A data analysis-based ship equipment monitoring and early warning system

Abstract

This invention relates to the field of ship equipment monitoring and early warning technology, specifically disclosing a ship equipment monitoring and early warning system based on data analysis. The system includes: a data acquisition module, which uses various sensors installed on the oil separator to collect various status parameters in real time during the operation of the oil separator; and an analysis and monitoring module, which analyzes the various status parameters collected in real time during the operation of the oil separator and compares the abnormal risk coefficient curve of the oil separator during the operation of the oil separator with the preset allowable error coefficient curve within a preset time period. This invention obtains the abnormal risk coefficient curve of the oil separator during the operation of the oil separator by analyzing the various status parameters collected in real time. By comparing the abnormal risk coefficient curve with the preset allowable error coefficient curve to obtain the number of abnormalities, time, and average abnormal risk reference of the oil separator, the maintenance and repair time cycle of the oil separator is adjusted.

Description

Technical Field

[0001] This invention relates to the field of ship equipment monitoring and early warning technology, specifically a ship equipment monitoring and early warning system based on data analysis. Background Technology

[0002] Oil separators are one of the important pieces of equipment on ships. They are relatively complex in structure and operate at high speeds. They are the main equipment that requires enhanced monitoring and maintenance in marine engineering management. Their function is to separate different components in fuel oil according to their boiling point range. The basic principle is to use the tiny gaps between the separating discs and the centrifugal force of the high-speed rotation of the oil separator to separate various components in crude oil for use in different parts of the ship.

[0003] In existing technologies, the monitoring of marine oil separator equipment during use mainly relies on sensors to monitor relevant status parameters during operation in real time and to monitor the cumulative usage time since the last maintenance. When the real-time monitored status parameters exceed a preset warning threshold or the cumulative usage time within the maintenance cycle reaches a preset fixed maintenance time cycle, an early warning is issued to facilitate maintenance by management personnel. However, this monitoring and early warning method uses a preset fixed maintenance time cycle and cannot intelligently adjust according to the status of the oil separator equipment during operation, which may lead to an increase in malfunctions and maintenance costs during the use of the oil separator. Summary of the Invention

[0004] The purpose of this invention is to provide a data analysis-based ship equipment monitoring and early warning system to solve the following technical problems:

[0005] How to intelligently adjust the maintenance cycle of the oil separator equipment.

[0006] The objective of this invention can be achieved through the following technical solutions:

[0007] A data analysis-based ship equipment monitoring and early warning system, the system comprising:

[0008] The data acquisition module consists of various sensors installed on the oil separator, used to collect various status parameters of the oil separator in real time during operation;

[0009] The analysis and monitoring module is used to analyze various status parameters collected in real time during the operation of the oil separator, obtain the abnormal risk coefficient curve of the oil separator during operation, compare the abnormal risk coefficient curve of the oil separator during the preset time period with the preset allowable error coefficient curve, and obtain the number of abnormalities, time and reference average value of abnormal risks during the operation of the oil separator; by analyzing the number of abnormalities, time, reference average value of abnormal risks and cumulative usage time, the maintenance and repair time cycle of the oil separator is adjusted, and an early warning maintenance signal is generated when the maintenance and repair time cycle is reached;

[0010] The early warning module is used to execute early warning maintenance signals.

[0011] In one embodiment, the process of obtaining the anomaly risk coefficient analysis includes:

[0012]

[0013] The abnormal risk coefficient curve C of the oil separator during operation is obtained through analysis and calculation using the above formula. risk (t);

[0014] Where i∈[1,N], and N is the number of state parameters monitored during the operation of the oil separator. The curve of the i-th state parameter during the operation of the oil separator. Let τ be the standard curve of the i-th state parameter during the operation of the oil separator. i is the weight coefficient corresponding to the i-th state parameter.

[0015] In one embodiment, the process of obtaining the number of anomalies, their duration, and the anomaly risk during the operation of the oil separator by means of average analysis includes:

[0016] Within a preset time period [t1, t2], the abnormal risk coefficient curve C risk (t) and the preset allowable error coefficient curve C sth (t) is compared, and curve segments with abnormal risk coefficient values ​​greater than or equal to the allowable error coefficient are extracted. Statistical analysis is performed on all curve segments extracted within the preset time period [t1, t2] to obtain the number of all curve segments extracted within the preset time period [t1, t2], the cumulative time, and the average height of the area formed between the two curves above the allowable error coefficient curve. Among these, the number of curve segments is the number of abnormalities M during the operation of the oil separator within the preset time period [t1, t2], the cumulative time is the abnormal time T during the operation of the oil separator, and the area between the two curves is the reference mean value V of the abnormal risk during the operation of the oil separator.

[0017] Furthermore, the process of obtaining the abnormal risk reference mean analysis includes:

[0018] Through formula The average reference value V for abnormal risk was obtained through analysis and calculation.

[0019] Where Δt = t2 - t1.

[0020] Furthermore, the process of obtaining the preset allowable error coefficient curve through analysis includes:

[0021]

[0022] The preset allowable error coefficient curve C is obtained through the above formula analysis and calculation. sth (t);

[0023] Among them, t sum (t) represents the cumulative usage time of the oil separator at time t, A(t) represents the cumulative number of repairs to the oil separator at time t, σ represents the coefficient for converting a single repair of the oil separator into usage time, a represents a preset constant, and a∈(0,1), and δ represents the limit standard value of the allowable error coefficient of the oil separator.

[0024] In one embodiment, the process of adjusting the maintenance and repair time cycle of the oil separator includes:

[0025]

[0026] The maintenance time cycle P after the oil separator adjustment is obtained through the above formula analysis and calculation. s (t);

[0027] Among them, P sth M(t) represents the standard maintenance time cycle of the oil separator, M(t) represents the number of anomalies analyzed at time point t within the maintenance time cycle, T(t) represents the cumulative anomaly time analyzed at time point t within the maintenance time cycle, and V(t) represents the average anomaly risk reference value analyzed at time point t within the maintenance time cycle.

[0028] Furthermore, the process of generating the early warning maintenance signal includes:

[0029] When the cumulative usage time of the oil separator within the current cycle is greater than or equal to the adjusted maintenance time cycle of the oil separator, a warning maintenance signal is generated.

[0030] In one embodiment, the system further includes:

[0031] The data storage module is used to record and store the data collected during the use of the oil separator, the cumulative usage time, the cumulative number of maintenance operations, and the average number of abnormalities, abnormal times, and abnormal risks within a preset time period.

[0032] The visualization display terminal is used to visually display the various status parameters and data analysis results of the oil separator.

[0033] The beneficial effects of this invention are:

[0034] (1) The various sensors installed on the oil separator of the present invention collect various status parameters in real time during the operation of the oil separator. The analysis and monitoring module is used to analyze the various status parameters collected in real time during the operation of the oil separator and obtain the abnormal risk coefficient curve of the oil separator during the operation process.

[0035] (2) This invention compares the abnormal risk coefficient curve with the preset allowable error coefficient curve to analyze and obtain the number of abnormalities, time and average abnormal risk reference of the oil separator within a preset time period. Then, it adds the cumulative usage time of the oil separator for comprehensive analysis and adjusts the maintenance and repair time cycle of the oil separator. When the cumulative usage time of the oil separator after the last maintenance reaches the adjusted maintenance and repair time cycle, an early warning maintenance signal is generated, and the early warning module executes the signal. Attached Figure Description

[0036] The invention will now be further described with reference to the accompanying drawings.

[0037] Figure 1 This is a schematic diagram of a ship equipment monitoring and early warning system based on data analysis proposed in this invention. Detailed Implementation

[0038] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0039] Please see Figure 1 As shown, in one embodiment, a ship equipment monitoring and early warning system based on data analysis is provided, the system comprising:

[0040] The data acquisition module consists of various sensors installed on the oil separator, used to collect various status parameters of the oil separator in real time during operation;

[0041] The analysis and monitoring module is used to analyze various status parameters collected in real time during the operation of the oil separator, obtain the abnormal risk coefficient curve of the oil separator during operation, compare the abnormal risk coefficient curve of the oil separator during the preset time period with the preset allowable error coefficient curve, and obtain the number of abnormalities, time and reference average value of abnormal risks during the operation of the oil separator; by analyzing the number of abnormalities, time, reference average value of abnormal risks and cumulative usage time, the maintenance and repair time cycle of the oil separator is adjusted, and an early warning maintenance signal is generated when the maintenance and repair time cycle is reached;

[0042] The early warning module is used to execute early warning maintenance signals.

[0043] Through the above technical solution, this embodiment provides a ship equipment monitoring and early warning system based on data analysis. Specifically, various sensors installed on the oil separator can collect various status parameters during the operation of the oil separator in real time. These status parameters include, but are not limited to, the inlet and outlet flow rates of the oil separator, internal pressure, internal temperature, rotational speed, and the quality of the separated oil. Then, the analysis and monitoring module analyzes the various status parameters collected in real time during the operation of the oil separator to obtain the abnormal risk coefficient curve of the oil separator during operation. This curve is then compared with the preset allowable error coefficient curve to analyze and obtain the number of abnormalities, time, and average abnormal risk reference of the oil separator within a preset time period. Finally, the cumulative usage time of the oil separator is added for comprehensive analysis to adjust the maintenance and repair time cycle of the oil separator. When the cumulative usage time of the oil separator since the last maintenance reaches the adjusted maintenance and repair time cycle, an early warning maintenance signal is generated. The early warning module executes the signal to notify the management personnel that the oil separator needs maintenance and repair.

[0044] In one embodiment, the process of obtaining the anomaly risk coefficient analysis includes:

[0045]

[0046] The abnormal risk coefficient curve C of the oil separator during operation is obtained through analysis and calculation using the above formula. risk (t);

[0047] Where i∈[1,N], and N is the number of state parameters monitored during the operation of the oil separator. The curve of the i-th state parameter during the operation of the oil separator. Let τ be the standard curve of the i-th state parameter during the operation of the oil separator. i is the weight coefficient corresponding to the i-th state parameter.

[0048] Through the above technical solution, this embodiment provides a method for obtaining the abnormal risk coefficient during the operation of an oil separator. Specifically, it can be achieved through the formula... The abnormal risk coefficient curve C during the operation of the oil separator was obtained through calculation and analysis. risk (t), where there are N state parameters monitored during the oil separator's operation, i∈[1,N]. The curve of the i-th state parameter during the operation of the oil separator can be obtained by a linear regression algorithm based on the real-time monitoring values ​​of various sensors installed on the oil separator. The standard curve for the i-th state parameter during the operation of the oil separator can be obtained through experimental analysis of monitoring various parameters during the operation of the oil separator. i The weighting coefficient corresponding to the i-th state parameter can be obtained by experimental fitting based on the analysis of the abnormal risk coefficient of the oil separator.

[0049] In one embodiment, the process of obtaining the number of anomalies, their duration, and the anomaly risk during the operation of the oil separator by means of average analysis includes:

[0050] Within a preset time period [t1, t2], the abnormal risk coefficient curve C risk (t) and the preset allowable error coefficient curve C sth (t) is compared, and curve segments with abnormal risk coefficient values ​​greater than or equal to the allowable error coefficient are extracted. Statistical analysis is performed on all curve segments extracted within the preset time period [t1, t2] to obtain the number of all curve segments extracted within the preset time period [t1, t2], the cumulative time, and the average height of the area formed between the two curves above the allowable error coefficient curve. Among these, the number of curve segments is the number of abnormalities M during the operation of the oil separator within the preset time period [t1, t2], the cumulative time is the abnormal time T during the operation of the oil separator, and the area between the two curves is the reference mean value V of the abnormal risk during the operation of the oil separator.

[0051] The process of obtaining the abnormal risk reference mean analysis includes:

[0052] Through formula The average reference value V for abnormal risk was obtained through analysis and calculation.

[0053] Where Δt = t2 - t1.

[0054] Through the above technical solution, this embodiment provides a method for analyzing and obtaining the number of anomalies, anomaly time, and anomaly risk reference mean during the operation of an oil separator. Within a preset time period, the anomaly risk coefficient curve is compared with a preset allowable error coefficient curve. The curve segments of the anomaly risk coefficient curve where the anomaly risk coefficient value is greater than or equal to the allowable error coefficient are extracted. Statistical analysis is performed on the extracted curve segments. The number of extracted curve segments represents the number of anomalies M during the operation of the oil separator within the preset time period. The cumulative length of the extracted curve segments along the X-axis represents the cumulative anomaly time T during the operation of the oil separator within the preset time period. The average height of the area formed between the extracted curve segments and the preset allowable error coefficient curve represents the anomaly risk reference mean V during the operation of the oil separator within the preset time period. Specifically, this can be obtained through the formula... Analysis and calculation yielded Δt = t2 - t1.

[0055] In one embodiment, the process of obtaining the preset allowable error coefficient curve analysis includes:

[0056]

[0057] The preset allowable error coefficient curve C is obtained through the above formula analysis and calculation. sth (t);

[0058] Among them, t sum (t) represents the cumulative usage time of the oil separator at time t, A(t) represents the cumulative number of repairs to the oil separator at time t, σ represents the coefficient for converting a single repair of the oil separator into usage time, a represents a preset constant, and a∈(0,1), and δ represents the limit standard value of the allowable error coefficient of the oil separator.

[0059] Through the above technical solution, this embodiment provides a method for analyzing and obtaining a preset allowable error coefficient curve. Specifically, it uses the formula... Analysis and calculation yielded the preset allowable error coefficient curve C sth (t), where t sumA(t) represents the cumulative usage time of the oil separator at time t, which can be obtained by accumulating the historical usage time of the oil separator. A(t) represents the cumulative number of repairs to the oil separator at time t, which can be obtained by statistical analysis of the repair records during the use of the oil separator. σ is the coefficient for converting a single repair of the oil separator into usage time, which can be obtained based on experimental analysis of the impact of a single repair on the service life of the oil separator. a is a preset constant, and a∈(0,1), which can be obtained based on experimental analysis of the impact of the cumulative usage time of the oil separator on its allowable error. δ is the limit standard value of the allowable error coefficient of the oil separator, which can be selected based on experimental analysis of the allowable error of the abnormal risk coefficient during the use of the oil separator. Regardless of how long the oil separator has been used or how many times it has been repaired, if the abnormal risk coefficient during its use is greater than this value, it is determined that the state of the oil separator is abnormal during operation.

[0060] In one embodiment, the process of adjusting the maintenance and repair time cycle of the oil separator includes:

[0061]

[0062] The maintenance time cycle P after the oil separator adjustment is obtained through the above formula analysis and calculation. s (t);

[0063] Among them, P sth M(t) represents the standard maintenance time cycle of the oil separator, M(t) represents the number of anomalies analyzed at time point t within the maintenance time cycle, T(t) represents the cumulative anomaly time analyzed at time point t within the maintenance time cycle, and V(t) represents the average anomaly risk reference value analyzed at time point t within the maintenance time cycle.

[0064] The process of generating the early warning maintenance signal includes:

[0065] When the cumulative usage time of the oil separator within the current cycle is greater than or equal to the adjusted maintenance time cycle of the oil separator, a warning maintenance signal is generated.

[0066] Through the above technical solution, this embodiment provides a method for generating early warning maintenance signals. Specifically, it first uses a formula... The maintenance time cycle P after the oil separator adjustment was obtained through analysis and calculation. s (t), where P sthThe standard maintenance time cycle for the oil separator can be obtained by analyzing the oil separator's maintenance manual. M(t) represents the number of anomalies analyzed at time point t within this maintenance time cycle, T(t) represents the cumulative anomaly time analyzed at time point t within this maintenance time cycle, and V(t) represents the average anomaly risk reference value analyzed at time point t within this maintenance time cycle. M(t), T(t), and V(t) are all time intervals from the initial time point of this maintenance time cycle to the latest time point t within a preset time period. Specifically, they can be obtained by analyzing the number of anomalies, anomaly time, and average anomaly risk reference value within the preset time period during the oil separator's operation.

[0067] In one embodiment, the system further includes:

[0068] The data storage module is used to record and store the data collected during the use of the oil separator, the cumulative usage time, the cumulative number of maintenance operations, and the average number of abnormalities, abnormal times, and abnormal risks within a preset time period.

[0069] The visualization display terminal is used to visually display the various status parameters and data analysis results of the oil separator.

[0070] Through the above technical solution, this embodiment provides a method for facilitating data analysis and visualization during the operation of a separator. The data storage module records and stores various status parameters collected by the data acquisition module during the separator's operation, the cumulative usage time and cumulative maintenance counts from the separator's historical data, and the average number of anomalies, anomaly times, and anomaly risk references within a preset time period obtained from the analysis and monitoring module. This facilitates analysis and data retrieval by the analysis and monitoring module. The visualization display terminal can visually present the various status parameters and data analysis results of the separator to management personnel, enabling them to understand the separator's operating status and any anomalies, and to control and adjust the separator's operation as needed.

[0071] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the claims of this invention should still fall within the patent coverage of this invention.

Claims

1. A ship equipment monitoring and early warning system based on data analysis, characterized in that, The system includes: The data acquisition module consists of various sensors installed on the oil separator, used to collect various status parameters of the oil separator in real time during operation; The analysis and monitoring module is used to analyze various status parameters collected in real time during the operation of the oil separator, obtain the abnormal risk coefficient curve of the oil separator during operation, compare the abnormal risk coefficient curve of the oil separator during the preset time period with the preset allowable error coefficient curve, and obtain the number of abnormalities, time and reference average value of abnormal risks during the operation of the oil separator; by analyzing the number of abnormalities, time, reference average value of abnormal risks and cumulative usage time, the maintenance and repair time cycle of the oil separator is adjusted, and an early warning maintenance signal is generated when the maintenance and repair time cycle is reached; The early warning module is used to execute early warning maintenance signals; The process of obtaining the abnormal risk coefficient curve analysis includes: ， The abnormal risk coefficient curve of the oil separator during operation was obtained through analysis and calculation using the above formula. ; in, N represents the number of status parameters monitored during the operation of the oil separator. The curve of the i-th state parameter during the operation of the oil separator. This is the standard curve for the i-th state parameter during the operation of the oil separator. The weight coefficient corresponding to the i-th state parameter; The process of obtaining the number of anomalies, their duration, and the risk of anomalies during the operation of the oil separator by means of average analysis includes: During the preset time period Inside, the abnormal risk coefficient curve Curve with preset allowable error coefficient A comparison is performed, and curve segments with an anomaly risk coefficient value greater than or equal to the allowable error coefficient are extracted for a preset time period. Statistical analysis was performed on all the curve segments extracted within the preset time period to obtain the results. The number of all curve segments captured within the time frame, the cumulative time, and the average height of the area formed between the two curves above the allowable error coefficient curve, where the number of curve segments represents the preset time period. The number of abnormalities M during the operation of the oil separator is the cumulative time, which is the abnormal time T during the operation of the oil separator. The area between the two curves is the reference mean value V of the abnormal risk during the operation of the oil separator.

2. The ship equipment monitoring and early warning system based on data analysis according to claim 1, characterized in that, The process of obtaining the abnormal risk reference mean analysis includes: Through formula The average reference value V for abnormal risk was obtained through analysis and calculation. in, .

3. The ship equipment monitoring and early warning system based on data analysis according to claim 2, characterized in that, The process of obtaining the preset allowable error coefficient curve analysis includes: ， The preset allowable error coefficient curve is obtained through analysis and calculation using the above formula. ; in, Let t be the cumulative usage time of the oil separator. The cumulative number of maintenance visits to the oil separator at time point t. The coefficient for converting a single maintenance of the oil separator into usage time. It is a preset constant, and , This represents the limit standard value of the permissible error coefficient for the oil separator.

4. The ship equipment monitoring and early warning system based on data analysis according to claim 3, characterized in that, The process of adjusting the maintenance and repair cycle of the oil separator includes: ， The maintenance time cycle after adjustment of the oil separator is obtained through the above formula analysis and calculation. ; in, This is the standard maintenance interval for the oil separator. The number of anomalies was analyzed at time point t within this maintenance period. To obtain the cumulative abnormal time at time point t within this maintenance period, The average reference value of abnormal risk is obtained by analyzing time point t within the maintenance period.

5. A ship equipment monitoring and early warning system based on data analysis according to claim 4, characterized in that, The process of generating the early warning maintenance signal includes: When the cumulative usage time of the oil separator within the current cycle is greater than or equal to the adjusted maintenance time cycle of the oil separator, a warning maintenance signal is generated.

6. The ship equipment monitoring and early warning system based on data analysis according to claim 1, characterized in that, The system also includes: The data storage module is used to record and store the data collected during the use of the oil separator, the cumulative usage time, the cumulative number of maintenance operations, and the average number of abnormalities, abnormal times, and abnormal risks within a preset time period. The visualization display terminal is used to visually display the various status parameters and data analysis results of the oil separator.

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