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A reciprocating compressor is frequently used in the petroleum and petrochemical industries, its structure is complex, and multiple reasons for failure exist, of which more than 60 % occur in the valve. The ability to quickly and accurately find and diagnose the fault type for reciprocating compressor fault diagnosis is of great significance [1]. Due to the nonlinearity, non-stationarity, and multi-component coupling characteristics of reciprocating compressor valve vibration signals, using the traditional linear theory of signal analysis methods for fault diagnosis has more limitations, and it is difficult to effectively extract fault features. Consequently, these feature extraction methods based on nonlinear dynamic parameters, such as fractal dimension, approximate entropy [2], and sample entropy [3], are gradually being introduced to the field of fault diagnosis. These methods can describe the nonlinear characteristics of vibration signals from different perspectives, but merely reflect the characteristics of vibration signals from a single scale of information. Multiscale entropy (MSE) is used by Costa et al. [4] and [5] and others to propose a nonlinear characteristics analysis method; it is a measurement method of the complexity and the random degree of time series under different scale factors. In the literature [6] to [8], the MSE method is used to quantify and extract the characteristics of vibration signals in the fault diagnosis of rolling bearings and gearboxes. The results show that MSE can describe the complexity of time series and effectively distinguish the types of faults. However, when different time scales are derived by calculating the coarse-grained time series, the length of the time series will be shortened as the scale factor increases, resulting in an imprecise estimation of entropy or undefined entropy when the scale factor is larger, and the endpoint “Flying wing” phenomenon occurs. Therefore, this paper proposes replacing the commonly used meanaverage coarse-grained procedure with a movingFault Diagnosis Method Based on Modified Multiscale Entropy and Global Distance Evaluation for the Valve Fault of a Reciprocating Compressor Li, Y. – Wang, J. – Zhao, H. – Song, M. – Lingfei Ou, L. Ying Li – Jindong Wang* – Haiyang Zhao – Meiping Song – Lingfei Ou Northeast Petroleum University, Mechanical Science and Engineering Institute, China

Fault Diagnosis Method Based on Modified Multiscale Entropy and Global Distance Evaluation for Valve Fault of Reciprocating Compressor