will enhance the reliability when extract and analyze the characteristics of fault signal.

Abstract: To effectively avoid the loss of useful information, in this paper, feature information has been extracted from the fault signal of rotating machinery in different aspects such as amplitude-domain, time-domain and time-frequency domain. Then, for the multi-dimensional feature extraction was prone to the problem of “dimension disaster”, the principles of FDR was introduced in data mining to determine the classification ability of each individual feature, and the cross correlation coefficient was adopted to solve the problem that dealing with individual feature. Neglected the interrelationship between the features, a new feature selection algorithm was constructed. Finally, the eigenvectors were used for training and recognizing of SVM model. The experimental results showed the fault diagnosis system was valid and robust.

Abstract: Concrete pump truck is a kind of mobile construction machinery, with the characteristic of complex structure, poor working condition and difficult to maintenance. Therefore, adopting appropriate health monitoring methods, and accurate grasping the running status information of the pump truck is significant to the pump truck’s safety use and pre-judgment maintenance plan arrangement. In this paper, the traditional structural health monitoring methods was studied. And the structure, load and work environment characteristics of concrete pump truck were analyzed. Taking into account the economy and reliability of the structural health monitoring system, the technical route of the concrete pump truck structural health monitoring system and health evaluation criteria were proposed. The evaluation criteria takes into account both the cumulative health effect and timely health status of concrete pump truck structure.

Abstract: Inner leakage of hydraulic cylinder is a very serious failure in the hydraulic system and it can lead to many problems.A important fault diagnosis way is to detect the pressure signal.But the pressure signal is seriously influenced by pressure fluctuation and other noises.It is difficult to extract features from pressure singal. Aiming at the difficulty in extracting feature from pressure singal in fault diagnosis for leakage of hydraulic cylinder,a fault diagnosis approch based on monitoring preesure singal and wavelet energy is proposed in this article.According to the method, the enegry of different frequency bands after wavelet decomposition costitutes the eigenvectors at first, then these eigenvectors are input into BP network to identify faults. The experimental results show that three modes of no leakage, slighter leakage and severe leakage were correctly identified.This approach can be used in the leakage fault diagnosis of hydraulic cylinder.

Abstract: In order to accurately estimate tool life for milling operation, a novel tool condition monitoring system was proposed to improve classifying precision in different cutting condition. Lots of features were extracted from cutting forces signal, vibration signal and acoustic emission signal by different signal processing method, only a few features selected by principal component analysis (PCA) according to contribution rate, and constructed as input vector. The relation between tool condition and features was built by radial basis probability neural network which control parameter of kernel function and hidden central vector were optimized by improved genetic algorithm. The experimental results show that the method proposed in the paper achieves higher recognition rate, good generalization ability and better available practicality.

Abstract: In order to estimate and predict the screw performance in the process of machining, a screw life monitoring system was built. Current signal was processed and features sensitive to cutting force were selected, a virtual force sensor was constructed to model the relation between cutting force and current by BPNN. Cutting force was indirectly calculated by the model, so the rating life of screw in different condition could be known and residual life also be reckoned by historical database. A three-way vibration sensor was installed on screw pair base; screw condition could be induced by HMM which input was 15 vibration signal features. As machining condition changed, corresponding new HMM would be built by adaptive method. Finally, the residual life of screw could be gotten by multi-HMM and BPNN. The experimental results show the model proposed in the paper is effective and high precision.

Abstract: This paper mainly introduced the basic theory of Hidden Markov Model (HMM) and Support Vector Machines (SVM). HMM has strong capability of handling dynamic process of time series and the timing pattern classification, particularly for the analysis of non-stationary, poor reproducibility signals. It has good ability to learn and re-learn and high adaptability. SVM has strong generalization ability of small samples, which is suitable for handling classification problems, to a greater extent, reflecting the differences between categories. Based on the advantages and disadvantages between the two models, this paper presented a hybrid model of HMM-SVM. Experiments showed that the HMM-SVM model was more effective and more accurate than the two single separate models. The paper also explored the application of its database system development, which could help the managers to get and handle the data quickly and effectively.

Abstract: The interoperability among diversified ubiquitous sensor network platforms can be solved well by employing XML technology. According to the requirement that implementing accurately remote XML configuration strategy for spot equipments, a new dynamic interpretation method of XML configuration strategy is proposed. First, an XML-based IP mode measurement & control system is introduced. On the basis of realizing graphical strategy editor, the innovative ways and technologies of some key problems for dynamic interpretation method were especially discussed in this paper, including configuration strategy conversion combining preorder binary tree traversal with stack, XML strategy decomposition based on DOM tree model and dynamical update of XML configuration strategy. Finally, an application example in IP mode ethanol concentration measurement & control system is introduced. Experimental results show that the new method can greatly simplify conversion from measurement & control strategy to microinstructions, which saves system resources and improves greatly the speed of interpretation.

Abstract: In this paper, a modeling about centrifugal compressor axial displacement fault diagnosis is proposed through investigating on the mechanical performance and failure mode of thrust bearing as well as the characteristics of axial displacement fault. In this paper, result of experimental studying of the force-displacement curve of rotor at different speed is presented. Numerical simulation method is used to predict the curve in diagnosis model. By comprising the experimental and numerical result, it can be seen that Finite Element Method(FEM) elastic-perfectly plastic material model can get more precise result than linear elastic model and Computational Fluid Dynamics(CFD) method provide a new insight to investigation the physics based diagnosis method. Result of the paper provides the foundation of axial displacement fault self-recovery.