Abstract
Aiming at the problem that ICA can only be confined to the condition that the number of observed signals is larger than the number of source signals; a single channel blind source separation method combining EEMD, PCA and RobustICA is proposed. Through the eemd decomposition of the single-channel mechanical vibration observation signal the multidimensional IMF components are obtained, and the principal component analysis (PCA) is performed on the matrix of these IMF components. The number of principal components is determined and a new matrix is generated to satisfy the overdetermined blind source separation conditions, the new matrix input RobustICA, to achieve the separation of the source signal. Finally, the isolated signals are respectively analyzed by the envelope spectrum, the fault frequency is extracted, and the fault type is judged according to the prior knowledge. The experiment was carried out by using the simulation signal and the mechanical signal. The results show that the algorithm is effective and can accurately diagnose the location of mechanical fault.
Highlights
In the traditional signal processing, it is generally necessary to know in advance some of the prior knowledge of the signal or the mathematical model of the signal mixing matrix, and estimate the source signal by filtering or transforming
Aiming at the problem that ICA can only be confined to the condition that the number of observed signals is larger than the number of source signals; a single channel blind source separation method combining EEMD, principal component analysis (PCA) and RobustICA is proposed
The isolated signals are respectively analyzed by the envelope spectrum, the fault frequency is extracted, and the fault type is judged according to the prior knowledge
Summary
In the traditional signal processing, it is generally necessary to know in advance some of the prior knowledge of the signal or the mathematical model of the signal mixing matrix, and estimate the source signal by filtering or transforming. Single channel blind separation is an extreme condition of the underdetermined condition, that is, only through single channel observation signal to estimate the multichannel source signals, in real life, due to environmental or cost constraints, often encountered such extreme problems. In this case, some scholars decompose the signal with wavelet, the resulting signal component is subjected to ICA processing, get the source signals [1]; Some scholars put forward the method of space-time, the method is to delay the mixed signal collected multi-channel signals, and use the independent component analysis algorithm of multiple mixed signal separation, realize the rotating machinery fault diagnosis [2]. The experimental results show that the method can effectively isolate the mechanical fault of each part
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