Optimal Filter Length to Identify Uninterpretable Electrocardiograms

Adaptive filtering algorithms can be used on the time-domain processing of navigation receivers to suppress interference and maintain the navigation and positioning function. The filter length can affect the interference suppression performance and hardware utilization simultaneously. In practical engineering, the filter length is usually set to a large number to guarantee anti-jamming performance, which means a high-performance receiver requires a high-complexity anti-jamming filter. The study aims at solving the problem by presenting a design method for the optimal filter order in the time-domain anti-jamming receiver, with no need for detailed interference information. According to interference bandwidth and jam-to-signal ratio (JSR), the approach designed a band-stop filter by Kaiser window for calculating the optimal filter order to meet interference suppression requirements. The experimental results show that the time-domain filtering processing has achieved good interference suppression performance for engineering requirements with optimal filter order in satellite navigation receivers.

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Research on Fault Extraction Method of CYCBD Based on Seagull Optimization Algorithm

Shock and Vibration ◽

10.1155/2021/8552024 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Qianqian Zhang ◽

Haochi Pan ◽

Qiuxia Fan ◽

Fujing Xu ◽

Yulong Wu

Keyword(s):

Optimization Algorithm ◽

Power Flow ◽

Blind Deconvolution ◽

Vibration Signal ◽

Optimal Filter ◽

Minimum Entropy ◽

Test Bed ◽

Maximum Correlation ◽

Filter Length ◽

Simulated Signal

Maximum cyclostationarity blind deconvolution (CYCBD) can recover the periodic impulses from mixed fault signals comprised by noise and periodic impulses. In recent years, blind deconvolution has been widely used in fault diagnosis. However, it requires a preset of filter length, and inappropriate filter length may cause the inaccurate extraction of fault signal. Therefore, in order to determine filter length adaptively, a method to optimize CYCBD by using the seagull optimization algorithm (SOA) is proposed in this paper. In this method, the ratio of SNR to kurtosis is used as the objective function; firstly, SOA is used to search the optimal filter length in CYCBD by iteration, and then it uses the optimal filter length to perform CYCBD; finally, the frequency-domain waveform is determined through Fourier transformation. The method proposed is applied to the fault extraction of a simulated signal and a test vibration signal of the closed power flow gearbox test bed, and the fault frequency is successfully extracted, in addition, using maximum correlation kurtosis deconvolution (MCKD) and multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) to compare with CYCBD-SOA, which validated availability of the proposed method.

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COMPOUND FAULT DIAGNOSIS OF BEARINGS USING AN IMPROVED SPECTRAL KURTOSIS BY MAXIMUM CORRELATION KURTOSIS DECONVOLUTION (MCKD).

June-2020 - International Journal of Engineering Sciences & Research Technology ◽

10.29121/ijesrt.v9.i8.2020.8 ◽

2020 ◽

Vol 9 (8) ◽

pp. 66-72

Keyword(s):

Fault Diagnosis ◽

Resonance Frequency ◽

Background Noise ◽

Maximum Correlation ◽

Vibration Signals ◽

Filter Length ◽

Frequency Bands ◽

Noise Interference ◽

Spectral Kurtosis

This paper discusses the use of Maximum Correlation kurtosis deconvolution (MCKD) method as a pre-processor in fast spectral kurtosis (FSK) method in order to find the compound fault characteristics of the bearing, by enhancing the vibration signals. FSK only extracts the resonance bands which have maximum kurtosis value, but sometimes it might possible that faults occur in the resonance bands which has low kurtosis value, also the faulty signals missed due to noise interference. In order to overcome these limitations FSK used with MCKD, MCKD extracts various faults present in different resonance frequency bands; also detect the weak impact component, as MCKD also dealt with strong background noise. By obtaining the MCKD parameters like, filter length & deconvolution period, we can extract the compound fault feature characteristics.

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