# Status Recognition of Marine Centrifugal Pumps Based on a Stacked Sparse Auto-Encoder
###### Abstract
A variety of frequency domain characteristics can be obtained from the frequency domain, such as the mean square frequency, frequency variance, root mean square frequency, frequency amplitude variance, etc. [PERSON] et al. [12] used the envelope spectrum of vibration data as the feature input for their convolutional neural network; [PERSON] et al. [13] used RMS mapping of the fast Fourier transform of vibration data collected by two sensors as the feature input for their convolutional neural network. [PERSON] et al. [14] used fast Fourier transform results of vibration data as a feature input to a multi-layer extreme learning machine. Feature extraction using time domain and frequency domain processing is better in stationary vibration signals than in non-stationary vibration signals. Because an MCP is affected by load, turbulence, friction, and other factors, its vibration signal will show a non-stationary and non-linear situation relative to flow, pressure, and other signals, so the traditional time domain and frequency domain feature extraction is not suitable for MCP vibration signal feature extraction. In order to better extract equipment operation features contained in non-stationary vibration signals, some scholars choose to use the time-frequency analysis method to extract features from original vibration signals. Common methods include short-time Fourier transform (STFT), wavelet transform (WT), and wavelet packet transform (WPT). [PERSON] et al. [15] chose to use the time spectrum diagram of STFT as a feature to achieve fault diagnosis for rolling bearings. [PERSON] et al. [16] chose to use WT as a feature and input it into a diagnostic model combining a convolutional neural network and extreme learning machine for further analysis and processing, so as to realize the diagnosis of rolling bearing health status. [PERSON] et al. [17] used discrete WPT and quantified each sub-band of the signal by defining a new evaluation index defect ratio, and finally input it into a new adaptive deep convolutional neural network to realize the identification of bearing health status. [PERSON] et al. [18] carried out wavelet packet decomposition of vibration signals, selected the optimal frequency band, and calculated their respective Renyi entropy to form feature vectors to complete the performance degradation evaluation of rolling bearings. [PERSON] et al. [19] used the Hilbert-Huang transform (HHT) to extract the characteristic information of the monitoring signal, and obtained the mean amplitude of the inherent mode function and the maximum value point of the marginal spectrum as the characteristic parameters of tool wear, so as to identify a tool's state. [PERSON] et al. [20] simultaneously used three time-frequency analysis methods (including STFT, WT, and HHT) to obtain a time-frequency spectrum diagram to obtain an image representation of the original vibration signal, and used this feature to input a deep convolutional neural network to identify the health state of a bearing, while comparing the diagnostic performance of the three methods. [PERSON] et al. [21] proposed a fault degradation assessment method combining empirical mode decomposition (EMD) feature extraction and K-means clustering, which achieved feature index extraction well. [PERSON] et al. [22] used variational mode decomposition (VMD) to extract component singular values, combined with root mean square values and sample entropy to form feature indicators, and verified the effectiveness of feature extraction through performance degradation assessments. [PERSON] et al. [23] identified the fault type by calculating the fuzzy entropy of vibration signal components of three-phase asynchronous motors, and their results show that the accuracy of fuzzy entropy feature identification is high. Aiming to solve the problems of sensitive parameters and insufficient noise suppression in traditional feature extraction, [PERSON] et al.