Earticle

Time domain features are employed for detection and identification of rolling element bearing faults in rotating machinery. Only five features with simple calculation are selected as features extracted directly from the original time domain vibration signals or preprocessed time domain vibration components. Three preprocessing techniques including high and band pass filtration, wavelet package transform (WPT) and envelope analysis are researched to achieve time domain features carrying the important diagnostic information of bearing conditions. An optimized artificial neural network (ANN) with rapid learning algorithm is designed and classification is performed using the ANN combined with time domain features. The model was evaluated on vibration data recorded using two accelerometers mounted on an induction motor housing subjected to a number of single point defects. The results demonstrate the proposed model is capable of high precision, fast processing and time savings in identification of bearing faults.

In this brief, a novel pathological continuous speech detection method based on time domain features weighted. First, different optimal threshold for time domain features, including zero crossing ratio, short-time energy and autocorrelation, are obtained from training speech data. Second, a difference evaluation technique is proposed, and with it, the difference of the same time domain feature selected from testing speech data and training speech data were obtained. Finally, to distinguish a given speech well, a novel weighting method based on difference evaluation for each kinds of time domain is employed, respectively. Experiments were conducted on the pathological speech database to prove the power and effectiveness of the proposed method. Results obtained shown that this method outperforms other early proposed time domain feature method, creating a more reliable technique for pathological continuous speech detection.

The dynamic structural responses are sensitive to the time-frequency content of seismic waves, and seismic input motions in time-history analysis are usually required to be compatible with design response spectra according to nuclear codes. In order to generate spectra-compatible input motions while maintaining the intrinsic non-stationarity of seismic waves, an improved time-domain approach is proposed in this paper. To maintain the nonstationary characteristics of the given seismic waves, a new time-frequency envelope function is constructed using the Hilbert amplitude spectrum. Based on the intrinsic mode functions (IMFs) obtained from given seismic waves through variational mode decomposition, a new corrective time history is constructed to locally modify the given seismic waves. The proposed corrective time history and time-frequency envelope function are unique for each earthquake records as they are extracted from the given seismic waves. In addition, a dimension reduction iterative technique is presented herein to simultaneously superimpose corrective time histories of all the damping ratios at a specific frequency in the time domain according to optimal weights, which are found by the genetic algorithm (GA). Examples are presented to show the capability of the proposed approach in generating spectra-compatible time histories, especially in maintaining the nonstationary characteristics of seismic records. And numerical results reveal that the modified time histories generated by the proposed method can obtain similar dynamic behaviors of AP1000 nuclear power plant with the natural seismic records. Thus, the proposed method can be efficiently used in the design practices.