Crane fault diagnosis method and system based on internet of things

Abstract

The application relates to the technical field of cranes and discloses a crane fault diagnosis method and system based on the Internet of Things, which comprises a hydraulic crane product identification module, a hydraulic crane jib bending fault diagnosis module and a hydraulic crane jib leakage fault diagnosis and fault feedback module; through dynamic detection of the bending overload fault of the jib of the automobile hydraulic crane according to the real-time curvature information of the jib of the target hydraulic crane and the bending overload curvature threshold value of the jib of the target hydraulic crane, the bending overload fault of the jib of the automobile hydraulic crane is intelligently and timely diagnosed; based on the bending overload appearance image information of the jib of the hydraulic crane and in combination with the image recognition algorithm and the hydraulic oil leakage standard image information of the jib of the hydraulic crane, the jib hydraulic oil leakage fault under the bending overload state of the jib of the automobile hydraulic crane is intelligently recognized, and the safety of the hydraulic system of the automobile hydraulic crane is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of cranes, in particular to a crane fault diagnosis method and system based on the Internet of Things. BACKGROUND

[0002] A crane refers to a multi-motion hoisting machine that vertically lifts and horizontally transports heavy objects within a certain range. The main feature of a tire crane is that its driving cab and hoisting control cab are combined into one, which is evolved from a caterpillar crane. The caterpillar and walking support part of the walking mechanism is changed into a chassis with tires, overcoming the disadvantage of caterpillar plate damage to the road surface. It belongs to material handling machinery. A bridge crane is a hoisting device that crosses above a workshop, warehouse or material yard to transport materials. It is shaped like a bridge because its two ends are seated on high cement columns or metal supports. The bridge of the bridge crane runs longitudinally along the tracks laid on the high supports on both sides, which can fully utilize the space below the bridge for material hoisting and is not hindered by ground equipment. The working characteristics of the hoisting device are intermittent motion, i.e., the corresponding mechanisms for taking, moving and unloading materials in a working cycle work alternately. Cranes are increasingly used and developed in the market. Among them, the automobile hydraulic crane is the most widely used. During hoisting operation, the crane arm structure is damaged and the hydraulic oil leaks due to the crane arm bearing overload during hoisting. The existing automobile hydraulic crane cannot intelligently detect the bending overload fault and bending overload hydraulic oil leakage of the crane arm during hoisting operation, reducing the safety and service life of the automobile hydraulic crane during hoisting operation.

[0003] Chinese patent application CN118771197A, published on October 15, 2024, discloses an electrical fault diagnosis system for crane equipment, which includes a data acquisition module, a client CMS system and a storage logic processing module. The data acquisition module is a professional power detection device that transmits real-time power quality data and is bound one-to-one with PLC fault points. The storage logic processing module stores points in a point-to-point manner, and the client CMS system is used for convenient and fast fault point searching. The electrical fault diagnosis system has real-time, high precision, flexibility and visualization functions. However, the above technical solution cannot accurately diagnose the bending overload of the hydraulic crane arm and the bending overload hydraulic oil leakage fault. SUMMARY

[0004] (I) Technical problems solved

[0005] To solve the above problems that the existing automobile hydraulic crane cannot realize intelligent detection of the bending overload fault of the lifting arm and the hydraulic oil leakage of the bending overload of the lifting arm during the lifting operation, the safety and service life of the automobile hydraulic crane are reduced, and the purposes of accurately identifying the product type of the hydraulic crane, dynamically matching the bending overload curvature threshold of the lifting arm of the hydraulic crane, dynamically measuring the real-time curvature of the lifting arm of the hydraulic crane, intelligently diagnosing the bending overload fault of the lifting arm of the hydraulic crane, collecting the appearance image information of the bending overload of the lifting arm of the hydraulic crane in real time, and intelligently diagnosing the hydraulic oil leakage of the lifting arm of the hydraulic crane are achieved.

[0006] (II) Technical solutions

[0007] The crane fault diagnosis method based on the Internet of Things is realized by the following technical solutions: the method comprises the following steps:

[0008] S1, collecting appearance image data of a hydraulic crane;

[0009] S2, performing product type identification processing of the automobile hydraulic crane based on the appearance image data of the hydraulic crane and appearance image data of hydraulic cranes of different product types, and generating target hydraulic crane product type data;

[0010] S3, performing lifting arm bending overload curvature threshold search processing of the target automobile hydraulic crane according to the target hydraulic crane product type data and the lifting arm bending overload curvature threshold of hydraulic cranes of different product types, and generating target hydraulic crane lifting arm bending overload curvature threshold;

[0011] S4, collecting hydraulic crane lifting arm entity model data and performing lifting arm axis direction sampling point coordinate establishment processing required for measuring the curvature radius of the lifting arm of the automobile hydraulic crane, and generating hydraulic crane lifting arm curvature radius measurement coordinate data;

[0012] S5, performing hydraulic crane lifting arm curvature radius measurement processing based on the hydraulic crane lifting arm curvature radius measurement coordinate data, generating target hydraulic crane lifting arm curvature radius data, and performing automobile hydraulic crane lifting arm curvature numerical measurement processing, and generating target hydraulic crane lifting arm real-time curvature data;

[0013] S6, performing automobile hydraulic crane lifting arm bending overload fault detection processing according to the target hydraulic crane lifting arm real-time curvature data and the target hydraulic crane lifting arm bending overload curvature threshold, and generating target hydraulic crane lifting arm bending overload fault detection data; when it is not overloaded, repeatedly performing S4, S5 and S6 until the automobile hydraulic crane lifting arm bending overload fault detection result is overloaded;

[0014] S7, when overloaded, collect the hydraulic crane boom bending overload appearance image data and the hydraulic crane boom hydraulic oil leakage standard image data, and the hydraulic crane boom bending overload state of the automobile hydraulic crane, generate the target hydraulic crane boom hydraulic oil leakage detection data;

[0015] S8, build the automobile hydraulic crane fault diagnosis result data and perform the crane fault diagnosis feedback work.

[0016] Preferably, the operation steps of collecting the hydraulic crane appearance image data are as follows:

[0017] S11, the whole appearance image information of the automobile hydraulic crane performing the lifting operation is collected online by the unmanned aerial vehicle carrying the cloud lens, and the hydraulic crane appearance image data is generated.

[0018] Preferably, the operation steps of collecting the hydraulic crane appearance image data are as follows:

[0019] S21, establish different product type hydraulic crane appearance image data set Among them v c The different product type hydraulic crane appearance image data corresponding to the cth automobile hydraulic crane product type, The maximum value of the number of automobile hydraulic crane types; the automobile hydraulic crane product type represents different models of automobile hydraulic cranes produced by different automobile hydraulic crane brands, and the different product type hydraulic crane appearance image data represents the appearance image information of the standard automobile hydraulic crane product set for different types of automobile hydraulic crane products;

[0020] S22, the ORB image recognition algorithm is used to match the image features of the hydraulic crane appearance image data and the different product type hydraulic crane appearance image data v c In the different product type hydraulic crane appearance image data set V, search out the different product type hydraulic crane appearance image data v c Corresponding automobile hydraulic crane product type information, and build the target hydraulic crane product type data.

[0021] Preferably, according to the target hydraulic crane product type data and the different product type hydraulic crane boom bending overload curvature threshold value, the target automobile hydraulic crane boom bending overload curvature threshold value searching process is performed, and the operation steps of generating the target hydraulic crane boom bending overload curvature threshold value are as follows:

[0022] S31, a set of different product type hydraulic crane boom bending overload curvature threshold values is established Where v' c represents the different product type hydraulic crane boom bending overload curvature threshold value corresponding to the cth automobile hydraulic crane product type, and the different product type hydraulic crane boom bending overload curvature threshold value represents the maximum curvature value of the boom bending of the different type automobile hydraulic crane product when the boom is in the overload critical state during the execution of the lifting operation; wherein the greater the bending curvature of the boom of the automobile hydraulic crane, the greater the degree of bending of the boom of the automobile hydraulic crane;

[0023] S32, the Aho-Corasick search algorithm is used to match the target hydraulic crane product type data with the different product type hydraulic crane boom bending overload curvature threshold value v' c in the set of different product type hydraulic crane boom bending overload curvature threshold values V' c , and the target hydraulic crane boom bending overload curvature threshold value v mubiao is searched out.

[0024] Preferably, the operation steps of collecting the hydraulic crane boom entity model data and measuring the curvature radius of the boom of the automobile hydraulic crane and establishing the sampling point coordinates of the boom axis direction required for measuring the curvature radius of the boom of the automobile hydraulic crane are as follows:

[0025] S41, the three-dimensional entity model information of the entire boom of the automobile hydraulic crane performing the lifting operation is collected online by the unmanned aerial vehicle carrying the laser scanner, and the hydraulic crane boom entity model data is generated;

[0026] S42, the hydraulic crane boom entity model data is imported into AutoCAD and run, and the spatial coordinates of the left and right sampling points of the boom axis of the automobile hydraulic crane and the spatial coordinates of the middle sampling point of the boom axis are collected respectively by using the AutoCAD coordinate measurement tool, and the set of hydraulic crane boom curvature radius measurement coordinate data O = (o zuo , o zhong , o you ) is generated, wherein ozuo , o zhong and o you respectively represent hydraulic crane boom curvature radius measurement left end coordinate data, hydraulic crane boom curvature radius measurement middle coordinate data and hydraulic crane boom curvature radius measurement right end coordinate data.

[0027] Preferably, the operation steps of generating target hydraulic crane boom curvature radius data and performing automobile hydraulic crane boom curvature numerical metrology processing to generate target hydraulic crane boom real-time curvature data based on the hydraulic crane boom curvature radius measurement coordinate data are as follows:

[0028] S51, import the hydraulic crane boom curvature radius measurement coordinate data set O into AutoCAD, and measure the curvature radius of the automobile hydraulic crane boom axis based on the hydraulic crane boom curvature radius measurement left end coordinate data o zuo , the hydraulic crane boom curvature radius measurement middle coordinate data o zhong and the hydraulic crane crane boom curvature radius measurement right end coordinate data o you corresponding coordinate parameters, and generate target hydraulic crane boom curvature radius data L;

[0029] S52, based on the target hydraulic crane boom curvature radius data L and combined with the curvature-curvature radius calculation formula, the curvature parameter of the automobile hydraulic crane boom axis bending is calculated, and the target hydraulic crane boom real-time curvature data J is generated, wherein

[0030] Preferably, according to the target hydraulic crane boom real-time curvature data, the target hydraulic crane boom bending overload curvature threshold, the automobile hydraulic crane boom bending overload fault detection processing is performed to generate the target hydraulic crane boom bending overload fault detection data, and when it is not overloaded, the operation steps of repeating S4, S5 and S6 until the automobile hydraulic crane boom bending overload fault detection result is overloaded are as follows:

[0031] S61, obtain the target hydraulic crane boom bending overload curvature threshold v mubiao and the target hydraulic crane boom real-time curvature data J;

[0032] S62, compare the target hydraulic crane boom real-time curvature data J with the target hydraulic crane boom bending overload curvature threshold v mubiao , and generate target hydraulic crane boom bending overload fault detection data according to the curvature value comparison result;

[0033] When J is not greater than v mubiao , indicating that the automobile hydraulic crane boom bending state does not reach the overload state, the target hydraulic crane boom bending overload fault detection data is output as non-overload, and S4, S5 and S6 are repeatedly executed until the target hydraulic crane boom bending overload fault detection data is overload;

[0034] When J is greater than v mubiao , indicating that the automobile hydraulic crane boom bending state reaches the overload state, the target hydraulic crane boom bending overload fault detection data is output as overload.

[0035] Preferably, when overloaded, the operation steps of collecting the hydraulic crane boom bending overload appearance image data and performing the hydraulic crane boom hydraulic oil leakage fault detection processing with the hydraulic crane boom hydraulic oil leakage standard image data under the boom bending overload state of the automobile hydraulic crane to generate the target hydraulic crane boom hydraulic oil leakage detection data are as follows:

[0036] S71, when the target hydraulic crane boom bending overload fault detection data is overload, the appearance image information of the automobile hydraulic crane boom in the overload state is collected online by the unmanned aerial vehicle carrying the cloud lens to generate the hydraulic crane boom bending overload appearance image data;

[0037] S72, a hydraulic crane boom hydraulic oil leakage standard image data set M=(m a ,…,m γ ) is established, a=1, 2, 3, …, γ; wherein m a represents the a-th hydraulic crane boom hydraulic oil leakage standard image data, and γ represents the maximum value of the number of hydraulic crane boom hydraulic oil leakage standard images; the hydraulic crane boom hydraulic oil leakage standard image data represents the standard image information of the set hydraulic crane boom hydraulic oil leakage state;

[0038] S73, the ORB image recognition algorithm is used to perform image feature matching between the hydraulic crane boom bending overload appearance image data and the hydraulic crane boom hydraulic oil leakage standard image data m a in the hydraulic crane boom hydraulic oil leakage standard image data set M, and the target hydraulic crane boom hydraulic oil leakage detection data is generated according to the image feature matching result;

[0039] When the hydraulic crane boom bending overload appearance image data and the hydraulic crane boom hydraulic oil leakage standard image data m aWhen the image feature matching is successful, it indicates that the current unmanned aerial vehicle carrying a cloud lens to shoot the automobile hydraulic crane boom position has a hydraulic oil leakage fault, and the target hydraulic crane boom hydraulic oil leakage detection data is output as existing leakage.

[0040] When the hydraulic crane boom bending overload appearance image data and the hydraulic crane boom hydraulic oil leakage standard image data m a When the image feature matching is successful, it indicates that the current unmanned aerial vehicle carrying a cloud lens to shoot the automobile hydraulic crane boom position has a hydraulic oil leakage fault, and the target hydraulic crane boom hydraulic oil leakage detection data is output as existing leakage.

[0041] Preferably, the operation steps of constructing automobile hydraulic crane fault diagnosis result data and performing crane fault diagnosis feedback operation are as follows:

[0042] S81, the generated target hydraulic crane boom bending overload fault detection data, the hydraulic crane boom bending overload appearance image data and the target hydraulic crane boom hydraulic oil leakage detection data are combined to construct automobile hydraulic crane fault diagnosis result data;

[0043] S82, the automobile hydraulic crane fault diagnosis result data is transmitted to the crane management center through the Internet of Things communication network to perform crane fault diagnosis feedback operation.

[0044] The crane fault diagnosis system based on the Internet of Things is used to realize the crane fault diagnosis method based on the Internet of Things, and the system comprises a hydraulic crane product identification module, a hydraulic crane boom bending fault diagnosis module, and a hydraulic crane boom leakage fault diagnosis and fault feedback module.

[0045] The hydraulic crane product identification module comprises a hydraulic crane appearance image acquisition unit, a different product type hydraulic crane appearance image storage unit, and a hydraulic crane product type identification unit.

[0046] The hydraulic crane appearance image acquisition unit acquires hydraulic crane appearance image data through an unmanned aerial vehicle carrying a cloud lens; the different product type hydraulic crane appearance image storage unit is used to store different product type hydraulic crane appearance image data; and the hydraulic crane product type identification unit performs product type identification processing of the automobile hydraulic crane based on the hydraulic crane appearance image data and the different product type hydraulic crane appearance image data, and generates target hydraulic crane product type data.

[0047] The hydraulic crane boom bending fault diagnosis module comprises a hydraulic crane boom bending overload curvature threshold storage unit of different product types, a hydraulic crane boom bending overload curvature threshold searching unit, a hydraulic crane boom entity model collecting unit, a hydraulic crane boom curvature radius measurement sampling point establishing unit, a hydraulic crane boom curvature radius measurement unit, a hydraulic crane boom real-time curvature measurement unit, and a hydraulic crane boom bending overload fault detection unit.

[0048] The hydraulic crane boom bending overload curvature threshold storage unit is configured to store hydraulic crane boom bending overload curvature thresholds of different product types. The hydraulic crane boom bending overload curvature threshold searching unit is configured to search for a boom bending overload curvature threshold of a target hydraulic crane according to target hydraulic crane product type data and the hydraulic crane boom bending overload curvature thresholds of different product types, and generate a target hydraulic crane boom bending overload curvature threshold. The hydraulic crane boom entity model collecting unit is configured to collect hydraulic crane boom entity model data by using a drone carrying a laser scanner. The hydraulic crane boom curvature radius measurement sampling point establishing unit is configured to establish sampling point coordinates of a boom axis direction required for measuring the curvature radius of a boom of a hydraulic crane according to the hydraulic crane boom entity model data and by using AutoCAD, and generate hydraulic crane boom curvature radius measurement coordinate data. The hydraulic crane boom curvature radius measurement unit is configured to measure the curvature radius of a boom of a hydraulic crane based on the hydraulic crane boom curvature radius measurement coordinate data and by using AutoCAD, and generate target hydraulic crane boom curvature radius data. The hydraulic crane boom real-time curvature measurement unit is configured to measure the curvature value of a boom of a hydraulic crane based on the target hydraulic crane boom curvature radius data and by using numerical processing, and generate target hydraulic crane boom real-time curvature data. The hydraulic crane boom bending overload fault detection unit is configured to detect a boom bending overload fault of a hydraulic crane according to the target hydraulic crane boom real-time curvature data and the target hydraulic crane boom bending overload curvature threshold, and generate target hydraulic crane boom bending overload fault detection data.

[0049] The hydraulic crane boom leakage fault diagnosis and fault feedback module comprises a hydraulic crane boom bending overload appearance image collecting unit, a hydraulic crane boom hydraulic oil leakage standard image storage unit, a hydraulic crane boom hydraulic oil leakage monitoring unit, and a hydraulic crane boom fault feedback unit.

[0050] The hydraulic crane boom bending overload appearance image acquisition unit collects hydraulic crane boom bending overload appearance image data through a cloud lens carried by a drone; the hydraulic crane boom hydraulic oil leakage standard image storage unit is used for storing hydraulic crane boom hydraulic oil leakage standard image data; the hydraulic crane boom hydraulic oil leakage monitoring unit performs boom hydraulic oil leakage fault detection processing of the boom of the automobile hydraulic crane under the bending overload state of the automobile hydraulic crane based on the hydraulic crane boom bending overload appearance image data and the hydraulic crane boom hydraulic oil leakage standard image data, generates target hydraulic crane boom hydraulic oil leakage detection data; and the hydraulic crane boom fault feedback unit is used for constructing automobile hydraulic crane fault diagnosis result data and performing crane fault diagnosis feedback work in combination with a crane management center.

[0051] (III) Beneficial effects

[0052] The present application provides a crane fault diagnosis method and system based on the Internet of Things. The following beneficial effects are provided:

[0053] I. Dynamic collection of hydraulic crane appearance image information through a cloud lens carried by a drone provides real data support for accurately identifying the product type of the automobile hydraulic crane; based on the hydraulic crane appearance image information, image recognition algorithms and different product type hydraulic crane appearance image data are combined to perform autonomous identification of the product type of the automobile hydraulic crane, realize fine fault diagnosis based on the product type of the automobile hydraulic crane, and improve the accuracy of the fault diagnosis of the automobile hydraulic crane.

[0054] II. Target automobile hydraulic crane boom bending overload curvature threshold precise matching is realized through target hydraulic crane product type information, intelligent search algorithms and different product type hydraulic crane boom bending overload curvature thresholds, the limit curvature parameters under the critical state of the boom bending overload are precisely screened based on the product type of the hydraulic crane; automobile hydraulic crane boom entity model information is dynamically collected through a laser scanner carried by a drone, the curvature radius information of the boom bending axis of the automobile hydraulic crane is precisely measured using AutoCAD, and the real-time curvature information of the automobile hydraulic crane boom is dynamically measured based on numerical calculation, precise monitoring of the bending curvature information of the automobile hydraulic crane boom is realized based on model numerical analysis; automobile hydraulic crane boom bending overload fault dynamic detection is realized according to the target hydraulic crane boom real-time curvature information and the target hydraulic crane boom bending overload curvature threshold, intelligent real-time diagnosis of the bending overload fault of the automobile hydraulic crane boom is realized, and the safety and stability of the operation of the automobile hydraulic crane are improved.

[0055] III. Through the unmanned aerial vehicle carrying the cloud lens, the appearance image information of the bending overload state of the automobile hydraulic crane jib is dynamically collected, to provide real data support for accurately monitoring the hydraulic oil leakage fault of the automobile hydraulic crane jib in the bending overload state; based on the appearance image information of the bending overload of the hydraulic crane jib and combined with the image recognition algorithm and the standard image information of the hydraulic oil leakage of the hydraulic crane jib, the intelligent identification of the hydraulic oil leakage fault of the jib of the automobile hydraulic crane in the bending overload state is carried out, and the safety of the hydraulic system of the automobile hydraulic crane is improved; based on the bending overload fault detection information of the automobile hydraulic crane jib, the appearance image information of the bending overload of the automobile hydraulic crane jib and the hydraulic oil leakage detection information of the automobile hydraulic crane jib, the automobile hydraulic crane fault diagnosis result information is accurately constructed by combining numerical analysis, and at the same time, combined with the autonomous and efficient execution of the crane fault diagnosis feedback work of the crane management center, the bending overload fault of the jib of the automobile hydraulic crane and the visual dynamic feedback of the hydraulic oil leakage fault in the bending overload state are realized, and the response speed of the automobile hydraulic crane fault diagnosis is improved. BRIEF DESCRIPTION OF DRAWINGS

[0056] Fig. 1 The module schematic diagram of the crane fault diagnosis system based on the Internet of Things provided by the present application is shown in the figure.

[0057] Fig. 2 The flow chart of the crane fault diagnosis method based on the Internet of Things provided by the present application is shown in the figure. DETAILED DESCRIPTION

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

[0059] The implementation of the crane fault diagnosis method and system based on the Internet of Things is as follows:

[0060] Embodiment 1:

[0061] Please refer to Figs. 1-2 The crane fault diagnosis method based on the Internet of Things, the method comprises the following steps:

[0062] S1, collecting hydraulic crane appearance image data;

[0063] S2, based on the hydraulic crane appearance image data and the appearance image data of different product types of hydraulic crane, the product type identification processing of the automobile hydraulic crane is carried out, and the target hydraulic crane product type data is generated;

[0064] S3, search the target automobile hydraulic crane boom bending overload curvature threshold value according to the target hydraulic crane product type data and the different product type hydraulic crane boom bending overload curvature threshold value, and generate the target hydraulic crane boom bending overload curvature threshold value;

[0065] S4, collect the hydraulic crane boom entity model data and perform the boom axis direction sampling point coordinate establishment processing required for the automobile hydraulic crane boom curvature radius measurement, and generate the hydraulic crane boom curvature radius measurement coordinate data;

[0066] S5, perform the hydraulic crane boom curvature radius measurement processing based on the hydraulic crane boom curvature radius measurement coordinate data, generate the target hydraulic crane boom curvature radius data, and perform the automobile hydraulic crane boom curvature numerical measurement processing, and generate the target hydraulic crane boom real-time curvature data;

[0067] S6, perform the automobile hydraulic crane boom bending overload fault detection processing according to the target hydraulic crane boom real-time curvature data and the target hydraulic crane boom bending overload curvature threshold value, and generate the target hydraulic crane boom bending overload fault detection data, when it is not overloaded, repeat S4, S5, S6 until the automobile hydraulic crane boom bending overload fault detection result is overloaded;

[0068] S7, when it is overloaded, collect the hydraulic crane boom bending overload appearance image data and perform the automobile hydraulic crane boom bending overload state boom hydraulic oil leakage fault detection processing with the hydraulic crane boom hydraulic oil leakage standard image data, and generate the target hydraulic crane boom hydraulic oil leakage detection data;

[0069] S8, build the automobile hydraulic crane fault diagnosis result data and perform the crane fault diagnosis feedback work.

[0070] Further, please refer to Figs. 1-2 The operation steps of collecting the hydraulic crane appearance image data are as follows:

[0071] S11, collect the overall appearance image information of the automobile hydraulic crane performing the lifting work by the unmanned aerial vehicle carrying the cloud lens online, and generate the hydraulic crane appearance image data.

[0072] The operation steps of collecting the hydraulic crane appearance image data are as follows:

[0073] S21, establish different product type hydraulic crane appearance image data set wherein v c represents the different product type hydraulic crane appearance image data corresponding to the cth automobile hydraulic crane product type, represents the maximum value of the automobile hydraulic crane type quantity; the automobile hydraulic crane product type represents different models of automobile hydraulic cranes produced by different automobile hydraulic crane brand manufacturers, and the different product type hydraulic crane appearance image data represents the appearance image information of the standard automobile hydraulic crane product set for different types of automobile hydraulic crane products;

[0074] S22, using the ORB image recognition algorithm to match the hydraulic crane appearance image data with the different product type hydraulic crane appearance image data v c in the different product type hydraulic crane appearance image data set V, searching out the different product type hydraulic crane appearance image data v c corresponding to the automobile hydraulic crane product type information, and constructing the target hydraulic crane product type data.

[0075] Through the hydraulic crane appearance image acquisition unit, the dynamic acquisition of the hydraulic crane appearance image information is carried out by using the unmanned aerial vehicle carrying the cloud lens, which provides real data support for accurately identifying the product type of the automobile hydraulic crane; the hydraulic crane product type recognition unit carries out autonomous identification of the product type of the automobile hydraulic crane based on the hydraulic crane appearance image information combined with the image recognition algorithm and the different product type hydraulic crane appearance image data, realizes fine fault diagnosis based on the product type of the automobile hydraulic crane, and improves the accuracy of the fault diagnosis of the automobile hydraulic crane.

[0076] Further, please refer to Figs. 1-2 , according to the target hydraulic crane product type data and the different product type hydraulic crane boom bending overload curvature threshold value, the target automobile hydraulic crane boom bending overload curvature threshold value search processing is carried out, and the operation steps of generating the target hydraulic crane boom bending overload curvature threshold value are as follows:

[0077] S31, establishing a different product type hydraulic crane boom bending overload curvature threshold value set wherein v′ c represents the different product type hydraulic crane boom bending overload curvature threshold value corresponding to the cth automobile hydraulic crane product type, and the different product type hydraulic crane boom bending overload curvature threshold value represents the maximum value of the curvature of the boom bending of the different type automobile hydraulic crane product in the overload critical state of the boom during the execution of the hoisting operation; wherein the greater the bending curvature of the automobile hydraulic crane boom, the greater the bending degree of the automobile hydraulic crane boom;

[0078] S32. Using the Aho-Corasick search algorithm, the target hydraulic crane product type data is compared with the set of bending overload curvature thresholds for different product types of hydraulic crane booms, V′, to obtain the bending overload curvature thresholds for different product types of hydraulic crane booms. c Perform character matching for automotive hydraulic crane product types to search for the hydraulic crane boom bending overload curvature threshold v′ corresponding to different product types of the target hydraulic crane product type data. c And construct the target hydraulic crane boom bending overload curvature threshold v mubiao .

[0079] The steps for collecting solid model data of a hydraulic crane boom, establishing and processing the coordinates of sampling points along the boom axis required for measuring the boom curvature radius of the truck-mounted hydraulic crane, and generating coordinate data for measuring the boom curvature radius of the hydraulic crane are as follows:

[0080] S41. Use a drone equipped with a laser scanner to collect online the three-dimensional solid model information of the entire boom of the truck hydraulic crane performing lifting operations, and generate the solid model data of the hydraulic crane boom.

[0081] S42. Import the solid model data of the hydraulic crane boom into AutoCAD and run it. Using the AutoCAD coordinate measuring tool, collect the spatial coordinates of the sampling points at the left and right ends of the boom axis and the spatial coordinates of the sampling point in the middle of the boom axis. Generate a set of coordinate data for measuring the curvature radius of the hydraulic crane boom, O = (o zuo ,o zhong ,o you ), where o zuo o zhong and o you These represent the coordinate data of the left end of the hydraulic crane boom curvature radius measurement, the middle coordinate data of the hydraulic crane boom curvature radius measurement, and the right end coordinate data of the hydraulic crane boom curvature radius measurement, respectively.

[0082] The following are the steps for generating real-time curvature data of a target hydraulic crane boom based on the coordinate data of the boom curvature measurement coordinates. This process involves measuring and processing the boom curvature radius of the hydraulic crane boom, generating target hydraulic crane boom curvature radius data, and then performing numerical measurement of the boom curvature of the automotive hydraulic crane.

[0083] S51. Import the coordinate data set O of the hydraulic crane boom curvature radius measurement into AutoCAD, and use the AutoCAD radius measurement tool to measure the left end coordinate data o based on the hydraulic crane boom curvature radius measurement. zuo Intermediate coordinate data for measuring the radius of curvature of the hydraulic crane boom.zhong and hydraulic crane boom curvature radius measurement right end coordinate data o you The corresponding coordinate parameters measure the curvature radius parameters of the automobile hydraulic crane boom axis, and generate target hydraulic crane boom curvature radius data L;

[0084] S52, based on the target hydraulic crane boom curvature radius data L and combined with the curvature parameter of the automobile hydraulic crane boom axis bending through the curvature-curvature radius calculation formula, and generate the target hydraulic crane boom real-time curvature data J, wherein

[0085] According to the target hydraulic crane boom real-time curvature data, the target hydraulic crane boom bending overload curvature threshold, the automobile hydraulic crane boom bending overload fault detection processing is carried out, and the target hydraulic crane boom bending overload fault detection data is generated. When it is not overloaded, repeat the execution of S4, S5, S6 until the automobile hydraulic crane boom bending overload fault detection result is overloaded. The operation steps are as follows:

[0086] S61, get the target hydraulic crane boom bending overload curvature threshold v mubiao and the target hydraulic crane boom real-time curvature data J;

[0087] S62, compare the target hydraulic crane boom real-time curvature data J with the target hydraulic crane boom bending overload curvature threshold v mubiao Compare the curvature value, and generate the target hydraulic crane boom bending overload fault detection data according to the curvature value comparison result;

[0088] When J is not greater than v mubiao , indicating that the automobile hydraulic crane boom bending state has not reached the overload state, then output the target hydraulic crane boom bending overload fault detection data as not overloaded, and repeat the execution of S4, S5, S6 until the target hydraulic crane boom bending overload fault detection data is overloaded.

[0089] When J is greater than v mubiao , indicating that the automobile hydraulic crane boom bending state has reached the overload state, then output the target hydraulic crane boom bending overload fault detection data as overloaded.

[0090] The target automobile hydraulic crane boom bending overload curvature threshold is accurately matched by the hydraulic crane boom bending overload curvature threshold searching unit according to the target hydraulic crane product type information, combined with the intelligent search algorithm and the hydraulic crane boom bending overload curvature threshold of different product types, the limit curvature parameter in the critical state of the boom bending overload of the hydraulic crane is accurately screened based on the product type of the hydraulic crane; the hydraulic crane boom entity model collection unit, the hydraulic crane boom curvature radius measurement sampling point establishment unit, the hydraulic crane boom curvature radius measurement unit cooperate with each other, and the real-time curvature measurement unit of the hydraulic crane boom cooperates with each other, the entity model information of the automobile hydraulic crane boom is dynamically collected by the unmanned aerial vehicle carrying the laser scanner, the curvature radius information of the bending axis of the automobile hydraulic crane boom is accurately measured by using AutoCAD, and the real-time curvature information of the automobile hydraulic crane boom is dynamically measured by combining numerical calculation, the bending curvature information of the automobile hydraulic crane boom is accurately monitored based on model numerical analysis, and the safety and stability of the operation of the automobile hydraulic crane are improved.

[0091] Further, please refer to Figs. 1-2 When overloaded, collect the hydraulic crane boom bending overload appearance image data and the hydraulic crane boom hydraulic oil leakage standard image data, and perform the boom hydraulic oil leakage fault detection processing of the automobile hydraulic crane in the boom bending overload state of the automobile hydraulic crane, and the operation steps of generating the target hydraulic crane boom hydraulic oil leakage detection data are as follows:

[0092] S71, when the target hydraulic crane boom bending overload fault detection data is overloaded, the appearance image information of the automobile hydraulic crane boom in the overload state is collected online by the unmanned aerial vehicle carrying the cloud lens, and the hydraulic crane boom bending overload appearance image data is generated;

[0093] S72, the hydraulic crane boom hydraulic oil leakage standard image data set M=(m1,…,m a ,…,m γ ), a=1,2,3,…,γ; wherein m a represents the a-th hydraulic crane boom hydraulic oil leakage standard image data, and γ represents the maximum value of the number of hydraulic crane boom hydraulic oil leakage standard images; the hydraulic crane boom hydraulic oil leakage standard image data represents the standard image information of the set hydraulic crane boom hydraulic oil leakage state;

[0094] S73, adopt ORB image recognition algorithm to match the hydraulic crane boom bending overload appearance image data with the hydraulic crane boom hydraulic oil leakage standard image data m in the hydraulic crane boom hydraulic oil leakage standard image data set M a Perform image feature matching, and generate target hydraulic crane boom hydraulic oil leakage detection data according to the image feature matching result;

[0095] When the hydraulic crane boom bending overload appearance image data matches the hydraulic crane boom hydraulic oil leakage standard image data m a When the image feature matching is successful, it indicates that there is a hydraulic oil leakage fault in the position of the automobile hydraulic crane boom photographed by the unmanned aerial vehicle carrying the cloud lens, and the target hydraulic crane boom hydraulic oil leakage detection data is output as existing leakage.

[0096] When the hydraulic crane boom bending overload appearance image data matches the hydraulic crane boom hydraulic oil leakage standard image data m a When the image feature matching is not successful, it indicates that there is a hydraulic oil leakage fault in the position of the automobile hydraulic crane boom photographed by the unmanned aerial vehicle carrying the cloud lens, and the target hydraulic crane boom hydraulic oil leakage detection data is output as non-leakage.

[0097] The operation steps of constructing automobile hydraulic crane fault diagnosis result data and performing crane fault diagnosis feedback work are as follows:

[0098] S81, combine the generated target hydraulic crane boom bending overload fault detection data, hydraulic crane boom bending overload appearance image data and target hydraulic crane boom hydraulic oil leakage detection data to construct automobile hydraulic crane fault diagnosis result data;

[0099] S82, transmit the automobile hydraulic crane fault diagnosis result data to the crane management center through the Internet of Things communication network to perform crane fault diagnosis feedback work.

[0100] The hydraulic crane boom bending overload appearance image acquisition unit adopts a dynamic collection of the appearance image information of the bending overload state of the automobile hydraulic crane boom by a UAV carrying a cloud lens, to provide real data support for the precise monitoring of the hydraulic oil leakage fault of the automobile hydraulic crane boom under the bending overload state of the crane boom; the hydraulic crane boom hydraulic oil leakage monitoring unit performs intelligent identification of the hydraulic oil leakage fault of the crane boom under the bending overload state of the crane boom of the automobile hydraulic crane based on the bending overload appearance image information of the crane boom of the hydraulic crane and in combination with the image recognition algorithm and the hydraulic oil leakage standard image information of the crane boom of the hydraulic crane, to improve the safety of the hydraulic system of the automobile hydraulic crane; the hydraulic crane boom fault feedback unit accurately constructs the fault diagnosis result information of the automobile hydraulic crane based on the bending overload fault detection information of the crane boom of the automobile hydraulic crane, the bending overload appearance image information of the crane boom of the automobile hydraulic crane, and the hydraulic oil leakage detection information of the crane boom of the automobile hydraulic crane in combination with numerical analysis, and simultaneously in combination with the autonomous and efficient execution of the crane fault diagnosis feedback operation by the crane management center, to realize the visual dynamic feedback of the bending overload fault of the crane boom of the automobile hydraulic crane and the hydraulic oil leakage fault under the bending overload state of the crane boom, and to improve the response speed of the fault diagnosis of the automobile hydraulic crane.

[0101] Embodiment 2:

[0102] Please refer to Figs. 1-2 , the crane fault diagnosis system based on the Internet of Things, for realizing the crane fault diagnosis method based on the Internet of Things, the system comprises a hydraulic crane product identification module, a hydraulic crane boom bending fault diagnosis module, and a hydraulic crane boom leakage fault diagnosis and fault feedback module.

[0103] The hydraulic crane product identification module comprises a hydraulic crane appearance image acquisition unit, a different product type hydraulic crane appearance image storage unit, and a hydraulic crane product type identification unit.

[0104] The hydraulic crane appearance image acquisition unit collects hydraulic crane appearance image data by a UAV carrying a cloud lens; the different product type hydraulic crane appearance image storage unit is used for storing different product type hydraulic crane appearance image data; and the hydraulic crane product type identification unit performs product type identification processing of the automobile hydraulic crane based on the hydraulic crane appearance image data and the different product type hydraulic crane appearance image data, to generate target hydraulic crane product type data.

[0105] The hydraulic crane boom bending fault diagnosis module comprises a different product type hydraulic crane boom bending overload curvature threshold storage unit, a hydraulic crane boom bending overload curvature threshold search unit, a hydraulic crane boom entity model acquisition unit, a hydraulic crane boom curvature radius measurement sampling point establishment unit, a hydraulic crane boom curvature radius measurement unit, a hydraulic crane boom real-time curvature measurement unit, and a hydraulic crane boom bending overload fault detection unit.

[0106] The different product type hydraulic crane boom bending overload curvature threshold storage unit is configured to store different product type hydraulic crane boom bending overload curvature thresholds. The hydraulic crane boom bending overload curvature threshold search unit is configured to search for a target automobile hydraulic crane boom bending overload curvature threshold based on target hydraulic crane product type data and the different product type hydraulic crane boom bending overload curvature thresholds, and generate a target hydraulic crane boom bending overload curvature threshold. The hydraulic crane boom entity model acquisition unit is configured to acquire hydraulic crane boom entity model data by using a drone carrying a laser scanner. The hydraulic crane boom curvature radius measurement sampling point establishment unit is configured to establish sampling point coordinates in the direction of the boom axis required for measuring the curvature radius of the automobile hydraulic crane boom based on the hydraulic crane boom entity model data and in combination with AutoCAD, and generate hydraulic crane boom curvature radius measurement coordinate data. The hydraulic crane boom curvature radius measurement unit is configured to measure the curvature radius of the hydraulic crane boom based on the hydraulic crane boom curvature radius measurement coordinate data and in combination with AutoCAD, and generate target hydraulic crane boom curvature radius data. The hydraulic crane boom real-time curvature measurement unit is configured to perform numerical value measurement of the curvature of the automobile hydraulic crane boom based on the target hydraulic crane boom curvature radius data and in combination with numerical processing, and generate target hydraulic crane boom real-time curvature data. The hydraulic crane boom bending overload fault detection unit is configured to perform automobile hydraulic crane boom bending overload fault detection processing based on the target hydraulic crane boom real-time curvature data and the target hydraulic crane boom bending overload curvature threshold, and generate target hydraulic crane boom bending overload fault detection data.

[0107] The hydraulic crane boom leakage fault diagnosis and fault feedback module comprises a hydraulic crane boom bending overload appearance image acquisition unit, a hydraulic crane boom hydraulic oil leakage standard image storage unit, a hydraulic crane boom hydraulic oil leakage monitoring unit, and a hydraulic crane boom fault feedback unit.

[0108] The hydraulic crane boom bending overload appearance image acquisition unit collects hydraulic crane boom bending overload appearance image data through a cloud lens carried by a drone; the hydraulic crane boom hydraulic oil leakage standard image storage unit is used for storing hydraulic crane boom hydraulic oil leakage standard image data; the hydraulic crane boom hydraulic oil leakage monitoring unit performs boom hydraulic oil leakage fault detection processing under the boom bending overload state of the automobile hydraulic crane based on the hydraulic crane boom bending overload appearance image data and the hydraulic crane boom hydraulic oil leakage standard image data, generates target hydraulic crane boom hydraulic oil leakage detection data; and the hydraulic crane boom fault feedback unit is used for constructing automobile hydraulic crane fault diagnosis result data and performing crane fault diagnosis feedback work in combination with the crane management center.

[0109] Although embodiments of the present application have been shown and described, it is to be understood that various modifications, substitutions, replacements and changes can be made to these embodiments without departing from the principles and spirit of the present application, and the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. A crane fault diagnosis method based on Internet of Things, characterized in that, The method comprises the following steps: S1, collecting hydraulic crane appearance image data; S2, performing product type identification processing of the automobile hydraulic crane to generate target hydraulic crane product type data; S3, performing crane arm bending overload curvature threshold search processing of the target automobile hydraulic crane to generate target hydraulic crane crane arm bending overload curvature threshold; S4, collecting hydraulic crane crane arm entity model data and performing crane arm axis direction sampling point coordinate establishment processing required for automobile hydraulic crane crane arm curvature radius measurement, and generating hydraulic crane crane arm curvature radius measurement coordinate data; S5, performing hydraulic crane crane arm curvature radius measurement processing to generate target hydraulic crane crane arm curvature radius data and performing automobile hydraulic crane crane arm curvature numerical measurement processing to generate target hydraulic crane crane arm real-time curvature data; S6, performing automobile hydraulic crane crane arm bending overload fault detection processing to generate target hydraulic crane crane arm bending overload fault detection data, when not overloaded, repeating S4, S5, S6 until the automobile hydraulic crane crane arm bending overload fault detection result is overloaded; S7, when overloaded, collecting hydraulic crane crane arm bending overload appearance image data and performing crane arm hydraulic oil leakage fault detection processing of the automobile hydraulic crane under the crane arm bending overload state with the hydraulic crane crane arm hydraulic oil leakage standard image data to generate target hydraulic crane crane arm hydraulic oil leakage detection data; S8, constructing automobile hydraulic crane fault diagnosis result data and performing crane fault diagnosis feedback work.

2. The Internet of Things based crane fault diagnostic method according to claim 1, characterized in that: The S1 comprises the following steps: S11, collecting overall appearance image information of the automobile hydraulic crane performing the lifting work online by the unmanned aerial vehicle carrying the cloud lens, and generating hydraulic crane appearance image data.

3. The Internet of Things based crane fault diagnostic method according to claim 2, characterized in that: The S2 comprises the following steps: S21, establishing a different product type hydraulic crane appearance image data set V, the V includes v c , wherein v c represents the different product type hydraulic crane appearance image data corresponding to the cth automobile hydraulic crane product type. S22, using ORB image recognition algorithm to match the hydraulic crane appearance image data with the v c Perform image feature matching to search out the v c Corresponding automobile hydraulic crane product type information, and build target hydraulic crane product type data.

4. The Internet of Things based crane fault diagnostic method according to claim 3, characterized in that: The S3 comprises the following steps: S31, establishing a set of different product type hydraulic crane boom bending overload curvature threshold values V', the V' including v' c wherein v' c represents the different product type hydraulic crane boom bending overload curvature threshold value corresponding to the cth automobile hydraulic crane product type. S32, using Aho-Corasick search algorithm to match the target hydraulic crane product type data with the v' in the V' c The target hydraulic crane product type data is searched out by matching the product type character of the automobile hydraulic crane, and the v' corresponding to the target hydraulic crane product type data is searched out c The target hydraulic crane product type data is searched out by matching the product type character of the automobile hydraulic crane, and the v' corresponding to the target hydraulic crane product type data is searched out mubiao The target hydraulic crane product type data is searched out by matching the product type character of the automobile hydraulic crane, and the v' corresponding to the target hydraulic crane product type data is searched out 5. The Internet of Things based crane fault diagnostic method according to claim 4, characterized in that: The S4 comprises the following steps: S41, collecting three-dimensional entity model information of the entire crane arm of the automobile hydraulic crane performing the lifting work online by the unmanned aerial vehicle carrying the laser scanner, and generating hydraulic crane crane arm entity model data; S42, the hydraulic crane boom entity model data is imported into AutoCAD for running, and the spatial coordinates of the left and right end sampling points of the boom axis and the spatial coordinates of the middle sampling point of the boom axis of the truck hydraulic crane are collected respectively by using the AutoCAD coordinate measuring tool, and the hydraulic crane boom curvature radius measurement coordinate data set O=(o zuo zhong you ), wherein o zuo , o zhong and o you respectively represent the left end coordinate data of the hydraulic crane boom curvature radius measurement, the middle coordinate data of the hydraulic crane boom curvature radius measurement and the right end coordinate data of the hydraulic crane boom curvature radius measurement.​​ 6. The Internet of Things based crane fault diagnostic method according to claim 5, characterized in that: The S5 comprises the following steps: S51, import the O into AutoCAD, and measure the radius based on the o zuo , the o zhong , and the o you Corresponding coordinate parameters are measured to obtain the curvature radius parameter of the boom axis of the hydraulic crane, and target hydraulic crane boom curvature radius data L is generated. S52, based on the L and in combination with the curvature parameter of the axis bending of the lifting boom of the truck hydraulic crane through the curvature-curvature radius calculation formula, a target hydraulic crane lifting boom real-time curvature data J is generated, wherein 7. The Internet of Things based crane fault diagnostic method according to claim 6, characterized in that: The S6 comprises the following steps: S61, obtaining the v mubiao and the J; S62, compare the J with the v mubiao The curvature value is compared, and target hydraulic crane boom bending overload fault detection data is generated according to the comparison result. when j is not greater than v mubiao If the target hydraulic crane boom bending overload fault detection data is not overloaded, repeat S4, S5 and S6 until the target hydraulic crane boom bending overload fault detection data is overloaded. When J is greater than v mubiao If the target hydraulic crane boom bending overload fault detection data is output as overload.

8. The Internet of Things based crane fault diagnostic method according to claim 7, characterized in that: The S7 comprises the following steps: S71, when the target hydraulic crane crane arm bending overload fault detection data is overloaded, collecting appearance image information of the automobile hydraulic crane crane arm in the overloaded state online by the unmanned aerial vehicle carrying the cloud lens, and generating hydraulic crane crane arm bending overload appearance image data; S72, a set of hydraulic crane boom hydraulic oil leakage standard image data M is established, and the M includes m a ; wherein m a represents the a-th hydraulic crane boom hydraulic oil leakage standard image data; S73, using the ORB image recognition algorithm to match the hydraulic crane boom bending overload appearance image data with the m in the M a carrying out image feature matching, and generating target hydraulic crane boom hydraulic oil leakage detection data according to the image feature matching result; When the hydraulic crane boom bending overload appearance image data and the m a When the image feature matching is successful, the target hydraulic crane boom hydraulic oil leakage detection data is output as existing leakage. When the hydraulic crane boom bending overload appearance image data and the m a When the image feature is not matched successfully, the target hydraulic crane boom hydraulic oil leakage detection data is output as no leakage.

9. The Internet of Things based crane fault diagnostic method according to claim 8, characterized in that: The S8 comprises the following steps: S81, data combination is performed on the generated target hydraulic crane crane arm bending overload fault detection data, the hydraulic crane crane arm bending overload appearance image data and the target hydraulic crane crane arm hydraulic oil leakage detection data to construct automobile hydraulic crane fault diagnosis result data; S82, the automobile hydraulic crane fault diagnosis result data is transmitted to the crane management center through the Internet of Things communication network to perform crane fault diagnosis feedback work.

10. A crane fault diagnosis system based on Internet of Things, for implementing the crane fault diagnosis method based on Internet of Things in any one of claims 1-9, characterized in that: The system comprises a hydraulic crane product identification module, a hydraulic crane boom bending fault diagnosis module and a hydraulic crane boom leakage fault diagnosis and fault feedback module.

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