Method and device for determining residual life of shear fork arm and high-altitude operation machine

Abstract

The embodiment of the invention provides a method and device for determining the remaining life of a shear fork arm and a high-altitude operation machine. The method is applied to the high-altitude operation machine, the high-altitude operation machine at least comprises a chassis and a shear fork arm, and the method comprises the steps that in the operation process of the high-altitude operation machine, operation data of the shear fork arm are obtained in real time, and the operation data at least comprise the operation angle and torque of the shear fork arm; real-time damage values corresponding to the operation data are determined on the basis of a fatigue damage library, the fatigue damage library comprises a plurality of corresponding relations between the operation angles and moments and the damage values of the shear fork arms, and the corresponding relations are obtained after fatigue test training is conducted according to a plurality of historical operation angles and historical moments meeting preset conditions; determining a historical accumulated damage value of the shear fork arm based on the model information of the high-altitude operation machine; and determining the residual life of the shear fork arm based on the real-time damage value and the historical accumulated damage value.

Description

Technical Field

[0001] The present application relates to the technical field of engineering machinery, and in particular to a method, a device, an aerial work machine and a storage medium for determining the remaining life of a scissor arm. Background Art

[0002] Aerial work machinery is an important branch of the field of engineering machinery. It is a general term for mechanical equipment used to transport workers and work equipment to a specified height for work. Scissor-type aerial work platforms are one of the most common forms of aerial work machinery. Its main components include chassis, scissor arms and working platforms. The working principle is to drive the hydraulic cylinder to drive the scissor arms to extend and fold, thereby realizing the lifting and lowering of the working platform. At the same time, as a complex mechanical equipment with interrelated and simultaneous structural, electrical and hydraulic systems, the fatigue life of any structure or the state of the system are important factors affecting the safety of the entire machine.

[0003] In the prior art, the finite element method is usually used to calculate the multi-channel and multi-posture unit force damage library and to arrange pin sensors at each hinge point of the equipment to obtain the hinge force in real time to calculate the damage and life in real time. However, this method is only suitable for monitoring the fatigue life of a certain local structure of the equipment, and is not suitable for predicting the life of complex aerial work machinery such as scissor-type aerial work platforms. It is necessary to arrange a large number of pin sensors and a large number of computing personnel and resources for each structure of the scissor-type aerial work platform, which will greatly increase the difficulty and cost of product development. Therefore, how to accurately predict the remaining life of the scissor arm in real time is a technical problem that needs to be solved urgently. Summary of the invention

[0004] The purpose of the embodiments of the present application is to provide a method, device, aerial work machinery and storage medium for determining the remaining life of a scissor arm, so as to solve the technical defect in the prior art that the fatigue life of a scissor arm cannot be accurately predicted in real time.

[0005] In order to achieve the above-mentioned object, the first aspect of the present application provides a method for determining the remaining life of a scissor arm, which is applied to an aerial work machine, wherein the aerial work machine at least includes a chassis and a scissor arm, and the method includes:

[0006] During the operation of the aerial work machine, the operation data of the scissor arm is obtained in real time, wherein the operation data at least includes the operation angle and torque of the scissor arm;

[0007] Determine the real-time damage value corresponding to the operation data based on the fatigue damage library, wherein the fatigue damage library includes the correspondence between multiple operation angles and moments and the damage value of the scissor arm, and the correspondence is obtained after fatigue test training based on multiple historical operation angles and historical moments that meet the preset conditions;

[0008] Determine the historical cumulative damage value of the scissor arm based on the model information of the aerial work machinery;

[0009] The remaining life of the scissor arm is determined based on the real-time damage value and the historical accumulated damage value.

[0010] In an embodiment of the present application, the method also includes a step of constructing a fatigue damage library, the construction step including: obtaining multiple historical operating data of the scissors arm of the aerial work machinery within a historical time period, the multiple historical operating data including multiple historical operating angles and multiple historical torques of the scissors arm; for multiple historical operating angles, determining the cumulative number of occurrences of each historical operating angle within the historical time period; eliminating historical operating angles whose cumulative number of occurrences is lower than a preset number threshold, and determining the remaining historical operating angles as target operating angles that meet preset conditions; analyzing multiple historical torques, and screening out target torques that meet the preset conditions; generating fatigue test conditions based on the target operating angles and target torques; for any fatigue test condition, repeatedly performing fatigue test training on the fatigue test condition until fatigue cracks exist in the scissors arm under the fatigue test condition, and determining the number of training times for the fatigue test condition as the fatigue life of the scissors arm under the fatigue test condition; determining the damage value corresponding to each fatigue test condition based on the fatigue life of the scissors arm under each fatigue test condition, and constructing a fatigue damage library based on the correspondence between each fatigue test condition and the damage value.

[0011] In an embodiment of the present application, analyzing multiple historical moments and screening out target moments that meet preset conditions includes: performing normal distribution analysis on multiple historical moments to obtain a normal distribution graph for multiple historical moments; based on the normal distribution graph, eliminating historical moments outside a preset interval, and determining the remaining historical moments as target moments that meet the preset conditions.

[0012] In an embodiment of the present application, generating fatigue test conditions based on target operating angles and target torques includes: for each target operating angle, combining the target operating angle with all other target operating angles except the target operating angle in turn to obtain multiple angle groups; sorting all target torques in ascending order to obtain torque intervals for all target torques; dividing the torque interval into multiple continuous sub-torque intervals based on a preset division ratio; combining each sub-torque interval with each angle group in turn to generate corresponding multiple fatigue test conditions.

[0013] In an embodiment of the present application, the method also includes: acquiring electrical data and hydraulic data of the aerial work machinery in real time during the operation of the aerial work machinery; preprocessing the electrical data and hydraulic data; inputting the preprocessed data into a deep learning model to output the current operating status of the aerial work machinery through the deep learning model, and the operating status includes any one of normal and faulty, wherein the deep learning model is trained based on historical electrical data and historical hydraulic data.

[0014] In an embodiment of the present application, the method further includes: generating a corresponding alarm signal when the remaining life of the scissor arm is lower than a preset threshold or a failure occurs in the aerial work machinery.

[0015] In an embodiment of the present application, the electrical data includes at least the rotor position data, speed data and temperature data of the motor, and the hydraulic data includes at least the pressure data and flow data of the hydraulic pump. The method also includes: when any one of the electrical data and the hydraulic data is in an abnormal state, determining that the aerial work machinery has a fault.

[0016] A second aspect of the present application provides a device for determining the remaining life of a scissor arm, comprising:

[0017] a memory configured to store instructions; and

[0018] The processor is configured to call the instructions from the memory and implement the above-mentioned method for determining the remaining life of the scissor arm when executing the instructions.

[0019] The third aspect of the present application provides an aerial work machine, comprising:

[0020] The above-mentioned device for determining the remaining life of a scissor arm;

[0021] Chassis;

[0022] Scissor arm, one end of the scissor arm is fixed to the chassis.

[0023] A fourth aspect of the present application provides a machine-readable storage medium having instructions stored thereon, which, when executed by a processor, configures the processor to execute the above-mentioned method for determining the remaining life of a scissor arm.

[0024] The above technical scheme obtains the operating angle and torque of the scissor arm in real time during the operation of the aerial work machinery, and determines the real-time damage value corresponding to the operating data based on the fatigue damage library, wherein the fatigue damage library includes the correspondence between multiple operating angles and torques and the damage values ​​of the scissor arm, and then further determines the historical cumulative damage value of the scissor arm based on the model information of the aerial work machinery, thereby determining the remaining life of the scissor arm based on the real-time damage value and the historical cumulative damage value, so as to realize real-time and accurate prediction of the fatigue life of the scissor arm, while effectively improving the safety of the aerial work machinery.

[0025] Other features and advantages of the embodiments of the present application will be described in detail in the subsequent specific implementation section. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The accompanying drawings are used to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the following specific implementations, they are used to explain the embodiments of the present application, but do not constitute a limitation on the embodiments of the present application. In the accompanying drawings:

[0027] Figure 1 A schematic diagram of an application environment of a method for determining the remaining life of a scissor arm according to an embodiment of the present application is schematically shown;

[0028] Figure 2 A schematic diagram of a process for determining the remaining life of a scissor arm according to an embodiment of the present application is schematically shown;

[0029] Figure 3 A schematic diagram of a normal distribution diagram according to an embodiment of the present application is schematically shown;

[0030] Figure 4 A schematic diagram of a torque interval according to an embodiment of the present application is schematically shown;

[0031] Figure 5 The internal structure diagram of a computer device according to an embodiment of the present application is schematically shown.

[0032] Description of Reference Numerals

[0033] 101 chassis 102 first scissor arm

[0034] 103 Second scissor arm 104 Angle sensor

[0035] 105 torque sensor 106 motor

[0036] 107 Hydraulic pump 108 Cylinder

[0037] 109 Work Platform DETAILED DESCRIPTION

[0038] In order to make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. It should be understood that the specific implementation methods described herein are only used to illustrate and explain the embodiments of the present application, and are not used to limit the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0039] It should be noted that if the embodiments of the present application involve directional indications (such as up, down, left, right, front, back...), the directional indications are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0040] In addition, if there are descriptions involving "first", "second", etc. in the embodiments of the present application, the descriptions of "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or suggesting their relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of such features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in the field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by this application.

[0041] The method for determining the remaining life of a scissor arm provided in the present application can be applied to Figure 1 In the application environment shown in the figure. Figure 1 As shown, a schematic diagram of the structure of an aerial work machine is provided, and the aerial work machine includes:

[0042] Chassis 101 , first scissor arm 102 , second scissor arm 103 , angle sensor 104 , torque sensor 105 , motor 106 , hydraulic pump 107 , oil cylinder 108 , working platform 109 .

[0043] In this embodiment, it should be noted that the aerial work machinery may refer to a scissor-type aerial work platform, for example, it may include a single-stage single-cylinder scissor-type aerial work platform. Specifically, the single-stage single-cylinder scissor-type aerial work platform includes a first scissor arm 102 and a second scissor arm 103 that are cross-connected, and one end of the first scissor arm 102 and the second scissor arm 103 is fixed to the chassis 101, and the other end is fixed to the work platform 109. The connection positions of the first scissor arm 102 and the second scissor arm 103 and the work platform 109 are both installed with a torque sensor 105, and the torque sensor 105 can be used to collect the torque of the corresponding scissor arm in real time, wherein, since the torque sensor 105 collects the torque of the connection position between the scissor arm and the work platform 109, and the pressure on the connection position is applied by the load borne by the work platform 109, therefore, the torque of the scissor arm collected by the torque sensor 105 can be the load of the work platform 109. The angle sensor 104 is installed at any position on any scissor arm, and is used to collect the operating angle of the corresponding scissor arm in real time.

[0044] The oil cylinder 108 is installed between the first scissor arm 102 and the second scissor arm 103, and is used to provide energy consumption for lifting the first scissor arm 102 and the second scissor arm 103. The chassis 101 is installed with a motor 106 and a hydraulic pump 107, wherein the motor 106 is arranged with a plurality of sensors, which may include a position sensor, a speed sensor and a temperature sensor to collect electrical data of the motor 106 in real time, and the hydraulic pump 107 is arranged with a plurality of sensors, which may include a pressure sensor and a flow sensor to collect hydraulic data of the hydraulic pump 107 in real time.

[0045] The aerial work platform 109 can be used to carry workers and work equipment. The first scissor arm 102 and the second scissor arm 103 can drive the aerial work platform 109 to rise through a lifting action, so as to transport the workers and work equipment to a target height.

[0046] Figure 2 The following is a schematic diagram showing a flow chart of a method for determining the remaining life of a scissor arm according to an embodiment of the present application. Figure 2 As shown, an embodiment of the present application provides a method for determining the remaining life of a scissor arm. The method is applied to an aerial work machine. The aerial work machine at least includes a chassis and a scissor arm. The method may include the following steps.

[0047] Step 201, during the operation of the aerial work machine, real-time acquisition of operating data of the scissor arm, wherein the operating data at least includes the operating angle and torque of the scissor arm.

[0048] In the embodiments of the present application, it should be noted that during the operation of the aerial work machinery, the operating data such as the operating angle θ and torque F of the scissor arm can be obtained in real time by collecting signal data of the angle sensor and the torque sensor in real time.

[0049] Step 202, determining a real-time damage value corresponding to the operating data based on a fatigue damage library, wherein the fatigue damage library includes a correspondence between a plurality of operating angles and moments and damage values ​​of the scissor arms, and the correspondence is obtained after fatigue test training based on a plurality of historical operating angles and historical moments that meet preset conditions.

[0050] In the embodiment of the present application, it should be noted that the fatigue damage library includes a plurality of correspondences between operating angles and moments and the damage values ​​of the scissor arms, and each set of correspondences has a corresponding damage value, wherein the correspondences are obtained after fatigue test training based on a plurality of historical operating angles and historical moments that meet the preset conditions, and the preset conditions can be set according to actual needs. Therefore, after obtaining the operating data of the scissor arm, the operating data can be matched with the fatigue damage library to determine which set of correspondences in the fatigue damage library the operating angle θ and moment F included in the operating data meet, and the damage value of the set of correspondences is determined as the real-time damage value corresponding to the operating data. Specifically, as shown in Table 1, a fatigue damage library is provided, which includes a plurality of sets of correspondences between operating angles and moments, and each set of correspondences has a corresponding damage value. For example, the damage value corresponding to (F1, θ1θ2), (F1, θ1θ3), (F1, θ1θ2) is D1, and the damage value corresponding to (F1, θ1θ3) is D2. Therefore, taking the obtained operating data T of the scissors arm as an example, the operating data T includes the torque F1 and the operating angle θ1θ3. The corresponding relationship of the operating data T can be matched to (F1, θ1θ3) through the fatigue damage library, and then the damage value D2 corresponding to (F1, θ1θ3) can be determined as the real-time damage value Dnow corresponding to the operating data T.

[0051] Table 1 Fatigue damage library

[0052] Torque\operating angle <![CDATA[θ1θ2]]> <![CDATA[θ1θ3]]> …… <![CDATA[θ m-1 i m ]]> F1 D1 D2 …… …… F2 …… …… …… …… …… …… …… …… …… Fj …… …… …… DK

[0053] In an embodiment of the present application, the method also includes a step of constructing a fatigue damage library, the construction step including: obtaining multiple historical operating data of the scissors arm of the aerial work machinery within a historical time period, the multiple historical operating data including multiple historical operating angles and multiple historical torques of the scissors arm; for multiple historical operating angles, determining the cumulative number of occurrences of each historical operating angle within the historical time period; eliminating historical operating angles whose cumulative number of occurrences is lower than a preset number threshold, and determining the remaining historical operating angles as target operating angles that meet preset conditions; analyzing multiple historical torques, and screening out target torques that meet the preset conditions; generating fatigue test conditions based on the target operating angles and target torques; for any fatigue test condition, repeatedly performing fatigue test training on the fatigue test condition until fatigue cracks exist in the scissors arm under the fatigue test condition, and determining the number of training times for the fatigue test condition as the fatigue life of the scissors arm under the fatigue test condition; determining the damage value corresponding to each fatigue test condition based on the fatigue life of the scissors arm under each fatigue test condition, and constructing a fatigue damage library based on the correspondence between each fatigue test condition and the damage value.

[0054] In this embodiment, the fatigue damage library is obtained after fatigue test training based on multiple historical operating angles and historical torques that meet the preset conditions. Therefore, when constructing the fatigue damage library, a historical time period can be selected first, and multiple historical operating data of the scissor arm of the aerial work machinery in the historical time period can be obtained. It should be noted that the selected historical time period should be a time period with a long time span to ensure that the number of historical operating data of the scissor arm in the historical time period is sufficient, for example, the number of historical operating angles and historical torques should be more than 1000 groups. After obtaining multiple historical operating data, the cumulative number of occurrences of each historical operating angle is counted respectively, and the historical operating angles whose cumulative number of occurrences is lower than the preset number threshold are eliminated, and the remaining historical operating angles are determined as target operating angles that meet the preset conditions. For example, the preset number threshold can be set to 10 times, that is, for any historical operating angle, if the cumulative number of occurrences of the historical operating angle is less than 10 times, it is an operating angle that does not meet the preset conditions and needs to be eliminated. If the cumulative number of occurrences is greater than or equal to 10 times, it is an operating angle that meets the preset conditions and needs to be retained. Specifically, as shown in Table 2, a cumulative number of occurrences of each historical operating angle in a historical time period is provided, for example, the cumulative number of occurrences of θ1 is k1, and the cumulative number of occurrences of θ2 is k2. After obtaining the cumulative number of occurrences of each historical operating angle, the operating angles with a cumulative number of occurrences less than 10 times are eliminated to obtain an angle real operating condition library composed of target operating angles that meet the preset conditions as shown in Table 3.

[0055] Table 2 Cumulative number of occurrences of each historical operation angle in the historical time period

[0056] Serial number θ[°] Cumulative number of occurrences 1 <![CDATA[θ1]]> k1 2 <![CDATA[θ2]]> k2 3 <![CDATA[θ3]]> k3 …… …… …… …… …… …… n <![CDATA[θ n ]]> k

[0057] Table 3 Angle real working condition library

[0058]

[0059]

[0060] At the same time, the obtained multiple historical moments are analyzed, and the target moments that meet the preset conditions are screened out. Specifically, normal distribution analysis can be performed on the multiple historical moments to screen out the target moments that meet the preset conditions.

[0061] In the present technical solution, after obtaining the target operating angle and target torque that meet the preset conditions, a fatigue test condition is generated based on the target operating angle and the target torque. For any fatigue test condition, fatigue test training is repeated for the fatigue test condition until fatigue cracks exist in the scissors arm under the fatigue test condition. The number of training times for the fatigue test condition is determined as the fatigue life of the scissors arm under the fatigue test condition, and then the damage value corresponding to each fatigue test condition is further determined based on the fatigue life of the scissors arm under each fatigue test condition. Specifically, taking the fatigue life as Ti as an example, the inverse of the fatigue life Ti 1 / Ti is taken to determine the damage value Di corresponding to the fatigue test condition. After determining the damage value corresponding to each fatigue test condition, a fatigue damage library is constructed based on the correspondence between each fatigue test condition and the damage value.

[0062] In an embodiment of the present application, analyzing multiple historical moments and screening out target moments that meet preset conditions includes: performing normal distribution analysis on multiple historical moments to obtain a normal distribution graph for multiple historical moments; based on the normal distribution graph, eliminating historical moments outside a preset interval, and determining the remaining historical moments as target moments that meet the preset conditions.

[0063] In this embodiment, it should be noted that a normal distribution analysis can be performed on the multiple historical moments obtained to screen out the target moments that meet the preset conditions. Specifically, a normal distribution analysis is performed on the multiple historical moments to obtain a normal distribution diagram for the multiple historical moments. For example, as shown in Table 4, multiple historical moments are provided, and a normal distribution analysis is performed on the multiple historical data in Table 4 to obtain the following: Figure 3 A schematic diagram of a normal distribution diagram is shown in Figure . Figure 3As shown in Figure 5, the expected value is 3σ. Therefore, the preset interval can be set to [-3σ, +3σ], the historical moments outside the interval [-3σ, +3σ] are eliminated, and the historical moments within the interval [-3σ, +3σ] are determined as the target moments that meet the preset conditions, and the torque real working condition library shown in Table 5 is obtained.

[0064] Table 4 Multiple historical moments

[0065] Serial number F[N] 1 F1 2 F2 3 F3 …… …… …… …… n Fn

[0066] Table 5 Torque real working condition library

[0067] Serial number F[N] 1 F1 2 F2 3 F3 …… …… …… …… m Fm

[0068] In an embodiment of the present application, generating fatigue test conditions based on target operating angles and target torques includes: for each target operating angle, combining the target operating angle with all other target operating angles except the target operating angle in turn to obtain multiple angle groups; sorting all target torques in ascending order to obtain torque intervals for all target torques; dividing the torque interval into multiple continuous sub-torque intervals based on a preset division ratio; and combining each sub-torque interval with each angle group in turn to generate corresponding multiple fatigue test conditions.

[0069] In this embodiment, after obtaining the target operating angle and target torque that meet the preset conditions, for each target operating angle, the target operating angle is sequentially combined with all other target operating angles except the target operating angle to obtain multiple angle groups. For example, as shown in Table 3, taking θ1 as an example, θ1 is combined with θ2…θ m By combining them in sequence, multiple lifting starting angle groups as shown in Table 6 can be obtained, such as θ1θ2, θ1θ3, etc.

[0070] Table 6 Lifting start angle group

[0071] Lifting start angle group <![CDATA[θ1θ2]]> <![CDATA[θ1θ3]]> <![CDATA[θ2θ3]]> …… …… <![CDATA[θ m-1 i m ]]>

[0072] At the same time, the obtained target torques are sorted in ascending order to obtain the maximum target torque and the minimum target torque, and are respectively determined as the maximum value Fmax and the minimum value Fmin of all target torques. Based on the maximum value Fmax and the minimum value Fmin, the following is obtained: Figure 4The torque interval schematic diagram for all target torques is shown, and the value of the torque interval is [Fmin, Fmax]. It should be noted that the preset ratio can be set to 100N, so the torque interval can be divided into equal proportions with 100N as the division interval to obtain multiple consecutive sub-torque intervals. For example, as shown in Table 7, a lifting load F table including multiple consecutive sub-torque intervals is provided.

[0073] Table 7 Lifting load F

[0074]

[0075]

[0076] Each sub-torque interval obtained by the division is sequentially combined with each angle group obtained by the combination to generate a corresponding plurality of fatigue test conditions. Specifically, each angle group in the lifting start angle group shown in Table 6 can be sequentially combined with each sub-torque interval in the lifting load F table shown in Table 7 to obtain the fatigue test condition table shown in Table 8.

[0077] Table 8 Fatigue test conditions

[0078] Load\Start Angle <![CDATA[θ1θ2]]> <![CDATA[θ1θ3]]> …… <![CDATA[θ m-1 i m ]]> F1 Condition 1 Condition 2 …… …… F2 …… …… …… …… …… …… …… …… …… Fj …… …… …… Working condition K

[0079] Specifically, for any fatigue test condition in Table 8, fatigue test training is repeated for the fatigue test condition, that is, each condition is repeatedly executed according to the load and starting angle in the fatigue test condition table until the scissors arm stops after fatigue cracks exist under the fatigue test condition. The number of repeated training Ti of the fatigue test condition at this time is recorded, and Ti is determined as the fatigue life of the scissors arm under the fatigue test condition. At the same time, the reciprocal of the fatigue life Ti 1 / Ti is taken as the damage value Di corresponding to the fatigue test condition. After determining the damage value corresponding to each fatigue test condition, a fatigue damage library as shown in Table 1 is constructed based on the correspondence between each fatigue test condition and the damage value.

[0080] Step 203: determining the historical cumulative damage value of the scissor arm based on the model information of the aerial work machine.

[0081] In the embodiment of the present application, it should be noted that the data information of any model of aerial work machinery will be stored in the remote server, so the historical cumulative damage value Dtot-last of the scissor arm of the aerial work machinery can be obtained from the remote server through the model information of the aerial work machinery. At the same time, the total life Ttot of the scissor arm of the aerial work machinery can also be obtained.

[0082] Step 204: determining the remaining life of the scissor arm based on the real-time damage value and the historical accumulated damage value.

[0083] In the embodiment of the present application, after determining the real-time damage value of the scissor arm under the current operation data based on the fatigue damage library, and obtaining the current historical cumulative damage value of the scissor arm based on the remote server, the real-time cumulative damage value Dtot=Dtot-last+Dnow can be determined based on the sum of the historical cumulative damage value Dtot-last and the real-time damage value Dnow, and the reciprocal of the real-time cumulative damage value 1 / Dtot is taken as the current cumulative consumed life TD=1 / Dtot. Thus, the remaining life of the scissor arm Tr=Ttot.-TD=Ttot-1 / Dtot can be further calculated based on the total life Ttot of the scissor arm and the cumulative consumed life TD.

[0084] This technical solution obtains the operating angle and torque of the scissor arm in real time during the operation of the aerial work machinery, and determines the real-time damage value corresponding to the operating data based on the fatigue damage library, wherein the fatigue damage library includes the correspondence between multiple operating angles and torques and the damage values ​​of the scissor arm, and then further determines the historical cumulative damage value of the scissor arm based on the model information of the aerial work machinery, and then determines the remaining life of the scissor arm based on the real-time damage value and the historical cumulative damage value, so as to achieve real-time and accurate prediction of the fatigue life of the scissor arm, while effectively improving the safety of the aerial work machinery.

[0085] In one embodiment, the method also includes: acquiring electrical data and hydraulic data of the aerial work machinery in real time during the operation of the aerial work machinery; preprocessing the electrical data and hydraulic data; inputting the preprocessed data into a deep learning model to output the current operating status of the aerial work machinery through the deep learning model, and the operating status includes any one of normal and faulty, wherein the deep learning model is trained based on historical electrical data and historical hydraulic data.

[0086] In this embodiment, during the operation of the aerial work machine, the electrical data of the aerial work machine can be obtained in real time through the position sensor, speed sensor and temperature sensor installed on the motor. At the same time, the hydraulic data of the aerial work machine can be obtained in real time through the pressure sensor and flow sensor installed on the hydraulic pump.

[0087] After obtaining the electrical data and hydraulic data, the obtained raw data needs to be pre-processed in sequence such as cleaning, normalization and segmentation. The pre-processed data is input into the deep learning model to output the current operating status of the aerial work machinery through the deep learning model. It should be noted that the deep learning model may refer to a hybrid model using a convolutional neural network (CNN) and a long short-term memory network (LSTM), where CNN is used to extract features and LSTM processes the correlation of time series. The model parameters of the hybrid model can be trained based on historical electrical data and historical hydraulic data.

[0088] In an embodiment of the present application, the electrical data includes at least the rotor position data, speed data and temperature data of the motor, and the hydraulic data includes at least the pressure data and flow data of the hydraulic pump. The method also includes: when any one of the electrical data and the hydraulic data is in an abnormal state, determining that the aerial work machinery has a fault.

[0089] In this embodiment, the electrical data of the aerial work machine can be obtained in real time through the position sensor, speed sensor and temperature sensor installed on the motor. Specifically, the electrical data may include the rotor position data U, the motor speed data V and the motor temperature data T of the motor. At the same time, the hydraulic data of the aerial work machine can be obtained in real time through the pressure sensor and flow sensor installed on the hydraulic pump. Specifically, the hydraulic data may include the hydraulic pump pressure P and the hydraulic pump flow Q. When any one of the electrical data and the hydraulic data is in an abnormal state, that is, when any one of the motor or the hydraulic pump fails, the deep learning model outputs that the aerial work machine is currently faulty.

[0090] In this embodiment, the deep learning model can be trained based on historical electrical data and historical hydraulic data. Specifically, the electrical and hydraulic databases in the remote server can be accessed to obtain sufficient historical electrical data and historical hydraulic data. The original historical electrical data and hydraulic data are classified into three categories of data: "normal", "caution" and "fault" according to the status. The classification criteria are shown in Table 9, including:

[0091] Table 9 Data classification table

[0092]

[0093]

[0094] The deep learning model to be trained is established based on convolutional neural network (CNN) and long short-term memory network (LSTM). After the original historical electrical data and historical hydraulic data are classified according to the classification table shown in Table 9, the classified original data are pre-processed such as cleaning, normalization and segmentation, and the pre-processed data are divided into training set, verification set and test set. The training set, verification set and test set are input into the deep learning model to be trained, and the model parameters are continuously adjusted and the loss function is minimized through the back propagation algorithm. At the same time, the mean square error (MSE) and accuracy are used to measure the model performance, requiring the MSE to be less than 0.1 and the classification accuracy to reach at least 90%. The model outputs the status result of the aerial work machinery, that is, "normal" or "fault". Among them, it should be noted that when the data state of the electrical data or the hydraulic data is normal or attention, the status result of the aerial work machinery output by the deep learning model is normal, and only when the data state of at least one of the electrical data or the hydraulic data is in fault, the status result of the aerial work machinery output by the deep learning model is fault.

[0095] In one embodiment, the method further comprises: generating a corresponding alarm signal when the remaining life of the scissor arm is lower than a preset threshold or a failure occurs in the aerial work machine.

[0096] In this embodiment, it should be noted that the preset threshold can be set according to actual needs, for example, it can include but is not limited to being set to half a year. Specifically, when the remaining life of the scissor arm is less than half a year or a failure occurs in the aerial work machinery, an alarm reminder mechanism is initiated. The configured alarm system will send an alarm signal to the service equipment via GPRS, triggering the alarm of the service equipment to alert the user to perform maintenance in time. At the same time, the alarm system will send text messages to the pre-bound mobile phone number and email address, and push alarm information. The alarm information contains the diagnosis results to remind the user to perform maintenance in time.

[0097] This technical solution can simultaneously monitor the life or status of the structure, electrical and hydraulic systems of aerial work machinery with high precision, and can simultaneously send warnings to the service equipment and mobile phones to remind users to perform maintenance in a timely manner, which can effectively prevent safety accidents from occurring.

[0098] The embodiment of the present application provides a device for determining the remaining life of a scissor arm, comprising:

[0099] a memory configured to store instructions; and

[0100] A processor is configured to call the instructions from the memory and implement a method for determining a remaining life of a scissor arm when executing the instructions.

[0101] An embodiment of the present application provides a machine-readable storage medium having a program stored thereon, which, when executed by a processor, implements the above-mentioned method for determining the remaining life of a scissor arm.

[0102] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 5 As shown. The computer device includes a processor A01, a network interface A02, a memory (not shown in the figure) and a database (not shown in the figure) connected via a system bus. Among them, the processor A01 of the computer device is used to provide computing and control capabilities. The memory of the computer device includes an internal memory A03 and a non-volatile storage medium A04. The non-volatile storage medium A04 stores an operating system B01, a computer program B02 and a database (not shown in the figure). The internal memory A03 provides an environment for the operation of the operating system B01 and the computer program B02 in the non-volatile storage medium A04. The database of the computer device is used to store method data for determining the remaining life of the scissor arm. The network interface A02 of the computer device is used to communicate with an external terminal through a network connection. When the computer program B02 is executed by the processor A01, a method for determining the remaining life of the scissor arm is implemented.

[0103] Those skilled in the art will understand that Figure 5 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0104] The present application also provides an aerial work machine, including:

[0105] Chassis;

[0106] a scissor arm having one end fixed to the chassis; and

[0107] The above-mentioned device for determining the remaining life of the scissor arm.

[0108] The present application also provides a computer program product, which, when executed on a data processing device, is suitable for executing a program for initializing the steps of the method for determining the remaining life of a scissor arm.

[0109] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0110] The present application is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0111] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0112] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0113] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0114] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0115] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0116] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0117] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A method for determining the remaining life of a scissor arm, characterized in that: The method is applied to an aerial work machine, the aerial work machine at least comprising a chassis and a scissor arm, and the method comprises: During the operation of the aerial work machine, the operation data of the scissor arm is acquired in real time, wherein the operation data at least includes the operation angle and torque of the scissor arm; Determine a real-time damage value corresponding to the operation data based on a fatigue damage library, wherein the fatigue damage library includes a correspondence between a plurality of operation angles and moments and a damage value of a scissor arm, and the correspondence is obtained after fatigue test training based on a plurality of historical operation angles and historical moments that meet preset conditions; Determining a historical cumulative damage value of the scissor arm based on the model information of the aerial work machinery; The remaining life of the scissor arm is determined based on the real-time damage value and the historical accumulated damage value.

2. The method for determining the remaining service life of a scissor arm according to claim 1, characterized in that: The method further comprises a step of constructing a fatigue damage library, wherein the step of constructing the fatigue damage library comprises: Acquire a plurality of historical operation data of the scissor arm of the aerial work machine within a historical time period, wherein the plurality of historical operation data includes a plurality of historical operation angles and a plurality of historical moments of the scissor arm; For a plurality of historical operation angles, determining the cumulative number of occurrences of each historical operation angle within the historical time period; Eliminate historical operating angles whose cumulative occurrence times are lower than a preset number threshold, and determine the remaining historical operating angles as target operating angles that meet the preset conditions; Analyze the multiple historical moments and select target moments that meet preset conditions; Generate fatigue test conditions based on target operating angle and target torque; For any fatigue test condition, fatigue test training is repeatedly performed on the fatigue test condition until fatigue cracks occur in the scissor arm under the fatigue test condition, and the number of training times for the fatigue test condition is determined as the fatigue life of the scissor arm under the fatigue test condition; Based on the fatigue life of the scissor arm under each fatigue test condition, a damage value corresponding to each fatigue test condition is determined, and a fatigue damage library is constructed based on the corresponding relationship between each fatigue test condition and the damage value.

3. The method for determining the remaining service life of a scissor arm according to claim 2, characterized in that: The analyzing the multiple historical moments and selecting the target moments meeting the preset conditions comprises: Performing normal distribution analysis on the multiple historical moments to obtain normal distribution graphs for the multiple historical moments; Based on the normal distribution diagram, historical moments outside a preset interval are eliminated, and the remaining historical moments are determined as target moments that meet preset conditions.

4. The method for determining the remaining service life of a scissor arm according to claim 2, characterized in that: The fatigue test condition generated based on the target operating angle and the target torque includes: For each target operating angle, sequentially combine the target operating angle with all other target operating angles except the target operating angle to obtain a plurality of angle groups; Sort all target moments in ascending order to obtain moment intervals for all target moments; Dividing the torque interval into a plurality of continuous sub-torque intervals in sequence based on a preset division ratio; Each sub-torque interval is combined with each angle group in sequence to generate corresponding multiple fatigue test conditions.

5. The method for determining the remaining service life of a scissor arm according to claim 1, characterized in that: The method further comprises: During the operation of the aerial work machine, the electrical data and hydraulic data of the aerial work machine are acquired in real time; Preprocessing the electrical data and the hydraulic data; The preprocessed data is input into a deep learning model to output the current operating status of the aerial work machinery through the deep learning model, wherein the operating status includes any one of normal and faulty, wherein the deep learning model is trained based on historical electrical data and historical hydraulic data.

6. The method for determining the remaining service life of a scissor arm according to claim 5, characterized in that: The method further comprises: When the remaining life of the scissor arm is lower than a preset threshold or the aerial work machine fails, a corresponding alarm signal is generated.

7. The method for determining the remaining life of a scissor arm according to claim 5, characterized in that: The electrical data at least includes rotor position data, speed data and temperature data of the motor, the hydraulic data at least includes pressure data and flow data of the hydraulic pump, and the method further includes: When any one of the electrical data and the hydraulic data is in an abnormal state, it is determined that the aerial work machine has a malfunction.

8. A device for determining the remaining life of a scissor arm, characterized in that: include: a memory configured to store instructions; as well as A processor is configured to call the instructions from the memory and implement the method for determining the remaining life of a scissor arm according to any one of claims 1 to 7 when executing the instructions.

9. A high-altitude working machine, characterized in that: include: The device for determining the remaining life of a scissor arm according to claim 8; Chassis; A scissor arm, one end of which is fixed to the chassis.

10. A machine-readable storage medium having instructions stored thereon, characterized in that: When the instructions are executed by a processor, the processor is configured to perform the method for determining the remaining life of a scissor arm according to any one of claims 1 to 7.

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