Research On Cloud-platform-based Centrifugal Pump Vibration Signal Monitoring And Operation Status Identification

This research was carried out under the funding of the National Natural Science Foundation of China(51879121)and the National Key Research and Development Program of China(2018YFB0606103).Centrifugal pumps are widely used in production and life,but due to their harsh working environment and long operating hours,they are prone to off-duty operation and failures.Therefore,effective condition monitoring and operating condition identification of centrifugal pumps is a prerequisite for ensuring safe production and life.The current centrifugal pump condition monitoring system has high costs,serious geographical restrictions,poor real-time and other shortcomings.This thesis first used the real-time interactivity of Io T technology and the powerful computing power of the cloud platform to independently develop a centrifugal pump vibration signal monitoring system to monitor the vibration signal generated by the centrifugal pump operation in real time,and the designed vibration signal monitoring system was verified through experiments.The experimental results showed that the vibration signal monitoring system designed in this thesis could monitor the vibration signal of centrifugal pump operation in real time and manage the monitored vibration signal data.The second step was to identify the operating conditions of the centrifugal pump based on the vibration signals obtained during the experiments.The vibration signals were extracted using time domain,frequency domain and time-frequency domain feature extraction methods respectively to construct feature vectors.And improved the binary tree support vector machine,used the feature vectors obtained by different feature extraction methods to train the improved binary tree support vector machine model to identify the operating conditions of the centrifugal pump.The research content and innovations of this thesis are as follows:1.Innovated and developed a real-time vibration signal monitoring system based on embedded technology,Internet of Things and cloud platform.The system contained three parts: a vibration signal monitoring hardware terminal composed of a microprocessor STM32F103RCT6,an analogue-to-digital conversion chip ADS127L01,a WIFI module ESP8266 and peripheral circuits;a custom-developed embedded software for the hardware terminal;and an upper computer designed based on the cloud platform.The hardware terminal mainly consisted of a power supply module,a programmable constant current source,a signal conditioning circuit,an analogue-to-digital conversion,a signal transmission module and a main control module;the embedded software implemented the specific functions of the hardware terminal and ensured the normal operation of the terminal;the upper computer on the cloud platform consisted of a TCP/IP server for receiving data,a My SQL database for storing data and a website for human-computer interaction.2.A centrifugal pump test bench was built.Firstly,it was verified through experiments that the self-designed centrifugal pump vibration signal monitoring system could achieve the expected functions of real-time centrifugal pump vibration signal monitoring,vibration data storage,vibration data display and data management.The vibration signal of the centrifugal pump at different speeds of the motor when the outlet valve was opened to 100% and the vibration signal of the centrifugal pump at different outlet valve openings when the motor speed was 6400 r/min were collected using the vibration signal monitoring system designed in this thesis.3.The vibration signal is a non-smooth and complex signal,and in order to analyse and process it,the collected vibration signal was extracted from three perspectives: time domain,frequency domain and time-frequency domain.The time domain feature vectors were constructed using five time domain statistical indicators: mean value,standard deviation,root mean square,kurtosis and skewness.Frequency domain feature vectors were constructed using four frequency domain statistical indicators: centre of gravity frequency,mean square frequency,root mean square frequency and frequency variance.Based on the complementary ensemble empirical mode decomposition,the energy features were used as the time-frequency domain feature vectors.The feature vectors constructed by the three methods were also used to improve the training and prediction of the support vector machine classification algorithm.4.The binary tree support vector machine model has a simple structure and high classification accuracy,but its errors are passed along the binary tree.For this reason,this thesis innovated the use of the k-means clustering algorithm to optimise the structure of the binary tree support vector machine model and constructed a binary tree support vector machine model that was optimal in the sample feature space.The improved binomial tree support vector machine model designed in this paper was used for centrifugal pump condition identification,and the improved model was trained and tested using the feature vectors constructed by the three feature extraction methods,while two other support vector machine multi-classification models were used for comparison.The results showed that among the three feature extraction methods,the feature vector constructed by the time-frequency domain method performs best in the working condition recognition;among the three multi-classification models,the improved algorithm designed in this paper has a simple structure and better comprehensive performance,with an accuracy rate of 92.5% when classifying different speed working conditions and 81.67% when classifying different valve opening working conditions.