CN109376877B - Equipment operation and maintenance early warning method and device, computer equipment and storage medium - Google Patents
Equipment operation and maintenance early warning method and device, computer equipment and storage medium Download PDFInfo
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Abstract
The application relates to an equipment operation and maintenance early warning method, an equipment operation and maintenance early warning device, computer equipment and a storage medium, wherein the method comprises the following steps: acquiring equipment alarm data and routing inspection data in the historical records, analyzing the normal operation probability of the equipment under the influence of alarm and the normal operation probability of the equipment under the influence of defects to obtain the overall health degree of the equipment, and carrying out operation and maintenance early warning on the equipment according to the overall health degree of the equipment. In the whole process, the alarm data and the routing inspection data of the equipment are comprehensively considered to obtain the overall health degree of the equipment, the operation and maintenance of the equipment are pre-warned based on the overall health of the equipment, and the health degree of the equipment is measured in a data mode, so that the operation and maintenance pre-warning can be effectively realized.
Description
Technical Field
The present application relates to the field of operation and maintenance early warning technologies, and in particular, to an apparatus operation and maintenance early warning method, an apparatus, a computer device, and a storage medium.
Background
With the development of science and technology, more and more fields and products in production and life are gradually automated, and the automated equipment is used for replacing complicated human labor, so that the productivity is greatly improved.
In the process of realizing automation of the equipment, the running state of the equipment is effectively acquired in time, and the running state of the equipment is managed. The conventional device operation and maintenance management mode is to record data of device failure or alarm, and also record a failure solution adopted for the current failure.
The above method only records the fault and the fault solution data, and the fault solution data are all processing methods after the fault occurs, so that the operation and maintenance early warning of the equipment cannot be realized, and the normal operation of the equipment is seriously influenced.
Disclosure of Invention
Therefore, it is necessary to provide an operation and maintenance early warning method and apparatus for a device, a computer device, and a storage medium, which can effectively implement the operation and maintenance early warning, in order to solve the above technical problems.
An equipment operation and maintenance early warning method comprises the following steps:
acquiring equipment alarm data and routing inspection data in a historical record;
according to the equipment alarm data, counting equipment fault shutdown probabilities caused by various alarms to obtain normal equipment operation probabilities under the influence of the alarms;
according to the equipment inspection data, counting equipment fault shutdown probabilities caused by various defects to obtain normal operation probabilities of the equipment under the influence of the defects;
obtaining the overall health degree of the equipment according to the normal operation probability of the equipment under the alarm influence and the normal operation probability of the equipment under the defect influence;
and carrying out operation and maintenance early warning on the equipment according to the overall health degree of the equipment.
In one embodiment, the counting, according to the device alarm data, the device outage probability caused by various types of alarms includes:
according to the equipment alarm data, the alarm types and the corresponding times are counted, and the equipment fault shutdown probability caused by various alarms is calculated.
In one embodiment, the formula for obtaining the normal operation probability of the device under the influence of the alarm specifically includes:
in the formula, FD is the normal operation probability of the equipment under the influence of the alarm, n is the alarm type, X is the alarm frequency, and P isnThe probability of equipment outage due to alarm type n.
In one embodiment, the formula for obtaining the normal operation probability of the device under the influence of the defect specifically includes:
FM＝∏(1-Qm)
in the formula, FM is the probability of normal operation of the equipment under the influence of defects, m is the defect type, and QmProbability of equipment downtime for defect type m.
In one embodiment, the obtaining of the overall health of the device further includes:
acquiring the total health degree of the equipment corresponding to each day, and drawing an equipment daily health degree curve;
and analyzing the equipment health degree change trend according to the equipment daily health degree curve.
In one embodiment, the performing operation and maintenance early warning on the device according to the overall health degree of the device includes:
acquiring a dangerous threshold value of the health degree of the equipment, wherein when the overall health degree of the equipment is greater than the dangerous threshold value of the health degree, the condition that the equipment fails and stops meets a small probability event;
and when the overall health degree of the equipment is lower than the equipment health degree danger threshold value, the early warning equipment is in fault shutdown.
An equipment operation and maintenance early warning device, the device comprises:
the acquisition module is used for acquiring equipment alarm data and routing inspection data in the history record;
the first statistical module is used for counting equipment fault shutdown probabilities caused by various alarms according to the equipment alarm data to obtain the normal operation probability of the equipment under the influence of the alarm;
the second statistical module is used for counting equipment fault shutdown probabilities caused by various defects according to the equipment routing inspection data to obtain normal operation probabilities of the equipment under the influence of the defects;
the health degree obtaining module is used for obtaining the overall health degree of the equipment according to the normal operation probability of the equipment under the alarm influence and the normal operation probability of the equipment under the defect influence;
and the early warning module is used for carrying out operation and maintenance early warning on the equipment according to the overall health degree of the equipment.
In one embodiment, the first statistical module is further configured to count alarm types and corresponding times according to the equipment alarm data, and calculate equipment outage probabilities caused by various types of alarms.
A computer device comprising a memory storing a computer program and a processor implementing the steps of the method as described above when executing the computer program.
A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method as described above.
According to the equipment operation and maintenance early warning method and device, the computer equipment and the storage medium, equipment alarm data and routing inspection data in the historical records are obtained, the equipment normal operation probability under the alarm influence and the equipment normal operation probability under the defect influence are analyzed to obtain the overall health degree of the equipment, and operation and maintenance early warning is carried out on the equipment according to the overall health degree of the equipment. In the whole process, the alarm data and the routing inspection data of the equipment are comprehensively considered to obtain the overall health degree of the equipment, the operation and maintenance of the equipment are pre-warned based on the overall health of the equipment, and the health degree of the equipment is measured in a data mode, so that the operation and maintenance pre-warning can be effectively realized.
Drawings
FIG. 1 is a diagram of an application environment of an early warning method for operation and maintenance of a device in an embodiment;
FIG. 2 is a schematic flow chart illustrating an early warning method for operation and maintenance of a device according to an embodiment;
FIG. 3 is a schematic flow chart illustrating an early warning method for operation and maintenance of a device according to another embodiment;
FIG. 4 is a block diagram of an embodiment of an early warning apparatus for operation and maintenance of a device;
FIG. 5 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The device operation and maintenance early warning method provided by the application can be applied to the application environment shown in fig. 1. The equipment operation and maintenance early warning server is connected with equipment, receives equipment alarm data and routing inspection data sent by an equipment working condition system and a self-checking system, analyzes the equipment normal operation probability under the alarm influence and the equipment normal operation probability under the defect influence to obtain the overall health degree of the equipment, performs operation and maintenance early warning on the equipment according to the overall health degree of the equipment, and sends prompt information to an operator when the equipment is about to fail and shut down in early warning. The operation and maintenance early warning server of the equipment can be used for uniformly managing a plurality of pieces of equipment of the same type by a single server, and can also be used for simultaneously supporting the operation and maintenance early warning of the single equipment by a plurality of servers.
In an embodiment, as shown in fig. 2, an operation and maintenance early warning method for a device is provided, which is described by taking an example that the method is applied to the operation and maintenance early warning server in fig. 1, and includes the following steps:
s100: and acquiring equipment alarm data and routing inspection data in the history record.
The history record can be data recorded by an equipment working condition system or data such as a log recorded by an equipment automatic inspection system, and the like, wherein the data can record data such as equipment voltage, current, oil pressure, power and the like collected at each time point, and can also record time, times and types of alarms of the equipment and adopted solutions, and record data such as time and types of defects found by equipment inspection and measures adopted for the defects. Optionally, to achieve accurate operation and maintenance early warning, the operation and maintenance early warning server may obtain the latest device alarm data and inspection data in the history record, for example, obtain device alarm data and inspection data in the history record within 30 days, and continuously update the device alarm data and inspection data in the history record within 30 days as time goes by.
S200: and according to the equipment alarm data, counting equipment fault shutdown probabilities caused by various alarms to obtain the normal operation probability of the equipment under the alarm influence.
The alarm type and the alarm frequency of the equipment are recorded in the alarm data of the equipment, and the equipment fault shutdown probability caused by various alarms is counted. Optionally, the system may issue multiple types of alarms or multiple times of alarms of the same type over a period of time, count the types of alarms and the corresponding times, and count the probability of equipment downtime due to each type of alarm. In practical application, because the probability that the failure type alarm may cause equipment failure is different, the probability of equipment failure shutdown caused by different types of alarms is counted based on historical experience, the probability of equipment failure shutdown caused by the same type of alarms occurring for multiple times can be considered in a centralized and one-time mode to obtain the probability of equipment failure shutdown under the alarm influence, and the probability of equipment failure shutdown under the alarm influence is subtracted from 1 to obtain the probability of normal operation of equipment under the alarm influence.
Unnecessary, the specific calculation formula of the normal operation probability of the equipment under the influence of the alarm is as follows:
in the formula, FD is the normal operation probability of the equipment under the influence of the alarm, n is the alarm type, X is the alarm frequency, and P isnThe probability of equipment outage due to alarm type n.
S300: and counting equipment fault shutdown probabilities caused by various defects according to the equipment inspection data to obtain the normal operation probability of the equipment under the influence of the defects.
The type and time of each defect type are recorded in the equipment inspection data, and the equipment fault shutdown probability caused by each defect can be obtained based on historical experience data. In practical application, for example, power station equipment is patrolled and examined, in power station production management, equipment can be patrolled and examined regularly, and after the equipment is patrolled and examined, equipment defects can be found, and equipment defect information is recorded. In the existing national standards, the equipment defects are classified into A, B, C, and each station will make a defect category table under these three categories. And counting the equipment fault shutdown probability caused by various defects to obtain the equipment fault shutdown probability under the influence of the defects, and subtracting the equipment fault shutdown probability under the influence of the defects from 1 to obtain the normal operation probability of the equipment under the influence of the defects.
Unnecessarily, the specific calculation formula of the normal operation probability of the equipment under the influence of the defects is as follows:
FM＝Π(1-Qm)
in the formula, FM is the probability of normal operation of the equipment under the influence of defects, m is the defect type, and QmProbability of equipment downtime for defect type m.
S400: and obtaining the overall health degree of the equipment according to the normal operation probability of the equipment under the influence of the alarm and the normal operation probability of the equipment under the influence of the defect.
And (4) obtaining the normal operation probability of the equipment under the influence of the alarm and the defect to obtain the overall health degree of the equipment. The overall health degree of the equipment represents the probability of normal operation of the equipment under the current state.
S500: and carrying out operation and maintenance early warning on the equipment according to the overall health degree of the equipment.
Based on the above analysis, it can be understood that the lower the overall health of the device, the lower the probability that the device is likely to operate properly. Optionally, a facility health risk threshold may be set based on historical empirical data, and when the overall facility health is below the facility health risk threshold, a downtime may occur, requiring a maintenance process. Furthermore, the overall health of the equipment during the fault shutdown in the history may be analyzed, and the risk threshold for the health of the equipment should be higher than the overall health of the equipment corresponding to the majority of the fault shutdown. In particular, most of the meaning here means that the case of a fault shutdown of the plant satisfies a small probability event in the case of a total health of the plant greater than a preset health risk threshold. It can be understood that in practical application, the health degree danger threshold of the equipment can be continuously learned, updated and adjusted to ensure that the setting is reasonable, and the early warning can be accurately carried out on the operation and maintenance of the equipment.
As shown in fig. 3, in one embodiment, the step S500 includes:
s520: and acquiring a dangerous threshold value of the health degree of the equipment, wherein when the overall health degree of the equipment is greater than the dangerous threshold value of the health degree, the condition that the equipment fails and stops meets a small probability event.
S540: when the overall health degree of the equipment is lower than the equipment health degree danger threshold value, the early warning equipment is in fault shutdown.
A small probability event is the probability of occurrence of an event that is nearly impossible to occur in one trial but is inevitable in multiple iterations. In probability theory we refer to events with probabilities very close to 0 (i.e. very low frequency of occurrence in a large number of repeated trials) as small probability events.
According to the equipment operation and maintenance early warning method, equipment alarm data and routing inspection data in the historical records are obtained, the normal operation probability of the equipment under the influence of alarm and the normal operation probability of the equipment under the influence of defects are analyzed, the overall health degree of the equipment is obtained, and operation and maintenance early warning is carried out on the equipment according to the overall health degree of the equipment. In the whole process, the alarm data and the routing inspection data of the equipment are comprehensively considered to obtain the overall health degree of the equipment, the operation and maintenance of the equipment are pre-warned based on the overall health of the equipment, and the health degree of the equipment is measured in a data mode, so that the operation and maintenance pre-warning can be effectively realized.
As shown in fig. 3, in one embodiment, after step S400, the method further includes:
s420: and acquiring the total health degree of the equipment corresponding to each day, and drawing an equipment daily health degree curve.
S440: and analyzing the change trend of the health degree of the equipment according to the daily health degree curve of the equipment.
The daily health degree curve of equipment reflects equipment health degree situation of change every day, can analyze out based on equipment daily health degree curve, if not carry out necessary operation and maintenance to equipment along with the time, equipment health degree can constantly descend to it is faster at later stage equipment health degree decline speed. Based on the daily health degree curve of the equipment, the change trend of the health degree of the equipment can be analyzed, the descending trend and descending amplitude of the health degree of the equipment can be specifically analyzed, and data support is provided for follow-up operation and maintenance inspection.
In order to further explain the technical scheme of the device operation and maintenance early warning method in the application in detail, the whole scheme will be explained in detail by taking a hydropower station as an example and a specific application example. Specifically, it comprises the following processing steps:
1. in a power station, background monitoring is often performed on power generation-related equipment, and the monitoring includes various data such as current, voltage, oil pressure, power and the like. When the monitored data exceeds the normal range, an alarm is often reported.
2. In the last 30 days, it is assumed that n kinds of alarms have occurred, and the number of times of occurrence is X ═ X1,X2,X3,...XnThe probability that they cause equipment to fail down is P ═ P, respectively1,P2,P3,...PnGet the probability of normal operation of the device
FD may be understood as the degree of operational health.
3. In the power station production management, equipment can be regularly inspected, equipment defects can be found after inspection, and equipment defect information is recorded. In the national standard, the equipment defects are classified into A, B, C, and each station makes a defect category table under these three categories.
4. In the last 30 days, it is assumed that m defects are found, and the probability of causing the equipment to be in a fault shutdown is Q ═ Q1,Q2,Q3,...QmII, the probability FM that the equipment normally operates is pi (1-Q)m). FM can be understood as maintaining health.
5. We define the overall health of the device as FT ═ FD × FM, and can find that 1-FT is the probability of the device being in problem.
6. For a piece of equipment, an overall health curve of the equipment can be formed according to the day, and the health of the equipment can be expected to continuously decline along with the operation of the equipment without maintaining the equipment.
7. Through calculation of health degrees of a large number of devices of the same type and fault shutdown records, a health degree high-risk threshold value can be obtained (generally, fault shutdown records which occur below the health account for 97% or 99.7% of total records, and the probability of fault shutdown meets the probability of a small-probability event). The threshold can be used to identify high risk operating conditions of the equipment and to allow the user to make early maintenance to reduce losses.
It should be understood that although the various steps in the flow charts of fig. 2-3 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in fig. 2-3 may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternating with other steps or at least some of the sub-steps or stages of other steps.
As shown in fig. 4, an apparatus operation and maintenance early warning device includes:
the acquisition module 100 is used for acquiring equipment alarm data and routing inspection data in the history record;
the first statistical module 200 is used for counting equipment fault shutdown probabilities caused by various alarms according to the equipment alarm data to obtain the normal operation probability of the equipment under the influence of the alarm;
the second statistical module 300 is used for counting equipment fault shutdown probabilities caused by various defects according to the equipment inspection data to obtain normal operation probabilities of the equipment under the influence of the defects;
the health degree obtaining module 400 is used for obtaining the overall health degree of the equipment according to the normal operation probability of the equipment under the influence of the alarm and the normal operation probability of the equipment under the influence of the defect;
and the early warning module 500 is used for carrying out operation and maintenance early warning on the equipment according to the overall health degree of the equipment.
According to the device operation and maintenance early warning device, the acquisition module 100 acquires device alarm data and routing inspection data in a historical record, the first statistical module 200 and the second statistical module 300 analyze the device normal operation probability under the alarm influence and the device normal operation probability under the defect influence, the health degree acquisition module 400 acquires the overall health degree of the device, and the early warning module 500 performs operation and maintenance early warning on the device according to the overall health degree of the device. In the whole process, the alarm data and the routing inspection data of the equipment are comprehensively considered to obtain the overall health degree of the equipment, the operation and maintenance of the equipment are pre-warned based on the overall health of the equipment, and the health degree of the equipment is measured in a data mode, so that the operation and maintenance pre-warning can be effectively realized.
In one embodiment, the first statistical module 200 is further configured to count alarm types and corresponding times according to the equipment alarm data, and calculate equipment outage probabilities caused by various types of alarms.
In one embodiment, the formula for obtaining the normal operation probability of the device under the influence of the alarm by the first statistical module 200 is specifically as follows:
in the formula, FD is the normal operation probability of the equipment under the influence of the alarm, n is the alarm type, X is the alarm frequency, and P isnThe probability of equipment outage due to alarm type n.
In one embodiment, the formula for obtaining the normal operation probability of the device under the influence of the defect by the second statistical module 300 is specifically as follows:
FM＝Π(1-Qm)
in the formula, FM is the probability of normal operation of the equipment under the influence of defects, m is the defect type, and QmProbability of equipment downtime for defect type m.
In one embodiment, the device operation and maintenance early warning apparatus further includes:
the analysis module is used for acquiring the total health degree of the equipment corresponding to each day and drawing an equipment daily health degree curve; and analyzing the change trend of the health degree of the equipment according to the daily health degree curve of the equipment.
In one embodiment, the early warning module 500 is further configured to obtain a health risk threshold of the device, and when the overall health of the device is greater than the health risk threshold, the device is in a fault shutdown state and meets a small probability event; when the overall health degree of the equipment is lower than the equipment health degree danger threshold value, the early warning equipment is in fault shutdown.
For specific limitations of the device operation and maintenance early warning apparatus, reference may be made to the above limitations on the device operation and maintenance early warning method, which is not described herein again. All modules in the device operation and maintenance early warning device can be completely or partially realized through software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 5. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer equipment is used for storing data such as equipment alarm logs, routing inspection logs and the like. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to realize a device operation and maintenance early warning method.
Those skilled in the art will appreciate that the architecture shown in fig. 5 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the following steps when executing the computer program:
acquiring equipment alarm data and routing inspection data in a historical record;
according to the equipment alarm data, counting equipment fault shutdown probabilities caused by various alarms to obtain normal equipment operation probabilities under the influence of the alarms;
according to the equipment inspection data, equipment fault shutdown probabilities caused by various defects are counted to obtain normal operation probabilities of the equipment under the influence of the defects;
obtaining the overall health degree of the equipment according to the normal operation probability of the equipment under the influence of the alarm and the normal operation probability of the equipment under the influence of the defect;
and carrying out operation and maintenance early warning on the equipment according to the overall health degree of the equipment.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
according to the equipment alarm data, the alarm types and the corresponding times are counted, and the equipment fault shutdown probability caused by various alarms is calculated.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
acquiring the total health degree of the equipment corresponding to each day, and drawing an equipment daily health degree curve; and analyzing the change trend of the health degree of the equipment according to the daily health degree curve of the equipment.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
acquiring a health degree danger threshold of the equipment, wherein when the overall health degree of the equipment is greater than the health degree danger threshold, the equipment is in a fault shutdown condition to meet a small probability event; when the overall health degree of the equipment is lower than the equipment health degree danger threshold value, the early warning equipment is in fault shutdown.
In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of:
acquiring equipment alarm data and routing inspection data in a historical record;
according to the equipment alarm data, counting equipment fault shutdown probabilities caused by various alarms to obtain normal equipment operation probabilities under the influence of the alarms;
according to the equipment inspection data, equipment fault shutdown probabilities caused by various defects are counted to obtain normal operation probabilities of the equipment under the influence of the defects;
obtaining the overall health degree of the equipment according to the normal operation probability of the equipment under the influence of the alarm and the normal operation probability of the equipment under the influence of the defect;
and carrying out operation and maintenance early warning on the equipment according to the overall health degree of the equipment.
In one embodiment, the computer program when executed by the processor further performs the steps of:
according to the equipment alarm data, the alarm types and the corresponding times are counted, and the equipment fault shutdown probability caused by various alarms is calculated.
In one embodiment, the computer program when executed by the processor further performs the steps of:
acquiring the total health degree of the equipment corresponding to each day, and drawing an equipment daily health degree curve; and analyzing the change trend of the health degree of the equipment according to the daily health degree curve of the equipment.
In one embodiment, the computer program when executed by the processor further performs the steps of:
acquiring a health degree danger threshold of the equipment, wherein when the overall health degree of the equipment is greater than the health degree danger threshold, the equipment is in a fault shutdown condition to meet a small probability event; when the overall health degree of the equipment is lower than the equipment health degree danger threshold value, the early warning equipment is in fault shutdown.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware related to instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. An equipment operation and maintenance early warning method comprises the following steps:
acquiring equipment alarm data and routing inspection data in a historical record;
according to the equipment alarm data, counting equipment fault shutdown probabilities caused by various alarms to obtain normal equipment operation probabilities under the influence of the alarms;
according to the equipment inspection data, counting equipment fault shutdown probabilities caused by various defects to obtain normal operation probabilities of the equipment under the influence of the defects;
obtaining the overall health degree of the equipment according to the product of the normal operation probability of the equipment under the alarm influence and the normal operation probability of the equipment under the defect influence;
and carrying out operation and maintenance early warning on the equipment according to the overall health degree of the equipment.
2. The method according to claim 1, wherein the step of counting the probability of equipment failure and shutdown caused by various types of alarms according to the equipment alarm data comprises the following steps:
according to the equipment alarm data, the alarm types and the corresponding times are counted, and the equipment fault shutdown probability caused by various alarms is calculated.
3. The method according to claim 2, wherein the formula for obtaining the probability of normal operation of the device under the influence of the alarm is specifically:
in the formula, FD is the normal operation probability of the equipment under the influence of the alarm, n is the alarm type, and X isnThe number of alarms corresponding to the alarm type n, PnThe probability of equipment outage due to alarm type n.
4. The method according to claim 1 or 2, wherein the formula for obtaining the normal operation probability of the device under the influence of the defect is specifically:
FM＝∏(1-Qm)
in the formula, FM is the probability of normal operation of the equipment under the influence of defects, m is the defect type, and QmProbability of equipment downtime for defect type m.
5. The method of claim 1 or 2, wherein the obtaining of the overall health of the device further comprises:
acquiring the total health degree of the equipment corresponding to each day, and drawing an equipment daily health degree curve;
and analyzing the equipment health degree change trend according to the equipment daily health degree curve.
6. The method according to claim 1 or 2, wherein the operation and maintenance early warning of the equipment according to the overall health degree of the equipment comprises the following steps:
acquiring a dangerous threshold value of the health degree of the equipment, wherein when the overall health degree of the equipment is greater than the dangerous threshold value of the health degree, the condition that the equipment fails and stops meets a small probability event;
and when the overall health degree of the equipment is lower than the equipment health degree danger threshold value, the early warning equipment is in fault shutdown.
7. The device is characterized in that the device comprises:
the acquisition module is used for acquiring equipment alarm data and routing inspection data in the history record;
the first statistical module is used for counting equipment fault shutdown probabilities caused by various alarms according to the equipment alarm data to obtain the normal operation probability of the equipment under the influence of the alarm;
the second statistical module is used for counting equipment fault shutdown probabilities caused by various defects according to the equipment routing inspection data to obtain normal operation probabilities of the equipment under the influence of the defects;
the health degree obtaining module is used for obtaining the overall health degree of the equipment according to the product of the normal operation probability of the equipment under the alarm influence and the normal operation probability of the equipment under the defect influence;
and the early warning module is used for carrying out operation and maintenance early warning on the equipment according to the overall health degree of the equipment.
8. The device according to claim 7, wherein the first statistic module is further configured to count types and corresponding times of alarms according to the equipment alarm data, and calculate the equipment outage probability caused by each type of alarm.
9. A computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor implements the steps of the method of any one of claims 1 to 6 when executing the computer program.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 6.
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| CN113541982B (en) * | 2020-04-14 | 2023-04-28 | 中国移动通信集团浙江有限公司 | Health early warning method and device for network element, computing equipment and computer storage medium |
| CN111626597A (en) * | 2020-05-22 | 2020-09-04 | 广东电网有限责任公司 | Equipment alarm method, device, equipment and medium |
| CN112214866B (en) * | 2020-08-25 | 2023-11-17 | 武汉船用机械有限责任公司 | Marine equipment health condition judging method and device and computer equipment |
| CN112433926B (en) * | 2020-11-27 | 2024-03-01 | 中国建设银行股份有限公司 | IT product-based fault analysis method, system, equipment and storage medium |
| CN112762990A (en) * | 2020-12-29 | 2021-05-07 | 陕西新泓水艺环境科技有限公司 | Prediction method and prediction system for equipment health degree |
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