A device state monitoring method, apparatus, device, and storage medium

Abstract

The application discloses a device state monitoring method and device, equipment and a storage medium, and relates to the technical field of computers, and comprises the following steps: determining a pre-warning event determination criterion corresponding to a target device, and monitoring a device pre-warning event in a running process of the target device based on the pre-warning event determination criterion; establishing a target reliability evaluation model, and determining the reliability of the target device when the device pre-warning event occurs based on the target reliability evaluation model; comparing the reliability with a pre-determined maintenance threshold to obtain a corresponding comparison result; judging whether the target device is in a fault state based on the comparison result, and if so, maintaining the target device. The application determines the pre-warning event determination criterion corresponding to the target device, solves the problem of inaccurate pre-warning event definition, and then determines the reliability of the target device when the device pre-warning event occurs based on the established target reliability evaluation model, so as to realize device state monitoring based on the combination of pre-warning events and device running reliability.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and in particular to a method, apparatus, device, and storage medium for monitoring device status. Background Technology

[0002] Currently, in the process of digital transformation, power plants are transitioning from traditional experience-based preventative maintenance to data-driven predictive maintenance to improve equipment reliability and reduce maintenance costs. Early warning technology is a crucial means of achieving predictive maintenance. This involves monitoring equipment status data to diagnose whether the current equipment status exceeds threshold limits, triggering an early warning event, and using algorithms to assess the health trend of the equipment over a period of time. However, simply comparing detected values ​​with threshold limits leads to a vague definition of early warning events. The impact of early warning events on equipment health is not reflected in reliability analysis, resulting in significant discrepancies between analyzed and actual data. Furthermore, the quantitative data generated by early warning events is often wasted, failing to effectively leverage its value. Additionally, there is a lack of analysis on the impact of early warning event data on equipment operational reliability, and early warning event data is not effectively integrated with reliability data.

[0003] In summary, existing early warning technologies do not effectively mine early warning event data, nor do they organically integrate it with power generation equipment reliability data, thus failing to fully realize equipment health management activities. Therefore, providing a solution to the above-mentioned technical problems is a problem that needs to be solved by those skilled in the art. Summary of the Invention

[0004] In view of this, the purpose of this invention is to provide a method, apparatus, device, and storage medium for equipment status monitoring, which can solve the problem of inaccurate definition of early warning events and realize equipment status monitoring based on a combination of early warning events and equipment operational reliability. The specific solution is as follows:

[0005] In a first aspect, this application discloses a method for monitoring equipment status, including:

[0006] Determine the criteria for judging early warning events corresponding to the target equipment, and monitor the equipment early warning events during the operation of the target equipment based on the criteria for judging early warning events;

[0007] Establish a target reliability assessment model, and determine the reliability of the target device when the device warning event occurs based on the target reliability assessment model;

[0008] The reliability is compared with a predetermined maintenance threshold to obtain the corresponding comparison result;

[0009] Based on the comparison results, it is determined whether the target device is in a faulty state. If so, maintenance is performed on the target device.

[0010] Optionally, the criteria for determining the early warning event corresponding to the target device include:

[0011] Determine the data type of the target device's status;

[0012] Based on the state data type, determine the upper and lower limits of the state data corresponding to the target device, and determine the state data control domain corresponding to the target device;

[0013] The criteria for determining the early warning event corresponding to the target device are determined based on the upper and lower limits of the status data and the status data control domain.

[0014] Optionally, determining the upper and lower limits of the state data corresponding to the target device based on the state data type includes:

[0015] The first state data of the target device at the time of its manufacture is determined based on the state data type, and the second state data is determined based on the state data generated by the target device during actual operation.

[0016] The upper and lower limits of the status data corresponding to the target device are determined based on the first status data and the second status data.

[0017] Optionally, determining the status data control domain corresponding to the target device includes:

[0018] Collect the status data generated by the target device during actual operation according to the preset data collection duration and data collection frequency, obtain the corresponding status dataset, and determine the corresponding data distribution parameters based on the status dataset;

[0019] The status data control domain corresponding to the target device is determined based on the data distribution parameters.

[0020] Optionally, establishing the target reliability assessment model includes:

[0021] Establish an inverse power law model based on the device's early warning events and a reliability model of the target device under normal operating conditions, and establish a corresponding reliability assessment model based on the inverse power law model and the reliability model;

[0022] Configure the corresponding model parameters for the reliability assessment model to obtain the corresponding target reliability assessment model.

[0023] Optionally, establishing a corresponding reliability assessment model based on the inverse power law model and the reliability model includes:

[0024] Establish the correlation function between the inverse power law model and the reliability model, and establish the corresponding transition function;

[0025] The corresponding reliability model is determined based on the correlation point function and the transition function.

[0026] Optionally, determining whether the target device is in a faulty state based on the comparison result, and if so, performing maintenance on the target device, includes:

[0027] If the comparison result indicates that the reliability is less than the preset threshold, then the target device is determined to be in a faulty state, and the target device is maintained.

[0028] If the comparison result indicates that the reliability is not less than the preset threshold, then the target device is determined to be in a non-faulty state.

[0029] Secondly, this application discloses an equipment status monitoring device, comprising:

[0030] The judgment criterion determination module is used to determine the judgment criteria for the early warning event corresponding to the target device;

[0031] The early warning event monitoring module is used to monitor equipment early warning events during the operation of the target equipment based on the early warning event judgment criteria.

[0032] The model building module is used to build a target reliability assessment model;

[0033] A reliability determination module is used to determine the reliability of the target device when the device warning event occurs, based on the target reliability evaluation model.

[0034] The comparison module is used to compare the reliability with a predetermined maintenance threshold to obtain a corresponding comparison result;

[0035] The status determination module is used to determine whether the target device is in a fault state based on the comparison result. If so, the target device is maintained.

[0036] Thirdly, this application discloses an electronic device, including:

[0037] Memory, used to store computer programs;

[0038] A processor is used to execute the computer program to implement the steps of the aforementioned disclosed device status monitoring method.

[0039] Fourthly, this application discloses a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, it implements the steps of the aforementioned disclosed device status monitoring method.

[0040] As can be seen, this application provides a method for monitoring equipment status, including: determining the judgment criteria for early warning events corresponding to a target device, and monitoring equipment early warning events during the operation of the target device based on the early warning event judgment criteria; establishing a target reliability assessment model, and determining the reliability of the target device when the equipment early warning event occurs based on the target reliability assessment model; comparing the reliability with a pre-determined maintenance threshold to obtain a corresponding comparison result; determining whether the target device is in a fault state based on the comparison result, and if so, performing maintenance on the target device. Therefore, this application monitors equipment early warning events during the operation of a target device based on the determined judgment criteria for early warning events corresponding to the target device, and then determines the reliability of the target device when the equipment early warning event occurs based on the established target reliability assessment model. Based on the comparison result of the reliability with a pre-determined maintenance threshold, it determines whether the target device is in a fault state, and if so, performs maintenance on the target device. Therefore, this application can solve the problem of inaccurate early warning event definitions and realize equipment status monitoring based on a combination of early warning events and equipment operational reliability. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0042] Figure 1 This is a flowchart of a device status monitoring method disclosed in this application;

[0043] Figure 2 This is a schematic diagram illustrating the reliability changes of a device disclosed in this application during actual operation.

[0044] Figure 3 This is a schematic diagram of the structure of an equipment status monitoring device disclosed in this application;

[0045] Figure 4 This is a structural diagram of an electronic device disclosed in this application. Detailed Implementation

[0046] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0047] Currently, existing early warning technologies do not effectively mine early warning event data, nor are they organically integrated with power generation equipment reliability data, thus failing to fully realize equipment health management activities. Therefore, this application provides an equipment status monitoring scheme that can solve the problem of inaccurate early warning event definitions and achieve equipment status monitoring based on a combination of early warning events and equipment operational reliability.

[0048] This invention discloses a method for monitoring device status, see [link to relevant documentation]. Figure 1 As shown, the method includes:

[0049] Step S11: Determine the judgment criteria for the early warning event corresponding to the target device, and monitor the device early warning events during the operation of the target device based on the early warning event judgment criteria.

[0050] In this embodiment, the early warning event judgment criteria corresponding to the target equipment are first determined to standardize the equipment early warning events and solve the problem of uncertain early warning event definitions. For example, when the target equipment is a diesel engine fire pump widely used in power plants, the early warning event judgment criteria corresponding to the diesel engine fire pump during daily operation are formulated. Specifically, the status data type of the target equipment is determined; the upper and lower limits of the status data corresponding to the target equipment are determined according to the status data type, and the status data control domain corresponding to the target equipment is determined; the early warning event judgment criteria corresponding to the target equipment are determined based on the upper and lower limits of the status data and the status data control domain. It can be understood that the process of determining the upper and lower limits of the status data may include: determining the first status data rated at the factory of the target equipment according to the status data type, and determining the second status data based on the status data generated by the target equipment during actual operation; and determining the upper and lower limits of the status data corresponding to the target equipment based on the first status data and the second status data. For example, based on the operating parameters provided by the diesel engine fire pump equipment manufacturer, the main status data type of the fire pump is determined to be pressure data. The rated pressure corresponding to the pressure data of the diesel engine fire pump is 1.365 MPa. Therefore, the first status data can be used as the upper limit of the status data. In other words, 1.365 MPa is determined as the upper limit of the pressure data. Since the daily power generation of the power plant changes with seasonal demand, the usage requirements of the fire pump also change accordingly, and the operating data will also differ. Based on the actual usage requirements of the diesel engine fire pump, the status data of the diesel engine fire pump during actual operation is determined. Analyzing the status data during actual operation, it is concluded that the minimum pressure of the diesel engine fire pump during actual operation must not be lower than 1.0 MPa. Therefore, the second status data can be used as the lower limit of the status data. In other words, 1.0 MPa is determined as the lower limit of the pressure data. Therefore, the determined upper and lower limits of the status data corresponding to the target equipment are the upper and lower limits of the pressure status data (1.0, 1.365), in MPa. Furthermore, the process of determining the aforementioned state data control domain may include: collecting the state data generated by the target device during actual operation according to a preset data acquisition duration and frequency, obtaining a corresponding state dataset, and determining the corresponding data distribution parameters based on the state dataset; and determining the state data control domain corresponding to the target device based on the data distribution parameters. For example, the data acquisition duration is determined to be 1 hour, and the data acquisition frequency is 5 data points randomly selected at a 5-minute interval. The collected data is shown in Table 1. The pressure data of the fire pump during actual operation is a continuous variable and is represented using a mean-range plot.

[0051] Table 1

[0052]

[0053] Since the collected pressure data follows a normal distribution, the mean value is determined to be:

[0054]

[0055] The determined standard deviation is:

[0056]

[0057] Then, based on the 3σ principle, the control domain for the pressure data is determined using the above mean and standard deviation:

[0058]

[0059] The upper limit for warning events can be:

[0060]

[0061] The lower limit for a warning event can be:

[0062]

[0063] Statistical analysis of the data shows that the probability of the data distribution within the control domain is 0.9973, meaning that the likelihood of the status data generated by the target device during actual operation exceeding the control domain is very small. Then, based on the upper and lower limits of the status data and the control domain, a judgment criterion for the corresponding early warning event of the target device is determined, enabling more refined and accurate early warning event management. This criterion may include, but is not limited to: First, the data is either outside or within the control domain; Second, within the control domain, there are nine consecutive status data points above or below the mean; Third, multiple status data points repeatedly approach the control domain, i.e., multiple data points are within a range of 2 to 3 standard deviations; Fourth, six or more consecutive data points show an upward or downward trend; Fifth, adjacent data points in 14 consecutive data points alternate between rising and falling; Sixth, the data points in the status data set are concentrated near the center line, i.e., within 0.5 standard deviations. In other words, this embodiment establishes reasonable and standardized standards for early warning events, defining what kind of early warning event is based on the stability of the data generated by the device during operation, in addition to judging the upper and lower thresholds. If the status data generated by the target device during actual operation is outside the defined upper and lower limits of the status data, then the status data is directly determined to be faulty data. Then, based on the aforementioned warning event determination criteria, the device warning events during the operation of the target device are monitored; that is, warning events are recorded during the actual operation of the target device according to the aforementioned warning event determination criteria.

[0064] Step S12: Establish a target reliability assessment model, and determine the reliability of the target device when the device warning event occurs based on the target reliability assessment model.

[0065] In this embodiment, a target reliability assessment model is established, and then the reliability of the target device when the device warning event occurs is determined based on the target reliability assessment model. This solves the problem of inaccuracy in the prior art that relies only on time parameters for device reliability assessment. By incorporating the warning event factor into the device reliability calculation, the device health assessment factors are made more multidimensional and accurate.

[0066] In this embodiment, establishing the target reliability assessment model may include: establishing an inverse power-law model based on the equipment warning event and a reliability model of the target equipment under normal operating conditions; and establishing a corresponding reliability assessment model based on the inverse power-law model and the reliability model; configuring corresponding model parameters for the reliability assessment model to obtain the corresponding target reliability assessment model. It is understood that when the target equipment is subjected to stress beyond its normal operating load, the remaining service life of the target equipment is often inversely proportional to the power of its corresponding dominant stress. Therefore, an inverse power-law model based on the equipment warning event can be established to reflect the relationship between the remaining service life of the target equipment and the dominant stress.

[0067] For example, the inverse power-law model based on the device's early warning event can be expressed as:

[0068]

[0069] Where V represents the stress level, K and n represent model parameters determined based on sample elements, and L represents a quantifiable life index.

[0070] Since the stress level V is generated with the occurrence of the warning event, and this stress level V is related to time t, the stress level V can be expressed as V(t).

[0071] And when When the inverse power law model is, it can be expressed as:

[0072]

[0073] Since the target equipment contains many components, and the failure patterns of each component are different, an exponential distribution model can be used to evaluate the reliability of the equipment under normal operation. For example, the reliability probability density model can be expressed as:

[0074] f(t)=λe -λt ;

[0075] when When, m represents the equipment's service life. Based on the properties of the exponential distribution, the above reliability probability density model can be expressed as:

[0076]

[0077] In this embodiment, establishing a corresponding reliability assessment model based on the inverse power law model and the reliability model may include: establishing a correlation point function between the inverse power law model and the reliability model, and establishing a corresponding transition function; determining a corresponding reliability model based on the correlation point function and the transition function. It is understood that when a warning event occurs, the equipment experiences accelerated failure on top of normal failures due to the warning event. Therefore, the inverse power law model and the reliability probability density model can be combined to assess the equipment's reliability when a warning event occurs. Thus, a target reliability assessment model involving the warning event in reliability is established based on the inverse power law model and the reliability model. For example, the reliability probability density model shows that the target equipment's lifespan m is related to time t and stress level V. Combining this with the inverse power law model, the correlation point function is obtained as follows:

[0078] m(t, V) = L(V(t));

[0079] The corresponding transition function established above can be expressed as:

[0080]

[0081] Therefore, the reliability assessment model for the participation reliability of early warning events, determined by combining the above correlation point function and the above transition function, is as follows:

[0082]

[0083] It should be noted that when configuring the corresponding model parameters for the reliability assessment model, the maximum likelihood method can be used to determine the model parameters. For example, for the above reliability assessment model f(t)... i The model selects z sample elements, namely: t1, t2, t3, ..., t z The above reliability assessment model f(t) i The result obtained by performing cumulative multiplication on the V) model is:

[0084]

[0085] Accordingly, taking the logarithm of this result yields the following likelihood function:

[0086]

[0087] Then, after taking the partial derivatives of the parameters A and n in the likelihood function respectively and setting the results to 0, we obtain:

[0088]

[0089]

[0090] Therefore, A0 and n0 that maximize the likelihood function are calculated, meaning the model parameters of the above reliability assessment model can be A0 and n0. Then, using A0 and n0, the reliability assessment model is configured, resulting in the target reliability assessment model:

[0091] R(t)=P(X≥t)=∫ t ∞ f(t, V)dx.

[0092] Step S13: Compare the reliability with the predetermined maintenance threshold to obtain the corresponding comparison result.

[0093] In this embodiment, the predetermined maintenance threshold is generally confirmed by the equipment reliability analysis team based on expert experience, and the fixed maintenance threshold L can be used as the standard for judging whether the equipment is healthy.

[0094] Step S14: Based on the comparison result, determine whether the target device is in a faulty state. If so, perform maintenance on the target device.

[0095] In this embodiment, if the comparison result indicates that the reliability is less than the preset threshold, the target device is determined to be in a faulty state, and maintenance is performed on the target device, indicating that the target device is unhealthy and requires maintenance. If the comparison result indicates that the reliability is not less than the preset threshold, the target device is determined to be in a non-faulty state, indicating that the target device is healthy and does not require maintenance. Figure 2 As shown, at time t0, the equipment reliability is about to fall below the maintenance threshold, and maintenance should be performed on the equipment at this time.

[0096] As can be seen, in this embodiment, the device early warning events during the operation of the target device are monitored based on the early warning event judgment criteria corresponding to the target device. Then, based on the established target reliability assessment model, the reliability of the target device at the time of the early warning event is determined. Finally, based on the comparison between this reliability and a pre-determined maintenance threshold, it is determined whether the target device is in a faulty state. If so, maintenance is performed on the target device. Therefore, this application can solve the problem of inaccurate early warning event definition and realize device status monitoring based on a combination of early warning events and device operational reliability.

[0097] Accordingly, this application also discloses a device for monitoring device status, see [link to relevant documentation]. Figure 3 As shown, the device includes:

[0098] The judgment criterion determination module 11 is used to determine the judgment criteria for the early warning event corresponding to the target device;

[0099] The early warning event monitoring module 12 is used to monitor equipment early warning events during the operation of the target equipment based on the early warning event judgment criteria.

[0100] Model building module 13 is used to build a target reliability assessment model;

[0101] Reliability determination module 14 is used to determine the reliability of the target device when the device warning event occurs based on the target reliability evaluation model;

[0102] Comparison module 15 is used to compare the reliability with a predetermined maintenance threshold to obtain a corresponding comparison result;

[0103] The status judgment module 16 is used to determine whether the target device is in a fault state based on the comparison result. If so, the target device is maintained.

[0104] As can be seen from the above, this embodiment monitors equipment warning events during the operation of the target equipment based on the judgment criteria for warning events corresponding to the target equipment. Then, based on the established target reliability assessment model, it determines the reliability of the target equipment when a warning event occurs. Finally, it determines whether the target equipment is in a faulty state based on the comparison result between this reliability and a pre-determined maintenance threshold. If so, maintenance is performed on the target equipment. Therefore, this application can solve the problem of inaccurate definition of warning events and realize equipment status monitoring based on a combination of warning events and equipment operational reliability.

[0105] In some specific embodiments, the determination criterion module 11 may specifically include:

[0106] A type determination unit is used to determine the state data type of the target device;

[0107] The upper and lower limit determination unit is used to determine the upper and lower limits of the state data corresponding to the target device based on the state data type.

[0108] A control domain determination unit is used to determine the state data control domain corresponding to the target device;

[0109] The judgment criterion determination unit is used to determine the judgment criterion for the early warning event corresponding to the target device based on the upper and lower limits of the status data and the status data control domain.

[0110] In some specific embodiments, the upper and lower limit determination unit may specifically include:

[0111] The first data determination subunit is used to determine the factory-rated first state data of the target device based on the state data type.

[0112] The second data determination subunit is used to determine the second state data based on the state data generated by the target device during actual operation.

[0113] The upper and lower limit determination subunit is used to determine the upper and lower limits of the state data corresponding to the target device based on the first state data and the second state data.

[0114] In some specific embodiments, the control domain determination unit may specifically include:

[0115] The data acquisition subunit is used to collect the status data generated by the target device during actual operation according to a preset data acquisition duration and data acquisition frequency, and obtain the corresponding status dataset.

[0116] The distribution parameter determination subunit is used to determine the corresponding data distribution parameters based on the state dataset.

[0117] The control domain determination subunit is used to determine the state data control domain corresponding to the target device based on the data distribution parameters.

[0118] In some specific embodiments, the model building module 13 may specifically include:

[0119] The first model building unit is used to build an inverse power law model based on the device's early warning events and a reliability model of the target device under normal operating conditions.

[0120] The second model building unit is used to build a corresponding reliability evaluation model based on the inverse power law model and the reliability model;

[0121] The model parameter configuration unit is used to configure the corresponding model parameters for the reliability assessment model to obtain the corresponding target reliability assessment model.

[0122] In some specific embodiments, the second model building unit may specifically include:

[0123] The function establishes a sub-unit, which is used to establish the correlation point function between the inverse power law model and the reliability model, and to establish the corresponding transition function;

[0124] The model determination sub-unit is used to determine the corresponding reliability model based on the correlation point function and the transition function.

[0125] In some specific embodiments, the state determination module 16 may specifically include:

[0126] The first determination unit is used to determine that the target device is in a fault state and to perform maintenance on the target device when the comparison result shows that the reliability is less than the preset threshold.

[0127] The first determination unit is used to determine that the target device is in a non-faulty state when the comparison result shows that the reliability is not less than the preset threshold.

[0128] Furthermore, embodiments of this application also provide an electronic device. Figure 4 This is a structural diagram of an electronic device 20 according to an exemplary embodiment. The content of the diagram should not be construed as limiting the scope of this application.

[0129] Figure 4 This is a schematic diagram of the structure of an electronic device 20 provided in an embodiment of this application. Specifically, the electronic device 20 may include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. The memory 22 stores a computer program, which is loaded and executed by the processor 21 to implement the relevant steps in the device status monitoring method disclosed in any of the foregoing embodiments. Furthermore, the electronic device 20 in this embodiment may specifically be an electronic computer.

[0130] In this embodiment, the power supply 23 is used to provide operating voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and external devices, and the communication protocol it follows can be any communication protocol applicable to the technical solution of this application, and is not specifically limited here; the input / output interface 25 is used to acquire external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs, and is not specifically limited here.

[0131] In addition, the memory 22, as a carrier for resource storage, can be a read-only memory, random access memory, disk or optical disk, etc. The resources stored thereon can include operating system 221, computer program 222, etc., and the storage method can be temporary storage or permanent storage.

[0132] The operating system 221 is used to manage and control the various hardware devices on the electronic device 20 and the computer program 222, which may be Windows Server, Netware, Unix, Linux, etc. In addition to including a computer program capable of performing the device status monitoring method executed by the electronic device 20 as disclosed in any of the foregoing embodiments, the computer program 222 may further include a computer program capable of performing other specific tasks.

[0133] Furthermore, embodiments of this application also disclose a computer-readable storage medium storing a computer program. When the computer program is loaded and executed by a processor, it implements the device status monitoring method steps disclosed in any of the foregoing embodiments.

[0134] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to in the method section.

[0135] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0136] The above provides a detailed description of the device status monitoring method, apparatus, equipment, and storage medium provided by the present invention. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for monitoring equipment status, characterized in that, include: Determine the criteria for judging early warning events corresponding to the target equipment, and monitor the equipment early warning events during the operation of the target equipment based on the criteria for judging early warning events; Establish a target reliability assessment model, and determine the reliability of the target device when the device warning event occurs based on the target reliability assessment model; The reliability is compared with a predetermined maintenance threshold to obtain the corresponding comparison result; Based on the comparison results, it is determined whether the target device is in a faulty state. If so, maintenance is performed on the target device. The criteria for determining the early warning event corresponding to the target device include: Determine the data type of the target device's status; Based on the state data type, determine the upper and lower limits of the state data corresponding to the target device, and determine the state data control domain corresponding to the target device; The warning event judgment criteria for the target device are determined based on the upper and lower limits of the status data and the status data control domain. The warning event judgment criteria include, but are not limited to: data outside or within the status data control domain; nine consecutive status data points within the status data control domain that are above or below the mean; multiple status data points within a range of 2 to 3 times the standard deviation; six or more consecutive data points showing an upward or downward trend; adjacent data points alternating between upward and downward movements in 14 consecutive data points; and data points in the status data set concentrated within a range of 0.5 times the standard deviation. The establishment of the target reliability assessment model includes: Establish an inverse power law model based on the device's early warning events and a reliability model of the target device under normal operating conditions, and establish a corresponding reliability assessment model based on the inverse power law model and the reliability model; Configure the corresponding model parameters for the reliability assessment model to obtain the corresponding target reliability assessment model; The establishment of a corresponding reliability assessment model based on the inverse power law model and the reliability model includes: Establish the correlation function between the inverse power law model and the reliability model, and establish the corresponding transition function; The corresponding reliability model is determined based on the correlation point function and the transition function; The step of determining the upper and lower limits of the state data corresponding to the target device based on the state data type includes: The first state data of the target device at the time of its manufacture is determined based on the state data type, and the second state data is determined based on the state data generated by the target device during actual operation. Determine the upper and lower limits of the status data corresponding to the target device based on the first status data and the second status data; The step of determining the status data control domain corresponding to the target device includes: Collect the status data generated by the target device during actual operation according to the preset data collection duration and data collection frequency, obtain the corresponding status dataset, and determine the corresponding data distribution parameters based on the status dataset; The status data control domain corresponding to the target device is determined based on the data distribution parameters.

2. The equipment status monitoring method according to claim 1, characterized in that, The step of determining whether the target device is in a faulty state based on the comparison result, and if so, performing maintenance on the target device, includes: If the comparison result indicates that the reliability is less than a preset threshold, then the target device is determined to be in a faulty state, and the target device is maintained. If the comparison result indicates that the reliability is not less than the preset threshold, then the target device is determined to be in a non-faulty state.

3. A device for monitoring equipment status, characterized in that, include: The judgment criterion determination module is used to determine the judgment criteria for the early warning event corresponding to the target device; The early warning event monitoring module is used to monitor equipment early warning events during the operation of the target equipment based on the early warning event judgment criteria. The model building module is used to build a target reliability assessment model; A reliability determination module is used to determine the reliability of the target device when the device warning event occurs, based on the target reliability evaluation model. The comparison module is used to compare the reliability with a predetermined maintenance threshold to obtain a corresponding comparison result; The status judgment module is used to determine whether the target device is in a fault state based on the comparison result; if so, the target device is maintained. The criteria for determining the early warning event corresponding to the target device include: Determine the data type of the target device's status; Based on the state data type, determine the upper and lower limits of the state data corresponding to the target device, and determine the state data control domain corresponding to the target device; The warning event judgment criteria for the target device are determined based on the upper and lower limits of the status data and the status data control domain. The warning event judgment criteria include, but are not limited to: data outside or within the status data control domain; nine consecutive status data points within the status data control domain that are above or below the mean; multiple status data points within a range of 2 to 3 times the standard deviation; six or more consecutive data points showing an upward or downward trend; adjacent data points alternating between upward and downward movements in 14 consecutive data points; and data points in the status data set concentrated within a range of 0.5 times the standard deviation. The establishment of the target reliability assessment model includes: Establish an inverse power law model based on the device's early warning events and a reliability model of the target device under normal operating conditions, and establish a corresponding reliability assessment model based on the inverse power law model and the reliability model; Configure the corresponding model parameters for the reliability assessment model to obtain the corresponding target reliability assessment model; The establishment of a corresponding reliability assessment model based on the inverse power law model and the reliability model includes: Establish the correlation function between the inverse power law model and the reliability model, and establish the corresponding transition function; The corresponding reliability model is determined based on the correlation point function and the transition function; The step of determining the upper and lower limits of the state data corresponding to the target device based on the state data type includes: The first state data of the target device at the time of its manufacture is determined based on the state data type, and the second state data is determined based on the state data generated by the target device during actual operation. Determine the upper and lower limits of the status data corresponding to the target device based on the first status data and the second status data; The step of determining the status data control domain corresponding to the target device includes: Collect the status data generated by the target device during actual operation according to the preset data collection duration and data collection frequency, obtain the corresponding status dataset, and determine the corresponding data distribution parameters based on the status dataset; The status data control domain corresponding to the target device is determined based on the data distribution parameters.

4. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor for executing the computer program to implement the steps of the device status monitoring method as described in claim 1 or 2.

5. A computer-readable storage medium, characterized in that, Used to store a computer program; wherein, when the computer program is executed by a processor, it implements the steps of the device status monitoring method as described in claim 1 or 2.

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