Life cycle prediction and auxiliary maintenance method for wave absorbing bank

Abstract

The invention discloses a life cycle prediction and auxiliary maintenance method for a wave absorbing bank, and the method comprises the steps: collecting parameter data, operation data and life maintenance data of wave absorbing bank equipment, and carrying out the data correction, thereby obtaining fault prediction data; performing feature analysis and unified representation on the processed parameter data, operation data and fault prediction data, and setting data labels and label weights of the operation data and the fault prediction data of the wave absorbing shore equipment according to actual requirements; according to the label weight, constructing a wave absorbing shore equipment portrait label system; carrying out dimension reduction processing to generate similar clusters, and mining association rules by using an FP-Tree algorithm; performing fault classification on the data attributes according to the association rule, constructing an equipment fault prediction model, and training to obtain a life cycle prediction model of the wave absorbing shore equipment; and life cycle prediction is carried out based on the life cycle prediction model, and a prediction result is fed back for auxiliary maintenance of the wave absorbing shore equipment.

Description

Technical Field

[0001] The invention relates to the field of wave-breaking banks, and in particular to a life cycle prediction and auxiliary maintenance method for wave-breaking banks. Background Art

[0002] The wave breaker is one of the key test equipment in the wave pool. Large wave pools at home and abroad are equipped with L-shaped wave makers on both sides of the pool, and the wave breaker is arranged on the opposite side of the wave maker. The wave breaker is mainly used to absorb the reflected waves in the pool during the model test to meet the requirements of the model test for wave simulation accuracy.

[0003] Different wave-breaking devices have different wave-breaking effects, and the quality of the wave-breaking performance will directly affect the accuracy of the wave tank test. Therefore, in order to improve the sustainable use of the wave-breaking device, the present invention proposes a life cycle prediction and auxiliary maintenance method for the wave-breaking bank. The life cycle of each device of the wave-breaking bank can be predicted based on this method, so as to provide a basis for the auxiliary maintenance of the wave-breaking bank. When the wave-breaking bank has life defects, human intervention can be used to ensure the continuous use of the wave-breaking bank equipment, so as to avoid temporary damage to the wave-breaking bank equipment during the wave tank test. Summary of the invention

[0004] The object of the present invention is to provide a life cycle prediction and auxiliary maintenance method for a wave-breaking bank to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solution: a life cycle prediction and auxiliary maintenance method of a wave-breaking bank, comprising the steps of:

[0006] S1: Collect parameter data, operation data and life maintenance data of wave-breaking bank equipment, where parameter data includes basic parameters and load parameters of each equipment of wave-breaking bank, operation data refers to the operation arrangement of wave-breaking bank equipment under different working conditions and post-operation status evaluation data, and life maintenance data refers to the life cycle regression data obtained through the historical maintenance arrangement of each equipment of wave-breaking bank under different maintenance types and post-maintenance status evaluation data;

[0007] S2: Determine the initial fault prediction data of each type of wave-breaking bank equipment according to the historical maintenance arrangement, and correct the initial fault prediction data according to the life cycle rollback data corresponding to each maintenance type arrangement of each type of wave-breaking bank equipment, so as to obtain the corrected fault prediction data of each type of wave-breaking bank equipment;

[0008] S3: Performing feature analysis and unified characterization on the processed parameter data, operation data and fault prediction data, and setting data labels and label weights of the wave-breaking bank equipment operation data and fault prediction data according to actual needs;

[0009] S4: Based on the above label weights, a label system for wave-breaking shore equipment portrait is constructed;

[0010] S5: Performing dimensionality reduction processing on the parameter data, operation data and fault prediction data after unified characterization by using K-means data clustering algorithm to generate similar clusters, and using FP-Tree algorithm to mine association rules between equipment abnormality or fault data and fault factor data;

[0011] S6: classifying the data attributes according to the association rules to build an equipment failure prediction model; training the equipment failure prediction model based on the parameter data, operation data and failure prediction data to obtain a life cycle prediction model for the wave-breaking bank equipment;

[0012] S7: Based on the life cycle prediction model of the wave-breaking bank equipment, the life cycle of the wave-breaking bank equipment is predicted, and the prediction result is fed back for auxiliary maintenance of the wave-breaking bank equipment.

[0013] Preferably, the process of obtaining the life cycle rollback data through the historical maintenance arrangements of each equipment of the wave-breaking bank under different maintenance types and the post-maintenance status evaluation data in step S1 includes: obtaining the actual life of each wave-breaking bank equipment in each type of wave-breaking bank equipment according to the historical maintenance arrangements of each type of wave-breaking bank equipment under each maintenance type arrangement; determining the equivalent life of each wave-breaking bank equipment in each type of wave-breaking bank equipment according to the post-maintenance status evaluation data of each type of wave-breaking bank equipment under each maintenance type arrangement; and determining the life cycle rollback data corresponding to each maintenance type arrangement of each type of wave-breaking bank equipment according to the above-mentioned actual life and equivalent life.

[0014] Preferably, the implementation process of the life cycle rollback data in step S1 includes:

[0015] In the preset time period, among the M types of wave-breaking shore equipment, a total of N equipment has been scheduled for overhaul. Therefore, the actual life of each wave-breaking shore equipment in the M types of wave-breaking shore equipment is recorded as t 1 ,t 2 ,t 3 …t n After the overhaul, the equivalent life of each wave-breaking bank equipment in the M-type wave-breaking bank equipment is determined based on the after-overhaul status evaluation data of the wave-breaking bank equipment.

[0016] By taking the average of the life cycle regression data of each type of wave-breaking bank equipment, the actual life cycle regression data of each type of wave-breaking bank equipment is obtained, that is, the life cycle regression data It can be expressed as:

[0017]

[0018] Preferably, the unified representation in step S3 is:

[0019] D j,t =(Par jt ,run jt ,fault jt ),j=1,2,3…Z;

[0020] In the above formula, D j,t represents the data set collected when a j-type fault occurs at time t, and Z represents the number of fault categories;

[0021] The above data can be expressed in a unified form as follows:

[0022] D j =(x j1 ,x j2 ,x j3 …x jn ),n=m*N*T

[0023] In the formula, m represents the number of single monitoring indicators, N represents the frequency of data collection under the fault surface, T represents the duration of data collection under the fault surface, and x j1 ,x j2 ,x j3 …x jn They respectively represent the relevant fault factor data collected when j type of fault occurs, and n represents the fault data category.

[0024] Preferably, the specific process of mining using the FP-Tree algorithm in step S5 includes: executing the FP-tree generation algorithm on each data cluster to generate FP-tree subtrees corresponding to multiple data clusters one by one; and mining association rules based on the correspondence between elements in different FP-tree subtrees.

[0025] Compared with the prior art, the present invention has the following beneficial effects:

[0026] The present invention can predict the life cycle of each device of the wave-breaking bank based on the method, so as to provide a basis for the auxiliary maintenance of the wave-breaking bank. When the wave-breaking bank has life defects, human intervention can be used to ensure the continuous use of the wave-breaking bank equipment, thereby avoiding temporary damage to the wave-breaking bank equipment during the wave tank test. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is the prediction flow chart of the present invention. DETAILED DESCRIPTION

[0028] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0029] See also Figure 1 The present invention provides a technical solution: a life cycle prediction and auxiliary maintenance method of a wave-breaking bank, comprising the steps of:

[0030] S1: Collect parameter data, operation data and life maintenance data of wave-breaking bank equipment, where parameter data includes basic parameters and load parameters of each equipment of wave-breaking bank, operation data refers to the operation arrangement of wave-breaking bank equipment under different working conditions and post-operation status evaluation data, and life maintenance data refers to the life cycle regression data obtained through the historical maintenance arrangement of each equipment of wave-breaking bank under different maintenance types and post-maintenance status evaluation data;

[0031] S2: Determine the initial fault prediction data of each type of wave-breaking bank equipment according to the historical maintenance arrangement, and correct the initial fault prediction data according to the life cycle rollback data corresponding to each maintenance type arrangement of each type of wave-breaking bank equipment, so as to obtain the corrected fault prediction data of each type of wave-breaking bank equipment;

[0032] S3: Performing feature analysis and unified characterization on the processed parameter data, operation data and fault prediction data, and setting data labels and label weights of the wave-breaking bank equipment operation data and fault prediction data according to actual needs;

[0033] S4: Based on the above label weights, a label system for wave-breaking shore equipment portrait is constructed;

[0034] S5: Performing dimensionality reduction processing on the parameter data, operation data and fault prediction data after unified characterization by using K-means data clustering algorithm to generate similar clusters, and using FP-Tree algorithm to mine association rules between equipment abnormality or fault data and fault factor data;

[0035] S6: classifying the data attributes according to the association rules to build an equipment failure prediction model; training the equipment failure prediction model based on the parameter data, operation data and failure prediction data to obtain a life cycle prediction model for the wave-breaking bank equipment;

[0036] S7: Based on the life cycle prediction model of the wave-breaking bank equipment, the life cycle of the wave-breaking bank equipment is predicted, and the prediction result is fed back for auxiliary maintenance of the wave-breaking bank equipment.

[0037] In this embodiment, the process of obtaining life cycle rollback data through the historical maintenance arrangement of each type of wave-breaking bank equipment under different maintenance type arrangements and the post-maintenance status evaluation data in step S1 includes: obtaining the actual life of each type of wave-breaking bank equipment in each type of wave-breaking bank equipment according to the historical maintenance arrangement of each type of wave-breaking bank equipment under each maintenance type arrangement; determining the equivalent life of each type of wave-breaking bank equipment in each type of wave-breaking bank equipment according to the post-maintenance status evaluation data of each type of wave-breaking bank equipment under each maintenance type arrangement; determining the life cycle rollback data corresponding to each maintenance type arrangement of each type of wave-breaking bank equipment according to the above actual life and equivalent life. .

[0038] In this embodiment, the implementation process of the life cycle rollback data in step S1 includes:

[0039] In the preset time period, among the M types of wave-breaking shore equipment, a total of N equipment has been scheduled for overhaul. Therefore, the actual life of each wave-breaking shore equipment in the M types of wave-breaking shore equipment is recorded as t 1 ,t 2 ,t 3 …t n After the overhaul, the equivalent life of each wave-breaking bank equipment in the M-type wave-breaking bank equipment is determined based on the after-overhaul status evaluation data of the wave-breaking bank equipment.

[0040] By taking the average of the life cycle regression data of each type of wave-breaking bank equipment, the actual life cycle regression data of each type of wave-breaking bank equipment is obtained, that is, the life cycle regression data It can be expressed as:

[0041]

[0042] In this embodiment, the unified representation in step S3 is:

[0043] D j,t =(Par jt ,run jt ,fault jt ),j=1,2,3…Z;

[0044] In the above formula, D j,t represents the data set collected when a j-type fault occurs at time t, and Z represents the number of fault categories;

[0045] The above data can be expressed in a unified form as follows:

[0046] D j =(x j1 ,x j2 ,x j3 …x jn ),n=m*N*T

[0047] In the formula, m represents the number of single monitoring indicators, N represents the frequency of data collection under the fault surface, T represents the duration of data collection under the fault surface, and x j1 ,x j2 ,x j3 …x jn They respectively represent the relevant fault factor data collected when j type of fault occurs, and n represents the fault data category.

[0048] In this embodiment, the specific process of mining using the FP-Tree algorithm in step S5 includes: executing the FP-tree generation algorithm on each data cluster to generate FP-tree subtrees corresponding to multiple data clusters one by one; and mining association rules based on the correspondence between elements in different FP-tree subtrees.

[0049] The above method can predict the life cycle of each device of the wave-breaking bank, so as to provide a basis for the auxiliary maintenance of the wave-breaking bank. When the wave-breaking bank has life defects, human intervention can be used to ensure the continuous use of the wave-breaking bank equipment, avoiding temporary damage to the wave-breaking bank equipment during the wave tank test.

[0050] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0051] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0052] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0053] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of the units is only one, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0054] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0055] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0056] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks or optical disks.

[0057] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0058] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

[0059] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

[0060] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for life cycle prediction and auxiliary maintenance of a wave-breaking bank, characterized in that: Includes steps: S1: Collect parameter data, operation data and life maintenance data of wave-breaking bank equipment, where parameter data includes basic parameters and load parameters of each equipment of wave-breaking bank, operation data refers to the operation arrangement of wave-breaking bank equipment under different working conditions and post-operation status evaluation data, and life maintenance data refers to the life cycle regression data obtained through the historical maintenance arrangement of each equipment of wave-breaking bank under different maintenance types and post-maintenance status evaluation data; S2: Determine the initial fault prediction data of each type of wave-breaking bank equipment according to the historical maintenance arrangement, and correct the initial fault prediction data according to the life cycle rollback data corresponding to each maintenance type arrangement of each type of wave-breaking bank equipment, so as to obtain the corrected fault prediction data of each type of wave-breaking bank equipment; S3: Performing feature analysis and unified characterization on the processed parameter data, operation data and fault prediction data, and setting data labels and label weights of the wave-breaking bank equipment operation data and fault prediction data according to actual needs; S4: Based on the above label weights, a label system for wave-breaking shore equipment portrait is constructed; S5: Performing dimensionality reduction processing on the parameter data, operation data and fault prediction data after unified characterization by using K-means data clustering algorithm to generate similar clusters, and using FP-Tree algorithm to mine association rules between equipment abnormality or fault data and fault factor data; S6: classifying the data attributes according to the association rules to build an equipment failure prediction model; training the equipment failure prediction model based on the parameter data, operation data and failure prediction data to obtain a life cycle prediction model for the wave-breaking bank equipment; S7: Based on the life cycle prediction model of the wave-breaking bank equipment, the life cycle of the wave-breaking bank equipment is predicted, and the prediction result is fed back for auxiliary maintenance of the wave-breaking bank equipment.

2. The method for life cycle prediction and auxiliary maintenance of a wave-breaking bank according to claim 1, characterized in that: The process of obtaining the life cycle rollback data through the historical maintenance arrangements of each type of wave-breaking bank equipment under different maintenance types and the post-maintenance status evaluation data in step S1 includes: obtaining the actual life of each type of wave-breaking bank equipment according to the historical maintenance arrangements of each type of wave-breaking bank equipment under each maintenance type arrangement; determining the equivalent life of each type of wave-breaking bank equipment according to the post-maintenance status evaluation data of each type of wave-breaking bank equipment under each maintenance type arrangement; and determining the life cycle rollback data corresponding to each maintenance type arrangement of each type of wave-breaking bank equipment according to the above actual life and equivalent life.

3. The method for life cycle prediction and auxiliary maintenance of a wave-breaking bank according to claim 1, characterized in that: The implementation process of the life cycle rollback data in step S1 includes: In the preset time period, among the M types of wave-breaking shore equipment, a total of N equipment has been scheduled for overhaul. Therefore, the actual life of each wave-breaking shore equipment in the M types of wave-breaking shore equipment is recorded as t1, t2, t3…t n After the overhaul, the equivalent life of each wave-breaking bank equipment in the M-type wave-breaking bank equipment is determined based on the after-overhaul status evaluation data of the wave-breaking bank equipment. By taking the average of the life cycle regression data of each type of wave-breaking bank equipment, the actual life cycle regression data of each type of wave-breaking bank equipment is obtained, that is, the life cycle regression data It can be expressed as:

4. The method for life cycle prediction and auxiliary maintenance of a wave-breaking bank according to claim 1, characterized in that: The unified representation in step S3 is: D j,t =(Par jt ,run jt ,fault jt ),j=1,2,3…Z; In the above formula, D j,t represents the data set collected when a j-type fault occurs at time t, and Z represents the number of fault categories; The above data can be expressed in a unified form as follows: D j =(x j1 ,x j2 ,x j3 …x jn ),n=m*N*T In the formula, m represents the number of single monitoring indicators, N represents the frequency of data collection under the fault surface, T represents the duration of data collection under the fault surface, and x j1 ,x j2 ,x j3 …x jn They respectively represent the relevant fault factor data collected when j type of fault occurs, and n represents the fault data category.

5. The method for life cycle prediction and auxiliary maintenance of a wave-breaking bank according to claim 1, characterized in that: The specific process of mining using the FP-Tree algorithm in step S5 includes: executing the FP-tree generation algorithm on each data cluster to generate FP-tree subtrees corresponding to multiple data clusters one by one; and mining association rules based on the corresponding relationship between elements in different FP-tree subtrees.

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