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Published April 2, 2018 | Version v1
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Data from: Improving the signal subtle feature extraction performance based on dual improved fractal box dimension eigenvectors

Authors/Creators

  • 1. State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System (CEMEE), Luoyang, Henan 471003, People's Republic of China*
  • 2. Shanghai Dianji University
  • 3. Shanghai University

Description

Aiming at the limitation of traditional fractal box-counting dimension algorithm in subtle feature extraction of radiation source signals, a dual improved generalized fractal box-counting dimension eigenvector algorithm was proposed in the paper. Firstly, the radiation source signal was preprocessed, and Hilbert transform was performed to obtain the instantaneous amplitude of the signal. Then, the improved fractal box-counting dimension of signal instantaneous amplitude was extracted as the first eigenvector. At the same time, the improved fractal box-counting dimension of the signal without Hilbert transform was extracted as the second eigenvector. Finally, the dual improved fractal box-counting dimension eigenvectors form the multi-dimensional eigenvectors as signal subtle features, used for radiation source signal recognition by the gray relation algoritm. The experimental results show that compared with the traditional fractal box-counting dimension algorithm and the single improved fractal box-counting dimension algorithm, the proposed dual improved fractal box-counting dimension algorithm can better extract the signal subtle distribution characteristics under different reconstruction phase space, and has a better recognition effect with good real-time performance.

Notes

the radiation source signals for testing
In the paper, the radiation source signals for testing are from Case Western Reserve University Bearing Data Center. The database has been a standard dataset for testing the effectiveness of feature extraction algorithm and pattern recognition algorithm. Besides, the sampled signals of the database are full of random mechanical noise, which makes the test closer to the real situation. The motor drive end rotor is supported by a test bearing, where a single point of failure is set through discharge machining. The radiation source signals of bearing vibration data used for analysis are obtained under the motor speed of 1797 r/min and load of 0 horsepower. An accelerometer is installed on the motor drive end housing with a bandwidth of up to 5000 Hz, and the vibration data for the test bearing under different fault patterns is collected by a recorder as the radiation source signals, in which the sampling frequency is 12 kHz. The fault types contain outer race fault, the inner race fault, and the ball fault, and the fault diameters, i.e., fault severities, contain 28 mils, 21 mils, 14 mils and 7 mils. Totally 11 types of radiation source signals of bearing vibration data considering different fault categories and fault severities are analyzed, as seen in Table 1. Each data sample is made up of 2048 time series points. For those 550 data samples, each of those 550 data sample are different with different random mechanical noise. Among them, 110 data samples are chosen randomly for the establishment of the knowledge base, with the rest 440 data samples taken as testing data samples.
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Is cited by
10.1098/rsos.180087 (DOI)