Online monitoring system for ship engine supply and post-processing system

Abstract

According to the on-line monitoring system for the ship engine supply and post-processing system, the monitoring module and the evaluation and decision-making module are integrated, through intelligent monitoring and fault diagnosis, the running state of an ammonia fuel supply and auxiliary system can be monitored in real time, potential safety hazards can be found in time, the running safety of a ship is guaranteed, and the service life of the ship is prolonged. And the evaluation and decision module can provide suggestions for optimizing the operation scheme by analyzing the operation data, so that sailors are helped to better manage and maintain the system, and the navigation efficiency of the ship is improved. On the whole, the monitoring module achieves comprehensive and real-time monitoring of the system, the assessment and decision module accurately locates fault positions and causes, a targeted maintenance scheme is provided for maintenance personnel, and therefore the maintenance time is shortened, and the maintenance cost is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of ship engines, and in particular to an online monitoring system for a ship engine supply and post-processing system. Background Art

[0002] The stable operation of the marine engine supply and after-treatment system is crucial to the engine, and the use of new fuels such as ammonia fuel and methanol has put forward higher requirements on the reliability of the supply system. Once the supply system or auxiliary equipment fails, such as fuel leakage, overheating, etc., it may cause serious safety accidents. Traditional fault diagnosis methods often rely on manual inspections and experience judgments, which are not only inefficient, but also easy to miss faults. Summary of the invention

[0003] The problem to be solved by the technical solution of the present invention is that fault diagnosis relies on manual inspection and empirical judgment, which is not only inefficient but also prone to missing faults.

[0004] In order to solve the above technical problems, the technical solution of the present invention provides an online monitoring system for a ship engine supply and post-processing system, including a monitoring module and an evaluation and decision module, wherein the evaluation and decision module includes a fault diagnosis module, a status evaluation module, a maintenance decision module, and a spare parts management module;

[0005] The monitoring module monitors key parameters in real time and transmits them to the fault diagnosis module;

[0006] The fault diagnosis module obtains key parameters and determines whether abnormal data is detected according to the fault diagnosis knowledge base. If no abnormal data is detected, a minor fault detection is performed. If no minor fault information is found, a conclusion is drawn that there is no abnormal data and no minor fault.

[0007] If abnormal data is detected, the fault phenomenon is extracted according to the fault diagnosis knowledge base, and the similarity is calculated with the fault data corresponding to the same fault phenomenon in the fault diagnosis knowledge base, and the fault location and fault prediction are performed, and the equipment corresponding to the fault phenomenon, the fault phenomenon and the possible cause of the fault are output as the abnormal data fault conclusion;

[0008] The fault diagnosis module issues corresponding fault alarms based on the conclusions of no abnormal data and no minor faults and the conclusions of abnormal data and faults;

[0009] The status assessment module obtains key parameters, and performs operating status assessment based on the FAHP multi-layer assessment structure, focusing on operating status prediction, degradation feature analysis, main state deviation and fuzzy evaluation structure, obtains status assessment conclusions, and issues corresponding status alarms based on the status assessment conclusions;

[0010] The maintenance decision module analyzes the conclusions of no abnormal data and no minor faults, the conclusions of faults with abnormal data, and the state assessment conclusions according to the maintenance suggestion framework, decision variable set, and distribution probability, and obtains maintenance suggestions after comprehensive probability values;

[0011] The spare parts management module analyzes equipment demand based on maintenance recommendations, obtains the quantity, location, and name of spare parts, and sets maximum and minimum inventory quantities;

[0012] When the number of spare parts does not reach the minimum inventory quantity, perform inventory age analysis and maximum inventory quantity optimization and update spare parts inventory information;

[0013] When the number of spare parts reaches the minimum inventory, determine the purchase quantity by referring to the spare parts manual to understand the detailed information and usage of the spare parts, and update the spare parts inventory information based on the actual number of spare parts after purchase.

[0014] Preferably, the key parameters include pressure, flow rate, and temperature of storage equipment, pump equipment, valve group equipment, and pipeline equipment.

[0015] Preferably, the monitoring module determines whether the key parameter exists in the signal list of the supply and post-processing system, and if so, the monitoring module obtains the key parameter in the supply and post-processing system.

[0016] Preferably, the monitoring module acquires key parameters in the supply and post-processing system through a communication protocol.

[0017] Preferably, if it does not exist, the monitoring module obtains key parameters through sensors and data acquisition systems.

[0018] Preferably, the maintenance decision module includes:

[0019] A maintenance recommendation framework is constructed based on enhanced monitoring, minor repairs and replacement parts maintenance, and a decision variable set is constructed based on the conclusions of no abnormal data and no minor faults, abnormal data fault conclusions and status assessment conclusions, and the corresponding allocation probabilities of the decision variables in the decision variable set are set.

[0020] Preferably, the similarity calculation is a Pearson similarity calculation.

[0021] The technical solution of the present invention proposes an online monitoring system for ship engine supply and post-processing system, which integrates monitoring module, evaluation and decision module. Through intelligent monitoring and fault diagnosis, it can monitor the operating status of ammonia fuel supply and auxiliary system in real time, timely discover potential safety hazards, and ensure the safe operation of the ship. The evaluation and decision module can provide suggestions for optimizing the operation plan through the analysis of operation data, help the crew to better manage and maintain the system, and improve the navigation efficiency of the ship. Overall, the monitoring module realizes comprehensive and real-time monitoring of the system, and the evaluation and decision module accurately locates the fault location and cause, and provides maintenance personnel with targeted maintenance plans, thereby shortening maintenance time and reducing maintenance costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 A module distribution diagram of an online monitoring system for a ship engine supply and post-processing system provided by an embodiment of the present invention;

[0023] Figure 2 A flowchart of the fault diagnosis module provided in the embodiment of the present invention;

[0024] Figure 3 A flowchart of the state assessment module implementation provided by the embodiment of the present invention;

[0025] Figure 4 A technical roadmap for the maintenance decision module provided by the embodiment of the present invention;

[0026] Figure 5 A technical roadmap for the spare parts management module provided for an embodiment of the present invention. DETAILED DESCRIPTION

[0027] The present invention will be further described below in conjunction with specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention. In addition, it should be understood that after reading the content taught by the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms fall within the scope limited by the appended claims of the application equally.

[0028] like Figure 1 As shown, an embodiment of the present invention provides an online monitoring system for a ship engine supply and post-processing system, which mainly consists of a monitoring module and an evaluation and decision-making module.

[0029] Among them, the evaluation and decision-making module is divided into four sub-modules: fault diagnosis module, status evaluation module, maintenance decision module, and spare parts management module.

[0030] The monitoring module provides operation information and data to the evaluation and decision-making module. The evaluation and decision-making module processes and analyzes the data and provides maintenance decisions based on the system status. The specific functional responsibilities and deployment plans are as follows:

[0031] The monitoring module monitors the key parameters of the supply system and post-processing system in real time, such as pressure, flow, temperature, etc., through sensors and data acquisition systems. If the key parameter monitoring points are included in the supply system and post-processing system signal list, the monitoring information can be obtained through the communication protocol. If the monitoring point is not in the control system and security system of the supply system and post-processing system, a data acquisition device is installed for monitoring.

[0032] The evaluation and decision-making module includes four sub-modules: fault diagnosis module, status evaluation module, maintenance decision module, and spare parts management module.

[0033] Among them, the fault diagnosis module is based on the fault diagnosis knowledge base, and takes the monitoring information of storage equipment, pump equipment, valve group equipment and pipeline equipment as input. After fault judgment on the data, the fault phenomenon is extracted and the Pearson similarity calculation is performed with the fault data corresponding to the same fault phenomenon in the knowledge base. The output is the faulty equipment, fault phenomenon and possible cause of the fault. The implementation process is as follows: Figure 2 shown.

[0034] The status assessment module establishes a multi-layer FAHP assessment structure based on data drive. The module input is the system monitoring parameter data, and the module output is the ammonia fuel supply and auxiliary system operation status assessment conclusion. The current system operation status is rated to achieve the flow chart as shown below: Figure 3 shown.

[0035] The maintenance decision module builds a maintenance decision framework, sets basic probability distribution for each decision plan based on the status evaluation and fault diagnosis results, and makes reasonable maintenance suggestions for faulty equipment in the system. The technical route is as follows Figure 4 shown.

[0036] The spare parts management module queries the quantity, location and name of spare parts according to the suggestions of the maintenance decision module, and understands the detailed information and usage of spare parts in conjunction with the spare parts manual. Spare parts management is implemented through the maximum and minimum inventory method. The technical route is as follows: Figure 5 shown.

Claims

1. An online monitoring system for ship engine supply and post-processing system, characterized in that: It includes monitoring module and evaluation and decision module. The evaluation and decision module includes fault diagnosis module, status evaluation module, maintenance decision module and spare parts management module. The monitoring module monitors key parameters in real time and transmits them to the fault diagnosis module; The fault diagnosis module obtains key parameters and determines whether abnormal data is detected according to the fault diagnosis knowledge base. If no abnormal data is detected, a minor fault detection is performed. If no minor fault information is found, a conclusion is drawn that there is no abnormal data and no minor fault. If abnormal data is detected, the fault phenomenon is extracted according to the fault diagnosis knowledge base, and the similarity is calculated with the fault data corresponding to the same fault phenomenon in the fault diagnosis knowledge base, and the fault location and fault prediction are performed, and the equipment corresponding to the fault phenomenon, the fault phenomenon and the possible cause of the fault are output as the abnormal data fault conclusion; The fault diagnosis module issues corresponding fault alarms based on the conclusions of no abnormal data and no minor faults and the conclusions of abnormal data and faults; The status assessment module obtains key parameters, and performs operating status assessment based on the FAHP multi-layer assessment structure, focusing on operating status prediction, degradation feature analysis, main state deviation and fuzzy evaluation structure, obtains status assessment conclusions, and issues corresponding status alarms based on the status assessment conclusions; The maintenance decision module analyzes the conclusions of no abnormal data and no minor faults, the conclusions of faults with abnormal data, and the state assessment conclusions according to the maintenance suggestion framework, decision variable set, and distribution probability, and obtains maintenance suggestions after comprehensive probability values; The spare parts management module analyzes equipment demand based on maintenance recommendations, obtains the quantity, location, and name of spare parts, and sets maximum and minimum inventory quantities; When the number of spare parts does not reach the minimum inventory quantity, perform inventory age analysis and maximum inventory quantity optimization and update spare parts inventory information; When the number of spare parts reaches the minimum inventory, determine the purchase quantity by referring to the spare parts manual to understand the detailed information and usage of the spare parts, and update the spare parts inventory information based on the actual number of spare parts after purchase.

2. An online monitoring system for ship engine supply and post-processing system according to claim 1, characterized in that: The key parameters include pressure, flow and temperature of storage equipment, pump equipment, valve group equipment and pipeline equipment.

3. An online monitoring system for ship engine supply and post-processing system according to claim 1, characterized in that: The monitoring module determines whether the key parameter exists in the signal list of the supply and post-processing system. If so, the monitoring module obtains the key parameter in the supply and post-processing system.

4. An online monitoring system for ship engine supply and post-processing system according to claim 3, characterized in that: The monitoring module acquires key parameters in the supply and post-processing system through a communication protocol.

5. An online monitoring system for ship engine supply and post-processing system according to claim 3, characterized in that: If not present, the monitoring module obtains key parameters through sensors and data acquisition systems.

6. An online monitoring system for ship engine supply and post-processing system according to claim 1, characterized in that: The maintenance decision module includes: A maintenance recommendation framework is constructed based on enhanced monitoring, minor repairs and replacement parts maintenance, and a decision variable set is constructed based on the conclusions of no abnormal data and no minor faults, abnormal data fault conclusions and status assessment conclusions, and the corresponding allocation probabilities of the decision variables in the decision variable set are set.

7. An online monitoring system for ship engine supply and post-processing system according to claim 1, characterized in that: The similarity calculation is Pearson similarity calculation.

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