CN112767379A - Method, system and computer readable storage medium for fault detection in ceramic manufacturing - Google Patents
Method, system and computer readable storage medium for fault detection in ceramic manufacturing Download PDFInfo
- Publication number
- CN112767379A CN112767379A CN202110117683.9A CN202110117683A CN112767379A CN 112767379 A CN112767379 A CN 112767379A CN 202110117683 A CN202110117683 A CN 202110117683A CN 112767379 A CN112767379 A CN 112767379A
- Authority
- CN
- China
- Prior art keywords
- node
- ceramic
- matching degree
- preset threshold
- fault detection
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0004—Industrial image inspection
- G06T7/001—Industrial image inspection using an image reference approach
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/11—Region-based segmentation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/136—Segmentation; Edge detection involving thresholding
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30108—Industrial image inspection
Landscapes
- Engineering & Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Quality & Reliability (AREA)
- Analysing Materials By The Use Of Radiation (AREA)
Abstract
The invention relates to the technical field of ceramic fault detection, in particular to a fault detection method, a system and a computer readable storage medium for ceramic manufacture, which comprises the steps of firstly collecting characteristic information of a ceramic body at each manufacturing node in the ceramic manufacturing production process; then, the characteristic information of each node is respectively matched with a standard model to obtain the characteristic matching degree of each node; and finally, obtaining a fault detection result of ceramic manufacture according to the feature matching degree of each node.
Description
Technical Field
The invention relates to the technical field of ceramic fault detection, in particular to a fault detection method and system for ceramic manufacturing and a computer readable storage medium.
Background
In the ceramic manufacturing process, how to monitor each node of the ceramic manufacturing in real time, grasp the possible faults in the ceramic manufacturing in time and reliably monitor the ceramic manufacturing state has very important significance.
In the prior art, only manual visual quality inspection is needed, so that error detection is easy to occur, time and labor are wasted, and the development requirements of current intelligent manufacturing cannot be met.
Disclosure of Invention
The present invention provides a method, system and computer readable storage medium for failure detection in ceramic manufacturing, which solves one or more of the problems of the prior art and provides at least one of the advantages.
In order to achieve the purpose, the invention provides the following technical scheme:
a method of fault detection in ceramic manufacture, the method comprising the steps of:
collecting characteristic information of a ceramic body at each manufacturing node in the ceramic manufacturing production process;
matching the characteristic information of each node with a standard model respectively to obtain the characteristic matching degree of each node;
and obtaining a fault detection result of the ceramic manufacture according to the feature matching degree of each node.
Further, the collecting characteristic information of the ceramic body at each manufacturing node in the ceramic manufacturing production process comprises:
respectively arranging an image acquisition module at each node of the ceramic manufacture, and controlling the image acquisition modules to acquire state images of the ceramic body on the node at preset time intervals;
segmenting the acquired image by adopting an Otsu algorithm and a particle swarm algorithm, and segmenting a region image of the ceramic body from the state image;
and extracting key features of the area image obtained by dividing to obtain feature information, wherein the key features comprise the outer contour, noise points, cracks and patterns of the ceramic body.
Further, the standard model is determined by:
acquiring a normal sample set of each node in the ceramic manufacturing production process;
extracting each key feature in each normal sample, determining the mean value of each key feature in the normal sample set, and taking the mean value of each key feature as a standard model.
Further, the obtaining of the fault detection result of the ceramic manufacturing according to the feature matching degree of each node includes:
in the ceramic manufacturing process, judging whether the feature matching degree of the current node is smaller than a first preset threshold value or not in real time, and if the feature matching degree of any node is smaller than the first preset threshold value, sending out a fault warning;
if the feature matching degrees of the current node are all larger than a first preset threshold, judging whether the feature matching degrees of the current node are smaller than a second preset threshold in real time, wherein the second preset threshold is larger than the first preset threshold;
adding nodes with the feature matching degree larger than a first preset threshold and smaller than a second preset threshold in real time to form a node set to be judged;
obtaining the matching degree of the features to be judged according to the matching degree of the features of the node set to be judged;
and determining a fault detection result of the ceramic manufacturing according to the matching degree of the features to be determined and a first preset threshold value.
Further, the obtaining the matching degree of the features to be determined according to the matching degree of the features of the node set to be determined includes:
sequencing all nodes in the ceramic manufacturing production process in sequence, and determining the fault weight of each node in the ceramic manufacturing production process according to the sequencing result;
respectively matching the characteristic information of each node in the node set to be judged with a standard model to obtain the characteristic matching degree of each node in the node set to be judged;
and obtaining the matching degree of the features to be judged according to the fault weight and the feature matching degree of each node in the node set to be judged.
Further, the step of sequentially sequencing each node in the ceramic manufacturing production process and determining the fault weight of each node in the ceramic manufacturing production process according to the sequencing result comprises the following steps:
let the total number of all manufactured nodes in the ceramic manufacturing production process be n, then the failure weight of the ith node is: wi ═ n-i)/n, where 1. ltoreq. i.ltoreq.n.
Further, the determining the fault detection result of the ceramic manufacturing according to the matching degree of the features to be determined and a first preset threshold value comprises:
determining whether the following formula holds:
if yes, a fault exists; if not, no fault exists;
and ti is the feature matching degree of the ith node, the ith node is in the node set to be determined, and t1 is a first preset threshold.
Further, the method further comprises: and when the fault detection result of the ceramic manufacture is that a fault exists, determining the last node in the node set to be determined, and taking the last node in the node set to be determined as the node for detecting the fault.
A ceramic fault detection system, the system comprising:
at least one processor;
at least one memory for storing at least one program;
when executed by the at least one processor, cause the at least one processor to implement the method of fault detection in ceramic manufacturing of any of the above.
A computer-readable storage medium having stored thereon a ceramic manufacturing failure detection program that, when executed by a processor, implements the steps of the ceramic manufacturing failure detection method of any one of the above.
The invention has the beneficial effects that: the invention discloses a fault detection method, a system and a computer readable storage medium for ceramic manufacture.A method for detecting the fault of the ceramic manufacture comprises the steps of firstly collecting the characteristic information of a ceramic body at each manufacture node in the ceramic manufacture production process; then, the characteristic information of each node is respectively matched with a standard model to obtain the characteristic matching degree of each node; and finally, obtaining a fault detection result of the ceramic manufacture according to the feature matching degree of each node. The invention has the advantages of improving the speed and the precision of fault detection in ceramic manufacture and improving the reliability of monitoring the ceramic manufacture state.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a flow chart illustrating a method for fault detection in ceramic manufacture according to an embodiment of the present invention.
Detailed Description
The conception, specific structure and technical effects of the present application will be described clearly and completely with reference to the following embodiments and the accompanying drawings, so that the purpose, scheme and effects of the present application can be fully understood. It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
Referring to fig. 1, a method for detecting a failure in ceramic manufacturing according to an embodiment of the present invention is shown in fig. 1, and includes the following steps:
s100, collecting characteristic information of a ceramic body at each manufacturing node in the ceramic manufacturing production process;
s200, respectively matching the characteristic information of each node with a standard model to obtain the characteristic matching degree of each node;
and step S300, obtaining a fault detection result of ceramic manufacture according to the feature matching degree of each node.
In a modified embodiment, the step S100 includes:
respectively arranging an image acquisition module at each node of the ceramic manufacture, and controlling the image acquisition modules to acquire state images of the ceramic body on the node at preset time intervals;
segmenting the acquired image by adopting an Otsu algorithm and a particle swarm algorithm, and segmenting a region image of the ceramic body from the state image;
and extracting key features of the area image obtained by dividing to obtain feature information, wherein the key features comprise the outer contour, noise points, cracks and patterns of the ceramic body.
In a modified embodiment, the step S200 includes:
acquiring a normal sample set of each node in the ceramic manufacturing production process;
extracting each key feature in each normal sample, determining the mean value of each key feature in the normal sample set, and taking the mean value of each key feature as a standard model.
In a modified embodiment, the step S300 includes:
in the ceramic manufacturing process, judging whether the feature matching degree of the current node is smaller than a first preset threshold value or not in real time, and if the feature matching degree of any node is smaller than the first preset threshold value, sending out a fault warning;
if the feature matching degrees of the current node are all larger than a first preset threshold, judging whether the feature matching degrees of the current node are smaller than a second preset threshold in real time, wherein the second preset threshold is larger than the first preset threshold;
adding nodes with the feature matching degree larger than a first preset threshold and smaller than a second preset threshold in real time to form a node set to be judged;
obtaining the matching degree of the features to be judged according to the matching degree of the features of the node set to be judged;
and determining a fault detection result of the ceramic manufacturing according to the matching degree of the features to be determined and a first preset threshold value.
In an improved embodiment, the obtaining the feature matching degree to be determined according to the feature matching degree of the node set to be determined includes:
sequencing all nodes in the ceramic manufacturing production process in sequence, and determining the fault weight of each node in the ceramic manufacturing production process according to the sequencing result;
respectively matching the characteristic information of each node in the node set to be judged with a standard model to obtain the characteristic matching degree of each node in the node set to be judged;
and obtaining the matching degree of the features to be judged according to the fault weight and the feature matching degree of each node in the node set to be judged.
In an improved embodiment, the sequentially sorting the nodes in the ceramic manufacturing process according to the sequence, and determining the fault weight of each node in the ceramic manufacturing process according to the sorting result includes:
let the total number of all manufactured nodes in the ceramic manufacturing production process be n, then the failure weight of the ith node is: wi ═ n-i)/n, where 1. ltoreq. i.ltoreq.n.
In an improved embodiment, the determining the fault detection result of the ceramic manufacturing according to the matching degree of the features to be determined and the first preset threshold value comprises:
determining whether the following formula holds:
if yes, a fault exists; if not, no fault exists;
and ti is the feature matching degree of the ith node, the ith node is in the node set to be determined, and t1 is a first preset threshold.
The method further comprises the following steps: and when the fault detection result of the ceramic manufacture is that a fault exists, determining the last node in the node set to be determined, and taking the last node in the node set to be determined as the node for detecting the fault.
In correspondence with the method of fig. 1, embodiments of the present invention also provide a ceramic manufactured fault detection system, the system comprising:
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 program causes the at least one processor to implement the method for detecting a failure in ceramic manufacturing according to any of the above embodiments.
The contents in the above method embodiments are all applicable to the present system embodiment, the functions specifically implemented by the present system embodiment are the same as those in the above method embodiment, and the beneficial effects achieved by the present system embodiment are also the same as those achieved by the above method embodiment.
In correspondence with the method of fig. 1, an embodiment of the present invention further provides a computer-readable storage medium, on which a failure detection program for ceramic manufacturing is stored, which, when executed by a processor, implements the steps of the failure detection method for ceramic manufacturing according to any one of the above-described embodiments.
The Processor may be a Central-Processing Unit (CPU), other general-purpose Processor, a Digital Signal Processor (DSP), an Application-Specific-Integrated-Circuit (ASIC), a Field-Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, a discrete hardware component, or the like. The general purpose processor may be a microprocessor or the processor may be any conventional processor or the like that is the control center for the ceramic fault detection system and connects the various parts of the overall ceramic fault detection system operational equipment using various interfaces and lines.
The memory may be used to store the computer programs and/or modules, and the processor may implement the various functions of the ceramic fault detection system by executing or executing the computer programs and/or modules stored in the memory and invoking the data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the cellular phone, and the like. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart-Media-Card (SMC), a Secure-Digital (SD) Card, a Flash-memory Card (Flash-Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
While the description of the present application has been made in considerable detail and with particular reference to a few illustrated embodiments, it is not intended to be limited to any such details or embodiments or any particular embodiments, but it is to be construed that the present application effectively covers the intended scope of the application by reference to the appended claims, which are interpreted in view of the broad potential of the prior art. Further, the foregoing describes the present application in terms of embodiments foreseen by the inventor for which an enabling description was available, notwithstanding that insubstantial changes from the present application, not presently foreseen, may nonetheless represent equivalents thereto.
Claims (10)
1. A method of fault detection in ceramic manufacture, the method comprising the steps of:
collecting characteristic information of a ceramic body at each manufacturing node in the ceramic manufacturing production process;
matching the characteristic information of each node with a standard model respectively to obtain the characteristic matching degree of each node;
and obtaining a fault detection result of the ceramic manufacture according to the feature matching degree of each node.
2. The method for detecting the failure in ceramic manufacturing according to claim 1, wherein the collecting the characteristic information of the ceramic body at each manufacturing node in the ceramic manufacturing process comprises:
respectively arranging an image acquisition module at each node of the ceramic manufacture, and controlling the image acquisition modules to acquire state images of the ceramic body on the node at preset time intervals;
segmenting the acquired image by adopting an Otsu algorithm and a particle swarm algorithm, and segmenting a region image of the ceramic body from the state image;
and extracting key features of the area image obtained by dividing to obtain feature information, wherein the key features comprise the outer contour, noise points, cracks and patterns of the ceramic body.
3. The method of claim 1, wherein the standard model is determined by:
acquiring a normal sample set of each node in the ceramic manufacturing production process;
extracting each key feature in each normal sample, determining the mean value of each key feature in the normal sample set, and taking the mean value of each key feature as a standard model.
4. The method of claim 1, wherein the obtaining of the fault detection result of the ceramic manufacture according to the feature matching degree of each node comprises:
in the ceramic manufacturing process, judging whether the feature matching degree of the current node is smaller than a first preset threshold value or not in real time, and if the feature matching degree of any node is smaller than the first preset threshold value, sending out a fault warning;
if the feature matching degrees of the current node are all larger than a first preset threshold, judging whether the feature matching degrees of the current node are smaller than a second preset threshold in real time, wherein the second preset threshold is larger than the first preset threshold;
adding nodes with the feature matching degree larger than a first preset threshold and smaller than a second preset threshold in real time to form a node set to be judged;
obtaining the matching degree of the features to be judged according to the matching degree of the features of the node set to be judged;
and determining a fault detection result of the ceramic manufacturing according to the matching degree of the features to be determined and a first preset threshold value.
5. The method according to claim 4, wherein the obtaining the degree of matching of the features to be determined according to the degree of matching of the features of the set of nodes to be determined includes:
sequencing all nodes in the ceramic manufacturing production process in sequence, and determining the fault weight of each node in the ceramic manufacturing production process according to the sequencing result;
respectively matching the characteristic information of each node in the node set to be judged with a standard model to obtain the characteristic matching degree of each node in the node set to be judged;
and obtaining the matching degree of the features to be judged according to the fault weight and the feature matching degree of each node in the node set to be judged.
6. The method for detecting the failure in the ceramic manufacturing according to claim 5, wherein the step of sequentially sequencing the nodes in the ceramic manufacturing production process and the step of determining the failure weight of each node in the ceramic manufacturing production process according to the sequencing result comprises:
let the total number of all manufactured nodes in the ceramic manufacturing production process be n, then the failure weight of the ith node is: wi ═ n-i)/n, where 1. ltoreq. i.ltoreq.n.
7. The method for detecting the faults of the ceramic manufacturing according to claim 6, wherein the step of determining the fault detection results of the ceramic manufacturing according to the matching degree of the features to be determined and a first preset threshold comprises the following steps:
determining whether the following formula holds:
if yes, a fault exists; if not, no fault exists;
and ti is the feature matching degree of the ith node, the ith node is in the node set to be determined, and t1 is a first preset threshold.
8. The method of claim 7, further comprising: and when the fault detection result of the ceramic manufacture is that a fault exists, determining the last node in the node set to be determined, and taking the last node in the node set to be determined as the node for detecting the fault.
9. A ceramic fault detection system, comprising:
at least one processor;
at least one memory for storing at least one program;
when executed by the at least one processor, cause the at least one processor to implement the method of fault detection in ceramic manufacture of any of claims 1 to 8.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored thereon a failure detection program for ceramic manufacture, which when executed by a processor, implements the steps of the failure detection method for ceramic manufacture according to any one of claims 1 to 8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110117683.9A CN112767379A (en) | 2021-01-28 | 2021-01-28 | Method, system and computer readable storage medium for fault detection in ceramic manufacturing |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110117683.9A CN112767379A (en) | 2021-01-28 | 2021-01-28 | Method, system and computer readable storage medium for fault detection in ceramic manufacturing |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112767379A true CN112767379A (en) | 2021-05-07 |
Family
ID=75706403
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110117683.9A Pending CN112767379A (en) | 2021-01-28 | 2021-01-28 | Method, system and computer readable storage medium for fault detection in ceramic manufacturing |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112767379A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114399237A (en) * | 2022-03-25 | 2022-04-26 | 张家港特恩驰电缆有限公司 | Intelligent quality detection method and system for cable production process |
-
2021
- 2021-01-28 CN CN202110117683.9A patent/CN112767379A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114399237A (en) * | 2022-03-25 | 2022-04-26 | 张家港特恩驰电缆有限公司 | Intelligent quality detection method and system for cable production process |
| CN114399237B (en) * | 2022-03-25 | 2022-08-05 | 上海电气集团腾恩驰科技(苏州)有限公司 | Intelligent quality detection method and system for cable production process |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109671078B (en) | Method and device for detecting product surface image abnormity | |
| CN111179263B (en) | Industrial image surface defect detection model, method, system and device | |
| CN110347700A (en) | Static vulnerability database matching process, device, electronic equipment and readable storage medium storing program for executing | |
| CN110599484A (en) | Defect detection method, device and storage medium | |
| CN113608916A (en) | Fault diagnosis method and device, electronic equipment and storage medium | |
| CN112767379A (en) | Method, system and computer readable storage medium for fault detection in ceramic manufacturing | |
| CN111583180A (en) | Image tampering identification method and device, computer equipment and storage medium | |
| CN111898754A (en) | Neural network model training method and device, storage medium and equipment | |
| CN115690475A (en) | Target detection method and device, electronic equipment and readable storage medium | |
| CN111027474A (en) | Face area acquisition method and device, terminal equipment and storage medium | |
| CN111812511A (en) | Motor fault diagnosis method and device based on big data | |
| CN111091068A (en) | Density estimation model training method and device, storage medium and electronic equipment | |
| CN114544184B (en) | Fault diagnosis method, device, electronic equipment, storage medium and engine | |
| CN112816959B (en) | Clustering method, device, equipment and storage medium for vehicles | |
| CN112308061B (en) | License plate character recognition method and device | |
| CN114612710A (en) | Image detection method, image detection device, computer equipment and storage medium | |
| CN110796129A (en) | Text line region detection method and device | |
| CN112785614A (en) | Image segmentation method and system for ceramic cracks | |
| CN113763305B (en) | Method and device for calibrating defect of article and electronic equipment | |
| CN114463345A (en) | Multi-parameter mammary gland magnetic resonance image segmentation method based on dynamic self-adaptive network | |
| CN113190851A (en) | Active learning method of malicious document detection model, electronic device and storage medium | |
| CN112903703A (en) | Ceramic surface defect detection method and system based on image processing | |
| CN113485287B (en) | Multi-granularity measurement dynamic system fault diagnosis method and system | |
| CN111159961A (en) | Abnormity detection method and system based on curve data | |
| CN113765915B (en) | Network event analysis method, system, readable storage medium and computer device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |