A noise-resistant Wigner-Vile spectrum analysis method based on cyclostationarity and its application in fault diagnosis of rotating

Rolling element bearing and gear are the most common used rotating parts in rotating machinery and they are also the fragile mechanical part. Studying the effective method of timely diagnosis of them is very necessary. The Wigner-Vile spectrum (WVS) is an effective time-frequency analysis and common used method for diagnosis of rotating machinery. However, it would not work effectively when the impulsion characteristic fault signal of rotating machinery is buried by strong background noise. To solve the above problem, the property of cyclostationarity of the rotating machinery signal is used, and the cyclic spectral density basing on second order cyclostationarity statistic is combined with the WVS, and the cyclic spectral density Wigner Vile spectrum (CSDWVS) time-frequency method is proposed in the paper. Through the analysis results of simulation and experiment, the CSDWVS method has the advantages of much more noise-resistant than traditional WVS method, and it could extract the fault feature of the vibration signal of rotating machinery buried in strong background noise. Besides, it also has better time frequency aggregation effect

Diagnosis of rolling element bearing fault arising in gearbox based on sparse morphological component analysis

It is hard to diagnose the rolling element bearing fault occurring in gearbox due to the complexity and the probable mutual coupling among the kinds of signals. A novel diagnosis method of rolling element bearing fault arising in gearbox based on morphological component analysis (MCA) originating from sparse representation theory is proposed in the paper. By selecting proper dictionaries, different morphological components can be separated successfully from the complex rolling fault signal arising in gearbox, which helps to improve the efficiency and accuracy of diagnosis result. The effectiveness of the proposed method is verified through simulations firstly. Then the proposed method is used in fault feature extracting of complex vibration signals collected from rotating machinery, and the effectiveness of the proposed method is further verified. Besides, the advantage of the proposed method over other relative method is presented

Study of the Kalman filter for arrhythmia detection with intracardiac electrograms

Third generation implantable antitachycardia devices offer tiered-therapy to reverse ventricular fibrillation (VF) by defibrillation and ventricular tachycardia (VT) by low-energy cardioversion or antitachycardia pacing. The schemes for detecting cardiac arrhythmias often realize nonpathologic tachycardia as serious arrhythmias and deliver false shocks. In this study, an arrhythmia classification technique has been developed with the use of Kalman filter applied on cyclostationary autoregressive model. This new algorithm was developed with a training set of 24 arrhythmia passages and tested on a different data set of 29 arrhythmia passages. The algorithm provides 100% detection of VF on the test set. 77.8% of VTs were detected correctly while 16.7% of VTs were diagnosed as sinus rhythm and 5.5% of VTs were detected as VF

機械学習を用いたコグニティブ無線における変調方式識別に関する研究

The current spectrum allocation cannot satisfy the demand for future wireless communications, which prompts extensive studies in search of feasible solutions for the spectrum scarcity. The burden in terms of the spectral efficiency on the radio frequency terminal is intended to be small by cognitive radio (CR) systems that prefer low power transmission, changeable carrier frequencies, and diverse modulation schemes. However, the recent surge in the application of the CR has been accompanied by an indispensable component: the spectrum sensing, to avoid interference towards the primary user. This requirement leads to a complex strategy for sensing and transmission and an increased demand for signal processing at the secondary user. However, the performance of the spectrum sensing can be extended by a robust modulation classification (MC) scheme to distinguish between a primary user and a secondary user along with the interference identification. For instance, the underlying paradigm that enables a concurrent transmission of the primary and secondary links may need a precise measure of the interference that the secondary users cause to the primary users. An adjustment to the transmission power should be made, if there is a change in the modulation of the primary users, implying a noise oor excess at the primary user location; else, the primary user will be subject to interference and a collision may occur.Alternatively, the interweave paradigm that progresses the spectrum efficiency by reusing the allocated spectrum over a temporary space, requires a classification of the intercepted signal into primary and secondary systems. Moreover, a distinction between noise and interference can be accomplished by modulation classification, if spectrum sensing is impossible. Therefore, modulation classification has been a fruitful area of study for over three decades.In this thesis, the modulation classification algorithms using machine learning are investigated while new methods are proposed. Firstly, a supervised machine learning based modulation classification algorithm is proposed. The higher-order cumulants are selected as features, due to its robustness to noise. Stacked denoising autoencoders,which is an extended edition of the neural network, is chosen as the classifier. On one hand stacked pre-train overcomes the shortcoming of local optimization, on the other, denoising function further enhances the anti-noise performance. The performance of this method is compared with the conventional methods in terms of the classification accuracy and execution speed. Secondly, an unsupervised machine learning based modulation classification algorithm is proposed.The features from time-frequency distribution are extracted. Density-based spatial clustering of applications with noise (DBSCAN) is used as the classifier because it is impossible to decide the number of clusters in advance. The simulation reveals that this method has higher classification accuracy than the conventional methods. Moreover, the training phase is unnecessary for this method. Therefore, it has higher workability then supervised method. Finally, the advantages and dis-advantages of them are summarized.For the future work, algorithm optimization is still a challenging task, because the computation capability of hardware is limited. On one hand, for the supervised machine learning, GPU computation is a potential solution for supervised machine learning, to reduce the execution cost. Altering the modulation pool, the network structure has to be redesigned as well. On the other hand, for the unsupervised machine learning, that shifting the symbols to carrier frequency consumes extra computing resources.電気通信大学201