Detection of speech signal in strong ship-radiated noise based on spectrum entropy

Comparing the frequency spectrum distributions calculated from several successive frames, the change of the frequency spectrum of speech frames between successive frames is larger than that of the ship-radiated noise. The aim of this work is to propose a novel speech detection algorithm in strong ship-radiated noise. As inaccurate sentence boundaries are a major cause in automatic speech recognition in strong noise background. Hence, based on that characteristic, a new feature repeating pattern of frequency spectrum trend (RPFST) was calculated based on spectrum entropy. Firstly, the speech is detected roughly with the precision of 1 s by calculating the feature RPFST. Then, the detection precision is up to 20 ms, the length of frames, by method of frame shifting. Finally, benchmarked on a large measured data set, the detection accuracy (92 %) is achieved. The experimental results show the feasibility of the algorithm to all kinds of speech and ship-radiated noise

Detection of speech signal in strong ship-radiated noise based on spectrum entropy

Comparing the frequency spectrum distributions calculated from several successive frames, the change of the frequency spectrum of speech frames between successive frames is larger than that of the ship-radiated noise. The aim of this work is to propose a novel speech detection algorithm in strong ship-radiated noise. As inaccurate sentence boundaries are a major cause in automatic speech recognition in strong noise background. Hence, based on that characteristic, a new feature repeating pattern of frequency spectrum trend (RPFST) was calculated based on spectrum entropy. Firstly, the speech is detected roughly with the precision of 1 s by calculating the feature RPFST. Then, the detection precision is up to 20 ms, the length of frames, by method of frame shifting. Finally, benchmarked on a large measured data set, the detection accuracy (92 %) is achieved. The experimental results show the feasibility of the algorithm to all kinds of speech and ship-radiated noise

Feature extraction method based on VMD and MFDFA for fault diagnosis of reciprocating compressor valve

Aiming at the nonlinearity, nonstationarity and multi-component coupling characteristics of reciprocating compressor vibration signals, an integrated feature extraction method based on the variational mode decomposition (VMD) and multi-fractal detrended fluctuation analysis (MFDFA) is proposed for a fault diagnosis for a reciprocating compressor valve. Firstly, to eliminate the noise interference, a novel VMD method with superior anti-interference performance was utilized to obtain several components of the quasi-orthogonal band-limited intrinsic mode function (BLIMF) from a strong non-stationarity vibration signal, and a consistent number K of BLIMFs was selected based on a novel criterion for all fault states. Secondly, the MFDFA method, which can describe the multi-fractal structure feature of non-stationary time series, was applied to analyze each BLIMF component, and the parameters of MFDFA were employed as the eigenvectors to reflect the structure characteristics and local scale behavior of the vibration signal. Then, the principal component analysis (PCA) was introduced to refine the eigenvectors for a higher recognition efficiency and accuracy. Finally, the vibration signals of four types of reciprocating compressor valve faults were analyzed by this method, and the faults were identified correctly by pattern classifiers of BTSVM and CNN. Further results comparison with other feature extraction methods verifies the superiority of the proposed method

Implementing a new fully stepwise decomposition-based sampling technique for the hybrid water level forecasting model in real-world application

Various time variant non-stationary signals need to be pre-processed properly in hydrological time series forecasting in real world, for example, predictions of water level. Decomposition method is a good candidate and widely used in such a pre-processing problem. However, decomposition methods with an inappropriate sampling technique may introduce future data which is not available in practical applications, and result in incorrect decomposition-based forecasting models. In this work, a novel Fully Stepwise Decomposition-Based (FSDB) sampling technique is well designed for the decomposition-based forecasting model, strictly avoiding introducing future information. This sampling technique with decomposition methods, such as Variational Mode Decomposition (VMD) and Singular spectrum analysis (SSA), is applied to predict water level time series in three different stations of Guoyang and Chaohu basins in China. Results of VMD-based hybrid model using FSDB sampling technique show that Nash-Sutcliffe Efficiency (NSE) coefficient is increased by 6.4%, 28.8% and 7.0% in three stations respectively, compared with those obtained from the currently most advanced sampling technique. In the meantime, for series of SSA-based experiments, NSE is increased by 3.2%, 3.1% and 1.1% respectively. We conclude that the newly developed FSDB sampling technique can be used to enhance the performance of decomposition-based hybrid model in water level time series forecasting in real world

Bearing Fault Diagnosis Based on Optimized Variational Mode Decomposition and 1-D Convolutional Neural Networks

Due to the fact that measured vibration signals from a bearing are complex and non-stationary in nature, and that impulse characteristics are always immersed in stochastic noise, it is usually difficult to diagnose fault symptoms manually. A novel hybrid fault diagnosis approach is developed for the denoising signals and fault classification in this work, which combines successfully the variational mode decomposition (VMD) and one dimensional convolutional neural network (1-D CNN). VMD is utilized to remove stochastic noise in the raw signal and to enhance the corresponding characteristics. Since the modal number and penalty parameter are very important in VMD, a particle swarm mutation optimization (PSMO) as a novel optimization method and the weighted signal difference average (WSDA) as a new fitness function are proposed to optimize the parameters of VMD. The reconstructed signals of mode components decomposed by optimized VMD are used as the input of the 1-D CNN to obtain fault diagnosis models. The performance of the proposed hybrid approach has been evaluated using the sets of experimental data of rolling bearings. The experimental results demonstrate that the VMD can eliminate signal noise and strengthen status characteristics, and the proposed hybrid approach has a superior capability for fault diagnosis from vibration signals of bearings.National Natural Science Foundation of China, Key Laboratory Project of Department of Education of Shaanxi Province, Brunel University London (UK), National Fund for Study Abroad (China)

Identification of time-varying cable forces based on parameter optimization variational mode decomposition.

Accumulated fatigue damage is one of the main causes of damage and destruction of actual bridge structures. for cable bridges, the cable is the key force component. the cumulative fatigue damage of the cable seriously threatens the safety of the bridge structure. the traditional cable force test based on the vibration method can only identify the average cable force of a bridge cable over a period of time. however, due to vehicle load and environmental factors, the cable force of the bridge cable is time-varying. time-varying cable force is the main cause of fatigue damage, and it is also the basis for the safety assessment of cable limit state and the evaluation of cumulative fatigue damage. to this end, this paper studies the identification method of bridge cable time-varying cable force based on variational mode decomposition. the main research contents of this article include: based on the time-frequency analysis method of variational mode decomposition, a new method for identifying time-varying cable forces is proposed. the time-frequency analysis method of variational modal decomposition is a new development method in the field of current signal processing. its principle is to obtain a limited number of imf and extract the instantaneous frequency of the time-varying system by performing hilbert transform on the obtained imf. firstly, according to the time-frequency analysis method of variational modal decomposition, the time-varying modal frequency is identified from the measured cable acceleration. then, the bridge cable is simplified into an ideal tension string, and the cable force is identified based on the relationship between the cable force and frequency established by the classic string vibration theory. the frequency-doubling relationship of the vibration of the cable is used to reduce the optimization variables of the method, improve the calculation efficiency, reduce the influence of noise on the different instantaneous frequencies of the cable, and improve the accuracy of frequency identification of time-varying modal. finally, the time-varying modal frequency of the identified cable is substituted into the cable force formula to obtain the time-varying cable force of the cable. in the practical application of variational modal decomposition, the choice of penalty factors and the number of components has a great influence on the final signal decomposition results. in order to automatically determine the best parameter combination, the particle swarm optimization algorithm is used to search for these two influencing parameters in parallel . simulation signal and engineering signal processing results show that the proposed method can achieve identification of time-varying frequency. for the end effect of the instantaneous frequency curve, the signal extension method is used. reduce the error of the instantaneous frequency at the end point. design and build a tilting cable vibration test platform, and compare the test results with the calculation results to further verify the correctness of the method in this paper. the results show that the cable force error is about ± 5.0%