A fault self-diagnosis method and system of a full-automatic hollow blow molding device

Abstract

The present application relates to the technical field of blow molding, in particular to a fault self-diagnosis method and system of a full-automatic hollow blow molding equipment. The present application firstly acquires all uneven wall thickness areas in the light transmission image of each blow molding product during wall thickness detection, further performs inter-frame matching on the uneven wall thickness areas in different light transmission images to acquire batch uneven wall thickness areas; then acquires a process fluctuation coefficient according to the fluctuation change of the monitoring curve of each process parameter in the blow molding process of the blow molding product; finally, according to the batch quantity of the blow molding product corresponding to the batch uneven wall thickness areas and the process fluctuation coefficient, combined with the gray scale distribution and area change characteristics in the uneven wall thickness areas, the blow molding equipment is given a fault early warning. The present application comprehensively evaluates the possibility of the equipment fault by analyzing the batch condition and cumulative characteristics of the uneven wall thickness areas in the blow molding product and combining the fluctuation change of the process parameters, so as to accurately perform fault self-diagnosis and early warning on the blow molding equipment.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of blow molding, in particular to a fault self-diagnosis method and system of a full-automatic hollow blow molding equipment. BACKGROUND

[0002] The full-automatic hollow blow molding equipment blows hollow plastic products by heating plastic raw materials and using compressed air. The uniformity of the wall thickness of the blow molding products has a great influence on the product quality such as strength and sealing performance, and abnormal failure of the blow molding equipment is one of the main reasons for the non-uniform wall thickness of the blow molding products. Therefore, it is crucial to evaluate the failure of the full-automatic hollow blow molding equipment during the blow molding process.

[0003] At present, the wall thickness uniformity of the blow molding products is mainly detected to evaluate the failure of the full-automatic hollow blow molding equipment, thereby triggering the related failure shutdown early warning mechanism. The blow molding pressure is also one of the main factors for controlling the uniform molding of the blow molding products. The accidental fluctuation of the blow molding pressure during the blow molding process may cause the strain rate of the parison to increase dramatically, thereby causing the local wall thickness of the blow molding products to be non-uniform. Such normal process fluctuation interference may cause misdiagnosis of the equipment failure, thereby possibly causing misshutdown and affecting the production efficiency. SUMMARY

[0004] In order to solve the technical problem of poor fault diagnosis effect of the full-automatic hollow blow molding equipment, the purpose of the present application is to provide a fault self-diagnosis method and system of a full-automatic hollow blow molding equipment, and the technical solution adopted is as follows:

[0005] A fault self-diagnosis method of a full-automatic hollow blow molding equipment, the method comprising:

[0006] Until the current time, all wall thickness non-uniform areas in the light transmission image of each blow molding product in the same batch during wall thickness detection are obtained. The monitoring curves of each process parameter during the blow molding process of each blow molding product are obtained, and the process parameters at least include the blow molding pressure and the locking current.

[0007] According to the position, area and gray scale distribution change of the wall thickness non-uniform area in the light transmission image, the wall thickness non-uniform areas in different light transmission images of the blow molding products are matched between frames, and each frame matching result is taken as a batch of wall thickness non-uniform areas.

[0008] For each blow molding product, the process fluctuation coefficient is obtained according to the fluctuation change of the monitoring curve of each process parameter during the blow molding process. At the current time, according to the batch quantity of the blow molding products corresponding to each batch of wall thickness non-uniform areas and the process fluctuation coefficient, the gray scale distribution and the area change characteristics in the wall thickness non-uniform area are combined to perform failure warning on the blow molding equipment.

[0009] Further, the method for obtaining the wall thickness non-uniform area comprises:

[0010] obtaining a continuous light transmission frame image and an infrared frame image of the blow molding product in a wall thickness detection conveying process, the continuous light transmission frame image including a light transmission image; determining a suspected wall thickness uneven area and uneven characteristic parameters thereof according to local gray scale distribution in each light transmission frame image, and obtaining temperature information of each suspected wall thickness uneven area by combining pixel values in the infrared frame image of the same frame number;

[0011] For each blow molding product, frame-to-frame matching is performed on the suspected wall thickness uneven area according to the position, area and temperature information of the suspected wall thickness uneven area in the light transmission frame image in which the suspected wall thickness uneven area is located, and a wall thickness uneven area in the corresponding light transmission frame image is determined based on the matching result.

[0012] Further, the method for obtaining the suspected wall thickness uneven area and the uneven characteristic parameters thereof includes:

[0013] In each light transmission frame image, all target points are screened out according to gradient amplitudes of pixel points, region connectedness detection is performed on the target points, uneven characteristic parameters of each connected domain are obtained according to distribution characteristics of gray scale ranges and gradient amplitudes of pixel points in each connected domain, and all suspected wall thickness uneven areas are screened out from all connected domains according to the uneven characteristic parameters.

[0014] Further, the method for obtaining the batch wall thickness uneven area includes:

[0015] Any light transmission image is taken as a target image, any wall thickness uneven area in the target image is taken as a target area, and each wall thickness uneven area in each non-target image is taken as a reference area.

[0016] Between the target area and each reference area, a first matching parameter is obtained by fusing center position difference and area difference between the areas, a second matching parameter is obtained by mapping a negative correlation between differences between the uneven characteristic parameters of the corresponding areas, and a matching coefficient between each reference area and the target area is obtained by fusing the first matching parameter and the second matching parameter.

[0017] In each non-target image, a reference area between the target area and which has the largest matching coefficient and is greater than a preset matching threshold is screened out as a matching area of the target area, and the target area and all matching areas thereof are taken as a batch wall thickness uneven area.

[0018] Further, the method for obtaining the process fluctuation coefficient includes:

[0019] For each blow molding product, a monitoring curve of each process parameter is segmented by using extreme points, and all abnormal growth sub-sections are screened out from all subsections according to growth amplitudes of each subsection, and a process fluctuation coefficient is obtained according to growth amplitudes and duration of all abnormal growth sub-sections in each monitoring curve.

[0020] Further, the method for obtaining the process fluctuation coefficient according to the growth amplitude and duration of all abnormal growth sub-sections in each monitoring curve comprises:

[0021] In each monitoring curve, the product of the mean value of the growth amplitude of the abnormal growth sub-section and the proportion of the duration of all abnormal growth sub-sections in the monitoring curve is taken as the fluctuation parameter; and the normalized result of the cumulative sum of the fluctuation parameters of all monitoring curves is taken as the process fluctuation coefficient.

[0022] Further, the method for prewarning the blow molding equipment failure comprises:

[0023] The batch quantity of the blow molding product corresponding to each type of batch wall thickness uneven area is negatively correlated to obtain a process disturbance weight; and the discrete eigenvalue of the process fluctuation coefficient of the corresponding blow molding product is weighted and normalized by using the process disturbance weight to obtain a process disturbance coefficient of each type of batch wall thickness uneven area.

[0024] According to the area of each type of batch wall thickness uneven area and the batch accumulation of the uneven characteristic parameter, a failure disturbance coefficient of each type of batch wall thickness uneven area is obtained, and a suspected failure influence area is determined from all types of batch wall thickness uneven areas in combination with the process disturbance coefficient.

[0025] According to the distribution characteristics of the failure disturbance coefficient of the suspected failure influence area in each blow molding product, in combination with the total number and total area of the failure influence area, a production abnormality coefficient of the blow molding product is obtained; and the blow molding equipment is prewarned of failure until the production abnormality coefficient of a preset number of blow molding products is greater than a preset prewarning threshold value.

[0026] Further, the method for obtaining the failure disturbance coefficient comprises:

[0027] For each type of batch wall thickness uneven area, the area and the uneven characteristic parameter are respectively constructed into corresponding change sequences based on the detection sequence of the blow molding product, and according to the number and accumulation characteristics of non-negative values in the first-order difference sequence of the change sequence, an area growth parameter and a characteristic growth parameter are respectively obtained; and the failure disturbance coefficient is obtained by fusing the area growth parameter and the characteristic growth parameter.

[0028] Further, the method for obtaining the suspected failure influence area comprises:

[0029] The batch wall thickness uneven area with the failure disturbance coefficient less than or equal to a preset failure threshold value and the process disturbance coefficient greater than a preset process threshold value is taken as a suspected process influence area; and the remaining batch wall thickness uneven areas are taken as suspected failure influence areas.

[0030] The application further provides a fault self-diagnosis system of the full-automatic hollow blow molding device, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, and the processor implements the steps of the fault self-diagnosis method of the full-automatic hollow blow molding device when executing the computer program.

[0031] The application has the following beneficial effects:

[0032] The application firstly acquires all wall thickness uneven areas in the light transmission image of each blow molding product in the same batch at the time of wall thickness detection, prepares for subsequent evaluation of batch characteristics, and acquires the monitoring curve of each process parameter in the blow molding process of each blow molding product to evaluate the fluctuation and change of the process fluctuation parameter; further based on the batch characteristics of wall thickness unevenness caused by equipment failure, the wall thickness uneven areas in different light transmission images of the blow molding product are matched between frames according to the position, area and gray scale distribution change of the wall thickness uneven areas in the light transmission image, and all batch wall thickness uneven areas are acquired; finally, at the current time, the process interference possibility is analyzed according to the batch quantity of the blow molding product corresponding to each batch wall thickness uneven area and the process fluctuation coefficient, and the batch accumulation characteristics are analyzed in combination with the gray scale distribution and area change characteristics of the wall thickness uneven area, so as to analyze the possibility of equipment failure, and then the blow molding equipment is fault warned. The application analyzes the batch situation and batch accumulation characteristics of the wall thickness uneven area in the blow molding product, and combines the fluctuation and change of the process parameter in the blow molding process, so as to comprehensively evaluate the possibility of equipment failure, thereby accurately performing fault self-diagnosis and warning of the blow molding equipment. BRIEF DESCRIPTION OF DRAWINGS

[0033] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, and the advantages thereof, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description only show some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained from these drawings without any creative effort.

[0034] Figure 1 A flow chart of a fault self-diagnosis method of a full-automatic hollow blow molding device provided by an embodiment of the present application;

[0035] Figure 2 A flow chart of a batch wall thickness uneven area acquisition method provided by an embodiment of the present application. DETAILED DESCRIPTION

[0036] In order to further illustrate the technical means and effects taken by the present application to achieve the predetermined inventive objectives, the following describes in detail the specific implementation, structure, features and effects of a fault self-diagnosis method and system of a full-automatic hollow blow molding equipment according to the present application, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0037] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0038] The specific scheme of the fault self-diagnosis method and system of a full-automatic hollow blow molding equipment provided by the present application is described in detail below in combination with the accompanying drawings.

[0039] Please refer to Figure 1 which shows a flowchart of a fault self-diagnosis method of a full-automatic hollow blow molding equipment provided by one embodiment of the present application, which specifically includes:

[0040] Step S1, at the current time, all uneven wall thickness areas in the light transmission image of each blow molding product in the same batch during wall thickness detection are obtained; the monitoring curve of each process parameter during the blow molding process of each blow molding product is obtained, and the process parameters at least include blow molding pressure and mold locking current.

[0041] In one embodiment of the present application, up to the current time, first, in the product quality inspection process of the same batch, the light transmission image collected at the wall thickness detection time of each blow molding product in all inspected products is obtained, so as to subsequently analyze the uneven wall thickness;

[0042] Among them, the collection of the light transmission image of the blow molding product is already a prior art well known to those skilled in the art, and the general scene and collection process are briefly described as follows: in the wall thickness detection process section of the conveying belt, a backlight light source is installed on one side, and a wall thickness detection device, i.e. an industrial camera and an infrared imaging device, is installed on the other side, so that the detection device and the backlight light source are coaxial, when the blow molding product reaches the specified station, the light source, the product and the device are coaxial, and the detection device will automatically collect continuous frame light transmission frame images and continuous frame infrared frame images.

[0043] As an example, the frame rate of the industrial camera and the infrared imaging device is specifically set to 30 fps, and continuous frame images within one second are collected, and the implementer also adjusts the collection process; then the first frame light transmission frame image is taken as the light transmission image.

[0044] It should be noted that the blow molding products are detected one by one, i.e. only one blow molding product is contained in the light transmission image.

[0045] After the light transmission image of each blow molding product is acquired, the uneven wall thickness area of the blow molding product can be further analyzed and determined, so as to analyze whether the uneven wall thickness area is caused by equipment failure, thereby giving a warning.

[0046] Specifically, first, the corresponding area of the blow molding product in each frame of the light transmission image is determined by semantic recognition, and a threshold segmentation algorithm can also be used, so as to analyze and determine all uneven wall thickness areas in the blow molding product; the uneven wall thickness areas are all known technologies, and will not be described in detail.

[0047] Considering that the blow molding product is usually detected for wall thickness after being demolded, the blow molding product still has a certain temperature during the conveying process, the temperature distribution of the uneven wall thickness area is obviously different from that of the uniform wall thickness area, and during the conveying process of the blow molding product, the position of the uneven wall thickness area in the continuous frame image only changes slightly; and considering that the product wall thickness is different, the light transmission rate is also different, and the uneven wall thickness area will be characterized by uneven texture distribution in the light transmission image.

[0048] Based on this, in one preferred embodiment of the present application, the method for acquiring the uneven wall thickness area comprises:

[0049] The continuous light transmission frame images and infrared frame images of the blow molding product during the conveying process of the wall thickness detection are acquired, the light transmission images are included in the continuous light transmission frame images; the suspected uneven wall thickness area and uneven characteristic parameters thereof are determined according to the local gray scale distribution in each frame of the light transmission frame image, and the temperature information of each suspected uneven wall thickness area is acquired according to the pixel value in the infrared frame image with the same frame number.

[0050] For each blow molding product, the suspected uneven wall thickness area is matched between frames according to the position, area and temperature information of the suspected uneven wall thickness area in the light transmission frame image in which the suspected uneven wall thickness area is located, and the uneven wall thickness area in the corresponding light transmission frame image is determined based on the matching result.

[0051] In one preferred embodiment of the present application, considering that the uneven wall thickness is mainly reflected in the change of the gray scale texture, and the gradient can reflect the gray scale change rate, the method for acquiring the suspected uneven wall thickness area and the uneven characteristic parameters thereof comprises:

[0052] In each frame of the light transmission frame image, all target points are screened out according to the gradient amplitude of the pixel points, the region connectedness detection is performed on the target points, and the uneven characteristic parameters of each connected domain are acquired according to the distribution characteristics of the gray scale range and the gradient amplitude of the pixel points in each connected domain, and all suspected uneven wall thickness areas are screened out from all connected domains according to the uneven characteristic parameters.

[0053] As an example, in each light transmission frame image, the gradient of each pixel point is obtained based on a Sobel operator, and then the pixel points with a gradient amplitude greater than an average level are screened out as target points, and region connectivity detection is performed on the target points to obtain all connected regions, and the implementer can also perform certain inflation processing on each connected region to improve the region integrity; then the product of the gray scale range of all pixel points and the mean value of the gradient amplitude in each connected region is multiplied, and the product is linearly normalized to obtain the uneven feature parameter of each connected region; and then all connected regions with an uneven feature parameter greater than a preset threshold such as 0.5 are screened out from all connected regions as suspected wall thickness uneven regions;

[0054] It should be noted that the linear normalization is performed on the dimensions of all connected regions in each light transmission frame image, and the gradient acquisition, connected region detection and inflation processing are all prior art and will not be described again; the implementer can also adjust the preset threshold or adjust the screening conditions of the target points;

[0055] After each suspected wall thickness region is screened out, the temperature information of each suspected wall thickness region can be further determined by combining the infrared frame images synchronously collected under the same frame number; specifically, the infrared pixel values of all pixel points in the same position region of the suspected wall thickness region in the infrared frame image can be obtained by the mask method, and the range of the infrared pixel values is taken as the temperature information, i.e., the temperature range, of the suspected wall thickness uneven region, reflecting the temperature unevenness characteristic of the suspected wall thickness region;

[0056] Further, in the continuous frame images of each blow molding product, an image coordinate system is constructed with the image center as the origin, the position coordinates of the region center of each suspected wall thickness uneven region in the corresponding frame image are determined, the total number of pixel points in each suspected wall thickness uneven region is taken as the area of the region, and the feature triple is constructed in combination with the temperature range of each suspected wall thickness uneven region;

[0057] In the continuous light transmission frame images, taking any suspected wall thickness uneven region in the first frame as a matching target, the feature difference (i.e., the Euclidean norm of the corresponding position coordinates, the area of the region and the temperature range, and the value range is adjusted by mapping the Euclidean norm into tanh) between the matching target and the corresponding feature triple of each suspected wall thickness uneven region in the remaining frame images is obtained, and the suspected wall thickness uneven region with the smallest feature difference and less than a preset threshold such as 0.2 is screened out in each of the remaining frames, which is taken as the matching region of the matching target in the frame. The above operation means is prior art well known to those skilled in the art and will not be described again.

[0058] It should be noted that all parameter operations in the embodiments of the present application are uniformly subjected to standardization processing before operation to remove the dimension influence.

[0059] It should be noted that the target to be matched may not exist in the remaining frame images, and when the number of matching regions is greater than a preset ratio, such as 70%, of the total number of continuous frame images, the target to be matched is determined as a wall thickness uneven area, and the preset ratio can be adjusted by the implementer.

[0060] At this point, the suspected wall thickness uneven area is matched between frames, and all wall thickness uneven areas in the light transmission image of each blow molding product are determined.

[0061] In another embodiment of the present application, all wall thickness uneven areas can also be determined directly using a pre-trained labeling model, which is prior art and will not be described again.

[0062] Considering that fluctuations in the blow molding process, such as random fluctuation factors such as blow molding pressure fluctuations due to unstable gas supply, can also cause quality problems of blow molding products and wall thickness uneven problems, in order to further screen out process influences and accurately assess equipment failures, the present embodiment further acquires monitoring curves of each process parameter during the blow molding process of each blow molding product.

[0063] Among them, considering that the mold remains firmly closed during the blow molding process, when the blow molding pressure fluctuates, the gas pressure inside the mold and the expansion force of the plastic are also fluctuating, thereby affecting the closing force required by the locking system, and further causing changes in the locking current; based on this, the process parameters at least include the blow molding pressure and the locking current, and the implementer can also increase other process fluctuation related process parameters.

[0064] Then trace back the blow molding process of each blow molding product to acquire its monitoring curve of each process parameter in the corresponding blow molding process, i.e. the blow molding pressure monitoring curve and the locking current monitoring curve; it should be noted that tracing and acquiring the monitoring curve are prior art means and will not be described again.

[0065] Step S2, according to the position, area and gray scale distribution change of the wall thickness uneven area in the light transmission image, the wall thickness uneven areas in different light transmission images of the blow molding product are matched between frames, and each frame matching result is taken as a batch of wall thickness uneven areas.

[0066] Considering that accidental process fluctuations and equipment abnormal failures in the same batch of blow molding products will cause batch problems of blow molding quality, the wall thickness unevenness problem will occur in batches in different blow molding products; the batch problem caused by accidental process fluctuations is random and less, and the wall thickness unevenness problem caused by equipment abnormal failure has a large batch and may have cumulative characteristics; considering that the wall thickness detection process of the same batch of blow molding products is consistent, when the position, area and gray texture of the wall thickness unevenness area are relatively similar or consistent, it means that it is more likely to be a batch problem; based on this, the wall thickness unevenness area in different transmission images of the blow molding product can be matched between frames based on the similar logic of the inter-frame matching of the suspected wall thickness unevenness area in step S1 to determine the batch wall thickness unevenness area, and prepare for subsequent evaluation of equipment abnormalities.

[0067] In a preferred embodiment of the present application, the method for obtaining a batch wall thickness unevenness area comprises:

[0068] Please refer to Figure 2 which shows a method flowchart for obtaining a batch wall thickness unevenness area according to an embodiment of the present application, which specifically comprises:

[0069] Step S201: taking any transmission image as a target image, and taking any wall thickness unevenness area in the target image as a target area, and taking each wall thickness unevenness area in each non-target image as a reference area.

[0070] First, determine the target area in the target image. Take the target area as an example for matching expression, and then change the target image and the target area, so that the wall thickness unevenness area can be matched between frames.

[0071] Step S202: between the target area and each reference area, fuse the center position difference and area difference between the areas to obtain a first matching parameter, and take the negative correlation mapping result of the difference between the uneven feature parameters of the corresponding areas as a second matching parameter; fuse the first matching parameter and the second matching parameter to obtain the matching coefficient between each reference area and the target area.

[0072] As an example, between the target area and each reference area, the sum of the Euclidean distance between the position coordinates of the area centers and the absolute value of the area difference is mapped into the exponential function exp(-x) with natural constant e as the base number to adjust the logic, and the mapping value is taken as the first matching parameter; the absolute value of the difference between the uneven feature parameters of the area is also mapped into exp(-x) to adjust the logic, and the mapping value is taken as the second matching parameter; finally, the first matching parameter and the second matching parameter are multiplied to obtain the matching parameter, so that the two areas with smaller area difference, position difference and uneven feature parameter difference have larger matching parameter.

[0073] In other examples, the implementer can also adjust the negative correlation mapping method by himself.

[0074] Step S203, in each non-target image, screening the reference region with the maximum matching coefficient between the target region and the reference region and greater than a preset matching threshold as the matching region of the target region; taking the target region and all the matching regions thereof as a batch of wall thickness uneven regions.

[0075] As an example, in each non-target image, the preset matching threshold is set to 0.7, so as to obtain the matching region of the target region; between the light transmission images of all the blow molding products, the target region and all the matching regions thereof are taken as a batch of wall thickness uneven regions.

[0076] It should be noted that there is a possibility that the wall thickness uneven region in part of the light transmission images has no matching result, and the wall thickness uneven region may be caused by accidental process fluctuation, and has no batch characteristics.

[0077] Step S3, for each blow molding product, obtaining a process fluctuation coefficient according to the fluctuation change of the monitoring curve of each process parameter in the blow molding process; at the current time, according to the batch quantity of the blow molding product corresponding to each batch of wall thickness uneven regions and the process fluctuation coefficient, combining the gray distribution and area change characteristics in the wall thickness uneven region, a fault warning is performed on the blow molding equipment.

[0078] Considering that the fluctuation of the process parameters will affect the diagnosis accuracy of the equipment fault, based on this, the embodiment of the present application first evaluates the process fluctuation coefficient in the blow molding process of each blow molding product, and the process fluctuation coefficient is the possibility of subsequent evaluation of the batch wall thickness uneven region caused by the process fluctuation interference, and prepares for accurate evaluation of the equipment anomaly.

[0079] Preferably, in an embodiment of the present application, considering that the sudden increase amplitude of the process parameter reflects the fluctuation degree, and the more frequent the sudden increase is and the longer the time proportion is, the more frequent and violent the fluctuation is, which reflects the possibility of being a cause of wall thickness unevenness; based on this, the method for obtaining the process fluctuation coefficient comprises:

[0080] For each blow molding product, the monitoring curve of each process parameter is segmented by using the extreme points, and all abnormal growth segments are screened from all segments according to the growth amplitude of each segment; the process fluctuation coefficient is obtained according to the growth amplitude and the duration of all abnormal growth segments in each monitoring curve.

[0081] As an example, taking any blow molding product as an example, the extreme value points in each monitoring curve are obtained, the extreme value points are taken as segmentation points, the curve sub-segments between adjacent extreme value points are taken as a segment, the difference between the last monitoring value and the first monitoring value of the segment is taken as the growth amplitude (the growth amplitude can be negative, indicating a decrease), and the growth rate (known technology) is calculated using the growth amplitude, and the segment with a growth rate greater than a threshold value such as 0.5 is taken as an abnormal growth segment, i.e., a segment with a sudden increase in process parameters;

[0082] In a preferred embodiment of the present application, in each monitoring curve, the product of the average of the growth amplitudes of the abnormal growth segments and the proportion of the duration of all abnormal growth segments in the monitoring curve is taken as a fluctuation parameter; the normalized result of the cumulative sum of the fluctuation parameters of all monitoring curves is taken as a process fluctuation coefficient; specifically, the cumulative sum can be mapped into a tanh function to adjust the value range for normalization to obtain the process fluctuation coefficient, or other normalization means can be used.

[0083] At the current time, after obtaining the process fluctuation coefficient of each blow molding product in the blow molding production process, the batch quantity of each batch wall thickness uneven region corresponding to the blow molding product can be used to preliminarily evaluate whether the batch wall thickness uneven region of the blow molding product is affected by process disturbance or equipment failure, and further combined with the gray scale distribution and area change characteristics in the wall thickness uneven region, the possibility of equipment failure influence is evaluated, and then the blow molding equipment is warned of failure.

[0084] Preferably, in an embodiment of the present application, considering that the wall thickness unevenness caused by process fluctuation usually has a small batch quantity, the process parameter fluctuation of the corresponding blow molding product is large and the consistency can be extremely low, so the process disturbance coefficient of each batch wall thickness uneven region can be obtained; when the equipment is abnormally faulty, the wall thickness uneven region of the same batch blow molding product can show an increasingly serious batch trend, specifically in that the area and uneven characteristic parameters of the wall thickness uneven region can be stable or increase, so that the failure disturbance coefficient of each batch wall thickness uneven region can be determined; then the batch region affected by the failure can be determined by comprehensively considering the two; then the distribution of the batch region affected by the failure in each blow molding product is analyzed, and the failure influence characteristics of the blow molding product are comprehensively evaluated, and then the equipment failure situation is comprehensively evaluated to warn;

[0085] Based on this, the method for warning the blow molding equipment of failure comprises:

[0086] The batch quantity of each batch wall thickness uneven region corresponding to the blow molding product is negatively correlated to obtain a process disturbance weight; the discrete characteristic value of the process fluctuation coefficient of the corresponding blow molding product is weighted and normalized using the process disturbance weight to obtain the process disturbance coefficient of each batch wall thickness uneven region;

[0087] According to the area of each batch wall thickness uneven area and the batch cumulative situation of uneven characteristic parameters, the fault interference coefficient of each batch wall thickness uneven area is obtained, and the suspected fault influence area is determined from all kinds of batch wall thickness uneven areas in combination with the process interference coefficient;

[0088] According to the distribution characteristics of the fault interference coefficient of the suspected fault influence area in each blow molding product, in combination with the total number and total area of the fault influence area, the production abnormality coefficient of the blow molding product is obtained; until the current time, when the production abnormality coefficients of a preset number of blow molding products are greater than a preset warning threshold value, the blow molding equipment is warned to be in failure.

[0089] As an example, the batch quantity of each batch wall thickness uneven area corresponding to the blow molding product is inversely related to the batch quantity, the reciprocal process interference weight is obtained, the variance is used to represent the discrete characteristic value, the process interference weight is multiplied by the variance of the process fluctuation coefficient of the corresponding blow molding product, and the product is linearly normalized to obtain the process interference coefficient of each batch wall thickness uneven area;

[0090] In a preferred embodiment of the present application, considering that the sequence is constructed according to the detection order of the blow molding product, the area change and uneven characteristic parameter change of the batch wall thickness uneven area can be more clearly observed, so that the batch cumulative situation is evaluated; the fault interference coefficient is obtained by the following method:

[0091] For each batch wall thickness uneven area, the area and uneven characteristic parameters are respectively sorted and constructed into corresponding change sequences based on the detection order of the blow molding product, and the area growth parameter and the characteristic growth parameter are respectively obtained according to the number of non-negative values and the cumulative characteristics in the first-order difference sequence of the change sequence; the fault interference coefficient is obtained by fusing the area growth parameter and the characteristic growth parameter;

[0092] Specifically, taking the area of each batch wall thickness uneven area as an example, the area is sorted according to the wall thickness detection order of the corresponding blow molding product to construct an area change sequence, then the area change sequence is first-order differentiated, the number of non-negative difference values is multiplied by the cumulative sum to obtain the area growth parameter; the more non-negative difference values and the larger the cumulative value are, the more stable or growing the wall thickness uneven characteristic is, and the more likely it is to be a batch problem caused by long-term accumulation of equipment failure; the characteristic growth parameter of the uneven characteristic parameter can be obtained in the same way; the area growth parameter and the characteristic growth parameter are multiplied and fused, and the product is linearly normalized to obtain the fault interference coefficient;

[0093] Further considering that process fluctuation and equipment failure may occur simultaneously in the blow molding process, and the process interference coefficient and the failure interference coefficient respectively reflect the possibility of affecting the wall thickness unevenness at different angles; when the process interference coefficient is high and the failure interference coefficient is low, the batch wall thickness uneven area is more likely to be caused by process fluctuation;

[0094] Therefore, in a preferred embodiment of the present application, the batch wall thickness uneven area with a failure interference coefficient less than or equal to a preset failure threshold and a process interference coefficient greater than a preset process threshold is regarded as a suspected process affected area; the remaining batch wall thickness uneven area is regarded as a suspected failure affected area; wherein the preset failure threshold is set to 0.3, and the preset process threshold is set to 0.6, so that the suspected process affected area and the suspected failure affected area of all batches can be determined; wherein the part of the wall thickness uneven area without matching results in step S2 is also a suspected process affected area;

[0095] After obtaining the suspected failure affected area of the batch in the batch blow molding product, it can be determined whether there is a suspected failure affected area in each blow molding product, and further according to the distribution characteristics of the failure interference coefficient of the suspected failure affected area in each blow molding product, combined with the total number and total area of the failure affected area, the production abnormality coefficient of each blow molding product is evaluated, which reflects the degree of blow molding product abnormality caused by equipment failure, and prepares for subsequent early warning of blow molding equipment failure;

[0096] Specifically, in each blow molding product, the total number and total area of the suspected failure affected area are summed, the sum value is multiplied by the average value of the failure interference coefficients of all suspected failure affected areas, and then the product is mapped into a sigmoid function to adjust the value range to obtain the production abnormality coefficient;

[0097] Further, by the end of the current time, when the production abnormality coefficients of a preset number of blow molding products, such as 10, continuously appear and are greater than a preset warning threshold, such as 0.6, it is automatically determined and warned that the blow molding equipment has failed; the implementer can also adjust the determination condition according to actual application; when the failure is warned, the relevant staff can be prompted to suspend processing to avoid production loss.

[0098] In other embodiments, the implementer can also develop a warning emergency degree based on the production abnormality coefficient, and can also perform process fluctuation warning when the process fluctuation coefficient is greater than a preset threshold.

[0099] Based on the same inventive concept, the present application also proposes a fault self-diagnosis system of a full-automatic hollow blow molding equipment, which comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the fault self-diagnosis method of the full-automatic hollow blow molding equipment described in steps S1-S4 when executing the computer program.

[0100] To sum up, the present application firstly acquires all wall thickness uneven areas in each blow molding product in the light transmission image when the wall thickness is detected, further matches the wall thickness uneven areas in different light transmission images, and acquires batch wall thickness uneven areas; then acquires the process fluctuation coefficient according to the fluctuation change of the monitoring curve of each process parameter in the blow molding process of the blow molding product; finally, according to the batch quantity of the batch wall thickness uneven areas corresponding to the blow molding product and the process fluctuation coefficient, combined with the gray distribution and area change characteristics in the wall thickness uneven areas, the blow molding equipment is fault early warned. The present application analyzes the batch situation and cumulative characteristics of the wall thickness uneven areas in the blow molding product, and combines the fluctuation change of the process parameters, comprehensively evaluates the possibility caused by the equipment fault, so as to accurately diagnose and early warn the blow molding equipment fault.

[0101] It should be noted that the above-mentioned embodiment sequence of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. 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, multi-task processing and parallel processing are also possible or can be advantageous.

[0102] Each of the embodiments in the specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other. Each embodiment focuses on the difference from other embodiments.

Claims

1. A self-diagnosis method for faults in a fully automatic hollow blow molding equipment, characterized in that, The method includes: As of the current moment, acquire all areas of uneven wall thickness in the transmitted image of each blow-molded product in the same batch during wall thickness detection; acquire the monitoring curve of each process parameter during the blow molding process of each blow-molded product, the process parameters including at least blow molding pressure and clamping current; Based on the location, area and grayscale distribution of the uneven wall thickness region in the transmitted image, frame matching is performed on the uneven wall thickness region in different transmitted images of the blow-molded product, and each frame matching result is used as a batch of uneven wall thickness regions. For each blow-molded product, the process fluctuation coefficient is obtained based on the fluctuation changes of the monitoring curve of each process parameter during the blow molding process; at the current moment, based on the batch quantity of blow-molded products corresponding to each type of batch wall thickness uneven area and the process fluctuation coefficient, combined with the gray scale distribution and area change characteristics in the wall thickness uneven area, the blow molding equipment is given a fault warning. The method for obtaining the process fluctuation coefficient includes: For each blow-molded product, the monitoring curves of each process parameter are segmented using extreme points, and all abnormal growth segments are selected from all segments based on the growth amplitude of each segment. In each monitoring curve, the product of the mean growth amplitude of the abnormal growth segments and the proportion of the duration of all abnormal growth segments in the monitoring curve is used as the fluctuation parameter. The normalized result of the sum of the fluctuation parameters of all monitoring curves is used as the process fluctuation coefficient.

2. The fault self-diagnosis method for a fully automatic hollow blow molding equipment according to claim 1, characterized in that, The method for obtaining the uneven wall thickness region includes: Acquire continuous light-transmitted frames and infrared frames of blow-molded products during the wall thickness detection and transfer process. The continuous light-transmitted frames include the light-transmitted image. Based on the local grayscale distribution in each light-transmitted frame, determine the suspected uneven wall thickness areas and their unevenness characteristic parameters, and combine the pixel values ​​in the infrared frame with the same frame number to obtain the temperature information of each suspected uneven wall thickness area. For each blow-molded product, based on the location, area, and temperature information of the suspected uneven wall thickness area in the corresponding light transmission frame, the suspected uneven wall thickness area is matched between frames, and the uneven wall thickness area in the corresponding light transmission frame is determined based on the matching results.

3. The fault self-diagnosis method for a fully automatic hollow blow molding equipment according to claim 2, characterized in that, The methods for obtaining the suspected uneven wall thickness regions and their unevenness characteristic parameters include: In each frame of the light-transmitting frame, all target points are selected based on the gradient magnitude of the pixels. Region connectivity detection is performed on the target points. Based on the distribution characteristics of the grayscale range and gradient magnitude of the pixels in each connected region, the non-uniformity feature parameters of each connected region are obtained. Based on the non-uniformity feature parameters, all suspected non-uniform wall thickness regions are selected from all connected regions.

4. The fault self-diagnosis method for a fully automatic hollow blow molding equipment according to claim 2, characterized in that, The method for obtaining the batch of uneven wall thickness regions includes: Take any transparent image as the target image, and take any uneven wall thickness region in the target image as the target region, and take each uneven wall thickness region in each non-target image as the reference region. Between the target region and each reference region, the difference in center position and area between the regions are fused to obtain a first matching parameter, and the negative correlation mapping result between the differences in the uneven feature parameters of the corresponding regions is used as a second matching parameter; the first matching parameter and the second matching parameter are fused to obtain the matching coefficient between each reference region and the target region; In each non-target image, a reference region with the largest matching coefficient with the target region that is greater than a preset matching threshold is selected as the matching region of the target region; the target region and all its matching regions are regarded as a batch of uneven wall thickness regions.

5. The fault self-diagnosis method for a fully automatic hollow blow molding equipment according to claim 2, characterized in that, Methods for providing fault warnings for blow molding equipment include: The batch quantity of blow-molded products corresponding to each type of uneven wall thickness region is negatively correlated to obtain the process interference weight; the discrete characteristic value of the process fluctuation coefficient of the corresponding blow-molded product is weighted and normalized using the process interference weight to obtain the process interference coefficient of each type of uneven wall thickness region. Based on the area of ​​each type of batch wall thickness unevenness region and the batch accumulation of the unevenness characteristic parameters, the fault interference coefficient of each type of batch wall thickness unevenness region is obtained, and combined with the process interference coefficient, the suspected fault-affected area is determined from all types of batch wall thickness unevenness regions. Based on the distribution characteristics of the fault interference coefficient of the suspected fault-affected area within each blow-molded product, and combined with the total number and total area of ​​the fault-affected area, the production abnormality coefficient of the blow-molded product is obtained; as of the current moment, when the production abnormality coefficient of a preset number of blow-molded products exceeds a preset warning threshold, a warning is issued that the blow-molding equipment has malfunctioned.

6. The fault self-diagnosis method for a fully automatic hollow blow molding equipment according to claim 5, characterized in that, The method for obtaining the fault interference coefficient includes: For each type of batch with uneven wall thickness, the area and unevenness characteristic parameters are sorted according to the detection order of blow-molded products to construct corresponding change sequences. Based on the number of non-negative values ​​and cumulative characteristics in the first-order difference sequence of the change sequence, the area growth parameter and the characteristic growth parameter are obtained respectively. The fault interference coefficient is obtained by fusing the area growth parameter and the characteristic growth parameter.

7. The fault self-diagnosis method for a fully automatic hollow blow molding equipment according to claim 5, characterized in that, The method for obtaining the suspected fault-affected area includes: The areas with uneven wall thickness in a batch that have a fault interference coefficient less than or equal to a preset fault threshold and a process interference coefficient greater than a preset process threshold are designated as suspected process-affected areas; the remaining areas with uneven wall thickness in a batch are designated as suspected fault-affected areas.

8. A fault self-diagnosis system for a fully automatic hollow blow molding equipment, characterized in that, The device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the fault self-diagnosis method for a fully automatic hollow blow molding equipment as described in any one of claims 1-7.

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