Intelligent hydraulic loom fault diagnosis method and system

Abstract

The invention provides an intelligent water-jet loom fault diagnosis method and system, and belongs to the technical field of data processing, and the method specifically comprises the steps: determining a reference diagnosis model of a remaining textile fabric type according to the similar conditions of the remaining textile fabric type and control parameters of different reference types, according to the identification result of the reference diagnosis model in the matching data of the remaining textile fabric types, the identification deviation conditions of the fault types in different dimensions are determined, and when it is determined that the reference diagnosis model with the identification reliability meeting the requirement does not exist in combination with the matching data of the fault types in different dimensions; and performing combination processing on the reference diagnosis model to generate a plurality of groups of identification schemes, determining a screening identification scheme by using identification deviation conditions of different identification schemes in fault types of different dimensions of remaining textile types, and performing fault identification processing on the remaining textile types by using the screening identification scheme. And the accuracy of the fault diagnosis result of the water-jet loom is ensured.

Description

Technical Field

[0001] The present invention belongs to the technical field of data processing, and in particular relates to a fault diagnosis method and system for an intelligent water jet loom. Background Art

[0002] A water jet loom is a shuttleless loom that uses a jet of water to pull the weft yarn through the shed. In order to identify the faults of the water jet loom, the invention patent application CN202310636798.8 "Intelligent water jet loom and its control system" realizes the automatic diagnosis of water jet loom faults based on vibration signals and sound signals, so as to timely detect the faults of the water jet loom and avoid equipment damage and reduced production efficiency caused by the faults. However, at the same time, there are the following technical problems: When processing different types of textiles, there are certain differences in the opening amount and opening time of the water jet loom, the tension of the cloth roller, the water spraying time, water spraying amount and water pressure of the water pump, so there will be certain differences in the vibration signals and sound signals. This makes it difficult to meet the accuracy of the fault diagnosis processing results if the water jet loom adopts a unified fault diagnosis model.

[0003] In view of the above technical problems, the present invention provides a fault diagnosis method and system for an intelligent water jet loom. Summary of the invention

[0004] To achieve the purpose of the present invention, the present invention adopts the following technical solutions: According to one aspect of the present invention, a fault diagnosis method for an intelligent water jet loom is provided.

[0005] A fault diagnosis method for an intelligent water jet loom, specifically comprising: S1 determines matching data of water jet looms for different types of textiles, and determines the type of textile for constructing a fault diagnosis model through analysis results of the matching data for different fault types, and uses it as a reference type; S2: taking the types of textiles excluding the reference type as the remaining textile types, determining similarities with the control parameters of different reference types based on the control parameters of the remaining textile types in the water jet loom, and determining a reference diagnosis model for the remaining textile types using the similarities; S3 determines the recognition deviation of the fault types in different dimensions based on the recognition result of the reference diagnosis model on the matching data of the remaining textile types, and proceeds to the next step when it is determined that there is no reference diagnosis model whose recognition reliability meets the requirements in combination with the matching data of the fault types in different dimensions; S4 combines the reference diagnostic models to generate multiple groups of identification schemes, uses different identification schemes to determine the identification deviations of the fault types in different dimensions of the remaining textile types, and uses the screening identification schemes to perform fault identification processing on the remaining textile types.

[0006] The beneficial effects of the present invention are: By analyzing the matching data in different fault types, the type of textile for constructing the fault diagnosis model is determined, thereby realizing the determination of the type of textile for constructing the fault diagnosis model from multiple angles such as the amount of matching data corresponding to different fault types and the degree of similarity of the matching data. This avoids the technical problem of low accuracy of the construction results of the fault diagnosis model due to the small amount of matching data or the relatively similar matching data, improves the reliability of the diagnosis results of the fault diagnosis model, and lays the foundation for further determination of the screening and identification scheme.

[0007] By utilizing different recognition schemes to identify the fault types in different dimensions of the remaining textile types, screening recognition schemes are determined, thereby realizing the recognition of the fault types of the remaining textile types by using recognition schemes constructed from multiple fault diagnosis models, avoiding the technical problem of low accuracy of recognition results caused by using a single fault diagnosis type, and further combining the recognition deviations of fault types in different dimensions, thereby realizing accurate determination of the recognition schemes for the remaining textile types from the perspective of recognition accuracy.

[0008] A further technical solution is that the matching data is operating data of the water jet loom in processing the type of textile.

[0009] A further technical solution is that the input quantity of the fault diagnosis model includes vibration data and sound data of the water jet loom.

[0010] A further technical solution is that the method for determining the screening and identification scheme is: Determine the number of identification deviations and the number of accurate identifications of the fault types in different dimensions according to the identification deviations of the fault types in different dimensions of the remaining textile types by the identification scheme; Determine the deviation coefficient of the fault type of different dimensions based on the ratio of the identification deviation number to the identification accurate number; The comprehensive deviation coefficients of different identification schemes are determined according to the average values ​​of the deviation coefficients of the fault types in different dimensions, and the screening identification scheme in the identification scheme is determined based on the comprehensive deviation coefficient.

[0011] A further technical solution is that the screening and identification scheme is an identification scheme with the smallest comprehensive deviation coefficient.

[0012] On the other hand, the present invention provides a computer system comprising: a memory and a processor connected in communication, and a computer program stored in the memory and capable of running on the processor, characterized in that: the processor executes the above-mentioned intelligent water jet loom fault diagnosis method when running the computer program.

[0013] Other features and advantages will be described in the following description. The objects and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description and drawings.

[0014] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The above and other features and advantages of the present invention will become more apparent by describing in detail exemplary embodiments thereof with reference to the accompanying drawings; Figure 1 It is a flow chart of a fault diagnosis method for an intelligent water jet loom; Figure 2 is a flow chart of a method for determining the type of textile material for constructing a fault diagnosis model; Figure 3 is a flow chart of a method for determining a reference diagnostic model for a remaining textile type; Figure 4 is a flow chart of a method for determining the identification reliability of a reference diagnostic model; Figure 5 It is a framework diagram of a computer system. DETAILED DESCRIPTION

[0016] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this specification. Embodiment 1

[0017] To solve the above problems, according to one aspect of the present invention, Figure 1 As shown, a fault diagnosis method for an intelligent water jet loom is provided, which specifically includes: S1 determines matching data of water jet looms for different types of textiles, and determines the type of textile for constructing a fault diagnosis model through analysis results of the matching data for different fault types, and uses it as a reference type; Furthermore, the matching data is operating data of the water jet loom in processing the type of textile.

[0018] It can be understood that the input quantity of the fault diagnosis model includes the vibration data and sound data of the water jet loom.

[0019] Specifically, Figure 2 As shown, the method for determining the type of textile for constructing the fault diagnosis model is: Based on the analysis results of the matching data under different fault types, determining matching data under different fault types; Determine the data volume of the matching data under different fault types by using the matching data under different fault types, and determine the data matching coefficients of the different fault types by using the data volume of the matching data under different fault types; A comprehensive matching coefficient of the matching data is determined based on data matching coefficients of different fault types, and the comprehensive matching coefficient is used to determine whether the type of the textile is the type of textile for constructing a fault diagnosis model.

[0020] Furthermore, the data matching coefficient of the fault type is determined according to the ratio of the data volume of the matching data of the fault type to a preset data volume.

[0021] It can be understood that the comprehensive matching coefficient is determined according to the average value of the data matching coefficients of different fault types of the matching data.

[0022] It should also be noted that the method for determining the type of textile for constructing the fault diagnosis model is: Based on the analysis results of the matching data under different fault types, determining matching data under different fault types; The data volume of the matching data under different fault types is determined by the matching data under different fault types, and the data volume of the matching data under different fault types is used to determine whether the type of the textile is the type of textile for constructing a fault diagnosis model.

[0023] It should also be noted that when there is a fault type whose amount of matching data is less than a matching data amount threshold, it is determined that the type of textile does not belong to the type of textile for constructing a fault diagnosis model.

[0024] Optionally, the method for determining the type of textile for constructing the fault diagnosis model is: S11 determines matching data under different fault types based on analysis results of the matching data under different fault types; Optionally, before entering step S12, the following contents are also included: Based on the matching data under different fault types, the data volume of the matching data under different fault types is determined. When the total data volume of the matching data under different fault types is less than the preset data volume threshold, it is determined that the type of the textile does not belong to the type of textile for constructing the fault diagnosis model. When the total data volume of the matching data under different fault types is not less than the preset data volume threshold, proceed to step S12.

[0025] Optionally, the above step S11 includes steps S111-S113, which are specifically: Based on the analysis results of the matching data under different fault types, the matching data under different fault types are determined. S111: obtaining the amount of matching data of the type of the textile; when the amount of matching data of the type of the textile is less than a preset matching data amount threshold, determining that the type of the textile does not belong to the type of textile for constructing a fault diagnosis model; when the amount of matching data of the type of the textile is not less than the preset matching data amount threshold, proceeding to step S112; S112: calculating the data similarity coefficient of the matching data of the type of the textile using the matching data of the type of the textile. When the data similarity coefficient of the matching data of the type of the textile does not meet the requirement, it is determined that the type of the textile does not belong to the type of the textile for constructing the fault diagnosis model. When the data similarity coefficient of the matching data of the type of the textile meets the requirement, the process proceeds to step S113. S113 determines the amount of matching data under different fault types based on the matching data under different fault types. When the total amount of matching data under different fault types is less than the preset data amount threshold, it is determined that the type of the textile does not belong to the type of textile for constructing the fault diagnosis model. When the total amount of matching data under different fault types is not less than the preset data amount threshold, proceed to step S12.

[0026] S12 determines the data volume of the matching data under different fault types based on the matching data under different fault types, and determines the data matching coefficients of the different fault types based on the data deviation between the matching data under different fault types; Optionally, the above step S12 includes steps S121-S123, specifically: S121 determines the amount of matching data under different fault types using the matching data under different fault types. When there is a fault type whose amount of matching data is less than a matching data amount threshold, it is determined that the type of the textile does not belong to the type of textile for constructing a fault diagnosis model. When the amount of matching data under different fault types is not less than the matching data amount threshold, the process proceeds to step S122. S122 performs data similarity coefficients of matching data under different fault types according to the data deviation between matching data under different fault types. When there is a fault type with a data similarity coefficient greater than a preset similarity coefficient, it is determined that the type of the textile does not belong to the type of textile for constructing a fault diagnosis model. When there is no fault type with a data similarity coefficient greater than the preset similarity coefficient, the process proceeds to step S123. S123 determines the data matching coefficients of different fault types according to the data volume of the matching data under different fault types and the data deviation between the matching data under different fault types. When there is a fault type whose data matching coefficient does not meet the requirements, it is determined that the type of the textile does not belong to the type of textile for constructing the fault diagnosis model. When there is no fault type whose data matching coefficient does not meet the requirements, proceed to step S13.

[0027] S13 determines a comprehensive matching coefficient of the matching data based on data matching coefficients of different fault types, and uses the comprehensive matching coefficient to determine whether the type of the textile is the type of textile for constructing a fault diagnosis model.

[0028] S2: taking the types of textiles excluding the reference type as the remaining textile types, determining similarities with the control parameters of different reference types based on the control parameters of the remaining textile types in the water jet loom, and determining a reference diagnosis model for the remaining textile types using the similarities; Furthermore, the data similarity coefficient of the matching data under the fault type is determined according to the proportion of the data deviation amount of the matching data under the fault type within a preset range.

[0029] Specifically, the control parameters include the opening amount and opening time of the water jet loom, the tension of the cloth roller, and the water spraying time, water spraying amount and water pressure of the water pump.

[0030] It can be understood that the similar situations of the control parameters include parameter deviations in different control parameters.

[0031] It should be noted that if Figure 3 As shown, the method for determining the reference diagnostic model of the remaining textile types is: Determine the parameter deviation of the remaining textile type and the reference type at different control parameters by using the similar situation; A control parameter deviation coefficient is determined according to an average value of parameter deviations at different control parameters, and whether the fault diagnosis model of the reference type is a reference diagnosis model of the remaining textile types is determined by the control parameter deviation coefficient.

[0032] Furthermore, the control parameter deviation coefficient is determined according to an average value of the ratio of the parameter deviation amounts of different control parameters to the control parameters of the remaining textile types.

[0033] It can be understood that when the control parameter deviation coefficient between the remaining textile type and the reference type is less than a preset deviation threshold, the fault diagnosis model of the reference type is determined as the reference diagnosis model of the remaining textile type.

[0034] It should also be noted that the method for determining the reference diagnostic model of the remaining textile types is: Determine the parameter deviation of the remaining textile type and the reference type at different control parameters by using the similar situation; Determining deviation coefficients of different control parameters according to parameter deviations of different control parameters; The deviation coefficient is used to determine whether the fault diagnosis model of the reference type is a reference diagnosis model of the remaining textile types.

[0035] Further, when there is a control parameter whose deviation coefficient does not meet the requirement, it is determined that the fault diagnosis model of the reference type does not belong to the reference diagnosis model of the remaining textile types.

[0036] S3 determines the recognition deviation of the fault types in different dimensions based on the recognition result of the reference diagnosis model on the matching data of the remaining textile types, and proceeds to the next step when it is determined that there is no reference diagnosis model whose recognition reliability meets the requirements in combination with the matching data of the fault types in different dimensions; Furthermore, the identification deviation conditions of the fault types in different dimensions include the number of identification deviations of the fault types in different dimensions.

[0037] Specifically, Figure 4 As shown, the method for determining the recognition reliability of the reference diagnosis model is: Determine the number of identification deviations of fault types in different dimensions based on the identification deviations of fault types in different dimensions by the reference diagnosis model; Determine the data volume of the matching data of the fault types in different dimensions by using the matching data of the fault types in different dimensions, and determine the weight coefficient of the matching data of the fault types in different dimensions by using the proportion of the data volume of the matching data of the fault types in different dimensions in the data volume of the remaining textile types; The correction deviation times of the reference diagnosis model are determined based on the weight coefficients of the matching data of the fault types in different dimensions and the identification deviation times of the fault types in different dimensions, and the identification reliability of the reference diagnosis model is determined using the correction deviation times.

[0038] Furthermore, the recognition reliability of the reference diagnostic model is determined according to a preset reliability coefficient corresponding to the number of correction deviations.

[0039] It should also be noted that the recognition reliability of the reference diagnostic model ranges from 0 to 1, wherein when the recognition reliability of the reference diagnostic model is greater than a preset reliability threshold, it is determined that the recognition reliability of the reference diagnostic model meets the requirements.

[0040] It can be understood that when there is a reference diagnosis model whose recognition reliability meets the requirements, the reference diagnosis model with the highest recognition reliability is used to perform fault recognition processing on the remaining textile types.

[0041] Optionally, the method for determining the recognition reliability of the reference diagnostic model is: S31 determines the number of identification deviations of the fault types in different dimensions based on the identification deviations of the reference diagnosis model in the fault types in different dimensions, and performs identification reliability coefficients of the fault types in different dimensions according to the number of identification deviations and the number of accurate identifications; S32: determining the data volume of the matching data of the fault types in different dimensions by using the matching data of the fault types in different dimensions, and determining the weight coefficient of the matching data of the fault types in different dimensions by using the proportion of the data volume of the matching data of the fault types in different dimensions to the data volume of the remaining textile types; S33 determines the recognition reliability of the reference diagnosis model based on the weight coefficients of the matching data of the fault types in different dimensions and the recognition reliability coefficients of the fault types in different dimensions.

[0042] S4 combines the reference diagnostic models to generate multiple groups of identification schemes, uses different identification schemes to determine the identification deviations of the fault types in different dimensions of the remaining textile types, and uses the screening identification schemes to perform fault identification processing on the remaining textile types.

[0043] Furthermore, the identification scheme is constructed based on multiple reference diagnostic models.

[0044] Specifically, the method for determining the screening and identification scheme is: Determine the number of identification deviations and the number of accurate identifications of the fault types in different dimensions according to the identification deviations of the fault types in different dimensions of the remaining textile types by the identification scheme; Determine the deviation coefficient of the fault type of different dimensions based on the ratio of the identification deviation number to the identification accurate number; The comprehensive deviation coefficients of different identification schemes are determined according to the average values ​​of the deviation coefficients of the fault types in different dimensions, and the screening identification scheme in the identification scheme is determined based on the comprehensive deviation coefficient.

[0045] It should be noted that the screening and identification scheme is an identification scheme with the smallest comprehensive deviation coefficient. Embodiment 2

[0046] On the other hand, Figure 5 As shown, the present invention provides a computer system, comprising: a memory and a processor connected in communication, and a computer program stored in the memory and capable of running on the processor, characterized in that: the processor executes the above-mentioned intelligent water jet loom fault diagnosis method when running the computer program.

[0047] In another embodiment, the method for determining the reference diagnostic model of the remaining textile types is: Determine the parameter deviation of the remaining textile type and the reference type in different control parameters by using the similar situation, and when there is a parameter deviation that does not meet the required control parameters, determine that the fault diagnosis model of the reference type does not belong to the reference diagnosis model of the remaining textile type; When there is no control parameter whose parameter deviation does not meet the requirements: Determining deviation coefficients of different control parameters according to ratios of parameter deviations at different control parameters to control parameters of the remaining textile types, and when there are control parameters whose deviation coefficients do not meet the requirements: determining that the fault diagnosis model of the reference type does not belong to the reference diagnosis model of the remaining textile type; When there is no control parameter whose deviation coefficient does not meet the requirements: Based on the deviation coefficients of different control parameters, the average values ​​of the deviation coefficients of different control parameters are determined. When the average values ​​of the deviation coefficients of different control parameters are less than the preset coefficient threshold value: then determining that the fault diagnosis model of the reference type does not belong to the reference diagnosis model of the remaining textile types; When the average value of the deviation coefficients of different control parameters is not less than the preset coefficient threshold: The control parameters are divided into different deviation coefficient intervals according to the deviation coefficients of different control parameters. When the number of control parameters in the preset deviation coefficient interval does not meet the requirements: then determining that the fault diagnosis model of the reference type does not belong to the reference diagnosis model of the remaining textile types; When the number of control parameters in the preset deviation coefficient range meets the requirements: Determining interval deviation coefficients in different deviation coefficient intervals based on the number of control parameters in different deviation coefficient intervals and the deviation coefficients of different control parameters, and when the interval deviation coefficient in a preset deviation coefficient interval is less than a preset deviation coefficient, determining that the fault diagnosis model of the reference type does not belong to the reference diagnosis model of the remaining textile type; When the interval deviation coefficient in the preset deviation coefficient interval is not less than the preset deviation coefficient: A comprehensive deviation coefficient is determined using interval deviation coefficients of different deviation coefficient intervals, and based on the comprehensive deviation coefficient, it is determined whether the fault diagnosis model of the reference type is a reference diagnosis model of the remaining textile types.

[0048] Optionally, the above step S31 includes steps S311-S313, which are specifically: S311 determines the number of identification deviations of fault types in different dimensions based on the identification deviations of the reference diagnostic model in the fault types in different dimensions. When there is a fault type whose identification deviation number does not meet the requirement, it is determined that the identification reliability of the reference diagnostic model does not meet the requirement. When there is no fault type whose identification deviation number does not meet the requirement, proceed to step S312. S312 determines the total number of identification deviations based on the number of identification deviations of faults of different dimensions. When the total number of identification deviations does not meet the requirement, it is determined that the identification reliability of the reference diagnosis model does not meet the requirement. When the total number of identification deviations meets the requirement, proceed to step S313. S313: performing identification reliability coefficients of fault types of different dimensions according to the identification deviation times and the identification accuracy times; when there is a fault type whose identification reliability coefficient does not meet the requirements, it is determined that the identification reliability of the reference diagnosis model does not meet the requirements; when there is no fault type whose identification reliability coefficient does not meet the requirements, proceeding to step S314; S314 determines the average value of the identification reliability coefficients of the fault types in different dimensions based on the identification reliability coefficients of the fault types in different dimensions. When the average value of the identification reliability coefficients of the fault types in different dimensions does not meet the requirements, it is determined that the identification reliability of the reference diagnostic model does not meet the requirements. When the average value of the identification reliability coefficients of the fault types in different dimensions meets the requirements, enters step S32.

[0049] Optionally, the above step S32 includes steps S321-S323, specifically: S321: determining the data volume of the matching data of the fault types in different dimensions by using the matching data of the fault types in different dimensions, and determining the weight coefficient of the matching data of the fault types in different dimensions by using the proportion of the data volume of the matching data of the fault types in different dimensions to the data volume of the remaining textile types; S322 determines the correction reliability coefficients of the fault types of different dimensions based on the weight coefficients and the identification reliability coefficients of the fault types of different dimensions. When there is a fault type whose correction reliability coefficient does not meet the requirements, it is determined that the identification reliability of the reference diagnosis model does not meet the requirements. When there is no fault type whose correction reliability coefficient does not meet the requirements, the process proceeds to step S323. S323 determines the average value of the corrected reliability coefficients of fault types in different dimensions based on the corrected reliability coefficients of fault types in different dimensions. When the average value of the corrected reliability coefficients of fault types in different dimensions does not meet the requirements, it is determined that the recognition reliability of the reference diagnostic model does not meet the requirements. When the average value of the corrected reliability coefficients of fault types in different dimensions meets the requirements, enters step S32.

[0050] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device, equipment, and non-volatile computer storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0051] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0052] The above description is only one or more embodiments of this specification and is not intended to limit this specification. For those skilled in the art, one or more embodiments of this specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of one or more embodiments of this specification shall be included in the scope of the claims of this specification.

Claims

1. A fault diagnosis method for an intelligent water jet loom, characterized in that: Specifically include: Determine matching data of water jet looms for different types of textiles, and determine the type of textile for constructing a fault diagnosis model through analysis results of the matching data for different fault types, and use it as a reference type; The types of textiles excluding the reference type are used as the remaining textile types, and based on the control parameters of the remaining textile types on the water jet loom, similar situations with the control parameters of different reference types are determined, and a reference diagnosis model of the remaining textile types is determined using the similar situations; Determine the recognition deviation of the fault types in different dimensions based on the recognition result of the reference diagnosis model on the matching data of the remaining textile types, and proceed to the next step when it is determined that there is no reference diagnosis model whose recognition reliability meets the requirements in combination with the matching data of the fault types in different dimensions; The reference diagnostic models are combined to generate multiple groups of identification schemes, and different identification schemes are used to determine the identification deviations of the fault types in different dimensions of the remaining textile types, and the screening identification schemes are used to perform fault identification processing on the remaining textile types.

2. The intelligent water jet loom fault diagnosis method according to claim 1, characterized in that: The matching data is the operating data of the water jet loom in processing the type of textile.

3. The fault diagnosis method of the intelligent water jet loom according to claim 1, characterized in that: The input quantity of the fault diagnosis model includes vibration data and sound data of the water jet loom.

4. The intelligent water jet loom fault diagnosis method according to claim 1, characterized in that: The method for determining the type of textile for constructing the fault diagnosis model is: Based on the analysis results of the matching data under different fault types, determining matching data under different fault types; Determine the data volume of the matching data under different fault types by using the matching data under different fault types, and determine the data matching coefficients of the different fault types by using the data volume of the matching data under different fault types; A comprehensive matching coefficient of the matching data is determined based on data matching coefficients of different fault types, and the comprehensive matching coefficient is used to determine whether the type of the textile is the type of textile for constructing a fault diagnosis model.

5. The fault diagnosis method of the intelligent water jet loom according to claim 4, characterized in that: The data matching coefficient of the fault type is determined according to the ratio of the data volume of the matching data of the fault type to a preset data volume.

6. The intelligent water jet loom fault diagnosis method according to claim 4, characterized in that: The comprehensive matching coefficient is determined according to an average value of data matching coefficients of different fault types of the matching data.

7. The intelligent water jet loom fault diagnosis method according to claim 1, characterized in that: The control parameters include the opening amount and opening time of the water jet loom, the tension of the cloth roller, the water spraying time, water spraying amount and water pressure of the water pump.

8. The intelligent water jet loom fault diagnosis method according to claim 1, characterized in that: The similarity of the control parameters includes parameter deviations in different control parameters.

9. The intelligent water jet loom fault diagnosis method according to claim 1, characterized in that: The method for determining the screening and identification scheme is: Determine the number of identification deviations and the number of accurate identifications of the fault types in different dimensions according to the identification deviations of the fault types in different dimensions of the remaining textile types by the identification scheme; Determine the deviation coefficient of the fault type of different dimensions based on the ratio of the identification deviation number to the identification accurate number; The comprehensive deviation coefficients of different identification schemes are determined according to the average values ​​of the deviation coefficients of the fault types in different dimensions, and the screening identification scheme in the identification scheme is determined based on the comprehensive deviation coefficient.

10. A computer system comprising: A memory and a processor connected in communication, and a computer program stored in the memory and capable of running on the processor, characterized in that the processor executes an intelligent water jet loom fault diagnosis method as described in any one of claims 1-9 when running the computer program.

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