A method for diagnosing cutting bed faults

Abstract

The application discloses a cutting bed fault diagnosis method, which comprises the following steps: S10, defining a fault diagnosis target type to be monitored; S20, defining a fault type; S30, setting a fault diagnosis target priority type; S40, constructing a fault diagnosis target priority decision tree and performing fault type diagnosis; and S50, outputting the decision tree, and outputting the fault type and the cause according to the branch logic and the priority setting of the decision tree. The application realizes monitoring and maintenance of the cutting bed equipment, guarantees cutting quality and equipment stability, improves cutting speed of the equipment and production efficiency, and reduces maintenance cost under the monitoring of various parameters, and the system feedback and diagnosis results are directly fed back to the equipment.

Description

Technical Field

[0001] This invention belongs to the field of textile processing technology, and specifically relates to a method for diagnosing cutting bed faults. Background Technology

[0002] In the textile processing industry, with the development of industrial automation and intelligence, higher demands are placed on the fault diagnosis and early warning capabilities of cutting bed equipment. Existing technologies include the following four methods for fault diagnosis of cutting bed equipment:

[0003] 1) Fault diagnosis method based on expert system: This type of method relies on the experience and knowledge of experts, but expert systems are difficult to cover all possible fault situations and variable combinations, and updating and maintaining expert systems requires a lot of time and resources.

[0004] 2) Fault diagnosis methods based on artificial neural networks: Although fault diagnosis methods based on artificial neural networks can handle complex nonlinear relationships, neural networks are generally regarded as "black box" models, making it difficult to explain their decision-making process. Training neural networks requires a large amount of data and computing resources, and handling abnormal data is quite difficult.

[0005] 3) Fault diagnosis methods based on fuzzy theory: Although capable of handling fuzzy and uncertain information, the formulation and updating of fuzzy rules require more specialized knowledge. The performance of fuzzy systems is significantly affected by the quality of input data and the selection of fuzzy rules.

[0006] 4) Fault diagnosis methods based on traditional statistical methods: such as regression analysis and analysis of variance. These methods typically assume that the data follows a certain distribution, but this assumption may not hold true in practical applications. Therefore, traditional statistical methods are difficult to handle high-dimensional data and complex nonlinear relationships.

[0007] In summary, existing diagnostic methods suffer from problems such as high data consumption, lack of clarity, and poor universality. To meet the modern textile industry's demand for rapid and accurate fault identification, a more efficient and adaptable fault diagnosis solution is urgently needed. This solution should be able to reduce data consumption while providing clear and interpretable results, and should be widely applicable to different types of cutting equipment. Summary of the Invention

[0008] This invention provides a method for diagnosing cutting bed faults, so as to achieve the purpose of quickly and accurately identifying equipment faults, thereby improving production efficiency and product quality.

[0009] The technical solution of the present invention is as follows:

[0010] A method for diagnosing cutting bed faults includes the following steps:

[0011] Step S10: Define the type of fault diagnosis target to be monitored;

[0012] Step S20: Define the fault type;

[0013] Step S30: Set the priority type of the fault diagnosis target;

[0014] Step S40: Based on the priority type of the fault diagnosis target, construct a fault diagnosis target priority decision tree and perform fault type diagnosis;

[0015] Step S50: Output the decision tree. Based on the branching logic and priority settings of the decision tree, output the fault type and cause.

[0016] Furthermore, the fault diagnosis target types in step S10 include: number of fabric layers, fabric size, fabric tightness, equipment status, vacuum level, vibrating blade speed, cutting speed, blade wear, adsorption negative pressure value, air pressure positive pressure value, and blade width sensor.

[0017] The fabric dimensions include the fabric length and the fabric width.

[0018] Furthermore, the fault types mentioned in step S20 include fabric abnormality faults, equipment status faults, and cutting system faults.

[0019] Furthermore, the criteria for identifying abnormal fabric faults include at least the number of fabric layers, fabric size, and fabric tightness;

[0020] The number of fabric layers is controlled and monitored by the cutting bed height limit module. When the height limit module setting value is exceeded, the fabric feeding system stops feeding, and the fault alarm module triggers an abnormal alarm.

[0021] The fabric size is controlled and monitored by the analytical cutting module. When the analytical cutting module detects that the rectangular area of ​​the fabric where a certain piece is located exceeds the cutting range that the cutting table can cut, the fabric cutting system stops cutting, and the fault alarm module triggers an abnormal alarm.

[0022] The fabric tension is controlled and monitored by a signal monitoring module, which is controlled by a vacuum adsorption unit. The signal monitoring module monitors the negative pressure sensor connected to the vacuum adsorption unit in real time. When the signal monitoring module detects that the pressure value of the vacuum adsorption unit drops to the set threshold of fabric tension pressure through the negative pressure sensor, the fabric cutting system suspends cutting, and the fault alarm module triggers an abnormal alarm.

[0023] Furthermore, the criteria for determining equipment status faults include at least the equipment status, which includes at least the equipment model, equipment checksum, equipment operating status, and operating time; wherein...

[0024] When determining equipment status faults, at least the following aspects need to be monitored, including:

[0025] The information recognition module reads the device model and the corresponding parameter configuration, and verifies whether the device model and its corresponding parameter configuration are complete and consistent. If the parameter configuration of the device model is abnormal, the fabric cutting system suspends cutting, and the fault alarm module triggers an abnormal alarm; and / or,

[0026] The information recognition module reads the dongle information to obtain the device verification code and detects whether there are any abnormalities in the dongle information reading process. If the dongle information reading process is abnormal and the device verification code cannot be obtained, the fabric cutting system suspends cutting, and the fault alarm module triggers an abnormal alarm; and / or,

[0027] The signal monitoring module reads the sensor I / O status of each component of the device to determine if any abnormality exists in each component; and / or,

[0028] During operation, the signal monitoring module initiates trigger commands for one or more target components, records the sensor trigger duration, scans the sensor status of the target component, and determines whether the sensor trigger duration exceeds the predetermined time period. If the sensor of the target component does not trigger within the predetermined time period, the operation time monitoring module judges it as abnormal, triggers the fabric cutting system stop command, and at the same time, the fault alarm module triggers an abnormal alarm.

[0029] Furthermore, the fault identification types of the cutting system include at least vacuum level, vibrating blade speed, cutting speed, and blade wear; wherein,

[0030] The vacuum level is achieved by the signal monitoring module monitoring the negative pressure sensor in real time. When the signal monitoring module detects that the negative pressure value of the vacuum adsorption unit is lower than the minimum threshold set by the negative pressure sensor, the fabric cutting system stops cutting, and the fault alarm module triggers an abnormal alarm.

[0031] The speed of the vibrating blade is monitored and read in real time by the signal monitoring module. When the speed of the vibrating blade is detected to be lower than the minimum value, the cutting is stopped and an abnormal alarm is triggered.

[0032] The cutting speed is achieved by the signal monitoring module detecting the interpolation feedback of each axis in real time. When an abnormality in the axis interpolation state is detected, the fabric cutting system suspends cutting, and the fault alarm module triggers an abnormal alarm.

[0033] The blade wear is monitored in real time by the signal monitoring module to detect the number of sharpening operations or by the displacement sensor to measure the blade width. When the number of sharpening operations reaches the maximum threshold or the blade width reaches the minimum threshold, the fabric cutting system pauses cutting, and the fault alarm module triggers an abnormal alarm.

[0034] Furthermore, the fault diagnosis target priority type in step S30 is divided into the highest priority target type, the second highest priority target type, the third priority target type, and other target types. The highest priority target type is the vacuum level, the second highest priority target type is the fabric tension, the third priority target type is the blade wear, and other target types include at least the number of fabric layers, fabric size, equipment status, vibrating blade speed, and cutting speed.

[0035] Furthermore, the specific steps of step S40 are as follows:

[0036] S41: Set the root node and begin fault type diagnosis;

[0037] S42: Construct the first-level branch, the highest priority target type, specifically:

[0038] Based on the vacuum index, the signal monitoring module monitors the negative pressure sensor to determine whether the negative pressure value of the vacuum adsorption unit is lower than the minimum threshold set for the fabric adsorption pressure, and diagnoses whether the vacuum level is abnormal.

[0039] If so, output a vacuum adsorption unit fault alarm and interrupt the judgment process;

[0040] If not, continue monitoring the next highest priority target type;

[0041] S43: Construct a second-level branch to diagnose the second-highest priority target type;

[0042] The signal monitoring module monitors the negative pressure sensor to determine whether the negative pressure value of the vacuum adsorption unit is lower than the set threshold for fabric tension, and diagnoses whether the fabric tension is abnormal. Specifically:

[0043] If so, output a fabric abnormality fault alarm and interact with the fabric cutting system to confirm whether the process has been interrupted;

[0044] No, continue monitoring the third priority target type;

[0045] S44: Construct a third-level branch to diagnose the third-priority target type, specifically:

[0046] The signal monitoring module monitors whether the number of sharpening cycles has reached the maximum threshold or whether the tool width has reached the minimum threshold, and diagnoses whether the tool wear has reached the usage threshold.

[0047] If so, the output cutting system is malfunctioning; interact with the fabric cutting system to confirm whether the process has been interrupted.

[0048] No, continue checking other target types;

[0049] S45, Diagnostic other monitoring quantities, specifically:

[0050] Monitor for any abnormalities in the number of fabric layers, fabric size, equipment status, vibrating blade speed, and cutting speed.

[0051] Based on the detected abnormal monitoring quantities, the fault diagnosis target type is output, and it is determined whether to perform fault diagnosis target processing.

[0052] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0053] Visual appeal and interpretability: The structure of a decision tree is intuitive and easy to understand and operate. Furthermore, the branching logic of a decision tree clearly demonstrates the process and basis for fault diagnosis.

[0054] The ability to integrate multiple variables: Decision trees can comprehensively consider multiple monitoring variables and make fault judgments based on the priority and importance of the variables.

[0055] Real-time performance and accuracy: The decision tree method can quickly respond to changes in input data and provide accurate fault judgment results.

[0056] Easy to update and maintain: The structure and rules of the decision tree can be adjusted and optimized according to actual conditions to adapt to changes in equipment characteristics and production needs. Attached Figure Description

[0057] Figure 1 This is a flowchart of the cutting bed fault diagnosis method of the present invention. Detailed Implementation

[0058] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0059] This invention discloses a method for diagnosing cutting bed faults, comprising the following steps:

[0060] Step S10: Define the type of fault diagnosis target to be monitored;

[0061] In this embodiment, the fault diagnosis target types include at least: number of fabric layers, fabric size, fabric tightness, equipment status, vacuum level, vibrating blade speed, cutting speed, and blade wear;

[0062] The fabric dimensions include the fabric length and the fabric width.

[0063] Step S20: Define the fault type;

[0064] In this embodiment, the fault types in step S20 include at least fabric abnormality fault, equipment status fault, and cutting system fault.

[0065] In this embodiment, the criteria for identifying fabric malfunctions include at least the number of fabric layers, fabric size, and fabric tightness.

[0066] The number of fabric layers is controlled and monitored by the cutting bed height limit module. When the height limit module setting is exceeded, the fabric feeding system stops feeding, and the fault alarm module triggers an abnormal alarm.

[0067] Fabric size is controlled and monitored by the analysis and cutting module. When the analysis and cutting module detects that the rectangular area of ​​a certain piece of fabric exceeds the cutting range of the cutting table, the fabric cutting system stops cutting, and the fault alarm module triggers an abnormal alarm.

[0068] The fabric tension is controlled and monitored by a signal monitoring module. The fabric tension is controlled by the vacuum adsorption unit. The signal monitoring module monitors the negative pressure sensor connected to the vacuum adsorption unit in real time. When the signal monitoring module detects that the pressure value of the vacuum adsorption unit drops to the set threshold of fabric tension pressure through the negative pressure sensor, the fabric cutting system stops cutting, and the fault alarm module triggers an abnormal alarm.

[0069] The criteria for determining equipment status faults include at least the equipment status, which includes at least the equipment model, equipment checksum, equipment operating status, and running time.

[0070] In this embodiment, the following aspects need to be monitored when determining equipment status faults:

[0071] The information identification module reads the device model and its corresponding parameter configuration, verifying the integrity and consistency between the device model and its corresponding parameter configuration. If the parameter configuration of the device model is abnormal, the fabric cutting system will be restricted from use, and the fault alarm module will trigger an abnormal alarm; and / or,

[0072] The information recognition module reads the dongle information to obtain the device verification code and checks for any abnormalities in reading the dongle information. If the dongle information reading is abnormal and the device verification code cannot be obtained, the fabric cutting system will be restricted from use, and the fault alarm module will trigger an abnormal alarm; and / or,

[0073] The signal monitoring module reads the sensor I / O status of each component of the device to determine if any abnormality exists in each component; and / or,

[0074] During operation, the signal monitoring module initiates trigger commands for one or more target components, records the sensor trigger duration, scans the sensor status of the target component, and determines whether the sensor trigger duration exceeds the predetermined time period. If the sensor of the target component does not trigger within the predetermined time period, the operation time monitoring module judges it as abnormal, triggers the fabric cutting system stop command, and at the same time, the fault alarm module triggers an abnormal alarm.

[0075] In this embodiment, the fault identification types of the cutting system include at least vacuum level, vibrating blade speed, cutting speed, and blade wear; wherein,

[0076] The vacuum level is achieved by the signal monitoring module monitoring the negative pressure sensor in real time. When the signal monitoring module detects that the negative pressure value of the vacuum adsorption unit is lower than the minimum threshold set by the negative pressure sensor, the fabric cutting system will be restricted from use, and the fault alarm module will trigger an abnormal alarm.

[0077] The vibrating knife speed is monitored and read in real time by the signal monitoring module. When the vibrating knife speed is detected to be lower than the minimum value, the fabric cutting system will be restricted from use, and the fault alarm module will trigger an abnormal alarm.

[0078] The cutting speed is achieved by the signal monitoring module detecting the interpolation feedback of each axis in real time. When an abnormality in the axis interpolation status is detected, the fabric cutting system will be restricted from use, and the fault alarm module will trigger an abnormal alarm.

[0079] Blade wear is monitored in real time by a signal monitoring module to detect the number of sharpening operations or by measuring the blade width using a displacement sensor. When the number of sharpening operations reaches the maximum threshold or the blade width reaches the minimum threshold, the fabric cutting system will be restricted from use, and the fault alarm module will trigger an abnormal alarm.

[0080] Step S30: Set the priority type of the fault diagnosis target;

[0081] In this embodiment, the priority types of fault diagnosis targets are divided into the highest priority target type, the second highest priority target type, the third priority target type, and other target types. The highest priority target type is the vacuum level, the second highest priority target type is the fabric tension, the third priority target type is the blade wear, and other target types include at least the number of fabric layers, fabric size, equipment status, vibrating blade speed, and cutting speed.

[0082] Step S40: Construct a fault diagnosis target priority decision tree and perform fault type diagnosis; the specific steps are as follows:

[0083] S41: Set the root node and begin fault type diagnosis;

[0084] S42: Construct the first-level branch, the highest priority target type, specifically:

[0085] Based on the vacuum index, the signal monitoring module monitors the negative pressure sensor to determine whether the negative pressure value of the vacuum adsorption unit is lower than the minimum threshold set for the fabric adsorption pressure, and diagnoses whether the vacuum level is abnormal.

[0086] If so, output a vacuum adsorption unit fault alarm and interrupt the judgment process;

[0087] If not, continue monitoring the next highest priority target type;

[0088] S43: Construct a second-level branch to diagnose the second-highest priority target type;

[0089] The signal monitoring module monitors the negative pressure sensor to determine whether the negative pressure value of the vacuum adsorption unit is lower than the set threshold for fabric tension, and diagnoses whether the fabric tension is abnormal. Specifically:

[0090] If so, output a fabric abnormality fault alarm and interact with the fabric cutting system to confirm whether the process has been interrupted;

[0091] No, continue monitoring the third priority target type;

[0092] S44: Construct a third-level branch to diagnose the third-priority target type, specifically:

[0093] The signal monitoring module monitors whether the number of sharpening cycles has reached the maximum threshold or whether the tool width has reached the minimum threshold, and diagnoses whether the tool wear has reached the usage threshold.

[0094] If so, output a cutting system malfunction and interact with the fabric cutting system to confirm whether the process should be interrupted; the cutting system malfunction specifically refers to severe blade wear, requiring blade replacement;

[0095] No, continue checking other target types;

[0096] S45, Diagnostic other monitoring quantities, specifically:

[0097] Monitor for any abnormalities in the number of fabric layers, fabric size, equipment status, vibrating blade speed, and cutting speed.

[0098] Based on the detected abnormal monitoring quantities, the fault diagnosis target type is output, and it is determined whether to perform fault diagnosis target processing.

[0099] Step S50: Output the decision tree. Based on the branching logic and priority settings of the decision tree, output the possible fault types and specific causes.

[0100] The cutting bed fault diagnosis method of this invention enables the monitoring and maintenance of the cutting bed equipment, ensuring cutting quality and equipment stability. Under the monitoring of various parameters, the system feedback and diagnosis results are directly fed back to the equipment, improving the cutting speed, increasing production efficiency, and reducing maintenance costs.

[0101] This invention also discloses a cutting bed fault diagnosis system, which is connected to both a fabric feeding system and a fabric cutting system. All three systems are connected to and controlled by a control system. The cutting bed fault diagnosis system includes multiple sub-modules, primarily a cutting bed height limit module, a cutting analysis module, a pressure monitoring module, an information recognition module, a signal monitoring module, and a sensor monitoring module. Each sub-module of the cutting bed fault diagnosis system is connected to the control system.

[0102] The above specific embodiments further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above are merely specific embodiments of the present invention and are 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 within the protection scope of the present invention.

Claims

1. A cutting table failure diagnosis method characterized by comprising: The method comprises the following steps: Step S10, defining the fault diagnosis target type to be monitored; Step S20, defining the fault type; Step S30, setting the fault diagnosis target priority type; Step S40, constructing a fault diagnosis target priority decision tree according to the fault diagnosis target priority type, and performing fault type diagnosis; Step S50, outputting the decision tree, and outputting the fault type and the cause according to the branch logic and the priority setting of the decision tree; The fault type in the step S20 includes cloth abnormal fault, equipment state fault, and cutting system fault; the cloth abnormal fault includes at least cloth layer number, cloth size, and cloth tightness; and the cutting system fault includes at least vacuum level, vibration knife speed, cutting speed, and knife wear; The specific steps of the step S40 are as follows: S41: setting a root node and starting fault type diagnosis; S42: constructing a first layer branch, the highest priority target type, specifically: Based on the vacuum index, the signal monitoring module monitors the negative pressure sensor to judge whether the vacuum adsorption unit negative pressure value is lower than the cloth adsorption pressure set minimum threshold value, and diagnose whether the vacuum level is abnormal; If yes, output the vacuum adsorption unit fault alarm and interrupt the judgment process; If no, continue to monitor the next high priority target type; S43: constructing a second layer branch, diagnosing the next high priority target type; The signal monitoring module monitors the negative pressure sensor to judge whether the vacuum adsorption unit negative pressure value is lower than the cloth tightness pressure set threshold value, and diagnose whether the cloth tightness is abnormal, specifically: If yes, output the cloth abnormal fault alarm and interact with the cloth cutting system to confirm whether to interrupt the process; If no, continue to monitor the third priority target type; S44: constructing a third layer branch, diagnosing the third priority target type, specifically: The signal monitoring module monitors whether the knife grinding frequency reaches the highest threshold value or the knife width reaches the lowest threshold value, and diagnoses whether the knife wear reaches the use threshold value, If yes, output the cutting system fault and interact with the cloth cutting system to confirm whether to interrupt the process; If no, continue to check other target types; S45, diagnosing other monitoring quantities, specifically: Monitoring whether the cloth layer number, cloth size, equipment state, vibration knife speed, and cutting speed are abnormal, According to the fault diagnosis target type output by the monitored abnormal monitoring quantity, it is decided whether to perform fault diagnosis target processing.

2. The cutting bed failure diagnosis method according to claim 1, wherein The fault diagnosis target type in the step S10 includes: cloth layer number, cloth size, cloth tightness, equipment state, vacuum level, vibration knife speed, cutting speed, knife wear, adsorption negative pressure value, air pressure positive pressure value, and knife width sensor; The cloth size includes cloth length and cloth width.

3. The method of claim 1, wherein the method further comprises: The cloth layer number is controlled and monitored by the cutting bed height limiting module; when the cloth layer number exceeds the set value of the height limiting module, the cloth feeding system stops feeding, and the fault alarm module triggers an abnormal alarm; The cloth size is controlled and monitored by the analysis cutting module; when the analysis cutting module identifies that a cloth rectangular area of a certain cutting piece exceeds the cutting range of the cutting table, the cloth cutting system pauses cutting, and the fault alarm module triggers an abnormal alarm. The fabric tightness is controlled and monitored by the signal monitoring module, the fabric tightness is controlled by the vacuum adsorption unit, and the signal monitoring module monitors the negative pressure sensor connected with the vacuum adsorption unit in real time; when the signal monitoring module detects that the pressure value of the vacuum adsorption unit decreases to the fabric tightness pressure set threshold value through the negative pressure sensor, the fabric cutting system suspends cutting, and the fault alarm module triggers an abnormal alarm.

4. The cutting bed failure diagnosis method according to claim 1, characterized by The device state fault identification target at least includes a device state, and the device state at least includes a device model, a device verification code, a device working state and a running time; Wherein When identifying the device state fault, at least the following aspects need to be monitored, including: The information identification module reads the device model, reads the parameter configuration corresponding to the device model, checks whether the device model and the parameter configuration corresponding to the device model maintain integrity and consistency, when the parameter configuration of the device model is abnormal, the fabric cutting system suspends cutting, and the fault alarm module triggers an abnormal alarm; and / or, The information identification module reads the encrypted lock information to obtain the device verification code, detects whether the process of reading the encrypted lock information is abnormal, when the process of reading the encrypted lock information is abnormal and the device verification code cannot be obtained, the fabric cutting system suspends cutting, and the fault alarm module triggers an abnormal alarm; and / or, The signal monitoring module reads the sensor IO state of each component of the device to determine whether each component is abnormal; and / or, The running time signal monitoring module initiates a trigger instruction to one or more target components, records the sensor trigger duration and scans the sensor state of the target component, determines whether the sensor trigger duration exceeds a predetermined time period, if the sensor of the target component does not trigger within the predetermined time period, the running time monitoring module determines that it is abnormal, and the fabric cutting system stopping instruction is triggered, and the fault alarm module triggers an abnormal alarm.

5. The method of claim 1, wherein the method further comprises: The vacuum level is monitored by the signal monitoring module in real time, and when the signal monitoring module detects that the negative pressure value of the vacuum adsorption unit is lower than the fabric adsorption pressure set minimum threshold value through the negative pressure sensor, the fabric cutting system suspends cutting, and the fault alarm module triggers an abnormal alarm; The vibration knife speed is monitored by the signal monitoring module in real time to read the feedback of the vibration knife motor encoder, and when it is detected that the vibration knife speed is lower than the minimum value, the cutting is stopped and an abnormal alarm is triggered; The cutting speed is detected by the signal monitoring module in real time to detect the interpolation feedback of each shaft, and when it is detected that there is an abnormal state of shaft interpolation, the fabric cutting system suspends cutting, and the fault alarm module triggers an abnormal alarm; The knife wear is monitored by the signal monitoring module in real time to measure the knife width through the displacement sensor, and when the number of knife grinding reaches the highest threshold value or the knife width reaches the lowest threshold value, the fabric cutting system suspends cutting, and the fault alarm module triggers an abnormal alarm.

6. The method of claim 1, wherein the method further comprises: The fault diagnosis target priority type in the step S30 is divided into a highest priority target type, a second highest priority target type, a third priority target type and other target types, wherein the highest priority target type is a vacuum level, the second highest priority target type is a cloth tightness, the third priority target type is a knife wear, and the other target types at least include a cloth layer number, a cloth size, an equipment state, a vibrating knife speed and a cutting speed.

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