Abstract
The multi-source impact signal of reciprocating compressor often represents nonlinear and non-stationary. For this reason, the fault features of the signal are difficult to quantitatively describe using conventional signal processing methods. In this paper, a novel adaptive waveform decomposition method was proposed to convert the strong non-stationary multi-component signal into stationary single-component signal. Subsequently, the signal was denoised with threshold-based mutual information to protect from being interfered by the noise. Finally, to measure the nonlinearity of reciprocating compressor signals in four states (normal valve sheets, gap valve sheets, fractured valve sheets, and bad spring), the normalized Lempel-Ziv complexity indexes were employed. The results reveal that the proposed method is capable of extracting the different faults states of reciprocating compressor accurately, which supplies a measure of fault diagnosis and maintenance strategy for the reciprocating compressor.
Highlights
Reciprocating compressors are vital to the oil and chemical production
It is generally known that in the field of mechanical fault diagnosis, the feature extraction method of vibration signal is continuously optimized with the advancement of signal processing technology
The rest of the paper is structured as follows: Section II introduces the architecture of the proposed solution; Section III presents the adaptive waveform decomposition (AWD) algorithm process and its application in simulation signal and experimental data of reciprocating compressor; Section IV discuss the characteristics of Lempel-Ziv complexity (LZC) method on measure nonlinear time series; Section V extracts the fault feature of reciprocating compressor gas valve based on the integration of AWD and LZC method; and Section VI concludes the paper
Summary
Reciprocating compressors are vital to the oil and chemical production. due to its more complex structure and motion, and the larger number of wearing parts, it is more difficult to diagnose than the rotating machines [1]. Though many related achievements have been obtained in the field, they have the following problems: 1) the magnitude of the feature amplitudes under different states are largely different, the real physical meaning is unclear, and the comparability is poor; 2) the feature amplitude under the same state may be significantly fluctuated with different samples, so the results exhibit poor repeatability and stability; 3) there may be different effects of noise interference on the accuracy of the results Those problems are closely associated with the nonlinear dynamic characteristics of reciprocation compressor. The rest of the paper is structured as follows: Section II introduces the architecture of the proposed solution; Section III presents the AWD algorithm process and its application in simulation signal and experimental data of reciprocating compressor; Section IV discuss the characteristics of LZC method on measure nonlinear time series; Section V extracts the fault feature of reciprocating compressor gas valve based on the integration of AWD and LZC method; and Section VI concludes the paper
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