Equipment health state prediction method, electronic equipment and system

Abstract

The invention provides an equipment health state prediction method, electronic equipment and a system. The method comprises the following steps: predicting the generated power of a first prediction time period based on the generated power of historical power generation of a generator set; calculating a health consumption value of the first prediction time period based on the generated power of the first prediction time period, and calculating a predicted health value of the first prediction time period based on the current health value of the generator set and the health consumption value of the first prediction time period; comparing the health value of the first prediction time period with a health threshold value, and judging whether the generator set needs to be maintained in the first prediction time period or not based on a comparison result; if it is judged that the generator set needs to be maintained in the first prediction time period, a first pre-maintenance instruction is sent to the control end, and the first pre-maintenance instruction is used for instructing a worker to maintain the generator set after the first prediction time period. According to the scheme, whether maintenance is carried out or not is determined by predicting the state of the generator set, the health state of the power generation equipment can be evaluated, and the maintenance opportunity is determined.

Description

Technical Field

[0001] The present invention relates to the technical field of power plant management, and in particular to a method for predicting the health status of equipment, electronic equipment and a system. Background Art

[0002] As the core component of the power system, the stable and efficient operation of power generation equipment is the cornerstone to ensure safe and reliable power supply. Therefore, regular and professional maintenance of power generation equipment is extremely important.

[0003] From a safety perspective, regular maintenance of power generation equipment to ensure that the equipment is in good working condition is an important measure to ensure production safety and prevent accidents.

[0004] Furthermore, from an environmental protection perspective, the maintenance of power generation equipment also helps reduce pollutant emissions. Regular maintenance can ensure the normal operation of these equipment, reduce pollutant emissions, and protect the ecological environment.

[0005] In summary, the maintenance of power generation equipment is not only necessary to ensure the stable operation of the power system and reduce economic costs, but also an inevitable requirement to ensure personnel safety and protect the ecological environment. However, in the actual maintenance of power generation equipment, it is often difficult for workers to assess the health status of power generation equipment, so it is difficult to accurately grasp the maintenance timing.

[0006] In view of this, the present invention is proposed. Summary of the invention

[0007] The purpose of the present invention is to provide a method, electronic device and system for predicting the health status of equipment. This solution can predict the consumption of a generator set by predicting the load, and predict the status of the generator set to determine whether maintenance is required. It can also pre-evaluate the health status of the power generation equipment and help staff accurately grasp the timing of maintenance.

[0008] The present invention provides a method for predicting the health status of equipment, the method comprising the following steps:

[0009] Predicting the power generation in the first prediction time period based on the power generation of the historical power generation of the power generating set;

[0010] Calculating the health consumption value of the first prediction period based on the power generation of the first prediction period, and calculating the estimated health value of the first prediction period based on the current health value of the generator set and the health consumption value of the first prediction period;

[0011] Comparing the health value of the first prediction time period with a preset health threshold, and determining whether maintenance of the generator set is required in the first prediction time period based on the comparison result;

[0012] If it is determined that the generator set needs to be maintained within the first predicted time period, a first pre-maintenance instruction is sent to the control end, wherein the first pre-maintenance instruction is used to instruct the staff to maintain the generator set after the first predicted time period.

[0013] The above scheme is adopted. After each maintenance of the generator set, the health value of the generator set is reset to a preset value. Furthermore, since the generator set has different consumption under different loads, the scheme predicts the power generation of the first prediction time period through the historical power generation, and further calculates the consumption required for the first prediction time period through the predicted power generation. The health value of the first prediction time period is estimated based on the current health value and the health value required for the first prediction time period. If the health value of the generator set in the first prediction time period is low, maintenance is required. The scheme can estimate the consumption of the generator set and the status of the generator set by estimating the load, determine whether maintenance is required, and pre-evaluate the health status of the power generation equipment to help staff accurately grasp the timing of maintenance.

[0014] In some embodiments of the present invention, in the step of predicting the power generation in the first prediction time period based on the power generation of the historical power generation of the generator set, the power generation at multiple time points in multiple historical time periods is constructed as a first input vector, and the first input vector is input into a pre-trained long short-term memory network model, and the long short-term memory network model outputs the predicted power generation at multiple time points in the first prediction time period.

[0015] In some embodiments of the present invention, in the step of calculating the health consumption value of the first prediction time period based on the power generation power of the first prediction time period, the power generation power at multiple time points in the first prediction time period is constructed as a second input vector, and the second input vector is input into a pre-trained convolutional neural network model, and the convolutional neural network model outputs the corresponding health consumption value.

[0016] The above scheme is adopted. This scheme performs joint processing through two models. The first model predicts the power generation in the future time period, which corresponds to the load intensity of the generator set. The second model determines the consumption of the generator set based on the load intensity, which corresponds to the decline in health level, that is, the health consumption value, and efficiently and accurately determines the consumption of the generator set.

[0017] In some embodiments of the present invention, in the step of calculating the estimated health value of the first prediction time period based on the current health value of the generator set and the health consumption value of the first prediction time period, the difference between the current health value of the generator set and the health consumption value of the first prediction time period is calculated to obtain the health value of the first prediction time period.

[0018] The above scheme is adopted. This scheme resets the health value of the generator set after each maintenance, and then calculates the consumption value of the health value after each time period. Different loads correspond to different consumption values. The consumption value can be 0 until the consumption value is lower than the threshold and a maintenance instruction is issued.

[0019] In some embodiments of the present invention, the method further comprises: receiving a maintenance completion instruction in real time, and resetting the health value of the generator set based on the maintenance completion instruction.

[0020] In some embodiments of the present invention, if it is determined that the generator set does not need to be maintained in the first prediction time period, the step of determining whether the generator set needs to be maintained in the first prediction time period based on the comparison result further includes:

[0021] Calculating the power generation for the second prediction time period based on the power generation of historical power generation and the power generation predicted for the first prediction time period;

[0022] Calculating the health consumption value of the second prediction time period based on the power generation of the second prediction time period, and calculating the estimated health value of the second prediction time period based on the current health value of the generator set, the health consumption value of the first prediction time period, and the health consumption value of the second prediction time period;

[0023] comparing the health value of the second prediction time period with a preset health threshold, and determining whether maintenance of the generator set is required in the second prediction time period based on the comparison result;

[0024] If it is determined that the generator set needs to be maintained within the second predicted time period, a second pre-maintenance instruction is sent to the control end, wherein the second pre-maintenance instruction includes an estimated maintenance time point.

[0025] By adopting the above scheme, when this scheme determines that the generator set does not need maintenance in the first prediction time period, the health value of the next prediction time period is estimated in the same way continuously until it is determined that a second pre-maintenance instruction needs to be issued. By continuously judging backward, a fuzzy estimate of the time point when maintenance is required can be made, and the estimated maintenance time can be fed back to the staff through the second maintenance instruction, so that the staff can estimate the maintenance for a longer time, so that the staff can make better work arrangements, or perform maintenance in advance, to avoid too many maintenance tasks and difficult to handle.

[0026] In some embodiments of the present invention, if it is determined that the generator set does not need to be maintained in the second prediction time period, then in the step of determining whether the generator set needs to be maintained in the first prediction time period based on the comparison result, the calculation method of the health value corresponding to the second prediction time period is adopted, and the health value of the next prediction time period is continuously calculated until it is determined that the generator set needs to be maintained in this time period, and a second pre-maintenance instruction is sent to the control end.

[0027] In some embodiments of the present invention, in the step of calculating the estimated health value of the second prediction time period based on the current health value of the generator set, the health consumption value of the first prediction time period, and the health consumption value of the second prediction time period, the difference between the current health value of the generator set and the sum of the health consumption values ​​of the first prediction time period and the second prediction time period is calculated to obtain the health value of the second prediction time period.

[0028] Another aspect of the present invention relates to an electronic device having a computer program stored thereon, which, when executed by a processor, implements the steps of the aforementioned device health status prediction method.

[0029] Another aspect of the present invention also relates to a device health status prediction system, which includes a computer device, wherein the computer device includes a processor and a memory, wherein the memory stores computer instructions, and the processor is used to execute the computer instructions stored in the memory. When the computer instructions are executed by the processor, the system implements the steps implemented by the method.

[0030] In summary, the present invention has the following beneficial effects:

[0031] 1. This solution can estimate the consumption of the generator set by estimating the load, and estimate the status of the generator set to determine whether maintenance is required. It can pre-evaluate the health status of the power generation equipment and help the staff to accurately grasp the maintenance timing. ;

[0032] 2. This solution uses two models for joint processing. The first model predicts the power generation in the future time period, which corresponds to the load intensity of the generator set. The second model determines the consumption of the generator set based on the load intensity. The corresponding decline in health level is the health consumption value, which efficiently and accurately determines the consumption of the generator set.

[0033] 3. This solution resets the health value of the generator set after each maintenance, and then calculates the consumption value of the health value after each time period. Different loads correspond to different consumption values. The consumption value can be 0 until the consumption value is lower than the threshold, and a maintenance instruction is issued;

[0034] 4. When this scheme determines that the generator set does not need to be maintained in the first forecast time period, the health value of the next forecast time period is estimated in the same way until it is determined that a second pre-maintenance instruction needs to be issued. By continuously judging backward, a fuzzy estimate of the time point when maintenance is required can be made, and the estimated maintenance time can be fed back to the staff through the second maintenance instruction, so that the staff can estimate the maintenance for a longer time, so that the staff can make better work arrangements, or perform maintenance in advance, to avoid too many maintenance tasks and difficult to handle. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0036] Figure 1 A schematic diagram of an implementation of a method for predicting the health status of equipment according to the present invention;

[0037] Figure 2 A schematic diagram of another implementation of the device health status prediction method of the present invention. DETAILED DESCRIPTION

[0038] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Instead, they are merely examples of devices and methods consistent with some aspects of the present invention as detailed in the appended claims.

[0039] The terms used in the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The singular forms "a", "the" and "the" used in the present invention and the appended claims are also intended to include plural forms unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0040] like Figure 1 As shown, the present invention provides a method for predicting the health status of equipment, the steps of the method comprising:

[0041] Step S100, predicting the power generation in the first prediction time period based on the historical power generation of the power generation set;

[0042] In a specific implementation process, the power generation of the historical power generation includes the power generation at multiple time points in history.

[0043] Step S200, calculating the health consumption value of the first prediction time period based on the power generation of the first prediction time period, and calculating the estimated health value of the first prediction time period based on the current health value of the generator set and the health consumption value of the first prediction time period;

[0044] Step S300, comparing the health value of the first prediction time period with a preset health threshold, and determining whether maintenance of the generator set is required in the first prediction time period based on the comparison result;

[0045] In the specific implementation process, in the step of determining whether maintenance of the generator set is required in the first prediction time period based on the comparison result, if the health value corresponding to the first prediction time period is less than a preset health threshold, it is determined that maintenance is required.

[0046] Step S410: If it is determined that the generator set needs to be maintained within the first predicted time period, a first pre-maintenance instruction is sent to the control end, wherein the first pre-maintenance instruction is used to instruct the staff to maintain the generator set after the first predicted time period.

[0047] In the specific implementation process, the control terminal can be a mobile terminal of a staff member or a server of a general control center.

[0048] The above scheme is adopted. After each maintenance of the generator set, the health value of the generator set is reset to a preset value. Furthermore, since the generator set has different consumption under different loads, the scheme predicts the power generation of the first prediction time period through the historical power generation, and further calculates the consumption required for the first prediction time period through the predicted power generation. The health value of the first prediction time period is estimated based on the current health value and the health value required for the first prediction time period. If the health value of the generator set in the first prediction time period is low, maintenance is required. The scheme can estimate the consumption of the generator set and the status of the generator set by estimating the load, determine whether maintenance is required, and pre-evaluate the health status of the power generation equipment to help staff accurately grasp the timing of maintenance.

[0049] In some embodiments of the present invention, in the step of predicting the power generation in the first prediction time period based on the power generation of the historical power generation of the generator set, the power generation at multiple time points in multiple historical time periods is constructed as a first input vector, and the first input vector is input into a pre-trained long short-term memory network model (Long Short-Term Memory, LSTM), and the long short-term memory network model outputs the predicted power generation at multiple time points in the first prediction time period.

[0050] LSTM controls the retention and forgetting of information between different moments by introducing three important gating mechanisms: Forget Gate, Input Gate, and Output Gate. These gating mechanisms allow the network to dynamically decide which information should be retained in memory and which should be forgotten, thereby effectively solving the long-term dependency problem.

[0051] In some embodiments of the present invention, in the step of calculating the health consumption value of the first prediction time period based on the power generation power of the first prediction time period, the power generation power at multiple time points in the first prediction time period is constructed as a second input vector, and the second input vector is input into a pre-trained convolutional neural network model, and the convolutional neural network model outputs the corresponding health consumption value.

[0052] In the specific implementation process, the convolutional neural network model (CNN) is a deep learning model, including multiple convolutional layers, pooling layers, fully connected layers and classification layers, and the health consumption value is output through the classification layer.

[0053] The above scheme is adopted. This scheme performs joint processing through two models. The first model predicts the power generation in the future time period, which corresponds to the load intensity of the generator set. The second model determines the consumption of the generator set based on the load intensity, which corresponds to the decline in health level, that is, the health consumption value, and efficiently and accurately determines the consumption of the generator set.

[0054] In some embodiments of the present invention, in the step of calculating the estimated health value of the first prediction time period based on the current health value of the generator set and the health consumption value of the first prediction time period, the difference between the current health value of the generator set and the health consumption value of the first prediction time period is calculated to obtain the health value of the first prediction time period.

[0055] The above scheme is adopted. This scheme resets the health value of the generator set after each maintenance, and then calculates the consumption value of the health value after each time period. Different loads correspond to different consumption values. The consumption value can be 0 until the consumption value is lower than the threshold and a maintenance instruction is issued.

[0056] In some embodiments of the present invention, the method further comprises: receiving a maintenance completion instruction in real time, and resetting the health value of the generator set based on the maintenance completion instruction.

[0057] like Figure 2 As shown, in some embodiments of the present invention, if it is determined that the generator set does not need to be maintained in the first prediction time period, the step of determining whether the generator set needs to be maintained in the first prediction time period based on the comparison result further includes:

[0058] Step S421, calculating the power generation in the second prediction time period based on the power generation of historical power generation and the power generation predicted for the first prediction time period;

[0059] Step S422, calculating the health consumption value of the second prediction time period based on the power generation of the second prediction time period, and calculating the estimated health value of the second prediction time period based on the current health value of the generator set, the health consumption value of the first prediction time period, and the health consumption value of the second prediction time period;

[0060] In a specific implementation process, the method of calculating the power generation in the second prediction time period is the same as the method of calculating the power generation in the first prediction time period.

[0061] In a specific implementation process, the method for calculating the health consumption value of the second prediction time period is the same as the method for calculating the health consumption value of the first prediction time period.

[0062] Step S423, comparing the health value in the second prediction time period with a preset health threshold, and determining whether maintenance of the generator set is required in the second prediction time period based on the comparison result;

[0063] Step S424: If it is determined that the generator set needs to be maintained within the second predicted time period, a second pre-maintenance instruction is sent to the control end, wherein the second pre-maintenance instruction includes an estimated maintenance time point.

[0064] In the specific implementation process, the maintenance time point is the last time point of the corresponding time period.

[0065] By adopting the above scheme, when this scheme determines that the generator set does not need maintenance in the first prediction time period, the health value of the next prediction time period is estimated in the same way continuously until it is determined that a second pre-maintenance instruction needs to be issued. By continuously judging backward, a fuzzy estimate of the time point when maintenance is required can be made, and the estimated maintenance time can be fed back to the staff through the second maintenance instruction, so that the staff can estimate the maintenance for a longer time, so that the staff can make better work arrangements, or perform maintenance in advance, to avoid too many maintenance tasks and difficult to handle.

[0066] In some embodiments of the present invention, if it is determined that the generator set does not need to be maintained in the second prediction time period, then in the step of determining whether the generator set needs to be maintained in the first prediction time period based on the comparison result, the calculation method of the health value corresponding to the second prediction time period is adopted, and the health value of the next prediction time period is continuously calculated until it is determined that the generator set needs to be maintained in this time period, and a second pre-maintenance instruction is sent to the control end.

[0067] In some embodiments of the present invention, in the step of calculating the estimated health value of the second prediction time period based on the current health value of the generator set, the health consumption value of the first prediction time period, and the health consumption value of the second prediction time period, the difference between the current health value of the generator set and the sum of the health consumption values ​​of the first prediction time period and the second prediction time period is calculated to obtain the health value of the second prediction time period.

[0068] Another aspect of the present invention relates to an electronic device having a computer program stored thereon, which, when executed by a processor, implements the steps of the aforementioned device health status prediction method. The electronic device may be a tangible storage medium, such as a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a floppy disk, a hard disk, a removable storage disk, a CD-ROM, or any other form of storage medium known in the art.

[0069] An embodiment of the present invention also provides an equipment health status prediction system, which includes a computer device, wherein the computer device includes a processor and a memory, wherein the memory stores computer instructions, and the processor is used to execute the computer instructions stored in the memory. When the computer instructions are executed by the processor, the system implements the steps implemented by the method.

[0070] It should be understood by those skilled in the art that the exemplary components, systems and methods described in conjunction with the embodiments disclosed herein can be implemented in hardware, software or a combination of the two. Whether it is performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention. When implemented in hardware, it can be, for example, an electronic circuit, an application specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, etc. When implemented in software, the elements of the present invention are programs or code segments used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or a communication link via a data signal carried in a carrier.

[0071] It should be clear that the present invention is not limited to the specific configuration and processing described above and shown in the figures. For the sake of simplicity, a detailed description of the known method is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present invention is not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between the steps after understanding the spirit of the present invention.

[0072] In the present invention, features described and / or illustrated for one embodiment may be used in the same or similar manner in one or more other embodiments, and / or combined with features of other embodiments or replace features of other embodiments.

[0073] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the embodiments of the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for predicting equipment health status, characterized in that: The steps of the method include: Predicting the power generation in the first prediction time period based on the power generation of the historical power generation of the power generating set; Calculating the health consumption value of the first prediction period based on the power generation of the first prediction period, and calculating the estimated health value of the first prediction period based on the current health value of the generator set and the health consumption value of the first prediction period; Comparing the health value of the first prediction time period with a preset health threshold, and determining whether maintenance of the generator set is required in the first prediction time period based on the comparison result; If it is determined that the generator set needs to be maintained within the first predicted time period, a first pre-maintenance instruction is sent to the control end, wherein the first pre-maintenance instruction is used to instruct the staff to maintain the generator set after the first predicted time period.

2. The device health status prediction method according to claim 1, characterized in that: In the step of predicting the power generation in the first prediction time period based on the power generation of the historical power generation of the power generator set, the power generation at multiple time points in multiple historical time periods is constructed as a first input vector, and the first input vector is input into a pre-trained long short-term memory network model, and the long short-term memory network model outputs the predicted power generation at multiple time points in the first prediction time period.

3. The device health status prediction method according to claim 1, characterized in that: In the step of calculating the health consumption value of the first prediction time period based on the power generation of the first prediction time period, the power generation at multiple time points in the first prediction time period is constructed as a second input vector, and the second input vector is input into a pre-trained convolutional neural network model, and the convolutional neural network model outputs the corresponding health consumption value.

4. The device health status prediction method according to claim 1, characterized in that: In the step of calculating the estimated health value of the first prediction time period based on the current health value of the generator set and the health consumption value of the first prediction time period, the difference between the current health value of the generator set and the health consumption value of the first prediction time period is calculated to obtain the health value of the first prediction time period.

5. The device health status prediction method according to claim 1, characterized in that: The method further includes: receiving a maintenance completion instruction in real time, and resetting the health value of the generator set based on the maintenance completion instruction.

6. The device health status prediction method according to any one of claims 1 to 5, characterized in that: If it is determined that the generator set does not need to be maintained in the first prediction time period, the step of determining whether the generator set needs to be maintained in the first prediction time period based on the comparison result further includes: Calculating the power generation for the second prediction time period based on the power generation of historical power generation and the power generation predicted for the first prediction time period; Calculating the health consumption value of the second prediction time period based on the power generation of the second prediction time period, and calculating the estimated health value of the second prediction time period based on the current health value of the generator set, the health consumption value of the first prediction time period, and the health consumption value of the second prediction time period; comparing the health value of the second prediction time period with a preset health threshold, and determining whether maintenance of the generator set is required in the second prediction time period based on the comparison result; If it is determined that the generator set needs to be maintained within the second predicted time period, a second pre-maintenance instruction is sent to the control end, wherein the second pre-maintenance instruction includes an estimated maintenance time point.

7. The device health status prediction method according to claim 6, characterized in that: If it is determined that the generator set does not need to be maintained in the second prediction time period, then in the step of determining whether the generator set needs to be maintained in the first prediction time period based on the comparison result, the calculation method of the health value corresponding to the second prediction time period is adopted, and the health value of the next prediction time period is continuously calculated until it is determined that the generator set needs to be maintained in this time period, and a second pre-maintenance instruction is sent to the control end.

8. The device health status prediction method according to claim 7, characterized in that: In the step of calculating the estimated health value of the second prediction time period based on the current health value of the generator set, the health consumption value of the first prediction time period and the health consumption value of the second prediction time period, the difference between the current health value of the generator set and the sum of the health consumption values ​​of the first prediction time period and the second prediction time period is calculated to obtain the health value of the second prediction time period.

9. An electronic device, characterized in that: A computer program is stored thereon, and when the computer program is executed by a processor, the steps of the device health status prediction method as described in any one of claims 1 to 8 are implemented.

10. A device health status prediction system, characterized in that: The system includes a computer device, which includes a processor and a memory, wherein the memory stores computer instructions, and the processor is used to execute the computer instructions stored in the memory. When the computer instructions are executed by the processor, the system implements the steps implemented by the device health status prediction method described in any one of claims 1 to 8.

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