Fault detection method, system and computer readable storage medium for ceramic manufacturing

Abstract

The present invention relates to the technical field of ceramic fault detection, and in particular to a ceramic manufacturing fault detection method, system and computer-readable storage medium. First, characteristic information of a ceramic blank at each manufacturing node in a ceramic manufacturing production process is collected; then the characteristic information of each node is matched with a standard model respectively to obtain the characteristic matching degree of each node; finally, the ceramic manufacturing fault detection result is obtained according to the characteristic matching degree of each node. The present invention has the beneficial effects of improving the speed and accuracy of fault detection in ceramic manufacturing and improving the reliability of ceramic manufacturing status monitoring.

Description

Technical Field

[0001] The present invention relates to the technical field of ceramic fault detection, and in particular to a ceramic manufacturing fault detection method, system and computer-readable storage medium. Background Art

[0002] In the ceramic manufacturing process, it is of great significance to monitor each node of ceramic manufacturing in real time, timely grasp the possible faults in ceramic manufacturing, and reliably monitor the ceramic manufacturing status.

[0003] In the existing technology, quality inspection by naked eye is only done manually, which is prone to false detection and is time-consuming and labor-intensive. It cannot meet the current development needs of intelligent manufacturing. Therefore, in the ceramic manufacturing process, an efficient and reliable fault detection method is urgently needed to solve the above problems. Summary of the invention

[0004] The present invention provides a ceramic manufacturing fault detection method, system and computer-readable storage medium to solve one or more technical problems existing in the prior art and at least provide a beneficial choice or create conditions.

[0005] In order to achieve the above object, the present invention provides the following technical solutions:

[0006] A ceramic manufacturing fault detection method, the method comprising the following steps:

[0007] Collect characteristic information of ceramic bodies at each manufacturing node during the ceramic manufacturing process;

[0008] Matching the feature information of each node with the standard model respectively to obtain the feature matching degree of each node;

[0009] The fault detection results of ceramic manufacturing are obtained according to the feature matching degree of each node.

[0010] Furthermore, the collection of characteristic information of the ceramic body at each manufacturing node during the ceramic manufacturing production process includes:

[0011] An image acquisition module is respectively provided at each node of ceramic manufacturing, and the image acquisition module is controlled to acquire a state image of the ceramic body at the node at a predetermined interval;

[0012] The Otsu algorithm and particle swarm algorithm are used to segment the collected images, and the regional image of the ceramic body is segmented from the state image.

[0013] Key features are extracted from the segmented regional image to obtain feature information, wherein the key features include the outer contour, noise points, cracks, and patterns of the ceramic body.

[0014] Furthermore, the standard model is determined by:

[0015] Obtain a normal sample set for each node in the ceramic manufacturing production process;

[0016] The key features in each normal sample are extracted, the mean of each key feature in the normal sample set is determined, and the mean of each key feature is used as the standard model.

[0017] Further, the ceramic manufacturing fault detection result is obtained according to the feature matching degree of each node, including:

[0018] In the ceramic manufacturing process, it is determined in real time whether the feature matching degree of the current node is less than the first preset threshold value. If the feature matching degree of any node is less than the first preset threshold value, a fault warning is issued;

[0019] If the feature matching degree of the current node is greater than the first preset threshold, it is determined in real time whether the feature matching degree of the current node is less than the second preset threshold, and the second preset threshold is greater than the first preset threshold;

[0020] In real time, nodes whose feature matching degree is greater than a first preset threshold and less than a second preset threshold are added to form a set of nodes to be determined;

[0021] Obtaining a feature matching degree to be determined according to the feature matching degree of the set of nodes to be determined;

[0022] The ceramic manufacturing fault detection result is determined according to the matching degree of the feature to be determined and a first preset threshold.

[0023] Further, obtaining the feature matching degree to be determined according to the feature matching degree of the set of nodes to be determined includes:

[0024] Sorting each node in the ceramic manufacturing production process in order of precedence, and determining the fault weight of each node in the ceramic manufacturing production process according to the sorting result;

[0025] Matching the feature information of each node in the set of nodes to be determined with the standard model respectively to obtain the feature matching degree of each node in the set of nodes to be determined;

[0026] The feature matching degree to be determined is obtained according to the fault weight and feature matching degree of each node in the set of nodes to be determined.

[0027] Furthermore, the nodes in the ceramic manufacturing production process are sorted in sequence, and the fault weight of each node in the ceramic manufacturing production process is determined according to the sorting result, including:

[0028] Assuming that the total number of all manufacturing nodes in the ceramic manufacturing production process is n, the fault weight of the i-th node is: Wi = (ni) / n, where 1≤i≤n.

[0029] Further, the determining of the ceramic manufacturing fault detection result according to the matching degree of the feature to be determined and the first preset threshold value includes:

[0030] Determine whether the following formula is true:

[0031]

[0032] If yes, there is a fault; if no, there is no fault;

[0033] Among them, ti is the feature matching degree of the i-th node, and the i-th node is in the set of nodes to be determined, and t1 is the first preset threshold.

[0034] Furthermore, the method further includes: when the fault detection result of the ceramic manufacturing is that a fault exists, determining the last node in the set of nodes to be determined, and using the last node in the set of nodes to be determined as the node where the fault is detected.

[0035] A ceramic manufacturing fault detection system, the system comprising:

[0036] at least one processor;

[0037] at least one memory for storing at least one program;

[0038] When the at least one program is executed by the at least one processor, the at least one processor implements any one of the ceramic manufacturing fault detection methods described above.

[0039] A computer-readable storage medium stores a ceramic manufacturing fault detection program, which, when executed by a processor, implements the steps of any one of the ceramic manufacturing fault detection methods described above.

[0040] The beneficial effects of the present invention are as follows: the present invention discloses a ceramic manufacturing fault detection method, system and computer-readable storage medium, the present invention first collects characteristic information of the ceramic blank at each manufacturing node during the ceramic manufacturing production process; then the characteristic information of each node is matched with the standard model respectively to obtain the characteristic matching degree of each node; finally, the ceramic manufacturing fault detection result is obtained according to the characteristic matching degree of each node. The present invention has the beneficial effects of improving the speed and accuracy of fault detection in ceramic manufacturing and improving the reliability of ceramic manufacturing status monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0042] Figure 1 1 is a flow chart of a fault detection method for ceramic manufacturing in an embodiment of the present invention. DETAILED DESCRIPTION

[0043] The following will be combined with the embodiments and drawings to clearly and completely describe the concept, specific structure and technical effects of this application, so as to fully understand the purpose, scheme and effect of this application. It should be noted that the embodiments and features in the embodiments of this application can be combined with each other without conflict.

[0044] refer to Figure 1 ,like Figure 1 A ceramic manufacturing fault detection method provided in an embodiment of the present application is shown, and the method comprises the following steps:

[0045] Step S100, collecting characteristic information of the ceramic body at each manufacturing node during the ceramic manufacturing production process;

[0046] Step S200, matching the feature information of each node with the standard model respectively to obtain the feature matching degree of each node;

[0047] Step S300: Obtain ceramic manufacturing fault detection results according to the feature matching degree of each node.

[0048] In an improved embodiment, the step S100 includes:

[0049] An image acquisition module is respectively provided at each node of ceramic manufacturing, and the image acquisition module is controlled to acquire a state image of the ceramic body at the node at a predetermined interval;

[0050] The Otsu algorithm and particle swarm algorithm are used to segment the collected images, and the regional image of the ceramic body is segmented from the state image.

[0051] Key features are extracted from the segmented regional image to obtain feature information, wherein the key features include the outer contour, noise points, cracks, and patterns of the ceramic body.

[0052] In an improved embodiment, the step S200 includes:

[0053] Obtain a normal sample set for each node in the ceramic manufacturing production process;

[0054] The key features in each normal sample are extracted, the mean of each key feature in the normal sample set is determined, and the mean of each key feature is used as the standard model.

[0055] In an improved embodiment, the step S300 includes:

[0056] In the ceramic manufacturing process, it is determined in real time whether the feature matching degree of the current node is less than the first preset threshold value. If the feature matching degree of any node is less than the first preset threshold value, a fault warning is issued;

[0057] If the feature matching degree of the current node is greater than the first preset threshold, it is determined in real time whether the feature matching degree of the current node is less than the second preset threshold, and the second preset threshold is greater than the first preset threshold;

[0058] In real time, nodes whose feature matching degree is greater than a first preset threshold and less than a second preset threshold are added to form a set of nodes to be determined;

[0059] Obtaining a feature matching degree to be determined according to the feature matching degree of the set of nodes to be determined;

[0060] The ceramic manufacturing fault detection result is determined according to the matching degree of the feature to be determined and a first preset threshold.

[0061] In an improved embodiment, obtaining the feature matching degree to be determined according to the feature matching degree of the set of nodes to be determined includes:

[0062] Sorting each node in the ceramic manufacturing production process in order of precedence, and determining the fault weight of each node in the ceramic manufacturing production process according to the sorting result;

[0063] Matching the feature information of each node in the set of nodes to be determined with the standard model respectively to obtain the feature matching degree of each node in the set of nodes to be determined;

[0064] The feature matching degree to be determined is obtained according to the fault weight and feature matching degree of each node in the set of nodes to be determined.

[0065] In an improved embodiment, the steps of sequentially sorting the nodes in the ceramic manufacturing production process and determining the fault weight of each node in the ceramic manufacturing production process according to the sorting result include:

[0066] Assuming that the total number of all manufacturing nodes in the ceramic manufacturing production process is n, the fault weight of the i-th node is: Wi = (ni) / n, where 1≤i≤n.

[0067] In an improved embodiment, the determining of the ceramic manufacturing fault detection result according to the matching degree of the feature to be determined and the first preset threshold value includes:

[0068] Determine whether the following formula is true:

[0069]

[0070] If yes, there is a fault; if no, there is no fault;

[0071] Among them, ti is the feature matching degree of the i-th node, and the i-th node is in the set of nodes to be determined, and t1 is the first preset threshold.

[0072] The method further includes: when the fault detection result of the ceramic manufacturing is that a fault exists, determining the last node in the set of nodes to be determined, and using the last node in the set of nodes to be determined as the node where the fault is detected.

[0073] and Figure 1 Corresponding to the method, an embodiment of the present invention further provides a ceramic manufacturing fault detection system, the system comprising:

[0074] at least one processor;

[0075] at least one memory for storing at least one program;

[0076] When the at least one program is executed by the at least one processor, the at least one processor implements the ceramic manufacturing fault detection method described in any of the above embodiments.

[0077] The contents of the above method embodiments are all applicable to the present system embodiments. The functions specifically implemented by the present system embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0078] and Figure 1 Corresponding to the method, an embodiment of the present invention further provides a computer-readable storage medium, on which a ceramic manufacturing fault detection program is stored. When the ceramic manufacturing fault detection program is executed by a processor, the steps of the ceramic manufacturing fault detection method as described in any of the above embodiments are implemented.

[0079] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the ceramic manufacturing fault detection system, and uses various interfaces and lines to connect various parts of the entire ceramic manufacturing fault detection system operable device.

[0080] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the ceramic manufacturing fault detection system by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart-Media-Card, SMC), a secure digital (Secure-Digital, SD) card, a flash card (Flash-Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0081] Although the description of the present application has been quite detailed and specifically describes several described embodiments, it is not intended to be limited to any of these details or embodiments or any particular embodiment, but should be regarded as providing a broad possible interpretation of these claims by reference to the attached claims, taking into account the prior art, so as to effectively cover the intended scope of the present application. In addition, the above description of the present application is based on the embodiments foreseeable by the inventor, and its purpose is to provide a useful description, and those non-substantial changes to the present application that have not yet been foreseen may still represent equivalent changes to the present application.

Claims

1. A ceramic manufacturing fault detection method, characterized in that: The method comprises the following steps: Collect characteristic information of ceramic bodies at each manufacturing node during the ceramic manufacturing process; Matching the feature information of each node with the standard model respectively to obtain the feature matching degree of each node; The fault detection results of ceramic manufacturing are obtained according to the characteristic matching degree of each node; The ceramic manufacturing fault detection result is obtained according to the characteristic matching degree of each node, including: In the ceramic manufacturing process, it is determined in real time whether the feature matching degree of the current node is less than the first preset threshold value. If the feature matching degree of any node is less than the first preset threshold value, a fault warning is issued; If the feature matching degree of the current node is greater than the first preset threshold, it is determined in real time whether the feature matching degree of the current node is less than the second preset threshold, and the second preset threshold is greater than the first preset threshold; In real time, nodes whose feature matching degree is greater than a first preset threshold and less than a second preset threshold are added to form a set of nodes to be determined; Obtaining a feature matching degree to be determined according to the feature matching degree of the set of nodes to be determined; The ceramic manufacturing fault detection result is determined according to the matching degree of the feature to be determined and a first preset threshold.

2. A ceramic manufacturing fault detection method according to claim 1, characterized in that: The collection of characteristic information of the ceramic body at each manufacturing node during the ceramic manufacturing production process includes: An image acquisition module is respectively provided at each node of ceramic manufacturing, and the image acquisition module is controlled to acquire a state image of the ceramic body at the node at a predetermined interval; The Otsu algorithm and particle swarm algorithm are used to segment the collected images, and the regional image of the ceramic body is segmented from the state image. Key features are extracted from the segmented regional image to obtain feature information, wherein the key features include the outer contour, noise points, cracks, and patterns of the ceramic body.

3. A ceramic manufacturing fault detection method according to claim 1, characterized in that: The Standard Model is determined by: Obtain a normal sample set for each node in the ceramic manufacturing production process; The key features in each normal sample are extracted, the mean of each key feature in the normal sample set is determined, and the mean of each key feature is used as the standard model.

4. A ceramic manufacturing fault detection method according to claim 1, characterized in that: The obtaining the feature matching degree to be determined according to the feature matching degree of the set of nodes to be determined comprises: Sorting each node in the ceramic manufacturing production process in order of precedence, and determining the fault weight of each node in the ceramic manufacturing production process according to the sorting result; Matching the feature information of each node in the set of nodes to be determined with the standard model respectively to obtain the feature matching degree of each node in the set of nodes to be determined; The feature matching degree to be determined is obtained according to the fault weight and feature matching degree of each node in the set of nodes to be determined.

5. A ceramic manufacturing fault detection method according to claim 4, characterized in that: The step of sequentially sorting the nodes in the ceramic manufacturing production process in order, and determining the fault weight of each node in the ceramic manufacturing production process according to the sorting result, comprises: Let the total number of all manufacturing nodes in the ceramic manufacturing production process be n, then the fault weight of the i-th node is: Wi = (ni) / n, where 1≤i≤n.

6. A ceramic manufacturing fault detection method according to claim 5, characterized in that: The method of determining a ceramic manufacturing fault detection result according to the matching degree of the feature to be determined and a first preset threshold value includes: Determine whether the following formula is true: If yes, there is a fault; if no, there is no fault; Among them, ti is the feature matching degree of the i-th node, and the i-th node is in the set of nodes to be determined, and t1 is the first preset threshold.

7. A ceramic manufacturing fault detection method according to claim 6, characterized in that: The method further includes: when the fault detection result of the ceramic manufacturing is that a fault exists, determining the last node in the set of nodes to be determined, and using the last node in the set of nodes to be determined as the node where the fault is detected.

8. A ceramic manufacturing fault detection system, characterized in that: The system comprises: at least one processor; at least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the ceramic manufacturing fault detection method according to any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a ceramic manufacturing fault detection program, and when the ceramic manufacturing fault detection program is executed by a processor, the steps of the ceramic manufacturing fault detection method as described in any one of claims 1 to 7 are implemented.

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