The rolling bearings are one of the most critical components in rotary machinery. To prevent unexpected bearing failure, it is crucial to develop the effective fault detection and diagnosis techniques to realize equipment’s near-zero downtime and maximum productivity. In this paper, a new fault detection and diagnosis method based on Wigner-Ville spectrum entropy (WVSE) is proposed. First, the local mean decomposition (LMD) and the Wigner-Ville distribution (WVD) are combined to develop a new feature extraction approach to extract the fault features in time-frequency domain of the bearing vibration signals. Second, the concept of the Shannon entropy is integrated into the WVD to define the Wigner-Ville spectrum entropy to quantify the energy variation in time-frequency distribution under different work conditions. The research results from the bearing vibration signals demonstrate that the proposed method based on WVSE can identify different fault patterns more accurately and effectively comparing with other methods based on singular spectrum entropy (SSE) or power spectrum entropy (PSE).

Cognitive radio (CR) is considered to be an effective approach to eliminate the dilemma of spectrum shortage. To meet the ever-increasing demands of instant and accurate spectrum sensing in CR with wideband and multi-frequency-slots, a novel wideband spectrum sensing algorithm based on bidirectional decision of normalized spectrum (BDNP) is proposed in this paper. The proposed algorithm takes the normalized power spectrum within the frequency slot as the detection statistics, and finds out all of the occupied frequency slots in the range of the target bandwidth by searching forward and backward in sequence. The asymptotic normality and independence of Fourier transform is proved firstly, and based on which the false alarm probability of single decision is derived. Additionally, the closed-form expression of decision threshold is obtained by using Neyman-Pearson criterion. Theoretical analysis and simulation results show that the BDNP algorithm can accurately identify occupied frequency slots, which provides the base of avoiding interference to the primary users. Furthermore, comparing with the spectrum sensing algorithm based on conventional spectral estimation (CSE), BDNP algorithm can effectively overcome noise uncertainty in spectrum sensing.

There are many parameters that be used to represent typical breath flow, mainly divided into three types: one is temporal, the second is frequency, and the last is pressure driven model. The temporal parameters include breath period, tidal volume, inspiration expiration ratio, and functional residual volume, to name a few. The frequency parameters mainly include every harmonic content variation. The pressure driven model use mechanical parameters to calculate flow. All the methods above have one common disadvantage: they do not take variation into account. We proposed here that the fourth type parameter should be also used to describe flow pattern, namely fractal parameter, fractal dimension of breath flow signal. The necessity and advantage of the fractal parameter is elucidated. The fractal dimension parameter is optional because of its stability compared with other parameters.

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In this paper how to generate respiratory flow that has fractal signal feature is introduced. Physiological signal have fractal feature have been verified by many researchers, such as heart beat rate, interbreath interval. Mechanical ventilators are used to provide life support for patients with respiratory failure. But these machines can damage the lung, causing them to collapse. On the other hand, fractal feature can be used as an indication of health situation; as a result in patient simulation the physiological signal should also have fractal features. The fractal feature is generated by fractional Brownian motion simulation. The fractal dimension is decided by Hurst exponent in routine. The algorithm is realized by R language and result is input into LabVIEW which have friendly interface and easy for simulation control usage. The method can be used in design of mechanical ventilator and medical patient simulator.