Intelligent control method for separated discharging platform

Abstract

The invention discloses an intelligent control method for a separated discharging platform, and relates to the technical field of equipment control. Constructing a digital twin model of the separated discharging platform, deploying a multi-source data acquisition end, obtaining different monitoring data, and obtaining an aging coefficient and a friction coefficient increase according to an actual acceleration and a theoretical acceleration under a standard working condition; the method comprises the following steps: acquiring expected driving force for reaching expected speed under different friction coefficient amplification and material weight, constructing an intelligent control model, intelligently controlling a separated discharging platform, acquiring a material weight threshold value, and judging whether the separated discharging platform reaches a scrap state and a fault state or not according to an aging coefficient and monitoring data. An early warning signal is generated and fed back; the control pertinence of the separated discharging platform can be obviously improved, and a flexible driving force control strategy can be provided for different material transportation conditions.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment control, and in particular to an intelligent control method for a separable unloading platform. Background Art

[0002] In the actual application scenarios of construction engineering, the unloading platform for material transfer from the inside of a building to the outside or from the outside of a building to the inside is indispensable. The traditional unloading platforms in the current construction industry are generally cantilever unloading platforms, which often have problems such as unstable platforms, low material transfer efficiency, inadequate safety monitoring measures, and long-term exposure of workers outdoors;

[0003] Therefore, the separable unloading platform came into being. It transports materials through a transportation structure driven by a servo motor. Due to the failure to consider the aging of the equipment itself and the impact of different transportation weights in the prior art, it often adopts a fixed driving force control strategy, resulting in a lack of flexibility in the driving force control of the transportation structure. In view of the deficiencies of the prior art, the present invention provides an intelligent control method for a separable unloading platform. Summary of the Invention

[0004] The purpose of the present invention is to provide an intelligent control method for a separable unloading platform.

[0005] The purpose of the present invention can be achieved through the following technical solutions: An intelligent control method for a separable unloading platform, comprising the following steps:

[0006] Step S1: Obtain the structural information of each component of the separable unloading platform, and construct a corresponding digital twin model. Deploy multi-source data acquisition terminals on the separable unloading platform, use the multi-source data acquisition terminals to obtain different monitoring data respectively, and synchronize them to the digital twin model;

[0007] Step S2: Obtain the actual acceleration and theoretical acceleration of the separable unloading platform under standard working conditions, and obtain the corresponding aging coefficient and friction coefficient increase. Obtain the expected driving force required to reach the expected speed under different friction coefficient increases and material weights in the digital twin model, and construct a corresponding intelligent control model;

[0008] Step S3: Use the intelligent control model to intelligently control the separable unloading platform in the actual application scenario, and obtain the corresponding vibration coefficient. Obtain the corresponding material weight threshold in the digital twin model in combination with different expected speeds and material weights;

[0009] Step S4: Set the scrapping threshold and the fault threshold, determine whether the split-type unloading platform reaches the scrapping state and the fault state according to the aging coefficient and the monitoring data, generate corresponding warning signals and give feedback.

[0010] Further, the process of obtaining the structural information of each component of the split-type unloading platform and constructing the corresponding digital twin model includes:

[0011] The split-type unloading platform includes a platform structure and a split-type transportation structure. Obtaining the structural information of the platform structure and the split-type transportation structure includes material specifications, connection relationships, and dimensional parameters;

[0012] Use digital twin technology to construct a physical model of the split-type unloading platform based on the obtained structural information, and use simulation software to simulate the constructed physical model to obtain the digital twin model.

[0013] Further, the process of deploying multi-source data acquisition terminals on the split-type unloading platform, using the multi-source data acquisition terminals to obtain different monitoring data respectively, and synchronizing them to the digital twin model includes:

[0014] The multi-source data acquisition terminals include a load monitoring unit, a speed monitoring unit, and a drive monitoring unit;

[0015] Set a load monitoring unit and a speed monitoring unit on the split-type transportation structure respectively, which are used to obtain the weight of the materials transported by the split-type transportation structure and the acceleration and speed of the split-type transportation structure in real time;

[0016] Set a drive monitoring unit on the servo motor of the split-type transportation structure, which is used to obtain the driving force provided by the servo motor for the split-type transportation structure in real time. The monitoring data includes material weight, acceleration, speed, and driving force, and upload each item of monitoring data to the digital twin model for synchronization.

[0017] Further, the process of obtaining the actual acceleration and the theoretical acceleration of the split-type unloading platform under standard working conditions, and obtaining the corresponding aging coefficient and the increase in friction coefficient includes:

[0018] The standard working condition refers to that the driving force provided by the servo motor and the weight of the materials transported by the split-type transportation structure are both preset fixed values;

[0019] Under standard working conditions, obtain the accelerations of the split-type transportation structure of the split-type unloading platform in the actual application scenario and in the digital twin model at the same moment, and record them as S aj and S az ;

[0020] Obtain the aging coefficient and the increase in friction coefficient of the split-type unloading platform in the actual application scenario, and record them as Hs and Δμ;

[0021]

[0022]

[0023] F y 、m y 、μ y are the driving force, material weight, and friction coefficient under standard working conditions, m0 is the self-weight of the split transportation structure, and g is the acceleration due to gravity.

[0024] Further, the process of obtaining the desired driving force required to reach the desired speed under different friction coefficient increases and material weights in the digital twin model and constructing the corresponding intelligent control model includes:

[0025] Taking the constant speed that the desired split transportation structure can reach during transportation as the desired speed, and using the control variable method in the digital twin model to obtain the desired driving force required to reach different desired speeds under different friction coefficient increases and material weights;

[0026] The desired driving force refers to the minimum driving force that the servo motor needs to provide for the split transportation structure to reach the desired speed. Generate an intelligent control set based on different desired speeds, friction coefficient increases, material weights, and their desired driving forces, and divide the intelligent control set into a training set and a test set;

[0027] Construct a convolutional neural network, use different desired speeds, friction coefficient increases, and material weights in the training set as the input data of the convolutional neural network, and use the corresponding desired driving force in the training set as the output data of the convolutional neural network;

[0028] Train the convolutional neural network to obtain an initial convolutional neural network, use the test set to verify the model of the initial convolutional neural network, and output the initial convolutional neural network with a test error threshold less than or equal to the preset value as the intelligent control model.

[0029] Further, the process of using the intelligent control model to perform intelligent control on the split unloading platform in the actual application scenario and obtaining the corresponding vibration coefficient includes:

[0030] In the actual application scenario, set the desired speed for the split transportation structure, and obtain its friction coefficient increase and material weight;

[0031] Input the above three pieces of data into the intelligent control model together, use the intelligent control model to output the corresponding desired driving force, and use the servo motor to provide the obtained desired driving force for the split transportation structure in real time to achieve intelligent control of the split unloading platform;

[0032] On each steel wire rope of the platform structure, a corresponding amplitude monitoring unit is set to obtain the vibration amplitude of each steel wire rope at the same moment in real time;

[0033] The average value of the vibration amplitudes of each steel wire rope at the end of driving is used as the corresponding amplitude coefficient, and the end of driving refers to the corresponding moment when the separated transportation structure reaches the designated position.

[0034] Further, the process of obtaining the corresponding material weight threshold by combining different expected speeds and material weights in the digital twin model includes:

[0035] Set an amplitude coefficient threshold. Keep a single expected speed unchanged in the digital twin model, continuously adjust the material weight to obtain the maximum material weight under the condition of not exceeding the amplitude coefficient threshold, and use it as the material weight threshold at this expected speed;

[0036] Adjust the expected speed to obtain the material weight thresholds at different expected speeds, and provide the corresponding material weight thresholds for the set expected speeds during the actual operation process.

[0037] Further, the process of setting a scrapping threshold and a fault threshold, judging whether the separated unloading platform reaches the scrapping state and the fault state according to the aging coefficient and the monitoring data, and generating corresponding warning signals and giving feedback includes:

[0038] Set a scrapping threshold, compare the aging coefficient of the separated unloading platform with the scrapping threshold. If the aging coefficient is greater than or equal to the scrapping threshold, mark it as the scrapping state and generate a scrapping warning signal;

[0039] Set fault thresholds, including an acceleration threshold, a speed threshold, and a driving force threshold. Compare the acceleration, speed, and driving force in the monitoring data with their respective fault thresholds. If the monitoring data is greater than or equal to its fault threshold, mark it as the fault state and generate a fault warning signal, and feedback the scrapping warning signal and the fault warning signal to the relevant personnel.

[0040] Compared with the prior art, the beneficial effects of the present invention are:

[0041] By constructing a digital twin model of the separated unloading platform, the present invention can simulate the relevant data generated by the separated unloading platform under various working conditions. According to the difference in acceleration between the actual and theoretical values of the separated unloading platform under standard working conditions, the aging coefficient and the increase in friction coefficient of the separated unloading platform can be obtained, which is beneficial to taking the aging situation of the equipment into consideration and can improve the pertinence of controlling the separated unloading platform;

[0042] By obtaining the expected driving force required to reach the expected speed under different friction coefficient increases and material weights in the digital twin model and constructing the corresponding intelligent control model, a flexible driving force control strategy can be provided for different material transportation situations. By setting the vibration coefficient threshold and obtaining the material weight threshold under different expected speeds and material weights, the stability of the separated unloading platform can be ensured while not exceeding its maximum transportation capacity, which is beneficial to improving the stability of the separated unloading platform. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 is a flowchart of the present invention;

[0044] Figure 2 is a schematic diagram of the separated unloading platform;

[0045] Figure 3 is a schematic diagram of the platform structure;

[0046] Figure 4 is a schematic diagram of the separated transportation structure. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0047] As Figure 1 shown, an intelligent control method for a separated unloading platform includes the following steps:

[0048] Step S1: Obtain the structural information of each component of the separated unloading platform and construct the corresponding digital twin model. Deploy a multi-source data acquisition terminal on the separated unloading platform, use the multi-source data acquisition terminal to obtain different monitoring data respectively, and synchronize it to the digital twin model;

[0049] Step S2: Obtain the actual acceleration and theoretical acceleration of the separated unloading platform under standard working conditions, and obtain the corresponding aging coefficient and friction coefficient increase. Obtain the expected driving force required to reach the expected speed under different friction coefficient increases and material weights in the digital twin model, and construct the corresponding intelligent control model;

[0050] Step S3: Use the intelligent control model to perform intelligent control on the separated unloading platform in the actual application scenario, and obtain the corresponding vibration coefficient. Obtain the corresponding material weight threshold in the digital twin model by combining different expected speeds and material weights;

[0051] Step S4: Set the scrapping threshold and failure threshold, judge whether the separated unloading platform reaches the scrapping state and failure state according to the aging coefficient and monitoring data, and generate the corresponding warning signal and give feedback.

[0052] It should be further noted that in the specific implementation process, the process of obtaining the structural information of each component of the separable unloading platform and constructing the corresponding digital twin model includes:

[0053] In the current traditional construction industry, the unloading platform is generally a cantilevered unloading platform, which often has various problems such as unstable platform, low material transfer efficiency, inadequate safety monitoring measures, and long-term exposure of workers;

[0054] In the embodiment of the present invention, a separable unloading platform for building construction, which is used to transfer materials from inside the building to outside the building or from outside the building to inside the building, is provided, as Figure 2 shown;

[0055] The separable unloading platform is composed of two components. One is the platform structure, as Figure 3 shown, and the other is the separable transportation structure, as Figure 4 shown;

[0056] The platform structure uses I-beams as the main load-bearing structure. The cantilever platform is fixed on the structural floor through the tail support vertical poles and anchor bolts, and multiple steel wires are installed on both sides of the cantilever end respectively to connect the cantilever end to the structural main body of the upper building;

[0057] The separable transportation structure is driven by a servo motor, separated and fixed from the platform structure through bolts. In the separated state, it can transport materials to the designated position, and corresponding limit devices are set to ensure that the material transportation is always within the safe range;

[0058] Obtain the structural information of the platform structure and the separable transportation structure. The structural information refers to the various data necessary for constructing the physical model of the separable unloading platform, including the material specifications, connection relationships, dimension parameters, etc. of the platform structure and the separable transportation structure;

[0059] Use digital twin technology to construct the physical model of the separable unloading platform according to the obtained structural information, and use simulation software to simulate the constructed physical model to obtain the digital twin model of the separable unloading platform. The digital twin model can obtain the transportation process of the separable unloading platform and the relevant data generated during transportation.

[0060] It should be further noted that in the specific implementation process, the process of deploying multi-source data acquisition terminals on the separable unloading platform, using the multi-source data acquisition terminals to obtain different monitoring data respectively, and synchronizing them to the digital twin model includes:

[0061] On the separated unloading platform, different multi-source data acquisition terminals are respectively set up, including a load monitoring unit, a speed monitoring unit, and a drive monitoring unit;

[0062] On the separated transportation structure, a load monitoring unit and a speed monitoring unit are respectively set up. The weight of the materials transported by the separated transportation structure is obtained in real time through the load monitoring unit, and the acceleration and speed of the separated transportation structure are obtained in real time through the speed monitoring unit;

[0063] A drive monitoring unit is set on the servo motor. The driving force provided by the servo motor for the separated transportation structure is obtained in real time through the drive monitoring unit. The monitoring data includes the above-mentioned material weight, acceleration, speed, and driving force, and the obtained monitoring data is uploaded to the digital twin model of the separated unloading platform for synchronization.

[0064] It should be further noted that in the specific implementation process, the process of obtaining the actual acceleration and theoretical acceleration of the separated unloading platform under standard working conditions, and obtaining the corresponding aging coefficient and friction coefficient increase includes:

[0065] The standard working condition refers to that the driving force provided by the servo motor and the weight of the materials transported by the separated transportation structure are both preset fixed values;

[0066] Under standard working conditions, the accelerations of the separated transportation structure of the separated unloading platform in the actual application scenario and in the digital twin model at the same moment are respectively obtained, and are denoted as S aj and S az , and the same moment is calculated based on the driving start moment;

[0067] The aging coefficient and friction coefficient increase of the separated unloading platform in the actual application scenario are obtained, and are respectively denoted as H s and Δμ;

[0068]

[0069]

[0070] Among them, F y , m y , μ y are the driving force, material weight, and friction coefficient under standard working conditions, m0 is the self-weight of the separated transportation structure, and g is the acceleration due to gravity.

[0071] It should be further noted that in the specific implementation process, the process of obtaining the expected driving force required to reach the expected speed under different friction coefficient increases and material weights in the digital twin model, and constructing the corresponding intelligent control model includes:

[0072] Set the desired speed, which refers to the constant speed that the relevant personnel expect the separable transportation structure to achieve during transportation. In the digital twin model, the control variable method is adopted to obtain the desired driving force required to reach the desired speed under different friction coefficient increases and material weights;

[0073] Keep the desired speed unchanged, continuously adjust the friction coefficient increase and material weight to obtain the corresponding desired driving force. The desired driving force refers to the minimum driving force that the servo motor needs to provide for the separable transportation structure to reach the desired speed;

[0074] Keep the friction coefficient increase and material weight unchanged, continuously adjust the desired speed to obtain the corresponding desired driving force, generate an intelligent control set according to different desired speeds, friction coefficient increases, material weights and their corresponding desired driving forces, and divide the intelligent control set into a training set and a test set;

[0075] Construct a convolutional neural network, use different desired speeds, friction coefficient increases, and material weights in the training set as the input data of the convolutional neural network, and use the corresponding desired driving force in the training set as the output data of the convolutional neural network;

[0076] Train the convolutional neural network to obtain an initial convolutional neural network, use the test set to verify the model of the initial convolutional neural network, and output the initial convolutional neural network with a test error threshold less than or equal to the preset value as the intelligent control model.

[0077] It should be further noted that in the specific implementation process, the process of using the intelligent control model to perform intelligent control on the separable unloading platform and obtaining the corresponding vibration coefficient in the actual application scenario includes:

[0078] In the actual application scenario, the relevant personnel set the corresponding desired speed for the separable transportation structure and obtain its friction coefficient increase and material weight;

[0079] Input the above three items of data into the intelligent control model together, use the intelligent control model to output the corresponding desired driving force, and use the servo motor to provide the output desired driving force for the separable transportation structure in real time to achieve the intelligent control of the separable unloading platform;

[0080] Number each steel wire rope on the platform structure, denoted as i, i = 1, 2,..., n, where n is the total number of steel wire ropes. Set corresponding amplitude monitoring units on each steel wire rope, and obtain the vibration amplitude of each steel wire rope at the same moment through the amplitude monitoring unit;

[0081] The average value of the vibration amplitude of each steel wire rope at the end of driving is used as the corresponding amplitude coefficient, and the end of driving refers to the corresponding moment when the separable transportation structure reaches the specified position.

[0082] It should be further noted that in the specific implementation process, the process of obtaining the corresponding material weight threshold by combining different expected speeds and material weights in the digital twin model includes:

[0083] Set an amplitude coefficient threshold, which is used to limit the vibration amplitude of the steel wire rope to ensure that the entire separable unloading platform remains as stable as possible during transportation;

[0084] Keep a single expected speed unchanged in the digital twin model, continuously adjust the material weight to obtain the maximum material weight under the condition of not exceeding the amplitude coefficient threshold, and use it as the material weight threshold at this expected speed;

[0085] Adjust the expected speed to obtain the material weight threshold at different expected speeds. During the actual operation process, provide the corresponding material weight threshold for the expected speed set by relevant personnel to ensure that the materials transported by the separable transportation structure do not exceed their corresponding material weight thresholds.

[0086] It should be further noted that in the specific implementation process, setting the scrapping threshold and the fault threshold, and judging whether the separable unloading platform reaches the scrapping state and the fault state according to the aging coefficient and the monitoring data, and generating the corresponding warning signals and feeding them back includes:

[0087] Set the scrapping threshold, compare the aging coefficient of the separable unloading platform with the scrapping threshold. If the aging coefficient is greater than or equal to the scrapping threshold, it is judged that the separable unloading platform is severely worn, and it is marked as the scrapping state, generating the corresponding scrapping warning signal. If the aging coefficient is less than the scrapping threshold, no other operations are performed on it;

[0088] Set the fault threshold, including the acceleration threshold, the speed threshold, and the driving force threshold. Compare the acceleration, speed, and driving force in the monitoring data with their corresponding fault thresholds respectively. If the monitoring data is greater than or equal to its fault threshold, it is judged that the separable unloading platform has the corresponding fault, and it is marked as the fault state, generating the corresponding fault warning signal;

[0089] The fault warning signals include the acceleration warning signal, the speed warning signal, and the driving force warning signal. Feed back each warning signal (including the scrapping warning signal and the fault warning signal) to relevant personnel to prompt relevant personnel to perform operation and maintenance on the separable unloading platform in time.

[0090] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical method of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical method of the present invention.

Claims

1. An intelligent control method for a separate unloading platform, characterized in that: The following steps are involved: Step S1: Obtain structural information of each component of the separate unloading platform, build a corresponding digital twin model, deploy a multi-source data acquisition terminal on the separate unloading platform, use the multi-source data acquisition terminal to obtain different monitoring data respectively, and synchronize them to the digital twin model; Step S2: Obtain the actual acceleration and theoretical acceleration of the separate unloading platform under standard working conditions, and obtain the corresponding aging coefficient and friction coefficient increase, obtain the expected driving force required to achieve the expected speed under different friction coefficient increases and material weights in the digital twin model, and build a corresponding intelligent control model; Step S3: In the actual application scenario, the intelligent control model is used to intelligently control the separate unloading platform, and the corresponding vibration coefficient is obtained. In the digital twin model, the corresponding material weight threshold is obtained by combining different expected speeds and material weights; Step S4: Set the scrap threshold and the fault threshold, determine whether the separate unloading platform has reached the scrap state and the fault state according to the aging coefficient and the monitoring data, generate the corresponding early warning signal and provide feedback.

2. The intelligent control method for a separate unloading platform according to claim 1, characterized in that: The process of building a digital twin model of the split unloading platform includes: The separate unloading platform includes a platform structure and a separate transport structure, and structural information of the platform structure and the separate transport structure is obtained, including material specifications, connection relationships, and size parameters; The digital twin technology is used to construct a physical model of the separate unloading platform according to the acquired structural information, and the constructed physical model is simulated by simulation software to obtain the digital twin model.

3. The intelligent control method for a separate unloading platform according to claim 2, characterized in that: The process of using a multi-source data collection terminal to obtain different monitoring data includes: The multi-source data acquisition terminal includes a load monitoring unit, a speed monitoring unit, and a drive monitoring unit; A load monitoring unit and a speed monitoring unit are respectively arranged on the separated transport structure to obtain the weight of the materials transported by the separated transport structure and the acceleration and speed of the separated transport structure in real time; A drive monitoring unit is set on the servo motor of the separate transport structure to obtain in real time the driving force provided by the servo motor for the separate transport structure. The monitoring data includes material weight, acceleration, speed, and driving force. The various monitoring data are uploaded to the digital twin model for synchronization.

4. The intelligent control method for a separate unloading platform according to claim 3, characterized in that: The process of obtaining the aging coefficient and the friction coefficient increase includes: The standard working condition means that the driving force provided by the servo motor and the weight of the material transported by the separate transport structure are both preset fixed values; Under standard working conditions, the acceleration of the separated transport structure of the separated unloading platform in the actual application scenario and in the digital twin model at the same time is obtained, which are denoted as S aj and S az ; Obtain the aging coefficient and friction coefficient increase of the separate unloading platform in the actual application scenario, denoted as H s and Δμ; F y 、m y , μ y are the driving force, material weight and friction coefficient under standard working conditions, m0 is the dead weight of the separated transport structure, and g is the acceleration due to gravity.

5. The intelligent control method for a separate unloading platform according to claim 4, characterized in that: The process of building an intelligent control model includes: The constant speed that the separated transport structure is expected to achieve during transportation is taken as the expected speed, and the control variable method is used in the digital twin model to obtain the expected driving force required to achieve different expected speeds under different friction coefficient increases and material weights; The expected driving force refers to the minimum driving force required by the servo motor for the separated transport structure to achieve the expected speed. An intelligent control set is generated according to different expected speeds, friction coefficient increases, material weights and expected driving forces, and the intelligent control set is divided into a training set and a test set. Construct a convolutional neural network, use different expected speeds, friction coefficient increases, and material weights in the training set as input data of the convolutional neural network, and use the corresponding expected driving forces in the training set as output data of the convolutional neural network; The convolutional neural network is trained to obtain an initial convolutional neural network, and the initial convolutional neural network is model verified using a test set, and an initial convolutional neural network that is less than or equal to a preset test error threshold is output as an intelligent control model.

6. The intelligent control method for a separate unloading platform according to claim 5, characterized in that: The process of intelligently controlling the separate unloading platform and obtaining the vibration coefficient includes: In the actual application scenario, set the expected speed for the separated transport structure and obtain its friction coefficient increase and material weight; The above three data are inputted into the intelligent control model, and the corresponding expected driving force is outputted by the intelligent control model. The servo motor provides the obtained expected driving force to the separated transport structure in real time to realize the intelligent control of the separated unloading platform. A corresponding amplitude monitoring unit is provided on each steel wire rope of the platform structure to obtain the vibration amplitude of each steel wire rope at the same time in real time; The average value of the vibration amplitude of each steel rope at the end of driving is taken as the corresponding amplitude coefficient, and the end of driving time refers to the corresponding time when the separated transport structure reaches the specified position.

7. The intelligent control method for a separate unloading platform according to claim 6, characterized in that: The process of obtaining the material weight threshold includes: Set the amplitude coefficient threshold, keep a single expected speed unchanged in the digital twin model, and continuously adjust the material weight to obtain the maximum material weight without exceeding the amplitude coefficient threshold, and use it as the material weight threshold at the expected speed; The expected speed is adjusted to obtain the material weight thresholds at different expected speeds, and the corresponding material weight thresholds are provided for the set expected speeds during actual operation.

8. The intelligent control method for a separate unloading platform according to claim 7, characterized in that: The process of determining whether the separate unloading platform has reached a scrap state or a fault state includes: A scrap threshold is set, and the aging coefficient of the separate unloading platform is compared with the scrap threshold. If the aging coefficient is greater than or equal to the scrap threshold, it is marked as scrapped and a scrap warning signal is generated; Set fault thresholds, including acceleration threshold, speed threshold, and driving force threshold, and compare the acceleration, speed, and driving force in the monitoring data with their fault thresholds respectively. If the monitoring data is greater than or equal to its fault threshold, it will be marked as a fault state, and a fault warning signal will be generated. The scrap warning signal and the fault warning signal will be fed back to relevant personnel.

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