# Status Recognition of Marine Centrifugal Pumps Based on a Stacked Sparse Auto-Encoder
###### Abstract  
Marine centrifugal pumps (MCPs) are widely used in ships, so it is important to identify their status accurately for their maintenance. Due to the influence of load, friction, and other nonlinear factors, the vibration signal of an MCP shows non-linear and non-stationary characteristics, and it is difficult to extract the state characteristics contained in the vibration signal. To solve the difficulty of feature extraction of non-linear non-stationary vibration signals generated by MCPs, a novel MCP frequency domain signal feature extraction method based on a stacked sparse auto-encoder (SSAE) is proposed. The characteristic parameters of MCP frequency domain signals are extracted via the SSAE model for classification training, and different statuses of MCPs are identified. The vibration signals in different MCP statuses were collected for feature extraction and classification training, and the MCP status recognition accuracy based on the time domain feature and fuzzy entropy feature was compared. According to the test data, the accuracy of MCP status recognition based on the time domain feature is 71.2%, the accuracy of MCP status recognition based on the fuzzy entropy feature is 87.7%, and the accuracy of MCP status recognition based on the proposed method is 100%. These results show that the proposed method can accurately identify each status of an MCP under test conditions. marine centrifugal pumps; stacked sparse auto-encoder; vibration signal; status recognition +
Footnote †: journal: Applied  
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between characteristics and state by analyzing the time domain and frequency domain characteristics of the vibration signal of the centrifugal pump. [PERSON] et al. [6] extracted 15 characteristic indexes of the vibration acceleration signal of the centrifugal pump, established the relationship between cavitation and characteristic indexes, and realized cavitation state monitoring of the centrifugal pump according to the characteristics of the vibration signal. By calculating the vibration signal characteristics of an MCP, [PERSON] et al. [7] realized the recognition of different statuses of the MCP by using multi-dimensional characteristic indexes. In the above literature, some scholars developed an online monitoring system for condition monitoring of MCPs, and realized online status identification of MCPs. Some scholars extract different features for state recognition according to the different states of centrifugal pumps, among which, vibration signal feature extraction is the most common. Condition monitoring via vibration signals is an important method for condition assessments of mechanical equipment that depends on feature extraction of the vibration signals of mechanical equipment. Common feature extraction methods mainly extract fault features from the time domain and frequency domain. The fault information of the equipment is hidden in the original time domain vibration data, and the fault information can be mined by calculating the time domain characteristic index [8]. [PERSON] et al. [9] proposed two new time domain indicators based on existing time domain eigenvalues, namely, the TALAF index based on the RMS value and kurtosis coefficient, and the THIKAT index based on kurtosis, RMS, and peak values, and used probabilistic neural networks to classify the health status of rolling bearings. [PERSON] et al. [10] calculated 25 statistical features of vibration signals in the time domain, used the Pearson correlation coefficient to determine the valid features among the 25 statistical features, and proposed a new method for diagnosing the spalling fault of ball bearings. [PERSON] et al. [11] converted their original vibration data into grayscale graphs through time domain calculations for feature extraction, and input them into the proposed LeNet-5-based convolutional neural network to identify the health status of bearings.