Switch fault prediction method and system based on Bayesian theory, and electronic equipment

Abstract

The invention discloses a Bayesian theory-based switch fault prediction method and system and electronic equipment, and relates to the technical field of switch fault prediction, and the method comprises the steps: judging whether a switch has a fault or not according to a driving current digital signal and a displacement digital signal of the switch during opening and closing, and obtaining a first judgment result, the opening and closing comprises opening or closing; and when the first judgment result is no, inputting the driving current digital signal and the displacement digital signal into a fault identification model based on a Bayesian theory, and performing fault prediction on the switch to obtain a predicted fault probability. Through the fault identification model based on the Bayesian theory, the fault probability of the switch after each time of opening and closing can be accurately predicted.

Description

Background Art

[0002] With the large-scale access of high-proportion new energy sources such as photovoltaics and wind power, new power systems are developing rapidly. However, the intermittent and random characteristics of renewable energy generation have brought new challenges to the safety and stability of the power system. In power transmission and distribution, high-voltage switchgear is a key control and protection device. Ensuring the stability of its mechanical reliability is crucial to the safety of the power system. Whether its performance is reliable is directly related to whether the power system can operate stably. Therefore, higher requirements are also placed on the fault detection and life prediction of high-voltage circuit breakers. Traditional fault detection methods often rely on field experience and physical testing, which are not only inefficient, but also difficult to deal with complex fault modes, which brings many difficulties to today's field operations. Therefore, the development of an efficient and intelligent fault detection method and device has become a demand that cannot be ignored at present.

[0003] To this end, relevant scholars and engineers have conducted many studies. In the invention patent with the publication number "CN111458629A" and the subject name "A method and device for automatically identifying mechanical faults of high-voltage switches", fault identification is mainly achieved by collecting current signals and sound signals at the scene, and performing neural network learning. However, the use of sound signals will be seriously interfered by the on-site environment, and the reliability of the identification results will be greatly affected; in the invention patent with the publication number "CN117493848A" and the subject name "A method and device for identifying faults of fast repulsive switches", fault identification is performed by collecting the displacement, vibration and current signals of the on-site switch, and matching the intelligent algorithm model, but there are many intelligent algorithm models, and some models are extremely inefficient and not suitable for rapid on-site diagnosis. Summary of the invention

[0004] The technical problem to be solved by the present invention is to address the deficiencies of the prior art and specifically provide a switch fault prediction method, system and electronic device based on Bayesian theory, as follows:

[0005] 1) In the first aspect, the present invention provides a switch fault prediction method based on Bayesian theory, and the specific technical solution is as follows:

[0006] According to the driving current digital signal and the displacement digital signal of the switch when opening and closing, whether the switch fails is judged to obtain a first judgment result, wherein opening and closing includes opening or closing;

[0007] When the first judgment result is no, the driving current digital signal and the displacement digital signal are input into a fault identification model based on Bayesian theory to perform fault prediction on the switch to obtain a predicted fault probability.

[0008] The beneficial effects of a switch fault prediction method based on Bayesian theory provided by the present invention are as follows:

[0009] The fault identification model based on Bayesian theory can accurately predict the fault probability of the switch after each opening and closing.

[0010] On the basis of the above scheme, the switch fault prediction method based on Bayesian theory of the present invention can also be improved as follows.

[0011] Furthermore, it also includes:

[0012] The predicted failure probability is input into the life prediction model based on Bayesian theory to predict the life of the switch.

[0013] Furthermore, the process of acquiring the driving current digital signal and the displacement digital signal when the switch is opened and closed includes:

[0014] Collect the driving current signal of the opening and closing control circuit to control the switch to open and close, and collect the displacement signal of the switch during opening and closing;

[0015] The driving current signal and the displacement signal are subjected to analog-to-digital conversion processing to obtain a driving current digital signal and a displacement digital signal.

[0016] Further, judging whether the switch fails based on the driving current digital signal and the displacement digital signal when the switch is opened and closed, and obtaining a first judgment result includes:

[0017] Determine whether the driving current digital signal is within a preset error range of a preset standard driving current digital signal to obtain a second determination result, and determine whether the displacement digital signal is within a preset error range of a preset standard displacement digital signal to obtain a third determination result;

[0018] When the second judgment result is no and / or the third judgment result is no, it is determined that the switch fails and the first judgment result is yes.

[0019] Further, judging whether the switch fails according to the driving current digital signal and the displacement digital signal when the switch is opened and closed to obtain a first judgment result, further comprising:

[0020] When the second judgment result and the third judgment result are both yes, it is determined that the switch is not faulty and the first judgment result is no.

[0021] Further, the driving current signal of the opening and closing control circuit controlling the switch to open and close is collected, and the displacement signal of the switch during opening and closing is collected, including:

[0022] The driving current signal of the opening and closing control circuit controlling the switch to open and close is collected through the Rogowski coil, and the displacement signal of the switch during opening and closing is collected through the displacement sensor.

[0023] Furthermore, the switch is a high voltage switch.

[0024] 2) In the second aspect, the present invention also provides a switch fault prediction system based on Bayesian theory, and the specific technical solution is as follows:

[0025] Including judgment module and fault prediction module;

[0026] The judgment module is used to: judge whether the switch fails according to the driving current digital signal and the displacement digital signal when the switch is opened and closed, and obtain a first judgment result, wherein the opening and closing includes opening or closing;

[0027] The fault prediction module is used to: when the first judgment result is no, input the drive current digital signal and the displacement digital signal into a fault identification model based on Bayesian theory, perform fault prediction on the switch, and obtain a predicted fault probability.

[0028] On the basis of the above solution, the switch fault prediction system based on Bayesian theory of the present invention can also be improved as follows.

[0029] Furthermore, it also includes a life prediction module, which is used to: input the predicted failure probability into a life prediction model based on Bayesian theory to predict the life of the switch.

[0030] Furthermore, it also includes a digital signal acquisition module, which is used to:

[0031] Collect the driving current signal of the opening and closing control circuit to control the switch to open and close, and collect the displacement signal of the switch during opening and closing;

[0032] The driving current signal and the displacement signal are subjected to analog-to-digital conversion processing to obtain a driving current digital signal and a displacement digital signal.

[0033] Furthermore, the judgment module is specifically used for:

[0034] Determine whether the driving current digital signal is within a preset error range of a preset standard driving current digital signal to obtain a second determination result, and determine whether the displacement digital signal is within a preset error range of a preset standard displacement digital signal to obtain a third determination result;

[0035] When the second judgment result is no and / or the third judgment result is no, it is determined that the switch fails and the first judgment result is yes.

[0036] Furthermore, the judgment module is also specifically used for:

[0037] When the second judgment result and the third judgment result are both yes, it is determined that the switch is not faulty and the first judgment result is no.

[0038] Furthermore, the digital signal acquisition module is also specifically used for:

[0039] The driving current signal of the opening and closing control circuit controlling the switch to open and close is collected through the Rogowski coil, and the displacement signal of the switch during opening and closing is collected through the displacement sensor.

[0040] Furthermore, the switch is a high voltage switch.

[0041] 3) In a third aspect, the present invention further provides an electronic device, comprising a processor, the processor being coupled to a memory, the memory storing at least one computer program, and the at least one computer program being loaded and executed by the processor so that the electronic device implements any of the above-mentioned switch fault prediction methods based on Bayesian theory.

[0042] 4) In a fourth aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, any of the above-mentioned switch fault prediction methods based on Bayesian theory is implemented.

[0043] It should be noted that the beneficial effects achieved by the technical solutions of the second to fourth aspects of the present invention and the corresponding possible implementation methods can be found in the above-mentioned technical effects of the first aspect and its corresponding possible implementation methods, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments of the present invention:

[0045] Figure 1 A schematic diagram of a flow chart of a switch fault prediction method based on Bayesian theory according to an embodiment of the present invention;

[0046] Figure 2 A schematic diagram of an envelope corresponding to a preset standard driving current digital signal;

[0047] Figure 3 This is a schematic diagram of the network structure of the first Bayesian network;

[0048] Figure 4 A schematic diagram of the structure of a switch fault prediction system based on Bayesian theory according to an embodiment of the present invention;

[0049] Figure 5 The figure is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0050] The principles and features of the present invention are described below. The examples given are only used to explain the present invention and are not used to limit the scope of the present invention.

[0051] The technical solution of the present invention and how the technical solution of the present invention solves the above-mentioned technical problems are described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present invention will be described below in conjunction with the accompanying drawings.

[0052] like Figure 1 As shown, a switch fault prediction method based on Bayesian theory in an embodiment of the present invention includes the following steps:

[0053] S1. judging whether a switch fails according to a driving current digital signal and a displacement digital signal when the switch is opened or closed, and obtaining a first judgment result, wherein opening or closing includes opening or closing;

[0054] S2. When the first judgment result is no, the drive current digital signal and the displacement digital signal are input into a fault identification model based on Bayesian theory to predict the fault of the switch and obtain a predicted fault probability.

[0055] Optionally, in the above technical solution, it also includes:

[0056] S3. Input the predicted failure probability into the life prediction model based on Bayesian theory to predict the life of the switch.

[0057] Optionally, in the above technical solution, the process of acquiring the driving current digital signal and the displacement digital signal when the switch is opened and closed includes:

[0058] S010, collecting the driving current signal of the opening and closing control circuit to control the switch to open and close, and collecting the displacement signal of the switch during opening and closing;

[0059] S011. Perform analog-to-digital conversion on the driving current signal and the displacement signal to obtain a driving current digital signal and a displacement digital signal.

[0060] Optionally, in S1, judging whether the switch fails based on the drive current digital signal and the displacement digital signal when the switch is opened or closed, and obtaining a first judgment result includes:

[0061] S10, determining whether the driving current digital signal is within a preset error range of a preset standard driving current digital signal, obtaining a second determination result, and determining whether the displacement digital signal is within a preset error range of a preset standard displacement digital signal, obtaining a third determination result;

[0062] S11. When the second judgment result is no and / or the third judgment result is no, it is determined that the switch fails and the first judgment result is yes.

[0063] Optionally, in S1, judging whether the switch fails according to the driving current digital signal and the displacement digital signal when the switch is opened or closed to obtain a first judgment result, further comprising:

[0064] S12. When the second judgment result and the third judgment result are both yes, it is determined that the switch is not faulty and the first judgment result is no.

[0065] Optionally, in S010, collecting a driving current signal of the opening and closing control circuit to control the switch to open and close, and collecting a displacement signal of the switch during opening and closing, including:

[0066] S0100, through the Rogowski coil, collect the driving current signal of the opening and closing control circuit to control the switch to open and close, and through the displacement sensor, collect the displacement signal of the switch when opening and closing.

[0067] Optionally, in the above technical solution, the switch is a high-voltage switch.

[0068] Taking a high-voltage switch as an example, the present invention is further explained, and specifically includes the following steps:

[0069] S101. The driving current signal of the closing and opening control circuit for controlling the high-voltage switch to close is collected through the Rogowski coil, and the displacement signal of the high-voltage switch when closing is collected through the displacement sensor. Both the driving current signal and the displacement signal are analog signals.

[0070] S102 , performing analog-to-digital conversion processing on the driving current signal and the displacement signal to obtain a driving current digital signal and a displacement digital signal.

[0071] S103, obtaining a first judgment result, specifically:

[0072] It is determined whether the driving current digital signal is within a preset error range of a preset standard driving current digital signal to obtain a second determination result, and it is determined whether the displacement digital signal is within a preset error range of a preset standard displacement digital signal to obtain a third determination result, then:

[0073] 1) When the second judgment result is no and / or the third judgment result is no, it is determined that the high-voltage switch is faulty and the first judgment result is yes;

[0074] 2) When the second judgment result and the third judgment result are both yes, it is determined that the high-voltage switch has not failed and the first judgment result is no.

[0075] The process of obtaining the preset standard driving current digital signal and the preset standard displacement digital signal is as follows:

[0076] After determining that the high-voltage switch and the opening and closing control circuit corresponding to the high-voltage switch are in normal working condition, the opening and closing control circuit sends out a driving current to control the closing of the high-voltage switch. The driving current signal of the opening and closing control circuit controlling the closing of the high-voltage switch is collected through the Rogowski coil as the standard driving current signal. The displacement signal of the high-voltage switch when closing is collected through the displacement sensor as the standard displacement signal. The standard driving current signal and the standard displacement signal are converted into digital form to obtain a preset standard driving current digital signal and a preset standard displacement digital signal.

[0077] Among them, the driving current signal is: the curve data of the driving current changing with time for controlling the closing of the high-voltage switch, in which the horizontal axis is the time and the vertical axis is the driving current. The time when the driving current is generated is taken as the starting time, and the time when the driving current is reduced to 0 is taken as the ending time.

[0078] Among them, the displacement signal is: when the high-voltage switch is closed, the displacement of the moving contact of the high-voltage switch changes with time, and the curve data is the time, and the vertical axis is the displacement of the moving contact. The time when the driving current is generated is still taken as the starting time, and the time when the driving current is reduced to 0 is taken as the ending time.

[0079] The process of obtaining the preset error range of the preset standard driving current digital signal includes:

[0080] According to the preset driving current error value of the preset standard driving current digital signal, the curve corresponding to the preset standard driving current digital signal is offset. Specifically, the preset driving current error value is added to the driving current at each moment in the preset standard driving current digital signal to obtain the upper limit offset curve corresponding to the preset standard driving current digital signal. The preset driving current error value is subtracted from the driving current at each moment in the preset standard driving current digital signal to obtain the lower limit offset curve corresponding to the preset standard driving current digital signal. The range between the upper limit offset curve and the lower limit offset curve is the preset error range of the preset standard driving current digital signal.

[0081] The horizontal axis of the preset standard driving current digital signal is time, and the vertical axis is driving current. The time when the driving current is generated is taken as the starting time, and the time when the driving current is reduced to 0 is taken as the ending time.

[0082] The second determination result is obtained by judging whether the driving current digital signal is within a preset error range of a preset standard driving current digital signal. The specific implementation method is as follows:

[0083] 1) The first implementation method:

[0084] After obtaining the driving current digital signal, first determine whether the duration between the start time and the end time in the driving current digital signal is equal to the duration between the start time and the end time of the preset standard driving current digital signal. If not, directly determine that the second determination result is no. If yes, continue to determine whether the driving current at each moment in the driving current digital signal is within the corresponding preset error range. If yes, the second determination result is yes. If the driving current at any moment is not within the corresponding preset error range, the second determination result is no. The preset error range at each moment can be obtained from the preset error range of the preset standard driving current digital signal.

[0085] like Figure 2 As shown, the upper limit offset curve corresponding to the preset standard driving current digital signal and the lower limit offset curve corresponding to the preset standard driving current digital signal are used as two envelopes, and then the starting data points and the ending data points of the upper limit offset curve corresponding to the preset standard driving current digital signal and the lower limit offset curve corresponding to the preset standard driving current digital signal are respectively connected by straight lines to form two envelopes, as shown in FIG. Figure 2 As shown by the dotted line in , the four envelope lines enclose an envelope. When the driving current digital signal is completely in the envelope, it is determined that the driving current at each moment in the driving current digital signal is within the corresponding preset error range, and the second judgment result is yes. When the driving current digital signal is not completely in the envelope, it is determined that the driving current at at least one moment is not within the corresponding preset error range, and the second judgment result is no.

[0086] 2) The second implementation method:

[0087] After obtaining the driving current digital signal, first determine whether the duration between the start time and the end time in the driving current digital signal is equal to the duration between the start time and the end time of the preset standard driving current digital signal. If not, directly determine that the second judgment result is no. If so, divide the driving current digital signal into N segments, and the duration Δt of each segment is:

[0088]

[0089] Wherein, t represents the duration between the start time t0 of the driving current digital signal and the end time t1 of the driving current digital signal.

[0090] The upper limit offset curve and the lower limit offset curve corresponding to the preset standard driving current digital signal are equally divided into N sections, and each section of the driving current digital signal is recorded as a driving current digital sub-signal. Each section of the upper limit offset curve and the lower limit offset curve corresponding to the preset standard driving current digital signal is recorded as: an upper limit offset sub-curve and a lower limit offset sub-curve, and then each driving current digital sub-signal is compared to see whether it is within the error range between the corresponding upper limit offset sub-curve and the lower limit offset sub-curve. If any driving current digital sub-signal is not within the error range between the corresponding upper limit offset sub-curve and the lower limit offset sub-curve, the second judgment result is no; if each driving current digital sub-signal is within the error range between the corresponding upper limit offset sub-curve and the lower limit offset sub-curve, the second judgment result is yes.

[0091] Among them, the upper limit offset sub-curve and the lower limit offset sub-curve with a corresponding relationship corresponding to any driving current digital sub-signal are used as two envelope lines, and then the starting data points and the ending data points of the upper limit offset sub-curve and the lower limit offset sub-curve are respectively connected by straight lines to form two envelope lines. The four envelope lines are combined to form an envelope, and it is judged that the driving current digital sub-signal is completely in the envelope to obtain a judgment result, and each driving current digital sub-signal is traversed to obtain N fourth judgment results. When the N fourth judgment results are all yes, the second judgment result is yes, and when one of the N fourth judgment results is no, the second judgment result is no.

[0092] The horizontal axis of the preset standard displacement digital signal is time, and the vertical axis is displacement. The time when the driving current is generated is taken as the starting time, and the time when the driving current is reduced to 0 is taken as the ending time.

[0093] Wherein, judging whether the displacement digital signal is within a preset error range of a preset standard displacement digital signal to obtain a third judgment result is specifically implemented as follows:

[0094] 1) The first implementation method:

[0095] After obtaining the displacement digital signal, first determine whether the duration between the start time and the end time in the displacement digital signal is equal to the duration between the start time and the end time of the preset standard displacement digital signal. If not, directly determine that the third judgment result is no. If yes, continue to determine whether the displacement at each moment in the displacement digital signal is within the corresponding preset error range. If yes, the third judgment result is yes. If the displacement at any moment is not within the corresponding preset error range, the third judgment result is no. The preset error range at each moment can be obtained from the preset error range of the preset standard displacement digital signal.

[0096] Among them, the upper limit offset curve corresponding to the preset standard displacement digital signal and the lower limit offset curve corresponding to the preset standard displacement digital signal are used as two envelope lines, and then the starting data points and the ending data points of the upper limit offset curve corresponding to the preset standard displacement digital signal and the lower limit offset curve corresponding to the preset standard displacement digital signal are respectively connected by straight lines to form two envelope lines. The four envelope lines are combined to form an envelope. When the displacement digital signal is completely in the envelope, it is determined that the displacement at each moment in the displacement digital signal is within the corresponding preset error range, and the third judgment result is yes. When the displacement digital signal is not completely in the envelope, it is determined that the displacement at at least one moment is not within the corresponding preset error range, and the third judgment result is no.

[0097] 2) The second implementation method:

[0098] First, determine whether the duration between the start time and the end time in the displacement digital signal is equal to the duration between the start time and the end time of the preset standard displacement digital signal. If not, directly determine that the third judgment result is no. If so, continue to divide the displacement digital signal into n segments, and the duration of each segment is Δt ′ for:

[0099]

[0100] Among them, t ′ Indicates: the starting time t0 of the displacement digital signal ′ The termination time t1 of the displacement digital signal ′ The length of time between.

[0101] The upper limit offset curve and the lower limit offset curve corresponding to the preset standard displacement digital signal are equally divided into n segments, and each segment of the displacement digital signal is recorded as a displacement digital sub-signal. Each segment of the upper limit offset curve and the lower limit offset curve corresponding to the preset standard displacement digital signal is recorded as an upper limit offset sub-curve and a lower limit offset sub-curve, respectively. Then, each displacement digital sub-signal is compared to see whether it is within the error range between the corresponding upper limit offset sub-curve and the lower limit offset sub-curve. If any displacement digital sub-signal is not within the error range between the corresponding upper limit offset sub-curve and the lower limit offset sub-curve, the third judgment result is no. If each displacement digital sub-signal is within the error range between the corresponding upper limit offset sub-curve and the lower limit offset sub-curve, the third judgment result is yes.

[0102] Among them, the upper limit offset sub-curve and the lower limit offset sub-curve with a corresponding relationship corresponding to any displacement digital sub-signal are used as two envelope lines, and then the starting data points and the ending data points of the upper limit offset sub-curve and the lower limit offset sub-curve are respectively connected by straight lines to form two envelope lines. The four envelope lines are combined to form an envelope, and it is judged that the displacement digital sub-signal is completely in the envelope to obtain a judgment result. Each displacement digital sub-signal is traversed to obtain n fifth judgment results. When the n fifth judgment results are all yes, the third judgment result is yes. When one of the n fifth judgment results is no, the third judgment result is no.

[0103] S104, obtaining a fault identification model based on Bayesian theory, specifically:

[0104] First, a Bayesian network is built. For the sake of distinction, the Bayesian network built here is recorded as the first Bayesian network. Each component or parameter in the first Bayesian network is a Bayesian node. Based on the relationship between different Bayesian nodes and the investigation of historical data (historical drive current digital signals, historical displacement digital signals, and historical faults of high-voltage switches), a suitable topological structure can be constructed as the first Bayesian network, such as Figure 3 As shown in the figure, T indicates fault, N1, N2, N3, X1, X2, X3 and X4 are all Bayesian nodes, N1 indicates energy storage unit fault, N2 indicates drive unit fault, N3 indicates motion unit fault, X1 indicates energy storage capacitor fault, X2 indicates opening and closing coil fault, X3 indicates repulsion disk fault, and X4 indicates transmission rod fault. N1 includes X1, N2 includes X2, and N3 includes X3 and X4.

[0105] Then, based on the historical driving current digital signal, historical displacement digital signal and historical faults of the high-voltage switch, the constructed first Bayesian network is trained, and the probability of each Bayesian node is gradually adjusted. After training, a fault recognition model based on Bayesian theory is obtained. The fault recognition model based on Bayesian theory can be used to predict faults based on the real-time driving current digital signal and displacement digital signal. Moreover, the fault recognition model based on Bayesian theory can be iteratively trained based on the newly obtained current digital signal, displacement digital signal and fault, which can ensure the fault prediction accuracy of the fault recognition model based on Bayesian theory.

[0106] The fault prediction logic of the fault identification model based on Bayesian theory is as follows:

[0107]

[0108] According to Bayesian theory, the algorithm should be divided into three parts: obtaining the failure probability of the grassroots node (prior probability), the impact of a certain condition on the failure probability (likelihood), and the prediction of the failure probability (posterior probability). Specifically:

[0109] 1) Prior probability: Establish a Bayesian network, based on previous equipment operation experience and data, and set component failures such as opening and closing coil failures and relay failures as X i .

[0110] 2) Likelihood: Each influencing factor such as usage time, maintenance times, etc. is denoted as Yi, and the influencing factors of different degrees are evaluated.

[0111] 3) Posterior probability: According to the refusal defect, the associated fault event X i Fault prediction is performed based on the probability of occurrence of the influencing factor Yi.

[0112] S105. When the first judgment result is no, the drive current digital signal and the displacement digital signal are input into a fault identification model based on Bayesian theory to predict the fault of the high-voltage switch and obtain a predicted fault probability. Then, S106 is executed. When the first judgment result is yes, it indicates that the high-voltage switch has failed and a fault reminder may be issued to remind the user to replace or repair the high-voltage switch to avoid further losses.

[0113] S106: Input the predicted failure probability into a life prediction model based on Bayesian theory to predict the life of the switch.

[0114] First, a Bayesian network is constructed. For the sake of distinction, the Bayesian network constructed here is recorded as the second Bayesian network. Each component or parameter in the second Bayesian network is a Bayesian node. According to the relationship between different Bayesian nodes, historical data (the historical failure probability of the high-voltage switch and the real life of the high-voltage switch corresponding to the historical failure probability) are investigated to construct a suitable topological structure as the second Bayesian network. Then, based on the historical failure probability of the high-voltage switch and the real life of the high-voltage switch corresponding to the historical failure probability, the constructed second Bayesian network is trained, and the probability of each Bayesian node is gradually adjusted to achieve algorithm optimization. After training, a life prediction model based on Bayesian theory is obtained. The life prediction model based on Bayesian theory can be used to predict faults according to the real-time driving current digital signal and displacement digital signal. Moreover, the life prediction model based on Bayesian theory can also be iteratively trained according to the newly obtained current digital signal, displacement digital signal and fault, which can ensure the fault prediction accuracy of the life prediction model based on Bayesian theory.

[0115] In the above embodiment, "the driving current digital signal and the displacement digital signal obtained when the high-voltage switch is closed" are used as an example for explanation. The implementation logic of the fault prediction process and the life prediction process of the high-voltage switch when it is opened are the same as those in the above embodiment and will not be repeated here.

[0116] In the above embodiments, although the steps are numbered S1, S2, etc., these are only specific embodiments given by the present invention. Those skilled in the art may adjust the execution order of S1, S2, etc. according to actual conditions, which is also within the protection scope of the present invention. It can be understood that in some embodiments, some or all of the above embodiments may be included.

[0117] like Figure 4 As shown, a switch fault prediction system 200 based on Bayesian theory according to an embodiment of the present invention includes a judgment module 201 and a fault prediction module 202;

[0118] The judgment module 201 is used to: judge whether the switch fails according to the driving current digital signal and the displacement digital signal when the switch is opened and closed, and obtain a first judgment result, wherein the opening and closing includes opening or closing;

[0119] The fault prediction module 202 is used to: when the first judgment result is no, input the drive current digital signal and the displacement digital signal into a fault identification model based on Bayesian theory, perform fault prediction on the switch, and obtain a predicted fault probability.

[0120] Optionally, the above technical solution further includes a life prediction module, which is used to: input the predicted failure probability into a life prediction model based on Bayesian theory to predict the life of the switch.

[0121] Optionally, in the above technical solution, a digital signal acquisition module is further included, and the digital signal acquisition module is used to:

[0122] Collect the driving current signal of the opening and closing control circuit to control the switch to open and close, and collect the displacement signal of the switch during opening and closing;

[0123] The driving current signal and the displacement signal are subjected to analog-to-digital conversion processing to obtain a driving current digital signal and a displacement digital signal.

[0124] Optionally, in the above technical solution, the determination module 201 is specifically used for:

[0125] Determine whether the driving current digital signal is within a preset error range of a preset standard driving current digital signal to obtain a second determination result, and determine whether the displacement digital signal is within a preset error range of a preset standard displacement digital signal to obtain a third determination result;

[0126] When the second judgment result is no and / or the third judgment result is no, it is determined that the switch fails and the first judgment result is yes.

[0127] Optionally, in the above technical solution, the determination module 201 is further specifically used for:

[0128] When the second judgment result and the third judgment result are both yes, it is determined that the switch is not faulty and the first judgment result is no.

[0129] Optionally, in the above technical solution, the digital signal acquisition module is further specifically used for:

[0130] The driving current signal of the opening and closing control circuit controlling the switch to open and close is collected through the Rogowski coil, and the displacement signal of the switch during opening and closing is collected through the displacement sensor.

[0131] Optionally, in the above technical solution, the switch is a high-voltage switch.

[0132] It should be noted that the beneficial effects of the switch fault prediction system 200 based on Bayesian theory provided in the above embodiment are the same as the beneficial effects of the switch fault prediction method based on Bayesian theory, which will not be described in detail here. In addition, when the system provided in the above embodiment realizes its functions, it only uses the division of the above functional modules as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the system can be divided into different functional modules according to actual conditions to complete all or part of the functions described above. In addition, the system and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be described in detail here.

[0133] Among them, the switch fault prediction system based on Bayesian theory of the present invention can be a computer program (including program code) running in a computer device. For example, the switch fault prediction system based on Bayesian theory of the present invention is an application software that can be used to execute the corresponding steps in the switch fault prediction method based on Bayesian theory of the present invention.

[0134] In some embodiments, the switch fault prediction system based on Bayesian theory of the present invention can be implemented in a combination of software and hardware. As an example, the switch fault prediction system based on Bayesian theory of the present invention can be a processor in the form of a hardware decoding processor, which is programmed to execute the switch fault prediction method based on Bayesian theory of the present invention. For example, the processor in the form of a hardware decoding processor can adopt one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field programmable gate arrays (FPGAs) or other electronic components.

[0135] The modules involved in the embodiments of the present invention may be implemented in software or hardware. The name of a module does not limit the module itself in some cases.

[0136] An electronic device according to an embodiment of the present invention includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, any of the above-mentioned switch fault prediction methods based on Bayesian theory is implemented. That is to say, an electronic device according to an embodiment of the present invention may include but is not limited to: a processor and a memory; a memory for storing a computer program; a processor for executing a switch fault prediction method based on Bayesian theory shown in any embodiment of the present invention by calling a computer program.

[0137] In an alternative embodiment, an electronic device is provided, such as Figure 5 As shown, Figure 5 The electronic device 4000 shown includes: a processor 4001 and a memory 4003. The processor 4001 and the memory 4003 are connected, such as through a bus 4002. Optionally, the electronic device 4000 may also include a transceiver 4004, which may be used for data interaction between the electronic device and other electronic devices, such as data transmission and / or data reception. It should be noted that in actual applications, the transceiver 4004 is not limited to one, and the structure of the electronic device 4000 does not constitute a limitation on the embodiments of the present invention.

[0138] Processor 4001 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It may implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of the present invention. Processor 4001 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0139] The bus 4002 may include a path to transmit information between the above components. The bus 4002 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus 4002 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 In the figure, only one thick line is used to represent the bus 4002, but this does not mean that there is only one bus or one type of bus.

[0140] The memory 4003 can be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compressed optical disk, laser disk, optical disk, digital versatile disk, Blu-ray disk, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited to this.

[0141] The memory 4003 is used to store the application code (computer program) for executing the solution of the present invention, and the execution is controlled by the processor 4001. The processor 4001 is used to execute the application code stored in the memory 4003 to implement the content shown in the above method embodiment.

[0142] Among them, the electronic device can also be a terminal device, and the terminal device can be any terminal device that can install applications and access web pages through applications, including at least one of a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, a smart TV, and a smart car device.

[0143] It should be noted that Figure 5 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0144] A computer-readable storage medium according to an embodiment of the present invention stores a computer program, and when the computer program is executed by a processor, any of the above-mentioned switch fault prediction methods based on Bayesian theory is implemented.

[0145] Optionally, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc (CD-ROM), a magnetic tape, a floppy disk, an optical data storage device, and the like.

[0146] In an exemplary embodiment, a computer program product or a computer program is also provided, the computer program product or the computer program comprising computer instructions, the computer instructions being stored in a computer-readable storage medium. A processor of an electronic device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the electronic device executes any of the above-mentioned switch fault prediction methods based on the Bayesian theory.

[0147] Computer program code for performing the operations of the present invention may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0148] It should be understood that the flow charts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the method and computer program product according to various embodiments of the present invention. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the module, the program segment, or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a sequence different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0149] The computer-readable storage medium provided by the embodiment of the present invention may be, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or component, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, device, or component.

[0150] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device executes the method shown in the above embodiment.

[0151] The above description is only a preferred embodiment of the present invention and an explanation of the technical principles used. Those skilled in the art should understand that the disclosure scope involved in the present invention is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in the present invention (but not limited to) by each other.

[0152] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application are used to distinguish similar objects and represent the definition of a specific order or sequence. The order of use of similar objects can be interchanged where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than the order shown or described.

[0153] Those skilled in the art know that the present invention can be implemented as a system, method or computer program product. Therefore, the present invention can be specifically implemented in the following forms, namely: it can be complete hardware, it can be complete software (including firmware, resident software, microcode, etc.), or it can be a combination of hardware and software, which is generally referred to as "circuit", "module" or "system" herein. In addition, in some embodiments, the present invention can also be implemented in the form of a computer program product in one or more computer-readable media, and the computer-readable medium contains computer-readable program code.

[0154] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A switch fault prediction method based on Bayesian theory, characterized in that: include: According to the driving current digital signal and the displacement digital signal of the switch when opening and closing, determine whether the switch fails, and obtain a first determination result, wherein opening and closing includes opening or closing; When the first judgment result is no, the drive current digital signal and the displacement digital signal are input into a fault identification model based on Bayesian theory to perform fault prediction on the switch to obtain a predicted fault probability.

2. A switch fault prediction method based on Bayesian theory according to claim 1, characterized in that: Also includes: The predicted failure probability is input into a life prediction model based on Bayesian theory to predict the life of the switch.

3. A switch fault prediction method based on Bayesian theory according to claim 1 or 2, characterized in that: The process of acquiring the driving current digital signal and displacement digital signal when the switch is opened and closed includes: Collecting the driving current signal of the opening and closing control circuit to control the switch to open and close, and collecting the displacement signal of the switch when opening and closing; The driving current signal and the displacement signal are subjected to analog-to-digital conversion processing to obtain the driving current digital signal and the displacement digital signal.

4. A switch fault prediction method based on Bayesian theory according to claim 1 or 2, characterized in that: According to the driving current digital signal and the displacement digital signal of the switch when the switch is opened and closed, it is judged whether the switch fails, and a first judgment result is obtained, including: Determine whether the driving current digital signal is within a preset error range of a preset standard driving current digital signal to obtain a second determination result, and determine whether the displacement digital signal is within a preset error range of a preset standard displacement digital signal to obtain a third determination result; When the second judgment result is no and / or the third judgment result is no, it is determined that the switch fails and the first judgment result is yes.

5. A switch fault prediction method based on Bayesian theory according to claim 4, characterized in that: Judging whether the switch fails according to the driving current digital signal and the displacement digital signal when the switch is opened or closed, and obtaining a first judgment result, further comprising: When the second judgment result and the third judgment result are both yes, it is determined that the switch has not failed, and the first judgment result is no.

6. A switch fault prediction method based on Bayesian theory according to claim 3, characterized in that: Collecting the driving current signal of the opening and closing control circuit to control the switch to open and close, and collecting the displacement signal of the switch during opening and closing, including: The driving current signal of the opening and closing control circuit controlling the switch to open and close is collected through the Rogowski coil, and the displacement signal of the switch during opening and closing is collected through the displacement sensor.

7. A switch fault prediction method based on Bayesian theory according to claim 1 or 2, characterized in that: The switch is a high voltage switch.

8. A switch fault prediction system based on Bayesian theory, characterized in that: Including judgment module and fault prediction module; The judgment module is used to: judge whether the switch fails according to the driving current digital signal and the displacement digital signal when the switch is opened and closed, and obtain a first judgment result, wherein opening and closing includes opening or closing; The fault prediction module is used to: when the first judgment result is no, input the drive current digital signal and the displacement digital signal into a fault identification model based on Bayesian theory, perform fault prediction on the switch, and obtain a predicted fault probability.

9. An electronic device, characterized in that: The invention comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, a switch fault prediction method based on Bayesian theory as claimed in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for predicting switch faults based on Bayesian theory as claimed in any one of claims 1 to 7 is implemented.

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