CN113311280B - A health grading monitoring device for complex electromechanical systems - Google Patents
A health grading monitoring device for complex electromechanical systems Download PDFInfo
- Publication number
- CN113311280B CN113311280B CN202110869129.6A CN202110869129A CN113311280B CN 113311280 B CN113311280 B CN 113311280B CN 202110869129 A CN202110869129 A CN 202110869129A CN 113311280 B CN113311280 B CN 113311280B
- Authority
- CN
- China
- Prior art keywords
- data
- health
- layer
- equipment
- network layer
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 230000036541 health Effects 0.000 title claims abstract description 108
- 238000012806 monitoring device Methods 0.000 title claims abstract description 11
- 238000012544 monitoring process Methods 0.000 claims abstract description 36
- 238000003745 diagnosis Methods 0.000 claims abstract description 29
- 238000004891 communication Methods 0.000 claims abstract description 27
- 238000013523 data management Methods 0.000 claims abstract description 21
- 238000004458 analytical method Methods 0.000 claims description 16
- 238000000034 method Methods 0.000 claims description 16
- 230000008569 process Effects 0.000 claims description 11
- 230000001052 transient effect Effects 0.000 claims description 11
- 230000007246 mechanism Effects 0.000 claims description 5
- 230000002159 abnormal effect Effects 0.000 claims description 3
- 230000005856 abnormality Effects 0.000 claims description 3
- 230000008859 change Effects 0.000 claims description 3
- 230000001960 triggered effect Effects 0.000 claims description 2
- 230000003862 health status Effects 0.000 claims 1
- 230000007774 longterm Effects 0.000 claims 1
- 238000013024 troubleshooting Methods 0.000 claims 1
- 238000012423 maintenance Methods 0.000 abstract description 4
- 239000010410 layer Substances 0.000 description 115
- 238000012545 processing Methods 0.000 description 5
- 230000008878 coupling Effects 0.000 description 2
- 238000010168 coupling process Methods 0.000 description 2
- 238000005859 coupling reaction Methods 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 238000001514 detection method Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000005611 electricity Effects 0.000 description 2
- 238000002955 isolation Methods 0.000 description 2
- 230000006855 networking Effects 0.000 description 2
- 238000011084 recovery Methods 0.000 description 2
- 230000004044 response Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 239000000306 component Substances 0.000 description 1
- 239000008358 core component Substances 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 230000005389 magnetism Effects 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000000926 separation method Methods 0.000 description 1
- 239000002344 surface layer Substances 0.000 description 1
- 238000012549 training Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M99/00—Subject matter not provided for in other groups of this subclass
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Computing Systems (AREA)
- General Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Testing And Monitoring For Control Systems (AREA)
Abstract
The invention provides a health grading monitoring device of a complex electromechanical system, which comprises an equipment layer, a communication network layer, a data management layer and a data application layer, wherein the equipment layer is used for monitoring the health of the complex electromechanical system; the communication network layer comprises a health network layer and a control network layer which are parallel and independently operated; the equipment layer is used for acquiring the running state information of the ship equipment, uploading health data in the running state information of the ship equipment to the health network layer, and uploading event data in the running state information of the ship equipment to the control network layer; the health network layer decodes the received health data and then sends the decoded health data to the data application layer; the control network layer decodes the received event data and then sends the decoded event data to the data application layer; the data application layer is used for carrying out fault diagnosis according to the received decoded health data and event data; the data management layer is used for receiving, storing and managing historical information. The invention has the characteristics of real time, reliability, easy maintenance and standardization.
Description
Technical Field
The invention belongs to the technical field of health state detection of complex electromechanical systems, and particularly relates to a health grading monitoring device for a complex electromechanical system.
Background
The electric ship is a new energy ship adopting an electric power system as a power source, and is one of the trends of intelligent and green development of the ship industry. The complex electromechanical equipment of the ship usually relates to mechanical, electrical and hydraulic equipment, consists of a plurality of parts, is compact in spatial distribution, multiple in structural hierarchy and strong in coupling, and mainly adopts a long-time operation mode, and needs to operate at an overspeed for a short time under special working conditions. The multi-working-condition use of the equipment also puts higher requirements on monitoring real-time performance, response capability and the like; the ship equipment has numerous heterogeneous devices, high automation degree, huge data information amount and more related communication protocols, and the interaction and sharing of internal and external real-time data and information are required to be realized in a unified standard mode. Due to the practical objective requirements, the existing monitoring system cannot meet the complex working conditions and special requirements for instantaneity, response capability, interconnection, reliability, maintainability and the like.
Disclosure of Invention
The invention aims to solve the defects in the background technology, provides a health grading monitoring device for a complex electromechanical system, meets the requirement of multi-working-condition operation of an electric ship, belongs to a standardized universal module for state detection and fault diagnosis, and has the characteristics of real time, reliability, easy maintenance and standardization.
The technical scheme adopted by the invention is as follows: a health grading monitoring device for a complex electromechanical system comprises an equipment layer, a communication network layer, a data management layer and a data application layer. Wherein, the communication network layer comprises a health network layer and a control network layer which are parallel and independently operated; the equipment layer is arranged corresponding to each ship equipment and used for correspondingly acquiring the running state information of the ship equipment, uploading the health data in the running state information of the ship equipment to the health network layer and uploading event data in the running state information of the ship equipment to the control network layer; the health network layer decodes the received health data and then sends the decoded health data to the data application layer; the control network layer decodes the received event data and then sends the decoded event data to the data application layer; the data application layer is used for carrying out fault diagnosis according to the received decoded health data and event data, generating control information and feeding the control information back to the equipment layer, and is used for controlling the running state of the ship equipment; the data management layer is used for receiving, storing and managing health data and event data from the communication network layer and historical information of fault diagnosis results from the data application layer; the health data refers to state data periodically uploaded by each ship device and is used for reflecting the health state of each device after the system is powered on; the event data refers to transient data uploaded by each ship device in real time in the working process and is used for reflecting the continuous change process of the working state of the system.
In the technical scheme, the equipment layer records and collects state data in a snapshot mode according to a certain frequency in the long-time running process of each ship equipment, and triggers a transient wave recording data recording mechanism when the state is abnormal, and stores the data before and after the abnormality in a wave recording mode to generate wave recording data; and meanwhile, state data which can cause serious faults of ship equipment is analyzed and judged, a judgment result of 'whether the system is allowed to work continuously' is generated, and the state data, the wave recording data and comprehensive information generated by the judgment result are used as health data. And the equipment layer controls the running state of the ship equipment corresponding to the equipment layer according to the judgment result.
In the technical scheme, the equipment layer records the wave recording data of each ship equipment in real time in a shooting mode in the transient working process of each ship equipment, the wave recording data are used for describing the working and running characteristics of each ship equipment and judging the control state of the equipment layer to generate a comprehensive signal, and the comprehensive information generated by the wave recording data and the comprehensive signal is used as event data. The equipment layer controls the running state of the ship equipment corresponding to the equipment layer based on the content of the comprehensive signal.
In the above technical solution, the health network layer includes a plurality of different protocol decoders; the health network layer analyzes the health data from different ship equipment through corresponding protocol decoders and sends the decoded health data to the data application layer according to a uniform communication protocol.
In the technical scheme, the control network layer comprises an RSLinx server; the RSLinx server is communicated with the equipment layer through an EIP (enhanced information platform) protocol and used for acquiring event data; the RSLinx server sends the decoded event data to the data application layer based on the EIP protocol.
In the technical scheme, the data application layer comprises a remote monitoring unit, a fault analysis unit and an expert system knowledge base; the remote monitoring unit is used for receiving the health data from the health network layer and sending the health data to the fault analysis unit; the expert system knowledge base is used for acquiring an expert experience model from the outside as the knowledge input of the fault analysis unit; and the fault analysis unit receives the event data from the control network layer and the health data from the remote monitoring unit, performs fault diagnosis according to the expert experience model, and stores the diagnosis result as new knowledge in an expert system knowledge base.
In the technical scheme, the fault analysis unit comprises a comprehensive database, a knowledge acquisition module, an interpreter and an inference machine; the comprehensive database receives event data from the control network layer based on an EIP protocol and receives health data from the remote monitoring unit; the knowledge acquisition module receives an expert experience model from an expert system knowledge base based on an RPC protocol; the reasoning machine is provided with a fault diagnosis reasoning model; and the interpreter calls a fault diagnosis inference model from the inference engine and an expert experience model from the knowledge acquisition module to calculate the health data and the event data in the comprehensive database to obtain a fault diagnosis result.
In the above technical solution, the data management layer uses an NTP protocol to synchronously acquire event data and health data received by the control network layer and the remote monitoring unit.
In the technical scheme, the data management layer is configured with a human-computer interface, and is used for configuring the data application layer according to an external instruction, and inquiring and displaying historical information of event data, health data and fault diagnosis results according to the external instruction.
The technical scheme comprises a display control module, a CPU module, a network module and a power supply module; the display control module is used for providing a human-computer interface for an operator, and the data management layer is configured on the display control module; the CPU module runs an operating system and fault diagnosis software, and the data application layer is configured on the CPU module; the network module comprises 2 switch units which are respectively used for connecting a health network layer and a control network layer; the power supply module is used for supplying power to the display control module, the CPU module and the network module.
The invention has the beneficial effects that: the invention provides a monitoring method combining steady state data and transient state time data, improves the efficiency of monitoring the health state of the system and the utilization rate of network resources and storage resources, and lays the foundation of system performance evaluation and equipment fault diagnosis. The invention constructs a platform compatible with multi-communication protocol data, so that multi-protocol data or standard communication standard data (SNMP, CAN, TCP/IP, EtherNet/IP, NTP, RPC, CIP, DDS, serial port, field bus protocol and the like) are completely transparent to the upper layer of the platform. The data source of the system is simplified, and the unique and consistent basic data and information are formed, so that the data are effectively shared, and an information isolated island is eliminated. The invention adopts a health network layer and control network layer double-network structure, and balances the data flow of the whole system. The classification of data information and data channels realizes measurement and control separation, network redundancy and rapid fault diagnosis, isolation and recovery, improves the reliability and safety of a system network, ensures the real-time performance of control instructions, and prevents the problem caused by untimely control instructions due to overlarge health monitoring information data volume and network congestion. The invention adopts a double-lock alarm mechanism, judges the abnormity obtained by analyzing the system equipment from the control network and the health network respectively, and effectively improves the reliability of the system operation from different control and monitoring angles through double-lock alarm information.
The use result of the method in the complex electromechanical system shows that the method reduces the network load, improves the efficiency of data analysis, reduces the requirement on storage space, optimizes the readability of data, enhances the centralized monitoring effect of state data, and can meet the maintenance requirement of the system.
Drawings
FIG. 1 is a schematic diagram of the data processing architecture of the present invention.
FIG. 2 is a hardware design diagram of the present invention.
Detailed Description
The invention will be further described in detail with reference to the following drawings and specific examples, which are not intended to limit the invention, but are for clear understanding.
As shown in fig. 1, the present invention provides a health classification monitoring apparatus for a complex electromechanical system, which includes an equipment layer, a communication network layer, a data management layer, and a data application layer; the communication network layer comprises a health network layer and a control network layer which are parallel and independently operated; the equipment layer is used for acquiring the running state information of the ship equipment, uploading health data in the running state information of the ship equipment to the health network layer, and uploading event data in the running state information of the ship equipment to the control network layer; the health network layer decodes the received health data and then sends the decoded health data to the data application layer; the control network layer decodes the received event data and then sends the decoded event data to the data application layer; the data application layer is used for carrying out fault diagnosis according to the received decoded health data and event data; the data management layer is used for receiving, storing and managing health data and event data from the communication network layer and historical information of fault diagnosis results from the data application layer; the health data refers to state data periodically uploaded by each ship device and is used for reflecting the health state of each device after the system is powered on; the event data refers to transient data uploaded by each ship device in real time in the working process and is used for reflecting the continuous change process of the working state of the system.
The invention designs a layered data processing structure model, and the system model comprises 4 layers, namely an equipment layer, a communication network layer, a data management layer and a data application layer. The equipment layer comprises sensors and equipment acquisition systems which are distributed on the site, and comprises bottom layer embedded control equipment, PLC monitoring equipment, PC104 monitoring equipment, network switch equipment, server equipment and the like, and relates to the acquisition of gas, heat, light, electricity, force, magnetism, flow/speed, motion quantity, network and other parameter states. The communication network layer is a transmission channel of information flow and comprises a health network layer and a control network layer, and the three networks are relatively independent. The data application layer is responsible for fault diagnosis and health management functions. The data management layer manages the snapshot of the real-time state information of the whole system and the log and the health information in a certain period for inquiry, analysis and training.
For a complex electromechanical system, a network topological structure not only determines the problems of intuitive surface layers of wiring, maintenance, cost and the like of the whole network, but also has important influence on network performances such as reliability, instantaneity and the like of the network, so that the multi-heterogeneous controller is required to be modularly networked, the coupling between equipment states is reduced, and the networking reliability is improved. The invention adopts a modular networking architecture which shares equipment health and control ring network, and equipment layers of a plurality of ship equipment share communication network layer equipment and single redundant equipment. The system has the functions of network redundancy, quick fault diagnosis, isolation and recovery, and improves the reliability and safety of the system network.
In the technical scheme, the equipment layer records and collects state data in a snapshot mode according to a certain frequency in the long-time running process of each ship equipment, a transient wave recording data recording mechanism is triggered when the state is abnormal, the data before and after the abnormality is stored in a wave recording mode to generate wave recording data, meanwhile, the state of serious failure of the ship equipment can be analyzed and judged, judgment information whether the system is allowed to continue working is generated, and the state data, the wave recording data and comprehensive information generated by the judgment information are used as health data. And the equipment layer controls the running state of the ship equipment corresponding to the equipment layer according to the judgment result.
In the technical scheme, the equipment layer records the wave recording data of each ship equipment in real time in a shooting mode in the transient working process of each ship equipment, the wave recording data are used for describing the working and running characteristics of each ship equipment and judging the control state of the equipment layer to generate a comprehensive signal, and the comprehensive information generated by the wave recording data and the comprehensive signal is used as event data. The equipment layer controls the running state of the ship equipment corresponding to the equipment layer based on the content of the comprehensive signal.
And if the equipment layer judges that the state of the ship equipment is not allowed to continue working according to the health data of the corresponding ship equipment, the equipment layer locally controls the corresponding ship equipment to automatically lock. Similarly, if the equipment layer judges that the state of the ship equipment is not allowed to continue working according to the event data of the corresponding ship equipment, the equipment layer locally controls the corresponding ship equipment to automatically lock.
Meanwhile, the data application layer receives health data and event data from the equipment layer of each ship device and comprehensively judges the overall operation state of the system, so that control information for each ship device is generated, and the integrated control of the complex electromechanical system is realized.
According to the double-network structure of the communication network layer, the invention provides a double-lock alarm mechanism, and the system equipment is analyzed from the control network and the health network respectively to generate a comprehensive signal and a signal for allowing the system to continue working or not, so that the reliable state monitoring of the system is completed. The invention adopts a monitoring method combining steady state data and transient state/fault recording data, effectively improves the utilization rate and monitoring efficiency of network resources, and can meet the monitoring requirement of a complex electromechanical system.
In the above technical solution, the health network layer includes a plurality of different protocol decoders; the health network layer analyzes the health data from different ship equipment through corresponding protocol decoders and sends the decoded health data to the data application layer according to a uniform communication protocol. The health network layer can respectively receive various state data from different ship equipment based on RPC protocol, SNMP protocol, TCP/IP protocol, serial port communication protocol and the like, and a platform compatible with multi-communication protocol data is constructed, so that the multi-protocol data or standard communication standard data is completely transparent to the upper layer of the platform.
In the technical scheme, the control network layer comprises an RSLinx server; the RSLinx server is communicated with the equipment layer through an EIP (enhanced information platform) protocol and used for acquiring event data; the RSLinx server sends the decoded event data to the data application layer based on the EIP protocol.
In the technical scheme, the data application layer comprises a remote monitoring unit, a fault analysis unit and an expert system knowledge base; the remote monitoring unit is used for receiving health data from the health network layer and sending the health data to the fault analysis unit; the expert system knowledge base is used for acquiring an expert experience model from the outside and sending the expert experience model to the fault analysis unit; and the fault analysis unit receives the event data from the control network layer and the health data from the remote monitoring unit and carries out fault diagnosis according to the expert empirical model.
In the technical scheme, the fault analysis unit comprises a comprehensive database, a knowledge acquisition module, an interpreter and an inference machine; the comprehensive database receives event data from the control network layer based on an EIP protocol and receives health data from the remote monitoring unit; the knowledge acquisition module receives an expert experience model from an expert system knowledge base based on an RPC protocol; the reasoning machine is provided with a fault diagnosis reasoning model; and the interpreter calls a fault diagnosis inference model from the inference engine and an expert experience module from the knowledge acquisition module to calculate the health data and events in the comprehensive database to obtain a fault diagnosis result.
In the above technical solution, the data management layer uses an NTP protocol to synchronously acquire event data and health data received by the control network layer and the remote monitoring unit.
In the technical scheme, the data management layer is configured with a human-computer interface, and is used for configuring the data application layer according to an external instruction, and inquiring historical information of event data, health data and fault diagnosis results according to the external instruction to display. Wherein, ordinary users can inquire historical information through the data management layer, and expert users can provide calculation models such as an expert system knowledge base, an inference engine, an interpreter and the like of the data application layer through the data management layer for manual configuration.
As shown in fig. 2, the hardware of the health classification monitoring device of the complex electromechanical system is composed of 5 modules: display control module, CPU module, network module, distribution module, UPS module. The power distribution module and the UPS module form a power supply module. The display control module comprises a display module and a control module shown in fig. 2, is configured with a data management layer, and mainly provides a human-computer interface for an operator, the display is divided into upper and lower screen display, the upper screen display displays the health state and the fault diagnosis result of the system, and the lower screen is a control input interface. The upper screen and the lower screen are mutually linked, the monitoring screen displays the control result of the control screen, and the comprehensive health state of the monitoring screen is displayed in the control screen. The CPU module is configured with a data application layer, which is the most core component of the device, and runs a domestic operating system and fault diagnosis software for the CPU module, so as to realize the main functions of the device. The network module includes 2 switch units. Respectively accessing a healthy ring network and a control ring network. The network module does not carry out redundancy because of the looped network; and the CPU module is dual-machine redundancy. The power distribution module is used for converting 220V AC to 24V DC power and distributing the power. The power distribution module provides two groups of completely independent 24V DC power supplies, one is obtained by directly converting an external 220V AC auxiliary power supply, and the other is obtained by converting a 220V AC power supply of the UPS. The former path is mainly supplied to two switch units and a CPU module, and the latter path is used for supplying power to all components including the switch, the CPU module and the like. The UPS module mainly provides stable 220VAC power frequency electricity for the device. The device can still complete the task when the auxiliary power supply is suddenly powered off, and shutdown is executed according to the operation flow.
The invention provides a health grading monitoring device of a complex electromechanical system, and the software functions comprise a user interface function, a command processing function, a state data processing function, an event data processing function, a communication function, an editable logic device type communication function, a bottom layer real-time controller communication function and an internal health monitoring function. The system has strong expandability, can add specific functions in each module, and can also expand the modules to complete more complex tasks.
Those not described in detail in this specification are within the skill of the art.
Claims (8)
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110869129.6A CN113311280B (en) | 2021-07-30 | 2021-07-30 | A health grading monitoring device for complex electromechanical systems |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110869129.6A CN113311280B (en) | 2021-07-30 | 2021-07-30 | A health grading monitoring device for complex electromechanical systems |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113311280A CN113311280A (en) | 2021-08-27 |
| CN113311280B true CN113311280B (en) | 2021-12-28 |
Family
ID=77382180
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110869129.6A Active CN113311280B (en) | 2021-07-30 | 2021-07-30 | A health grading monitoring device for complex electromechanical systems |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113311280B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114124987B (en) * | 2021-09-07 | 2023-06-20 | 湖北三江航天万峰科技发展有限公司 | Distributed vehicle control system |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN201637827U (en) * | 2009-11-04 | 2010-11-17 | 河北凯翔科技有限公司 | Intelligent alternating-current load cabinet for large-power marine generator unit |
| CN101968531A (en) * | 2010-09-29 | 2011-02-09 | 广州中船黄埔造船有限公司 | Load cabinet system |
| CN111903220B (en) * | 2009-12-31 | 2012-11-21 | 中国人民解放军海军工程大学 | Take linear electric motor real time monitoring system of dicyclo net |
| CN104965464A (en) * | 2015-07-08 | 2015-10-07 | 福建唐力电力设备有限公司 | Marine machinery and port machinery smart system and implementation method |
| CN106124982A (en) * | 2016-06-14 | 2016-11-16 | 都城绿色能源有限公司 | Automatic expert's resultant fault diagnostic system of a kind of Wind turbines and diagnostic method |
| CN112084711A (en) * | 2020-09-07 | 2020-12-15 | 哈尔滨工程大学 | Ship electric propulsion system fault diagnosis method based on ABC-SVM (active-support vector machine) expert system |
| CN112083355A (en) * | 2020-09-09 | 2020-12-15 | 江苏杰瑞信息科技有限公司 | Ship cabin equipment health management and fault prediction system and method |
| CN112596496A (en) * | 2020-12-08 | 2021-04-02 | 中国船舶重工集团公司第七0四研究所 | Health management platform and management method for ship electric propulsion system |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7092848B2 (en) * | 2003-12-22 | 2006-08-15 | Caterpillar Inc. | Control system health test system and method |
| US7684936B2 (en) * | 2008-05-07 | 2010-03-23 | Simmonds Precision Products, Inc. | Method, apparatus and computer program product for determining a future time of a component |
| FR2986507B1 (en) * | 2012-02-06 | 2014-01-17 | Eurocopter France | METHOD AND DEVICE FOR REALIZING A HEALTH CONTROL OF A TURBOMOTEUR OF AN AIRCRAFT COMPRISING AT LEAST ONE TURBOMOTEUR |
-
2021
- 2021-07-30 CN CN202110869129.6A patent/CN113311280B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN201637827U (en) * | 2009-11-04 | 2010-11-17 | 河北凯翔科技有限公司 | Intelligent alternating-current load cabinet for large-power marine generator unit |
| CN111903220B (en) * | 2009-12-31 | 2012-11-21 | 中国人民解放军海军工程大学 | Take linear electric motor real time monitoring system of dicyclo net |
| CN101968531A (en) * | 2010-09-29 | 2011-02-09 | 广州中船黄埔造船有限公司 | Load cabinet system |
| CN104965464A (en) * | 2015-07-08 | 2015-10-07 | 福建唐力电力设备有限公司 | Marine machinery and port machinery smart system and implementation method |
| CN106124982A (en) * | 2016-06-14 | 2016-11-16 | 都城绿色能源有限公司 | Automatic expert's resultant fault diagnostic system of a kind of Wind turbines and diagnostic method |
| CN112084711A (en) * | 2020-09-07 | 2020-12-15 | 哈尔滨工程大学 | Ship electric propulsion system fault diagnosis method based on ABC-SVM (active-support vector machine) expert system |
| CN112083355A (en) * | 2020-09-09 | 2020-12-15 | 江苏杰瑞信息科技有限公司 | Ship cabin equipment health management and fault prediction system and method |
| CN112596496A (en) * | 2020-12-08 | 2021-04-02 | 中国船舶重工集团公司第七0四研究所 | Health management platform and management method for ship electric propulsion system |
Non-Patent Citations (1)
| Title |
|---|
| 船舶机电系统综合状态评估与健康管理系统的开发与应用;张成伟 等;《电脑知识与技术》;20171130;第13卷(第33期);第104-105页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113311280A (en) | 2021-08-27 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN101782755B (en) | Embedded type integrated monitoring and maintenance system of concrete block forming production line | |
| CN110733038A (en) | Industrial robot remote monitoring and data processing system | |
| CN112731876A (en) | Industrial equipment management system based on production data | |
| CN201354933Y (en) | Automatic monitoring system of booster pump station based on PLC and Internet | |
| CN103019155B (en) | A kind of test unit for reliability of numerical control system | |
| CN108681288A (en) | A kind of novel maintenance system based on cloud platform | |
| CN106774220A (en) | A kind of thermal power plant's dcs on-line operation status monitoring platform | |
| CN109847200B (en) | A vacuum monitoring system for medical heavy ion accelerator | |
| CN110501965A (en) | Embedded remote PLC data acquisition and early warning system | |
| CN209170419U (en) | One kind is based on the twinborn apparatus remote maintenance system of digitlization | |
| CN101221438A (en) | Electrical control workstation of macrotype isostatic pressing machine | |
| CN111749878B (en) | An integrated modular group-controlled air compressor optimization system | |
| CN113311280B (en) | A health grading monitoring device for complex electromechanical systems | |
| CN108227657A (en) | A kind of dynamic environment monitoring system | |
| CN202275310U (en) | Intelligent monitor system of cold storage warehouse assembly | |
| CN103777610A (en) | Intelligent monitoring and maintaining system of large-sized frame type hydraulic machine | |
| CN106559501B (en) | Flat type industrial control method and system | |
| CN111600923A (en) | An OPC UA protocol server system based on .NET Core | |
| CN103926898A (en) | System for monitoring power quality of ocean module drilling machine | |
| CN115480542A (en) | Production line running state and related process data acquisition system | |
| CN205507515U (en) | Data center power environment monitoring system | |
| CN106707997A (en) | Hydropower station remote monitoring system | |
| CN107632575A (en) | Intelligent operation post | |
| CN207764643U (en) | A kind of remote centralized monitoring system of intelligence manufacture | |
| CN111903220B (en) | Take linear electric motor real time monitoring system of dicyclo net |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |