An intelligent detection method for production quality of aerial work vehicle structural parts

Abstract

The invention relates to the technical field of production quality detection, and discloses an intelligent detection method for the production quality of aerial work vehicle structural parts, comprising step one, using an infrared thermal imager to detect the temperature of the structural parts, and summarizing the detection heat maps of multiple surfaces, step two, analyzing the detection heat maps of multiple surfaces through an image processing system, and calculating the heat ratio of the structural parts, step three, judging the preheating effect of the structural parts according to the heat ratio of the structural parts, and judging the structural parts with unqualified preheating effect as poorly preheated structural parts, step four, judging whether preheating equipment diagnosis is required according to the data of the poorly preheated structural parts, step five, when preheating equipment diagnosis is required, evaluating the preheating equipment, and judging whether there is a preheating equipment failure, and step six, when it is judged that the preheating equipment fails, sending a fault signal for maintenance, thereby avoiding the problem of poorly preheated structural parts flowing into the production line to cause welding defects.

Description

Technical Field

[0001] The invention relates to the technical field of production quality detection, and in particular to an intelligent detection method for production quality of aerial work vehicle structural parts. Background Art

[0002] Aerial work vehicles are an important preheating equipment for construction machinery, which are widely used in the fields of construction, maintenance, installation, etc. It has many types, such as scissor type, self-propelled, telescopic arm type, curved arm type, cylinder type, etc. The structural parts of aerial work vehicles are the basis for their normal operation. Among them, the chassis is one of the key components of aerial work vehicles, and the power take-off device is an important part of aerial work vehicles. The relative rotational movement of the upper part of the aerial work vehicle relative to the lower part is called slewing, and the mechanism set up to achieve slewing is called slewing mechanism. The working mechanism is set up to achieve different movement requirements of aerial work vehicles, including working arms, lifting mechanisms, power systems, hydraulic systems, electrical systems, and control devices. The working arms, slewing platforms, subframes and other metal structures of aerial work vehicles are also important parts of aerial work vehicles. In addition, there are many other structural parts.

[0003] Welding is an important step in the production process of aerial work vehicle structural parts. Aerial work vehicle structural parts are usually preheated before welding to reduce the cooling rate of the welded joint, avoid the formation of hardened structure and reduce welding stress and deformation. This is crucial to prevent welding cracks. However, when there is a malfunction in the heating and preheating equipment, such as temperature control failure, damage to the heating element, etc., which makes it impossible to provide uniform heat, or the shape of the structural parts is more complex, making it difficult to evenly transfer heat to all areas, it will cause uneven heating. This uneven preheating will affect the welding quality, increase the risk of welding deformation and stress concentration, increase production risks, and reduce production efficiency. Summary of the invention

[0004] 1. Technical issues to be solved

[0005] In view of the deficiencies in the prior art, the present invention provides an intelligent detection method for the production quality of aerial work vehicle structural parts, which has the function of timely analyzing and understanding the preheating effect of the structural parts, avoiding the structural parts with poor preheating effect from flowing into the production line. When monitoring that the preheating of the structural parts is poor, the performance of the preheating equipment is analyzed, and it is determined whether the preheating equipment is faulty. The performance degradation or abnormal operation of the equipment can be discovered in time, so that measures can be taken quickly to adjust or repair it, avoiding the problems such as uneven preheating that will affect the welding quality, increase the risk of welding deformation and stress concentration, increase production risks, and reduce production efficiency.

[0006] (II) Technical solution

[0007] To achieve the above object, the present invention provides the following technical solution: an intelligent detection method for production quality of aerial work vehicle structural parts, comprising the following steps:

[0008] Step 1: Use an infrared thermal imager to detect the temperature of the structural parts and summarize the detection heat maps of multiple surfaces;

[0009] Step 2: Analyze the detected thermal images of multiple surfaces through an image processing system to calculate the heat ratio of the structural component;

[0010] Step 3: judging the preheating effect of the structural part according to the heat ratio of the structural part, and judging the structural part with unqualified preheating effect as a poorly preheated structural part;

[0011] Step 4: Determine whether preheating equipment diagnosis is required based on the data of the poorly preheated structural parts;

[0012] Step 5: If preheating equipment diagnosis is required, evaluate the preheating equipment to determine whether there is a preheating equipment failure;

[0013] Step 6: If it is determined that the preheating equipment is faulty, send a fault signal for maintenance.

[0014] Preferably, in the step 1, the detected thermal images of multiple surfaces are aggregated to form an infrared thermal image set, and the expression of the infrared thermal image set is: {HWrx1, HWrx2, . . . , HWrx n}, where HWrx1 represents the detection heat map of the first surface in the infrared thermal image set, and HWrx n It represents the detection heat map of the nth surface in the infrared thermal image set, and n represents that the temperature of n surfaces of the structural component has been detected.

[0015] Preferably, in step 2, the heat ratio is calculated as:

[0016]

[0017] In the above calculation formula, RLb represents the heat ratio, represents the complete area of ​​the i-th face of the structural component extracted by the image processing system, represents the high temperature area of ​​the i-th surface of the structural component extracted by the image processing system, represents the high temperature ratio of the i-th surface of the structural component, Represents the total value of the high temperature ratio of all surfaces of the structural component starting from the i=1th surface to the i=nth surface.

[0018] Preferably, in step 3, the method for judging the preheating effect of the structural component is:

[0019] When the calculated result of the heat ratio is less than the standard threshold value of the heat ratio of the structural component, it means that the preheating effect of the structural component is unqualified, and the structural component is determined to be a poorly preheated structural component.

[0020] Preferably, in step 4, the method for determining whether preheating equipment diagnosis is required is:

[0021] When two structural parts with poor preheating appear consecutively, preheating equipment diagnosis is performed.

[0022] Preferably, in step 5, the preheating equipment is evaluated in the following manner:

[0023] A. Monitor various data of the preheating equipment and integrate and summarize the monitoring data to form an equipment data set;

[0024] B. Analyze the integrated and summarized data and calculate the abnormal values ​​of the preheating equipment;

[0025] C. Determine whether the preheating equipment is faulty based on the abnormal value of the preheating equipment.

[0026] Preferably, in step A, the expression of the device data set is: {WDsj, DLsj, DYsj, LLsj, YLsj}, wherein WDsj represents the temperature data of the preheating device, DLsj represents the current data of the preheating device, DYsj represents the voltage data of the preheating device, LLsj represents the water flow data of the preheating device, and YLsj represents the water pressure data of the preheating device.

[0027] Preferably, in step B, the calculation formula for the abnormal value of the preheating equipment is:

[0028]

[0029] In the above calculation formula, SByc represents the abnormal value of the preheating equipment, WDsj s Represents the temperature data of the current preheating device, WDsj m Represents the average value of the temperature data of the preheating equipment, WDsj b Represents the standard deviation of the temperature data of the preheating equipment, DLsj s Represents the current data of the current preheating device, DLsj m Represents the average value of the current data of the preheating equipment, DLsj b Represents the standard deviation of the current data of the preheating equipment, DYsj s Represents the voltage data of the current preheating equipment, DYsj m Represents the average value of the voltage data of the preheating equipment, DYsj b Represents the standard deviation of the voltage data of the preheating equipment, LLsj sRepresents the water flow data of the current preheating equipment, LLsj m Represents the average value of the water flow data of the preheating equipment, LLsj b Represents the standard deviation of the water flow data of the preheating equipment, YLsj s Represents the water pressure data of the current preheating equipment, YLsj m Represents the average value of the water pressure data of the preheating equipment, YLsj b represents the standard deviation of the water pressure data of the preheating equipment, α represents the weight of the temperature factor, β represents the weight of the current factor, γ represents the weight of the voltage factor, δ represents the weight of the water flow factor, and ε represents the weight of the water pressure factor.

[0030] Preferably, when the abnormal value of the preheating device is greater than the abnormal threshold of the preheating device, it means that there is a fault in the preheating device, and a fault signal is sent for maintenance.

[0031] Compared with the prior art, the present invention provides an intelligent detection method for the production quality of aerial work vehicle structural parts, which has the following beneficial effects:

[0032] 1. The present invention first analyzes the preheating effect of the structural parts through thermal imaging, timely understands the preheating effect of the structural parts, and avoids the structural parts with poor preheating effect from flowing into the production line, affecting the welding quality, increasing the risk of welding deformation and stress concentration, increasing production risks, and reducing production efficiency.

[0033] 2. When the preheating of the structural parts is poorly monitored, the present invention analyzes the performance of the preheating equipment through temperature data, current data, voltage data, water flow data, and water pressure data to determine whether the preheating equipment is faulty. It can promptly discover the decline in equipment performance or abnormal operation, so as to quickly take measures to adjust or repair and reduce production interruption time. At the same time, multi-faceted comprehensive analysis can reduce the errors caused by the analysis results, reduce the subsequent generation of defective products, and comprehensively improve product performance and production quality efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 It is a step diagram of the method of the present invention. DETAILED DESCRIPTION

[0035] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0036] See also Figure 1, an intelligent detection method for the production quality of aerial work vehicle structural parts, comprising the following steps:

[0037] Step 1: Use an infrared thermal imager to detect the temperature of the structural parts and summarize the detection heat maps of multiple surfaces;

[0038] The expression of infrared thermal image set is: {HWrx1, HWrx2, ···, HWrx n}, where HWrx1 represents the detection heat map of the first surface in the infrared thermal image set, and HWrx n Represents the detection heat map of the nth surface in the infrared thermal image set, where n represents that the temperature of the structural component has been detected on n surfaces;

[0039] Infrared thermal imagers can monitor whether the best preheating effect is achieved during the production process, which helps reduce production interruptions and delays caused by insufficient or overheating, thereby improving overall production efficiency. Data sets facilitate multi-faceted analysis of temperature conditions to make monitoring results more accurate. At the same time, infrared thermal imagers use non-contact measurement methods to obtain temperature data without interfering with the production process, avoiding damage to structural parts.

[0040] Step 2: Analyze the detected thermal images of multiple surfaces through an image processing system to calculate the heat ratio of the structural component;

[0041] The heat ratio is calculated as:

[0042]

[0043] In the above calculation formula, RLb represents the heat ratio, represents the complete area of ​​the i-th face of the structural component extracted by the image processing system, represents the high temperature area of ​​the i-th surface of the structural component extracted by the image processing system, represents the high temperature ratio of the i-th surface of the structural component, Represents the total value of the high temperature ratio of all surfaces of the structural part starting from the i=1th surface to the i=nth surface;

[0044] The heat ratio reflects the result of a comprehensive analysis of the internal data of the infrared thermal image set, directly reflects the test results of the preheating effect of the structural parts, and can accurately reflect the products that are not preheated well;

[0045] Step 3: judging the preheating effect of the structural part according to the heat ratio of the structural part, and judging the structural part with unqualified preheating effect as a poorly preheated structural part;

[0046] The method for judging the preheating effect of the structural part is:

[0047] When the calculated result of the heat ratio is less than the standard threshold value of the heat ratio of the structural part, it means that the preheating effect of the structural part is unqualified, and the structural part is judged as a poorly preheated structural part;

[0048] Step 4: Determine whether preheating equipment diagnosis is required based on the data of the poorly preheated structural parts;

[0049] The way to determine whether preheating equipment diagnosis is needed is:

[0050] When two structural parts with poor preheating occur consecutively, perform preheating equipment diagnosis;

[0051] Preheating equipment diagnosis can timely find out whether there is a preheating equipment failure. Preheating equipment failure may cause uneven temperature distribution of the preheating parts, reduce preheating efficiency, and thus affect the preheating effect of the structural parts. By judging whether preheating equipment diagnosis is needed, it can be distinguished whether it is a problem with the preheating parts or the equipment when the preheating parts are not well preheated, and the equipment failure can be discovered in time to reduce the subsequent generation of defective products.

[0052] Step 5: If preheating equipment diagnosis is required, evaluate the preheating equipment to determine whether there is a preheating equipment failure;

[0053] The preheating equipment is evaluated by:

[0054] A. Monitor various data of the preheating equipment and integrate and summarize the monitoring data to form an equipment data set;

[0055] The expression of the equipment data set is: {WDsj, DLsj, DYsj, LLsj, YLsj}, where WDsj represents the temperature data of the preheating equipment, DLsj represents the current data of the preheating equipment, DYsj represents the voltage data of the preheating equipment, LLsj represents the water flow data of the preheating equipment, and YLsj represents the water pressure data of the preheating equipment;

[0056] B. Analyze the integrated and summarized data and calculate the abnormal values ​​of the preheating equipment;

[0057] The calculation formula for the abnormal value of the preheating equipment is:

[0058]

[0059] In the above calculation formula, SByc represents the abnormal value of the preheating equipment, WDsjs represents the temperature data of the current preheating equipment, and WDsj m Represents the average value of the temperature data of the preheating equipment, WDsj b Represents the standard deviation of the temperature data of the preheating equipment, DLsj s Represents the current data of the current preheating device, DLsj mRepresents the average value of the current data of the preheating equipment, DLsj b Represents the standard deviation of the current data of the preheating equipment, DYsj s Represents the voltage data of the current preheating equipment, DYsj m Represents the average value of the voltage data of the preheating equipment, DYsj b Represents the standard deviation of the voltage data of the preheating equipment, LLsj s Represents the water flow data of the current preheating equipment, LLsj m Represents the average value of the water flow data of the preheating equipment, LLsj b Represents the standard deviation of the water flow data of the preheating equipment, YLsj s Represents the water pressure data of the current preheating equipment, YLsj m Represents the average value of the water pressure data of the preheating equipment, YLsj b represents the standard deviation of the water pressure data of the preheating equipment, α represents the weight of the temperature factor, β represents the weight of the current factor, γ represents the weight of the voltage factor, δ represents the weight of the water flow factor, and ε represents the weight of the water pressure factor;

[0060] C. Determine whether the preheating equipment is faulty based on the abnormal value of the preheating equipment;

[0061] When the preheating device abnormal value is greater than the preheating device abnormal threshold, it means that the preheating device is faulty;

[0062] The performance of the preheating equipment can be analyzed through the temperature data, current data, voltage data, water flow data, and water pressure data of the preheating equipment to determine whether the preheating equipment is faulty. The equipment performance degradation or abnormal operation can be discovered in time, so that measures can be taken quickly to adjust or repair it, reducing production interruption time. At the same time, comprehensive analysis from multiple aspects can reduce the errors caused by the analysis results;

[0063] Step 6: If it is determined that the preheating equipment is faulty, send a fault signal for maintenance.

[0064] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent detection method for production quality of aerial work vehicle structural parts, characterized in that: The following steps are involved: Step 1: Use an infrared thermal imager to detect the temperature of the structural parts and summarize the detection heat maps of multiple surfaces; The detected thermal images of multiple surfaces are aggregated to form an infrared thermal image set, and the expression of the infrared thermal image set is: {HWrx1, HWrx2, . . . , HWrx n }, where HWrx1 represents the detection heat map of the first surface in the infrared thermal image set, and HWrx n Represents the detection heat map of the nth surface in the infrared thermal image set, where n represents that the temperature of the structural component has been detected on n surfaces; Step 2: Analyze the detected thermal images of multiple surfaces through an image processing system to calculate the heat ratio of the structural component; The heat ratio is calculated as: In the above calculation formula, RLb represents the heat ratio, represents the complete area of ​​the i-th face of the structural component extracted by the image processing system, represents the high temperature area of ​​the i-th surface of the structural component extracted by the image processing system, represents the high temperature ratio of the i-th surface of the structural component, Represents the total value of the high temperature ratio of all surfaces of the structural part starting from the i=1th surface to the i=nth surface; Step 3: judging the preheating effect of the structural part according to the heat ratio of the structural part, and judging the structural part with unqualified preheating effect as a poorly preheated structural part; Step 4: Determine whether preheating equipment diagnosis is required based on the data of the poorly preheated structural parts; Step 5: If preheating equipment diagnosis is required, evaluate the preheating equipment to determine whether there is a preheating equipment failure; Step 6: If it is determined that the preheating equipment is faulty, send a fault signal for maintenance.

2. The intelligent detection method for production quality of aerial work vehicle structural parts according to claim 1 is characterized by: In step 3, the method for judging the preheating effect of the structural part is: When the calculated result of the heat ratio is less than the standard threshold value of the heat ratio of the structural component, it means that the preheating effect of the structural component is unqualified, and the structural component is determined to be a poorly preheated structural component.

3. The intelligent detection method for production quality of aerial work vehicle structural parts according to claim 2 is characterized in that: In step 4, the method for determining whether preheating equipment diagnosis is required is: When two structural parts with poor preheating appear consecutively, preheating equipment diagnosis is performed.

4. The intelligent detection method for production quality of aerial work vehicle structural parts according to claim 3 is characterized by: In step 5, the preheating equipment is evaluated as follows: A. Monitor various data of the preheating equipment and integrate and summarize the monitoring data to form an equipment data set; B. Analyze the integrated and summarized data and calculate the abnormal values ​​of the preheating equipment; C. Determine whether the preheating equipment is faulty based on the abnormal value of the preheating equipment.

5. The intelligent detection method for production quality of aerial work vehicle structural parts according to claim 4 is characterized in that: In step A, the expression of the device data set is: {WDsj, DLsj, DYsj, LLsj, YLsj}, wherein WDsj represents the temperature data of the preheating device, DLsj represents the current data of the preheating device, DYsj represents the voltage data of the preheating device, LLsj represents the water flow data of the preheating device, and YLsj represents the water pressure data of the preheating device.

6. The intelligent detection method for production quality of aerial work vehicle structural parts according to claim 5 is characterized by: In step B, the calculation formula for the abnormal value of the preheating equipment is: In the above calculation formula, SByc represents the abnormal value of the preheating equipment, WDsj s Represents the temperature data of the current preheating device, WDsj m Represents the average value of the temperature data of the preheating equipment, WDsj b Represents the standard deviation of the temperature data of the preheating equipment, DLsj s Represents the current data of the current preheating device, DLsj m Represents the average value of the current data of the preheating equipment, DLsj b Represents the standard deviation of the current data of the preheating equipment, DYsj s Represents the voltage data of the current preheating equipment, DYsj m Represents the average value of the voltage data of the preheating equipment, DYsj b Represents the standard deviation of the voltage data of the preheating equipment, LLsj s Represents the water flow data of the current preheating equipment, LLsj m Represents the average value of the water flow data of the preheating equipment, LLsj b Represents the standard deviation of the water flow data of the preheating equipment, YLsj s Represents the water pressure data of the current preheating equipment, YLsj m Represents the average value of the water pressure data of the preheating equipment, YLsj b represents the standard deviation of the water pressure data of the preheating equipment, α represents the weight of the temperature factor, β represents the weight of the current factor, γ represents the weight of the voltage factor, δ represents the weight of the water flow factor, and ε represents the weight of the water pressure factor.

7. The intelligent detection method for production quality of aerial work vehicle structural parts according to claim 6 is characterized by: When the abnormal value of the preheating device is greater than the abnormal threshold of the preheating device, it means that there is a fault in the preheating device, and a fault signal is sent for maintenance.

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