Method, device, medium and electronic equipment for monitoring the force of bolt groups of generator sets

Abstract

The present disclosure relates to a method, device, medium and electronic equipment for monitoring the force of a bolt group of a generator set. The method includes: obtaining the stress of each bolt to be tested in the bolt group to be tested; determining the stress difference between each bolt to be tested based on the stress of each bolt to be tested; and judging whether the bolt group to be tested is in an uneven force state based on the stress difference between each bolt to be tested. Through the above technical solution, whether the bolt group to be tested is in an uneven force state is judged based on the stress difference between each bolt to be tested. In this way, the overall force condition of the bolt group to be tested is analyzed, and the presence of uneven force in the bolt group to be tested can be discovered in a timely manner, providing a basis for the maintenance and repair of the generator set.

Description

Technical Field

[0001] The present disclosure relates to the field of generator sets, and in particular to a method, device, medium and electronic equipment for monitoring the force of bolt groups in generator sets. Background Art

[0002] Bolts are commonly used components in generator sets, and the stress on them affects the safe operation of the generator set. Currently, relevant technicians are conducting research on the stress on bolts on generator sets to reduce the occurrence of generator set failures and ensure safer and more stable operation of the generator set. Summary of the Invention

[0003] The purpose of the present disclosure is to provide a method, device, medium and electronic equipment for monitoring the force of the bolt group of a generator set, which can promptly detect the uneven force of the bolt group of the generator set.

[0004] In order to achieve the above objectives, the present disclosure provides a method for monitoring the force of bolt groups in a generator set, comprising:

[0005] Obtaining the stress of each bolt to be tested in the bolt group to be tested;

[0006] Determining the stress difference between the bolts to be tested according to the stress of each bolt to be tested;

[0007] Whether the bolt group to be tested is in an uneven stress state is determined according to the stress difference between each of the bolts to be tested.

[0008] Optionally, the method further includes:

[0009] If the bolt group to be tested is in the uneven force state, it is determined that the generator set is abnormal.

[0010] Optionally, the generator set is a hydro-turbine generator set, and the bolt group to be tested is a turbine top cover bolt group of the hydro-turbine generator set.

[0011] Optionally, judging whether the bolt group to be tested is in an uneven stress state based on the stress difference between each bolt to be tested includes:

[0012] Obtain historical data of stress differences between bolts, and perform normal transformation on the historical data of stress differences between bolts;

[0013] Determining normal distribution parameters based on the transformed historical data of stress differences between bolts, wherein the normal distribution parameters include a mean parameter and a standard deviation parameter;

[0014] Performing a normal transformation on the stress difference between each of the bolts to be tested to obtain a stress difference verification data set;

[0015] For each data to be verified in the stress difference verification data set, determining a density value of the data to be verified according to the data to be verified and the normal distribution parameter;

[0016] For each of the data to be verified in the stress difference verification data set, if the data to be verified is greater than a mean parameter and a density value of the data to be verified is less than or equal to a density threshold, determining that the data to be verified is abnormal data;

[0017] If the abnormal data exists in the stress difference verification data set, it is determined that the bolt group to be tested is in the uneven stress state.

[0018] Optionally, performing normal transformation on the historical data of stress differences between bolts includes:

[0019] The normal transformation of the historical data of the stress difference between the bolts is performed according to the following formula:

[0020] ΔF i ′ =lnΔF i

[0021] Where ΔF i ′ is the historical data of the stress difference between bolts after the i-th transformation, ΔF i is the historical data of stress difference between the i-th bolts;

[0022] The performing normal transformation on the stress difference between each of the bolts to be tested includes:

[0023] The stress difference between each of the bolts to be tested is normally transformed according to the following formula:

[0024] X j =lnΔFY j

[0025] Among them, X j is the jth data to be verified, ΔFY j is the stress difference between the jth bolts to be tested.

[0026] Optionally, determining normal distribution parameters based on the transformed historical data of stress differences between bolts includes:

[0027] The mean parameter is determined according to the following formula:

[0028]

[0029] Wherein, m is the number of transformed historical data of stress difference between bolts, and μ is the mean parameter;

[0030] The standard deviation parameter is determined according to the following formula:

[0031]

[0032] Wherein, σ is the standard deviation parameter.

[0033] Optionally, determining the density value of the data to be verified based on the data to be verified and the normal distribution parameter includes:

[0034] The density value of the data to be verified is determined according to the following formula:

[0035]

[0036] Among them, p(X j ) is the density value of the j-th data to be verified.

[0037] The present disclosure also provides a generator set bolt group force monitoring device, comprising:

[0038] an acquisition module configured to acquire the stress of each bolt to be tested in the bolt group to be tested;

[0039] A first determining module is configured to determine the stress difference between the bolts to be tested according to the stress of each bolt to be tested;

[0040] The judgment module is configured to judge whether the bolt group to be tested is in an uneven stress state according to the stress difference between each of the bolts to be tested.

[0041] The present disclosure also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-mentioned generator set bolt group force monitoring method.

[0042] The present disclosure also provides an electronic device, comprising:

[0043] a memory having a computer program stored thereon;

[0044] A processor is used to execute the computer program in the memory to implement the steps of the above-mentioned generator set bolt group force monitoring method.

[0045] Through the above technical solution, whether the bolt group to be tested is in an uneven stress state is determined based on the stress difference between each bolt to be tested. In this way, the overall stress condition of the bolt group to be tested is analyzed, and any uneven stress condition of the bolt group to be tested can be discovered in a timely manner, providing a basis for the maintenance and repair of the generator set.

[0046] Other features and advantages of the present disclosure will be described in detail in the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The accompanying drawings are used to provide a further understanding of the present disclosure and constitute a part of the specification. Together with the following detailed description, they are used to explain the present disclosure but do not constitute a limitation of the present disclosure. In the accompanying drawings:

[0048] Figure 1 The present invention is a flowchart of a method for monitoring the force of bolt groups in a generator set provided in an exemplary embodiment of the present invention.

[0049] Figure 2 The present invention is a block diagram of a generator set bolt group force monitoring device provided in accordance with an exemplary embodiment of the present disclosure.

[0050] Figure 3 It is a block diagram of an electronic device according to an exemplary embodiment. DETAILED DESCRIPTION

[0051] The following describes the specific embodiments of the present disclosure in detail with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only used to illustrate and explain the present disclosure and are not intended to limit the present disclosure.

[0052] It should be noted that all actions of acquiring signals, information or data in the present disclosure are carried out in compliance with the corresponding data protection laws and policies of the country where they are located and with the authorization given by the owner of the corresponding device.

[0053] Figure 1 This is a flow chart of a method for monitoring the force of bolt groups in a generator set according to an exemplary embodiment of the present disclosure. Figure 1 As shown, the generator set bolt group force monitoring method includes steps S101 to S103.

[0054] In step S101, the stress of each bolt to be tested in the bolt group to be tested is obtained. In the process of implementing the generator set bolt group force monitoring method provided by the present disclosure, a specific bolt group in the generator set can be monitored, and the specific bolt group is the bolt group to be tested. For example, if the generator set is a hydro-turbine generator set, since the stress condition of the turbine top cover bolts of the hydro-turbine generator set is crucial to the operating safety of the generator set, the turbine top cover bolt group can be monitored according to the generator set bolt group force monitoring method provided by the present disclosure, that is, the bolt group to be tested is the turbine top cover bolt group. It should be noted that the bolt groups at other positions of the generator set can be monitored according to the generator set bolt group force monitoring method provided by the present disclosure, and this is not limited here.

[0055] The bolt to be tested is a bolt that needs to be monitored according to actual conditions. In one embodiment, the bolt to be tested can be each bolt in a group of turbine top cover bolts.

[0056] In step S101, the stress of each bolt in the bolt group to be tested is obtained. For example, after the turbine top cover bolt group is installed, the stress of each bolt in the turbine top cover bolt group is the initial preload force. The stress change of each bolt can be measured using a sensor. Based on the initial preload force and the stress change of each bolt, the stress of each bolt in the turbine top cover bolt group can be determined.

[0057] In step S102 , the stress difference between the bolts to be tested is determined based on the stress of each bolt to be tested.

[0058] For example, if there are n bolts to be tested, the stress difference between each of the n bolts to be tested and the remaining n-1 bolts to be tested can be determined, and a total of [n*(n-1)] / 2 stress differences can be obtained.

[0059] The stress difference between the bolts under test is the difference in stress between the bolts under test. For example, for two bolts under test, bolt #1 (#1 is the bolt number under test) and bolt #2, the stress difference between the bolts under test is the difference between the stress of bolt #1 and the stress of bolt #2. It should be noted that the stress difference between the bolts under test is the absolute value of the difference between the stresses of the two bolts under test.

[0060] In step S103 , it is determined whether the bolt group to be tested is in an uneven stress state according to the stress difference between each bolt to be tested.

[0061] Conditions for determining whether the bolt group to be tested is in an uneven stress state can be preset. For example, the condition for determining whether the bolt group to be tested is in an uneven stress state can be preset as whether the stress difference between each bolt to be tested is greater than a preset stress difference threshold. If the stress differences between each bolt to be tested are all less than or equal to the stress difference threshold, it is determined that the bolt group to be tested is not in an uneven stress state; if the stress difference between each bolt to be tested is greater than the stress difference threshold, it is determined that the bolt group to be tested is in an uneven stress state. Steps S101 to S103 can be cyclically executed at certain time intervals to promptly detect the uneven stress of the bolt group to be tested.

[0062] Through the above technical solution, whether the bolt group to be tested is in an uneven stress state is determined based on the stress difference between each bolt to be tested. In this way, the overall stress condition of the bolt group to be tested is analyzed, and any uneven stress condition of the bolt group to be tested can be discovered in a timely manner, providing a basis for the maintenance and repair of the generator set.

[0063] Optionally, the method further comprises:

[0064] If the bolt group to be tested is in an uneven stress state, it is determined that there is an abnormality in the generator set.

[0065] In this embodiment, when the bolt group to be tested is in a state of uneven force, it can be determined that there is an abnormality in the generator set. In this way, when the bolt group to be tested is in a state of uneven force, relevant personnel can deal with it in a timely manner, reduce safety hazards, and improve the safety and stability of the unit operation.

[0066] Optionally, the generator set is a hydro-generator set, and the bolt group to be tested is a turbine top cover bolt group of the hydro-generator set.

[0067] After the turbine has been used for a long time, the turbine top cover bolts may have excessive pre-tightening force or become loose, resulting in uneven force on the turbine top cover bolt group. The misalignment of the shaft system caused by slight differences in the installation process of the turbine unit, or the tilt of the turbine top cover relative to the shaft system, will cause uneven force on the turbine top cover bolt group. If the turbine top cover bolt group is unevenly stressed, there will be safety hazards. In this embodiment, the bolt group to be tested is the turbine top cover bolt group in the waterwheel chamber of the hydro-generator set. The turbine top cover bolt group can be monitored to promptly detect the uneven force on the turbine top cover bolt group, thereby improving the safety of the operation of the hydro-generator set.

[0068] Optionally, judging whether the bolt group to be tested is in an uneven stress state based on the stress difference between each bolt to be tested includes:

[0069] Obtain historical data of stress differences between bolts, and perform normal transformation on the historical data of stress differences between bolts;

[0070] Determine normal distribution parameters based on the transformed historical data of stress differences between bolts, wherein the normal distribution parameters include a mean parameter and a standard deviation parameter;

[0071] Perform normal transformation on the stress difference between each bolt to be tested to obtain a stress difference verification data set;

[0072] For each data to be verified in the stress difference verification data set, determining a density value of the data to be verified according to the data to be verified and a normal distribution parameter;

[0073] For each data to be verified in the stress difference verification data set, if the data to be verified is greater than the mean parameter and the density value of the data to be verified is less than or equal to the density threshold, the data to be verified is determined to be abnormal data;

[0074] If there are abnormal data in the stress difference verification data set, it is determined that the bolt group to be tested is in an uneven stress state.

[0075] For bolts of the same type as the bolt to be tested, the inter-bolt stress differences of these bolts can be collected in advance. The pre-collected inter-bolt stress differences are the inter-bolt stress difference historical data.

[0076] For example, if the bolts to be tested are turbine top cover bolts, the stress difference between the turbine top cover bolts of the turbine generator sets that are operating well can be collected.

[0077] For example, a certain number of well-performing hydro-generator sets can be selected as samples to test the stress of the turbine cover bolts of these hydro-generator sets and determine the stress difference between the bolts in the turbine cover bolt group. For a particular turbine in the selected hydro-generator set, the stress value of each bolt in the turbine cover bolt group can be obtained, and the stress difference between the bolts in the turbine cover bolt group can be determined.

[0078] The method for collecting historical data on stress differences between bolts is similar to the method for determining the stress differences between each bolt to be tested, as described above. That is, if a turbine in a selected hydro-turbine generator set has s turbine cover bolts, for each of these s turbine cover bolts, historical data on the stress differences between that turbine cover bolt and the remaining s-1 bolts is determined. In this way, a total of [s*(s-1)] / 2 historical data on stress differences between bolts are obtained. For each turbine in the sample hydro-turbine generator set, historical data on stress differences between bolts can be obtained for its cover bolts. In one embodiment, the turbines in the sample hydro-turbine generator set are of the same model. If the number of sample turbines is k, then [k*s*(s-1)] / 2 historical data on stress differences between bolts can be obtained. In one embodiment, the samples can be sampled at different times, that is, the same turbine can be sampled multiple times at different times to obtain more historical data on stress differences between bolts. For example, if each turbine is sampled at different times for a total of t times, [t*k*s*(s-1)] / 2 bolt stress difference history data can be obtained. In this way, a large amount of bolt stress difference history data can be obtained.

[0079] The historical data of stress difference between bolts may be pre-stored, and the pre-stored historical data of stress difference between bolts may be obtained during the execution of the method for monitoring the force of bolt groups of a generator set provided by the present disclosure.

[0080] The historical data on the stress differences between bolts may not follow a normal distribution. For example, the historical data may follow a chi-square distribution. A normal transformation can be performed on the historical data to make it follow a normal distribution (or be considered to approximately follow a normal distribution), facilitating further analysis. The transformation function used in performing the normal transformation on the historical data is not specifically limited herein.

[0081] The normal distribution parameters include a mean parameter and a standard deviation parameter. The mean parameter is the average value of the transformed historical data on stress differences between bolts, and the standard deviation parameter is the standard deviation of the transformed historical data on stress differences between bolts. Since the transformed historical data on stress differences between bolts follows a normal distribution, the normal distribution parameters can be determined based on the transformed historical data on stress differences between bolts. An expression for solving the normal distribution density function can be established based on the mean parameter and the standard deviation parameter. In this way, a normal distribution model is established based on the historical data on stress differences between bolts. This normal distribution model can be used to analyze the stress differences between the various bolts to be tested, as determined in step S102.

[0082] The stress differences between the bolts to be tested, determined in step S102, can be subjected to a normal transformation to obtain a stress difference validation dataset. The stress difference validation dataset is a collection of stress differences between the bolts to be tested that have undergone a normal transformation. When performing a normal transformation on the stress differences between the bolts to be tested, the transformation function used can be the same as the transformation function used when performing a normal transformation on the historical data of stress differences between the bolts.

[0083] Each data point to be verified is a data point obtained by normal transformation of one of the stress differences between the bolts to be tested determined in step S102. Since a normal distribution model has been established based on the historical data of stress differences between bolts, each data point to be verified in the stress difference verification dataset can be input into the normal distribution model to obtain a density value for each data point to be verified. In other words, each data point to be verified can be substituted into the density function of the normal distribution model to obtain the density value corresponding to each data point to be verified.

[0084] The density threshold can be preset, or it can be the density value corresponding to data that is three standard deviation parameters larger than the mean parameter. If the data to be verified is greater than the mean parameter and the density value of the data to be verified is less than or equal to the density threshold, the data to be verified can be considered as abnormal data. In other words, it can be considered that the stress difference between the two bolts to be tested represented by the data to be verified before the normal transformation (for example, the difference between the stress of the "No. 1" bolt and the stress of the "No. 5" bolt represented by the data to be verified before the normal transformation) exceeds the normal range. If there is abnormal data in the stress difference verification data set, it is determined that the bolt group to be tested is in an uneven stress state.

[0085] In this embodiment, a normal distribution model is established based on the historical data of stress differences between bolts. The historical data of stress differences between bolts can be regarded as the past data of the bolt group to be tested. In this way, the current data of the bolt group to be tested is analyzed based on the data established based on the past data, so that it can be accurately and efficiently determined whether the bolt group to be tested is in an uneven stress state.

[0086] Optionally, perform normal transformation on the historical data of stress difference between bolts, including:

[0087] Perform normal transformation on the historical data of stress difference between bolts according to the following formula:

[0088] ΔF i ′ =lnΔF i (1)

[0089] Where ΔF i ′ is the historical data of the stress difference between bolts after the i-th transformation, ΔF i is the historical data of stress difference between the i-th bolts;

[0090] Perform normal transformation on the stress difference between each bolt to be tested, including:

[0091] Perform normal transformation on the stress difference between the bolts to be tested according to the following formula:

[0092] X j =lnΔFY j (2)

[0093] Among them, X j is the jth data to be verified, ΔFY j is the stress difference between the jth bolts to be tested.

[0094] It should be noted that because the logarithm method is used to perform a normal transformation on the historical data of stress differences between bolts and the stress differences between each bolt to be tested, data with a value of 0 in the historical data of stress differences between bolts can be deleted. Similarly, if there is data with a value of 0 in the stress differences between each bolt to be tested, this data with a value of 0 can be deleted.

[0095] In addition, it should be noted that the i-th bolt stress difference historical data refers to the i-th bolt stress difference historical data among all bolt stress difference historical data. The i-th transformed bolt stress difference historical data refers to the i-th transformed bolt stress difference historical data among all transformed bolt stress difference historical data. The j-th stress difference between bolts to be tested refers to the j-th stress difference data between bolts to be tested among all bolt stress differences to be tested (stress differences between each bolt to be tested). The j-th data to be verified refers to the j-th data to be verified among all data to be verified.

[0096] In this embodiment, a method for performing normal transformation on historical data of stress differences between bolts and stress differences between bolts to be tested is provided, which can efficiently realize normal transformation of data and has a fast response speed.

[0097] Optionally, determining normal distribution parameters based on the transformed historical data of stress differences between bolts includes:

[0098] The mean parameter is determined according to the following formula:

[0099]

[0100] Where m is the number of transformed historical data of stress difference between bolts, and μ is the mean parameter;

[0101] The standard deviation parameter is determined according to the following formula:

[0102]

[0103] Where σ is the standard deviation parameter.

[0104] In this embodiment, a method for determining normal distribution parameters based on transformed historical data of stress differences between bolts is provided. The method is simple and has good implementation effect.

[0105] Optionally, determining the density value of the data to be verified according to the data to be verified and normal distribution parameters includes:

[0106] The density value of the data to be verified is determined according to the following formula:

[0107]

[0108] Among them, p(X j ) is the density value of the jth data to be verified.

[0109] In this embodiment, the density value of each data to be verified can be determined quickly and accurately according to formula (5), and the method is simple and has a fast response speed.

[0110] Figure 2 This is a block diagram of a generator set bolt group force monitoring device provided in an exemplary embodiment of the present disclosure. Figure 2 As shown, the generator set bolt group force monitoring device 200 includes an acquisition module 201, a first determination module 202 and a judgment module 203.

[0111] The acquisition module 201 is configured to acquire the stress of each bolt to be tested in the bolt group to be tested.

[0112] The first determining module 202 is configured to determine the stress difference between the bolts to be tested according to the stress of each bolt to be tested.

[0113] The judgment module 203 is configured to judge whether the bolt group to be tested is in an uneven stress state according to the stress difference between each bolt to be tested.

[0114] In yet another embodiment, the generator set bolt group force monitoring device 200 further includes a second determination module.

[0115] The second determination module is configured to determine that an abnormality exists in the generator set if the bolt group to be tested is in an uneven force state.

[0116] In another embodiment, the generator set is a hydro-turbine generator set, and the bolt group to be tested is a turbine top cover bolt group of the hydro-turbine generator set.

[0117] In yet another embodiment, the judgment module 203 includes a first control submodule, a first determination submodule, a second control submodule, a second determination submodule, a third determination submodule, and a fourth determination submodule.

[0118] The first control submodule is configured to obtain historical data of stress differences between bolts and perform normal transformation on the historical data of stress differences between bolts.

[0119] The first determination submodule is configured to determine normal distribution parameters according to the transformed historical data of stress differences between bolts, wherein the normal distribution parameters include a mean parameter and a standard deviation parameter.

[0120] The second control submodule is configured to perform a normal transformation on the stress difference between each bolt to be tested to obtain a stress difference verification data set.

[0121] The second determination submodule is configured to determine, for each data to be verified in the stress difference verification data set, a density value of the data to be verified according to the data to be verified and a normal distribution parameter.

[0122] The third determination submodule is configured to determine, for each data to be verified in the stress difference verification data set, that the data to be verified is abnormal data if the data to be verified is greater than the mean parameter and the density value of the data to be verified is less than or equal to the density threshold.

[0123] The fourth determining submodule is configured to determine that the bolt group to be tested is in an uneven stress state if there is abnormal data in the stress difference verification data set.

[0124] In yet another embodiment, the first control submodule is further configured to perform a normal transformation on the historical data of stress differences between bolts according to the following formula:

[0125] ΔF i ′ =lnΔF i

[0126] Where ΔF i ′ is the historical data of the stress difference between bolts after the i-th transformation, ΔF i is the historical data of stress difference between the i-th bolts.

[0127] The second control submodule is further configured to perform a normal transformation on the stress difference between each bolt to be tested according to the following formula:

[0128] X j =lnΔFY j

[0129] Among them, X j is the jth data to be verified, ΔFY j is the stress difference between the jth bolts to be tested.

[0130] In yet another embodiment, the first determining submodule is further configured to determine the mean parameter according to the following formula:

[0131]

[0132] Where m is the number of transformed historical data of stress difference between bolts, and μ is the mean parameter.

[0133] The first determining submodule is further configured to determine a standard deviation parameter according to the following formula:

[0134]

[0135] Where σ is the standard deviation parameter.

[0136] In yet another embodiment, the second determining submodule is further configured to determine the density value of the data to be verified according to the following formula:

[0137]

[0138] Among them, p(X j ) is the density value of the jth data to be verified.

[0139] Regarding the apparatus in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0140] Through the above technical solution, whether the bolt group to be tested is in an uneven stress state is determined based on the stress difference between each bolt to be tested. In this way, the overall stress condition of the bolt group to be tested is analyzed, and any uneven stress condition of the bolt group to be tested can be discovered in a timely manner, providing a basis for the maintenance and repair of the generator set.

[0141] The present disclosure also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-mentioned generator set bolt group force monitoring method.

[0142] The present disclosure also provides an electronic device, comprising:

[0143] a memory having a computer program stored thereon;

[0144] The processor is used to execute the computer program in the memory to implement the steps of the above-mentioned generator set bolt group force monitoring method.

[0145] Figure 3 FIG. 7 is a block diagram of an electronic device 700 according to an exemplary embodiment. Figure 3 As shown, the electronic device 700 may include: a processor 701 , a memory 702 , and may further include one or more of a multimedia component 703 , an input / output (I / O) interface 704 , and a communication component 705 .

[0146] The processor 701 is used to control the overall operation of the electronic device 700 to complete all or part of the steps in the generator set bolt group force monitoring method described above. The memory 702 is used to store various types of data to support the operation of the electronic device 700. This data may include, for example, instructions for any application or method operating on the electronic device 700, as well as application-related data, such as contact information, sent and received messages, images, audio, video, etc. The memory 702 can be implemented by any type of volatile or non-volatile storage device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The multimedia component 703 may include a screen and an audio component. The screen may be, for example, a touch screen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signal may be further stored in the memory 702 or sent via the communication component 705. The audio component also includes at least one speaker for outputting audio signals. The I / O interface 704 provides an interface between the processor 701 and other interface modules. The above-mentioned other interface modules may be a keyboard, a mouse, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 705 is used for wired or wireless communication between the electronic device 700 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, 4G, NB-IOT, eMTC, or other 5G, etc., or a combination of one or more thereof, is not limited here. Therefore, the corresponding communication component 705 may include: a Wi-Fi module, a Bluetooth module, an NFC module, etc.

[0147] In an exemplary embodiment, the electronic device 700 can be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components to execute the above-mentioned generator set bolt group force monitoring method.

[0148] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided. When executed by a processor, the program instructions implement the steps of the aforementioned generator set bolt group force monitoring method. For example, the computer-readable storage medium may be the aforementioned memory 702 including the program instructions. The program instructions may be executed by the processor 701 of the electronic device 700 to implement the aforementioned generator set bolt group force monitoring method.

[0149] In another exemplary embodiment, a computer program product is provided. The computer program product includes a computer program executable by a programmable device, and the computer program has a code portion for executing the above-mentioned generator set bolt group force monitoring method when executed by the programmable device.

[0150] The preferred embodiments of the present disclosure are described in detail above in conjunction with the accompanying drawings. However, the present disclosure is not limited to the specific details of the above embodiments. Within the technical concept of the present disclosure, various simple modifications can be made to the technical solutions of the present disclosure, and these simple modifications all fall within the scope of protection of the present disclosure.

[0151] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any appropriate manner without contradiction. In order to avoid unnecessary repetition, the present disclosure will not further describe various possible combinations.

[0152] In addition, the various embodiments of the present disclosure may be arbitrarily combined, and as long as they do not violate the concept of the present disclosure, they should also be regarded as the contents disclosed by the present disclosure.

Claims

1. A method for monitoring the force of bolt groups in a generator set, characterized in that: include: Obtaining the stress of each bolt to be tested in the bolt group to be tested; Determining the stress difference between the bolts to be tested according to the stress of each bolt to be tested; Determining whether the bolt group to be tested is in an uneven stress state according to the stress difference between each bolt to be tested; Wherein, judging whether the bolt group to be tested is in an uneven stress state according to the stress difference between each bolt to be tested includes: Obtain historical data of stress differences between bolts, and perform normal transformation on the historical data of stress differences between bolts; Determining normal distribution parameters based on the transformed historical data of stress differences between bolts, wherein the normal distribution parameters include a mean parameter and a standard deviation parameter; Performing a normal transformation on the stress difference between each of the bolts to be tested to obtain a stress difference verification data set; For each data to be verified in the stress difference verification data set, determining a density value of the data to be verified according to the data to be verified and the normal distribution parameter; For each of the data to be verified in the stress difference verification data set, if the data to be verified is greater than a mean parameter and a density value of the data to be verified is less than or equal to a density threshold, determining that the data to be verified is abnormal data; If the abnormal data exists in the stress difference verification data set, it is determined that the bolt group to be tested is in the uneven stress state; Among them, the mean parameter is the average value of the transformed historical data of stress difference between bolts, and the standard deviation parameter is the standard deviation of the transformed historical data of stress difference between bolts; the density value is determined by a normal distribution density function constructed based on the mean parameter and the standard deviation parameter.

2. The method according to claim 1, characterized in that The method further comprises: If the bolt group to be tested is in the uneven force state, it is determined that the generator set is abnormal.

3. The method according to claim 1, characterized in that The generator set is a hydro-generator set, and the bolt group to be tested is a turbine top cover bolt group of the hydro-generator set.

4. The method according to claim 1, wherein The normal transformation of the historical data of stress differences between bolts includes: The normal transformation of the historical data of the stress difference between the bolts is performed according to the following formula: in, is the historical data of stress difference between bolts after the i-th transformation, is the historical data of stress difference between the i-th bolts; The performing normal transformation on the stress difference between each of the bolts to be tested includes: The stress difference between each of the bolts to be tested is normally transformed according to the following formula: in, is the jth data to be verified, is the stress difference between the jth bolts to be tested.

5. The method according to claim 4, characterized in that The determining of normal distribution parameters based on the transformed historical data of stress differences between bolts includes: The mean parameter is determined according to the following formula: Where m is the number of transformed bolt stress difference history data, is the mean parameter; The standard deviation parameter is determined according to the following formula: in, is the standard deviation parameter.

6. The method according to claim 5, characterized in that The determining the density value of the data to be verified according to the data to be verified and the normal distribution parameter includes: The density value of the data to be verified is determined according to the following formula: in, is the density value of the j-th data to be verified.

7. A generator set bolt group force monitoring device, characterized in that: include: an acquisition module configured to acquire the stress of each bolt to be tested in the bolt group to be tested; A first determining module is configured to determine the stress difference between the bolts to be tested according to the stress of each bolt to be tested; a judgment module configured to judge whether the bolt group to be tested is in an uneven stress state according to the stress difference between each of the bolts to be tested; Wherein, judging whether the bolt group to be tested is in an uneven stress state according to the stress difference between each bolt to be tested includes: Obtain historical data of stress differences between bolts, and perform normal transformation on the historical data of stress differences between bolts; Determining normal distribution parameters based on the transformed historical data of stress differences between bolts, wherein the normal distribution parameters include a mean parameter and a standard deviation parameter; Performing a normal transformation on the stress difference between each of the bolts to be tested to obtain a stress difference verification data set; For each data to be verified in the stress difference verification data set, determining a density value of the data to be verified according to the data to be verified and the normal distribution parameter; For each of the data to be verified in the stress difference verification data set, if the data to be verified is greater than a mean parameter and a density value of the data to be verified is less than or equal to a density threshold, determining that the data to be verified is abnormal data; If the abnormal data exists in the stress difference verification data set, it is determined that the bolt group to be tested is in the uneven stress state; Among them, the mean parameter is the average value of the transformed historical data of stress difference between bolts, and the standard deviation parameter is the standard deviation of the transformed historical data of stress difference between bolts; the density value is determined by a normal distribution density function constructed based on the mean parameter and the standard deviation parameter.

8. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

9. An electronic device, characterized in that: include: a memory having a computer program stored thereon; A processor, configured to execute the computer program in the memory to implement the steps of the method according to any one of claims 1 to 6.

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