CN115511237A - Device operation condition monitoring method and system - Google Patents
Device operation condition monitoring method and system Download PDFInfo
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Abstract
The invention provides a device operation condition monitoring method and system, and belongs to the field of petrochemical industry. The monitoring method comprises the following steps: establishing a device risk assessment model instance and a plurality of equipment risk assessment model instances, wherein each equipment risk assessment model instance has a first preset parameter and a second preset parameter; acquiring real-time operation parameters of a plurality of devices, inputting the real-time operation parameters of the devices into corresponding device risk assessment model examples, and performing device operation risk calculation by combining the first preset parameters and/or the second preset parameters to obtain a device operation risk calculation result; inputting the equipment operation risk calculation results of all the equipment into the device risk evaluation model example to carry out device operation risk calculation so as to obtain a device operation risk calculation result; and determining the running state of the device according to the running risk calculation result of the device. The method calculates the running risk state of the device and the equipment in real time, carries out real-time early warning, is not influenced by human experience, and has high checking and evaluating efficiency.
Description
Technical Field
The invention relates to the field of petrochemical industry, in particular to a device operation condition monitoring method and a device operation condition monitoring system of a petrochemical refining device.
Background
Each equipment in a refinery (such as a desulfurization unit) has its own life cycle, and the state of the equipment changes frequently during operation, which affects the operation condition of the unit. At present, the running condition of the refining and chemical device is evaluated manually and subjectively based on the fault phenomenon and the consequence, or indirectly according to the high and low product quality, so that the running condition of the device cannot be reflected in real time due to time lag, and the inspection and evaluation efficiency is low.
In addition, the data required for evaluation is usually collected from the existing production real-time system and analysis and assay database, the existing online sensors and monitoring instruments of the device are few in number, single in type and function and low in integration degree, so that the evaluation data source is not representative and targeted, the troubleshooting and regulation feedback of faults are not timely and incomplete, and the running state or the existing risks of the device cannot be effectively and accurately predicted.
Therefore, how to efficiently and accurately monitor and evaluate the running state of the refining device in real time is a problem which is needed to be solved intelligently, conveniently and quickly, and carry out problem diagnosis, risk management and control and running optimization.
Disclosure of Invention
In order to solve the above problems, embodiments of the present invention provide a method and a system for monitoring an operating condition of a device.
In order to achieve the above object, a first aspect of the present invention provides a method for monitoring an operation condition of a plant for a petrochemical refining plant, the plant including a plurality of equipments, the monitoring method comprising:
establishing a device risk assessment model instance and a plurality of equipment risk assessment model instances, wherein each equipment risk assessment model instance has a first preset parameter and a second preset parameter;
acquiring real-time operation parameters of a plurality of devices, inputting the real-time operation parameters of the devices into corresponding device risk assessment model examples, and performing device operation risk calculation by combining the first preset parameters and/or the second preset parameters to obtain device operation risk calculation results;
inputting the equipment operation risk calculation results of all the equipment into the device risk evaluation model example to carry out device operation risk calculation so as to obtain a device operation risk calculation result;
and determining the running state of the device according to the running risk calculation result of the device.
Optionally, the method further includes: and determining the equipment running state of each equipment according to the equipment running risk calculation result of the equipment, and outputting equipment risk prevention and control measures corresponding to the equipment running state.
Optionally, the real-time operation parameters of the input device risk assessment model instance are processed real-time operation parameters;
the method further comprises the following steps: processing the acquired real-time operation parameters of the equipment, wherein the processing comprises the following steps: and calculating the reliability and the confidence coefficient of the real-time operation parameters.
Optionally, the first preset parameter is composed of an operation parameter of the corresponding device under a standard operation condition; the second preset parameter consists of the operation parameters of the corresponding equipment under the worst operation condition.
Optionally, the equipment risk assessment model example determines the degree of deviation of the actual operation condition of the equipment from the standard operation condition based on the real-time operation parameter of the equipment and the first preset parameter; and/or determining the degree of the actual operation condition of the equipment deviating from the worst operation condition of the equipment based on the real-time operation parameter and the second preset parameter of the equipment; and obtaining an equipment operation risk calculation result according to the degree of the actual operation working condition deviating from the standard operation working condition and/or the degree of the actual operation working condition deviating from the worst operation working condition of the equipment.
Optionally, the device risk assessment model instance obtains a device operation risk calculation result by combining an apparatus operation risk calculation result of each apparatus based on importance, number, distribution, and apparatus function of apparatuses in the device corresponding to the multiple apparatus risk assessment model instances.
Optionally, the obtaining of the calculation result of the equipment running risk according to the degree of the actual running condition deviating from the standard running condition and/or the degree of the actual running condition deviating from the worst running condition of the equipment includes:
taking the ratio of the real-time operation parameter of the equipment to the first preset parameter as an equipment real-time reliability index of the equipment, wherein the equipment real-time reliability index is used for expressing the risk degree of the equipment;
taking the ratio of the second preset parameter to the first preset parameter of the equipment as the worst reliability index of the equipment;
and comparing the real-time reliability index of the equipment with the worst reliability index of the equipment to obtain the running state index of the equipment, wherein the running state index of the equipment is used for indicating that the equipment is in a running state or a fault state.
Optionally, the obtaining, by the device risk assessment model instance, a device operation risk calculation result based on importance, number, distribution, and device functions of devices in the device corresponding to the multiple device risk assessment model instances, and by combining the device operation risk calculation result of each device, includes:
the device risk evaluation model instance determines the device running state index of the device based on the importance, the quantity, the distribution and the device functions of the devices corresponding to the multiple device risk evaluation model instances in the device and by combining the device running state index of each device, and obtains a device running risk calculation result.
Optionally, the method further includes:
counting the actual operation time T of the device in the standard operation time T by using the device operation state index, calculating the device reliability index of the device according to the actual operation time T and the standard operation time T, and determining the device operation risk level of the device according to the device reliability index.
Optionally, the monitoring method further includes: and selecting key attention equipment according to feedback information for eliminating the running risk of the equipment, wherein the feedback information comprises the risk elimination duration.
A second aspect of the present invention provides a system for monitoring an operating condition of a device, the system comprising:
an online monitoring component, a memory, and a processor;
the online monitoring component is arranged on the corresponding equipment and is used for acquiring real-time operation parameters of the corresponding equipment;
the memory comprises a data memory and an instruction memory, wherein the data memory is used for storing real-time operation parameters of the equipment; the instruction memory stores program instructions for a risk assessment model instance and an equipment risk assessment model instance;
and when the processor executes the program instructions of the risk assessment model examples and the equipment risk assessment model examples, the device operation condition monitoring method is realized.
Optionally, the processor includes a risk processor and a data processor, where the data processor is configured to process the acquired real-time operation parameters of the device, and the processing includes performing reliability and confidence calculation on the real-time operation parameters; and the risk processor is used for calculating the equipment operation risk and the device operation risk of the processed real-time operation parameters.
Optionally, the data processor includes a data state identification module, a data configuration module, and a data storage module; the data processor processes the acquired real-time operation parameters of the equipment, and comprises the following steps:
the data state identification module compares the acquired real-time operation parameter change of the equipment, analyzes the working state of the online monitoring component, and calculates the reliability and confidence of the real-time operation parameter of the equipment acquired by the online monitoring component;
the data configuration module is used for classifying the real-time operation parameters and making a storage plan of the real-time operation parameters;
and the data storage module stores the real-time operation parameters into a data storage according to a storage plan provided by the data configuration module.
Optionally, the monitoring system further includes:
the display module and/or the sound module are used for prompting the running state of the device and/or the running state of the equipment;
the alarm module is used for carrying out corresponding alarm prompt according to the running state of the device and/or the running state of the equipment;
and the alarm elimination analysis module is used for counting the risk elimination duration according to the equipment operation risk elimination feedback information and feeding the risk elimination duration back to the risk processor.
Optionally, the monitoring system further includes:
an expert intelligence library, wherein historical information of equipment and devices is stored in the expert intelligence library;
and the risk processor is also used for carrying out equipment operation risk calculation and device operation risk calculation on the processed real-time operation parameters by combining the historical information of the equipment and/or the device extracted from the expert intelligence library.
Optionally, the devices in the apparatus are classified according to importance level, and the online monitoring component is arranged on the device with the highest importance level of the apparatus.
Optionally, the monitoring system further comprises a machine learning-based image recognition module, and the online monitoring component comprises a video monitoring device and/or an integrated probe;
the video monitoring device is connected with the image identification module and is used for acquiring the field video information of the equipment with the highest importance level;
the image recognition module is used for analyzing the field video information and acquiring the implementation condition of field prevention and control measures;
the integrated probe is internally provided with a plurality of sensors and is used for acquiring real-time operation parameters of different types at the same part of the equipment at the same time.
By the technical scheme, whether risks and faults exist in the operation conditions of the device and the equipment can be calculated in real time based on the real-time operation parameters of the multiple pieces of equipment, the calculation is simple and quick, the real-time early warning of the operation conditions of the device and the equipment can be realized, and the checking and evaluating efficiency is high. The problem of among the prior art carry out the influence of artificial experience and subjective consciousness that the manual judgement leads to based on the fault phenomenon and consequence to and judge the time lag is solved.
Additional features and advantages of embodiments of the invention will be set forth in the detailed description which follows.
Drawings
The accompanying drawings, which are included to provide a further understanding of the embodiments of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the embodiments of the invention without limiting the embodiments of the invention. In the drawings:
FIG. 1 is a block diagram of a method for monitoring device operation according to an embodiment of the present invention;
FIG. 2 is a flowchart of a method for monitoring operation status of a device according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a data processor of a system for monitoring device operation according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a system for monitoring the operation status of a device according to an embodiment of the present invention;
fig. 5 is a flowchart of a system for monitoring device operation according to an embodiment of the present invention.
Detailed Description
The following detailed description of embodiments of the invention refers to the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating the present invention, are given by way of illustration and explanation only, not limitation.
Example one
Fig. 1 is a block diagram of a method for monitoring an operating condition of a device according to an embodiment of the present invention. As shown in fig. 1, an embodiment of the present invention provides a method for monitoring an operation status of a plant for a petrochemical refining plant, the plant including N devices, the method including:
establishing a device risk assessment model instance and N equipment risk assessment model instances, wherein each equipment risk assessment model instance has a first preset parameter and a second preset parameter;
acquiring real-time operation parameters of N devices, inputting the real-time operation parameters of the devices into corresponding device risk assessment model examples, and performing device operation risk calculation by combining the first preset parameters and/or the second preset parameters to obtain a device operation risk calculation result;
inputting the equipment operation risk calculation results of all the equipment into the device risk evaluation model example to carry out device operation risk calculation so as to obtain a device operation risk calculation result;
determining the running state of the device according to the running risk calculation result of the device;
wherein N is more than or equal to 2.
And calculating the operation risk calculation results of the device and the equipment in real time based on the acquired real-time operation parameters of the equipment through the established device risk evaluation model example and the equipment risk evaluation model examples, and confirming whether the device and the equipment have the operation risk or not through the operation risk calculation results of the device and the equipment. The method can realize real-time early warning of the operation conditions of the device and the equipment according to the operation risk calculation results of the device and the equipment, is not influenced by human experience, and has high inspection and evaluation efficiency.
Further, in order to ensure that the obtained real-time operation parameters of the equipment can accurately represent the actual operation state of the equipment, the obtained real-time operation parameters of the equipment are processed before the real-time operation parameters are transmitted to an equipment risk assessment model instance, and the processing comprises the following steps:
and calculating the reliability and the confidence coefficient of the acquired real-time operation parameters of the equipment by using methods such as F test, T test or P test in statistics, wherein the reliability and the confidence coefficient can be used for judging the accuracy and the reliability of the real-time operation parameters of the equipment.
Example two
In an embodiment of the present invention, the first preset parameter and the second preset parameter of each equipment risk assessment model instance are defined as: the first preset parameter consists of operation parameters of corresponding equipment under a standard operation condition; the second preset parameter consists of the operation parameters of the corresponding equipment under the worst operation condition; the worst operating condition is the worst operating condition of the equipment within an acceptable range determined based on historical operating conditions of the equipment, risk tolerance capability, emergency prevention and control measures, enterprise management regulations, and/or expert experience.
Furthermore, the equipment risk evaluation model example can determine the degree of deviation of the actual operation condition of the equipment from the standard operation condition based on the real-time operation parameter and the first preset parameter of the equipment; and/or determining the degree of the actual operation condition of the equipment deviating from the worst operation condition of the equipment based on the real-time operation parameter and the second preset parameter of the equipment; and obtaining an equipment operation risk calculation result according to the degree of the actual operation working condition deviating from the standard operation working condition and/or the degree of the actual operation working condition deviating from the worst operation working condition of the equipment.
Specifically, when the difference between the real-time operation parameter of the device and the first preset parameter of the device is larger, the actual operation condition of the device deviates from the standard operation condition. On the contrary, when the difference between the real-time operation parameter of the equipment and the first preset parameter of the equipment is smaller, the actual operation condition of the equipment is closer to the standard operation condition. The running risk of the equipment is smaller when the actual running condition is closer to the standard running condition, and conversely, the running risk of the equipment is larger when the actual running condition is further away from the standard running condition.
When the difference between the real-time operation parameter of the equipment and the second preset parameter of the equipment is larger, the actual operation condition of the equipment deviates from the worst operation condition, and otherwise, when the difference between the real-time operation parameter of the equipment and the second preset parameter of the equipment is smaller, the actual operation condition of the equipment is closer to the worst operation condition. When the actual operation condition deviates from the worst operation condition and is closer to the standard operation condition, the equipment operation risk is smaller. Conversely, when the actual operating conditions deviate from the worst operating conditions and approach an unacceptable operating range, it may be determined that the equipment risk is greater.
Preferably, the risk early warning and equipment risk prevention and control measures can be output to equipment with high equipment running risk, and operators are prompted to pay attention to the implementation of the risk prevention and control measures.
Through real-time calculation, real-time early warning and prevention and control measure prompt of the equipment operation risk, the equipment operation risk can be eliminated before the equipment fails, and equipment failure caused by the fact that the equipment fails to eliminate the operation risk in time is prevented.
Further, the device risk assessment model instance obtains a device operation risk calculation result by combining the device operation risk calculation result of each device based on the importance, the number, the distribution and the device functions of the devices corresponding to the multiple device risk assessment model instances in the device.
EXAMPLE III
In an embodiment of the present invention, inputting real-time operation parameters of a device into a corresponding device risk assessment model instance, and performing device operation risk calculation by combining first preset parameters and/or second preset parameters to obtain a device operation risk calculation result, including:
taking the ratio of the real-time operation parameter of the equipment to the first preset parameter as an equipment real-time reliability index of the equipment, wherein the equipment real-time reliability index is used for representing the risk degree of the equipment as shown in the following formula (1):
device real-time reliability index = first preset parameter of device/real-time operation parameter of device (1)
Taking the ratio of the first preset parameter to the second preset parameter of the equipment as the worst reliability index of the equipment, as shown in the following formula (2):
device worst reliability index = second preset parameter of device/first preset parameter of device (2)
And comparing the real-time reliability index of the equipment with the worst reliability index of the equipment to obtain the running state index of the equipment, wherein the running state index of the equipment is used for indicating that the equipment is in a running state or a fault state.
Specifically, when the real-time reliability index of the equipment is smaller than the worst reliability index of the equipment, the running state index value of the equipment is 0, which indicates that the current equipment is in a fault state; and when the real-time reliability index of the equipment is greater than or equal to the worst reliability index of the equipment, the running state index value of the equipment is 1, which indicates that the current equipment is in a running state.
Furthermore, the device running state index of the device is determined based on the importance, the number, the distribution and the device functions of the devices corresponding to the multiple device risk assessment model instances in the device, and the device running state index of each device is combined, wherein the device running state index is used for indicating that the device is in a running state or a fault state.
Specifically, the importance, the number, the distribution AND the device functions of each device in the device are combined, a logical operator (AND OR) corresponding to each device is selected, AND a logical operation is performed on the running state index value (0 OR 1) of each device calculated by the device risk assessment model example to obtain the running state index of the device. When the device operation state index value is 0, the current device is in a fault state as a whole; when the device operation state index value is 1, the device operation state index value indicates that the whole device is in an operation state.
Further, the actual operation time T of the device in the standard operation time T is counted by using the device operation state index, and the device reliability index of the device is calculated according to the actual operation time T and the standard operation time T.
Specifically, the method for acquiring the actual operation time length T includes detecting the device operation state index every fixed time length m in the time length T, when the device operation state index is 1, indicating that the device is currently in an operation state, increasing the actual operation time length of the device by the fixed time length m, namely T = T + m, and when the device operation state index is 0, indicating that the device is currently in a fault state, wherein the actual operation time length of the device is kept unchanged. Taking the ratio of the actual operation time T counted in the whole time length T to the standard operation time length T as a device reliability index, as shown in the following formula (3):
device reliability index = actual operation duration T/standard operation duration T (3)
The fixed time length m can be in the unit of minutes, hours, days, months or years, and represents that the actual operation time length statistics of the device is carried out every fixed time length of m minutes, m hours, m days, m months or m years.
Further, according to the reliability index of the device, the device operation risk level of the device is determined by combining the device operation condition over the years and/or expert experience.
Preferably, the following table of the reliability index of the plant and the grade of the operational risk of the plant may be referred to, as shown in table 1.
| Device reliability index | 0-0.3 | 0.3-0.5 | 0.5-0.8 | 0.8-1 |
| Device risk rating | Ultra-high | Height of | In | Is low in |
TABLE 1
The device reliability index or device risk level may be indicative of a risk level of the device over a period of time and may be used to alert an operator of the frequency of failure of the device over a period of time. When the reliability index value of the device is closer to 0 or the risk level indicates a high risk, the frequency of faults occurring in the device within a period of time is very high, and the operation personnel with low operation reliability of the device need special attention. Otherwise, the device is in good operation.
Fig. 2 is a flowchart of a method for monitoring an operating condition of an apparatus according to an embodiment of the present invention, and as shown in fig. 2, a real-time operating parameter of an apparatus is acquired and transmitted to a corresponding apparatus risk assessment model instance, the apparatus risk assessment model instance calculates an apparatus reliability index and an apparatus operating state index of the apparatus, the apparatus operating state indexes of a plurality of apparatuses are input to the apparatus risk assessment model instance, the apparatus risk assessment model instance calculates an apparatus operating state index of the apparatus based on importance, quantity, distribution, and apparatus functions of the apparatus, an actual operating time T of the apparatus within a standard operating time T is counted according to the apparatus operating state index, the apparatus reliability index is obtained according to a ratio of T to T, and an apparatus risk level can be further determined according to the apparatus reliability index.
The device and the equipment have simple operation risk calculation method, can calculate the real-time output result in real time, have high examination and evaluation efficiency and are not influenced by human factors.
Preferably, the risk prevention and control measures can also be output to the equipment with the operation risk. The operation risk condition of the equipment can be determined according to the real-time reliability index, the worst reliability index and the operation state index of the equipment, an equipment risk early warning signal can be provided when the equipment has operation risk, an operator is prompted which equipment has operation risk, and a risk prevention and control measure for eliminating the specific equipment risk is generated according to the specific equipment operation risk condition. And the operator can perform risk prevention and control operation according to the provided risk prevention and control measures, so that the running risk of the equipment is eliminated.
Preferably, the important attention device can be selected according to feedback information for eliminating the running risk of the device, wherein the feedback information comprises the risk elimination duration. Specifically, the time from the occurrence of the operation risk of a certain device to the elimination of the operation risk of the device is counted as the risk elimination time. And determining the risk prevention and control capability of the equipment according to the risk elimination duration of the equipment. When the time length of risk elimination is shorter, the stronger the risk prevention and control capability of the equipment is, and conversely, the weaker the risk prevention and control capability of the equipment is. The equipment with weak risk prevention and control capability is taken as the equipment needing important attention, and the equipment determined as the equipment needing important attention can be prominently prompted in the aspects of equipment early warning and equipment risk prevention and control measures.
Example four
The embodiment of the invention also provides a device running condition monitoring system which comprises an online monitoring component, a memory and a processor.
The online monitoring part is arranged on corresponding equipment in the device and is used for acquiring real-time operation parameters of the corresponding equipment.
The memory includes a data memory for storing real-time operating parameters of the device and an instruction memory.
The instruction memory stores program instructions of a risk assessment model example and an equipment risk assessment model example in the device operation condition monitoring method.
And when the processor executes the program instructions of the risk assessment model examples and the equipment risk assessment model examples, the method for monitoring the running condition of the device is realized.
According to the type and the characteristics of the equipment, a temperature sensor, a pressure sensor, a flow sensor, a liquid level sensor, a flow velocity sensor, a vibration sensor, an oil product quality online detector, a pH meter, a water quality online analysis multi-parameter probe, a corrosion probe, an online thickness gauge, an infrared probe, a video monitoring device or an integrated probe and the like can be selected as online monitoring components according to requirements.
The integrated probe is internally provided with a plurality of sensors, can realize monitoring of various types of running parameters at the same part, and ensures the consistency and synchronism of monitoring time and space.
Preferably, the integrated probe can be integrated according to actual monitoring requirements, for example, temperature, pressure, flow and flow velocity sensors are integrated into an integrated probe, and on-line analyzers for monitoring ammonia nitrogen, pH, cl-, COD and the like in water are integrated into an integrated probe.
Preferably, if changes in one operating parameter affect other types of operating parameters, the sensors for monitoring these types of operating parameters may be integrated into an integrated probe, such as integrating a sensor for monitoring temperature with a sensor for monitoring other operating parameters affected by temperature, such as integrating a temperature sensor, a pH meter, and a density sensor into an integrated probe.
Further, the processor comprises a risk processor and a data processor, wherein the data processor is used for processing the real-time operation parameters of the equipment acquired by the online monitoring component, and the processing comprises the calculation of credibility and confidence degree of the real-time operation parameters.
And the risk processor is used for calculating the equipment operation risk and the device operation risk of the processed real-time operation parameters of the equipment. Preferably, the operational risk calculation results of the devices and apparatuses may be stored in a data storage.
Fig. 3 is a schematic diagram of a data processor of a system for monitoring device operation status according to an embodiment of the present invention. As shown in fig. 3, the data processor includes a data state identification module, a data configuration module, and a data storage module, and specifically functions as follows:
and the data state identification module is used for eliminating invalid or wrong numerical values by comparing the range of the obtained real-time operation parameter values of the equipment based on the fact that the real-time operation parameters cannot be collected if the online monitoring component fails, and calculating the reliability and confidence coefficient of the obtained real-time operation parameters of the equipment by using methods such as F test, T test or P test in statistics.
And the data configuration module is used for classifying the real-time operation parameters of the equipment and making a storage plan of the real-time operation parameters, preferably, the real-time operation parameters are stored according to the type of each equipment in time sequence, so that the stored data can be conveniently extracted and summarized and counted subsequently.
And the data storage module stores the real-time operation parameters into the data storage according to the storage plan provided by the data configuration module.
Further, the monitoring system further comprises a display module and/or a sound module, which are used for prompting the running state of the device and/or the running state of the equipment, wherein the prompting comprises risk early warning prompting and fault prompting;
the alarm module is used for carrying out corresponding alarm prompt according to the running state of the device and/or the running state of the equipment, wherein the prompt comprises a risk and/or fault alarm prompt of the equipment or the device;
and the alarm elimination analysis module is used for counting the risk elimination duration according to the equipment operation risk elimination feedback information and feeding the risk elimination duration back to the risk processor, and the risk processor can determine the key attention equipment according to the equipment risk elimination duration.
Further, the monitoring system further comprises an expert intelligence library, historical information of the equipment and the device is stored in the expert intelligence library, and the risk processor is further used for calculating the equipment running risk and calculating the device running risk according to the processed real-time running parameters by combining the historical information of the equipment and/or the device extracted from the expert intelligence library.
Specifically, the risk processor can determine the corresponding relation between the worst working condition operation parameters of the equipment and the device reliability index and the risk level of the device during equipment operation risk calculation and device operation risk calculation according to historical information of the equipment and the device stored in the Chi library.
Preferably, in the monitoring system provided in the embodiment of the present invention, all of the online monitoring components are disposed on a key device of the apparatus. The devices in the apparatus may be ranked in order of importance, with the device of the highest importance ranking in the apparatus being the key device for the apparatus. The importance level of the equipment in the device can be selected in multiple directions according to the functional importance of the equipment, the purchase cost of the equipment, the difficulty level of the maintenance of the equipment and the like. For example, a device that plays a significant role in the operation of the apparatus or that causes an operational failure of the apparatus when the device fails is selected as a critical device.
EXAMPLE five
Fig. 4 is a schematic diagram of a system for monitoring an operation status of a device according to an embodiment of the present invention. As shown in fig. 4, the on-line monitoring component is mounted on the apparatus, in particular on the corresponding device of the apparatus.
The on-line monitoring part acquires real-time operation parameter signals of the equipment, performs signal conversion and transmission, and sends the signals to the data management and storage unit.
The data management and storage unit comprises a data processor and a data memory and is used for processing and storing real-time operation parameters of the equipment.
The operation risk analysis unit comprises a risk processor, an instruction memory and an expert intelligence library, wherein the risk processor acquires real-time operation parameters of equipment in the data memory in real time and performs operation risk calculation of the equipment and the device by combining the expert intelligence library.
The early warning and response unit comprises a display module and/or a sound module, an alarm module and a warning analysis module, wherein the display module displays the operation risk calculation result and/or the equipment risk prevention and control measure of the equipment and the device. And the sound module carries out sound prompt on the operation risk calculation result of the equipment and/or the device. The alarm module carries out corresponding alarm prompt, such as risk alarm prompt, on the device running state and/or the equipment running state. And the alarm elimination analysis module is used for counting the risk elimination duration according to the equipment operation risk elimination feedback information and feeding the risk elimination duration back to the risk processor.
Preferably, the online monitoring component includes a video monitoring device arranged in the device for obtaining the field video information of the equipment with the highest importance level. The early warning and response unit is internally provided with an image recognition module based on machine learning, the image recognition module is connected with a video monitoring device, and the recognition of field risk prevention and control measures including maintenance behaviors of operators, process parameter regulation and control behaviors of external operators, application of prevention and control equipment and the like is realized by extracting image characteristics and setting a classification decision algorithm.
Preferably, the signals monitored by the online monitoring component can be transmitted to an enterprise production real-time system or a remote diagnosis center through an external interface with a plurality of parallel output ports.
As shown in fig. 5, a flowchart of a system for monitoring device operation status according to an embodiment of the present invention is shown in fig. 5:
1) Arranging on-line monitoring components on corresponding equipment of the device;
2) The online monitoring part acquires signals in real time to obtain real-time operation parameters of corresponding equipment;
3) Carrying out data conversion and transmission on the acquired real-time operation parameter signals of the corresponding equipment;
4) Performing data processing and storage on the acquired real-time operation parameters of the equipment;
5) Calculating the operation risk of the equipment and the device according to the real-time operation parameters of the equipment;
6) Carrying out risk early warning and risk prevention and control measure prompting on corresponding equipment according to the operation risk calculation result;
7) An operator prompts manual regulation and control on equipment with running risk according to corresponding equipment risk early warning and risk prevention and control measures;
8) And manually regulating and controlling until the police are cleared.
The process from 2) to 7) is always circulated throughout the operation of the monitoring system.
Those skilled in the art will appreciate that all or part of the steps in the method for implementing the above embodiments may be implemented by a program, which is stored in a storage medium and includes several instructions to enable a single chip, a chip, or a processor (processor) to execute all or part of the steps in the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
While the embodiments of the present invention have been described in detail with reference to the accompanying drawings, the embodiments of the present invention are not limited to the details of the above embodiments, and various simple modifications can be made to the technical solution of the embodiments of the present invention within the technical idea of the embodiments of the present invention, and the simple modifications are within the scope of the embodiments of the present invention. It should be noted that the various features described in the foregoing embodiments may be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, the embodiments of the present invention will not be described separately for the various possible combinations.
In addition, any combination of the various embodiments of the present invention is also possible, and the same should be considered as disclosed in the embodiments of the present invention as long as it does not depart from the spirit of the embodiments of the present invention.
Claims (17)
1. A method for monitoring an operation state of a plant for a petrochemical refining plant, the plant including a plurality of equipments, the method comprising:
establishing a device risk assessment model instance and a plurality of equipment risk assessment model instances, wherein each equipment risk assessment model instance has a first preset parameter and a second preset parameter;
acquiring real-time operation parameters of a plurality of devices, inputting the real-time operation parameters of the devices into corresponding device risk assessment model examples, and performing device operation risk calculation by combining the first preset parameters and/or the second preset parameters to obtain device operation risk calculation results;
inputting the equipment operation risk calculation results of all the equipment into the device risk evaluation model example to carry out device operation risk calculation so as to obtain a device operation risk calculation result;
and determining the running state of the device according to the running risk calculation result of the device.
2. The monitoring method of claim 1, further comprising: and determining the equipment running state of each equipment according to the equipment running risk calculation result of the equipment, and outputting equipment risk prevention and control measures corresponding to the equipment running state.
3. The monitoring method according to claim 1, wherein the real-time operating parameters of the input device risk assessment model instance are processed real-time operating parameters;
the method further comprises the following steps: processing the acquired real-time operation parameters of the equipment, wherein the processing comprises the following steps: and carrying out reliability and confidence calculation on the real-time operation parameters.
4. The monitoring method according to claim 1, wherein the first preset parameter consists of an operating parameter of the corresponding device under a standard operating condition; the second preset parameter consists of the operation parameters of the corresponding equipment under the worst operation condition.
5. The monitoring method according to claim 4, wherein the equipment risk assessment model instance determines the degree to which the actual operating condition of the equipment deviates from the standard operating condition based on the real-time operating parameter of the equipment and a first preset parameter; and/or determining the degree of the actual operation condition of the equipment deviating from the worst operation condition of the equipment based on the real-time operation parameter and the second preset parameter of the equipment;
and obtaining an equipment operation risk calculation result according to the degree of the actual operation working condition deviating from the standard operation working condition and/or the degree of the actual operation working condition deviating from the worst operation working condition of the equipment.
6. The monitoring method according to claim 5, wherein the device risk assessment model instance obtains a device operation risk calculation result by combining an equipment operation risk calculation result of each equipment based on importance, number, distribution and equipment function of equipment corresponding to the multiple equipment risk assessment model instances in the device.
7. The monitoring method according to claim 5, wherein obtaining the equipment operation risk calculation result according to the degree of deviation of the actual operation condition from a standard operation condition and/or the degree of deviation of the actual operation condition from the worst operation condition of the equipment comprises:
taking the ratio of the real-time operation parameter of the equipment to the first preset parameter as an equipment real-time reliability index of the equipment, wherein the equipment real-time reliability index is used for expressing the risk degree of the equipment;
taking the ratio of the second preset parameter to the first preset parameter of the equipment as the worst reliability index of the equipment;
and comparing the real-time reliability index of the equipment with the worst reliability index of the equipment to obtain the running state index of the equipment, wherein the running state index of the equipment is used for indicating that the equipment is in a running state or a fault state.
8. The monitoring method according to claim 7, wherein the obtaining of the device operational risk calculation result by the device risk evaluation model instance based on the importance, number, distribution and device functions of the devices in the device corresponding to the multiple device risk evaluation model instances in combination with the device operational risk calculation result of each device comprises:
the device risk evaluation model instance determines the device running state index of the device based on the importance, the quantity, the distribution and the device functions of the devices corresponding to the multiple device risk evaluation model instances in the device and by combining the device running state index of each device, and obtains a device running risk calculation result.
9. The monitoring method of claim 8, further comprising:
counting the actual operation time T of the device in the standard operation time T by using the device operation state index, calculating the device reliability index of the device according to the actual operation time T and the standard operation time T, and determining the device operation risk level of the device according to the device reliability index.
10. A monitoring method according to any one of claims 1-9, characterized in that the monitoring method further comprises: and selecting key attention equipment according to feedback information for eliminating the running risk of the equipment, wherein the feedback information comprises the risk elimination duration.
11. A system for monitoring the operation of a device, the system comprising:
an online monitoring component, a memory, and a processor;
the online monitoring component is arranged on the corresponding equipment and is used for acquiring real-time operation parameters of the corresponding equipment;
the memory comprises a data memory and an instruction memory, wherein the data memory is used for storing real-time operation parameters of the equipment; the instruction memory stores program instructions for a risk assessment model instance and an equipment risk assessment model instance;
the processor, when executing the program instructions of the risk assessment model instance and the equipment risk assessment model instance, implements the monitoring method of any one of claims 1-10.
12. The monitoring system of claim 11, wherein the processor comprises a risk processor and a data processor for processing the acquired real-time operating parameters of the device, the processing comprising performing confidence and confidence calculations on the real-time operating parameters; and the risk processor is used for calculating the equipment operation risk and the device operation risk of the processed real-time operation parameters.
13. The monitoring system of claim 12, wherein the data processor comprises a data state identification module, a data configuration module, and a data storage module; the data processor processes the acquired real-time operation parameters of the equipment, and comprises the following steps:
the data state identification module compares the acquired real-time operation parameter change of the equipment, analyzes the working state of the online monitoring component, and calculates the reliability and confidence of the real-time operation parameter of the equipment acquired by the online monitoring component;
the data configuration module is used for classifying the real-time operation parameters and making a storage plan of the real-time operation parameters;
and the data storage module stores the real-time operation parameters into a data storage according to a storage plan provided by the data configuration module.
14. The monitoring system of claim 13, further comprising:
the display module and/or the sound module are used for prompting the running state of the device and/or the running state of the equipment;
the alarm module is used for carrying out corresponding alarm prompt according to the running state of the device and/or the running state of the equipment;
and the alarm elimination analysis module is used for counting the risk elimination duration according to the equipment operation risk elimination feedback information and feeding the risk elimination duration back to the risk processor.
15. The monitoring system of claim 14, further comprising:
an expert intelligence library, wherein historical information of equipment and devices is stored in the expert intelligence library;
and the risk processor is also used for carrying out equipment operation risk calculation and device operation risk calculation on the processed real-time operation parameters by combining the historical information of the equipment and/or the device extracted from the expert intelligence library.
16. A monitoring system according to any one of claims 11-15, characterised in that the devices in the apparatus are classified according to the level of importance, and that the online monitoring means are arranged on the device of the highest level of importance of the apparatus.
17. The monitoring system of claim 16, further comprising a machine learning based image recognition module, the online monitoring component comprising a video monitoring device and/or an integrated probe;
the video monitoring device is connected with the image identification module and is used for acquiring the field video information of the equipment with the highest importance level;
the image recognition module is used for analyzing the field video information and acquiring the implementation condition of field prevention and control measures;
the integrated probe is internally provided with a plurality of sensors and is used for acquiring real-time operation parameters of different types at the same part of the equipment at the same time.
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| CN202110695778.9A CN115511237A (en) | 2021-06-23 | 2021-06-23 | Device operation condition monitoring method and system |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116820014A (en) * | 2023-08-24 | 2023-09-29 | 山西交通科学研究院集团有限公司 | Intelligent monitoring and early warning method and system for traffic electromechanical equipment |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116820014A (en) * | 2023-08-24 | 2023-09-29 | 山西交通科学研究院集团有限公司 | Intelligent monitoring and early warning method and system for traffic electromechanical equipment |
| CN116820014B (en) * | 2023-08-24 | 2023-11-14 | 山西交通科学研究院集团有限公司 | Intelligent monitoring and early warning method and system for traffic electromechanical equipment |
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