Stacker abnormal vibration monitoring and early warning method based on digital twinning

Abstract

The invention discloses a stacking machine abnormal vibration monitoring and early warning method based on digital twinning. The method comprises the steps that S1, a plurality of sensors are installed on a stand column, a walking mechanism and a pallet fork motor of the stacking machine to collect vibration parameters in real time, wherein the vibration parameters comprise but are not limited to amplitude and vibration frequency; s2, establishing a digital twinborn model of the stacker, and transmitting the collected vibration parameter data to the digital twinborn model of the stacker; s3, establishing a vibration fault service system in the platform of the digital twin model, and diagnosing the collected vibration parameter data; and S4, an early warning system is arranged in the digital twin model, an early warning threshold value is set, and when the vibration parameter of the stacking machine reaches or exceeds the early warning threshold value, an early warning signal is sent out. According to the invention, key components of the stacking machine can be covered, the operation state can be monitored, and early warning can be carried out before abnormal vibration of the stacking machine or at the initial stage, so that maintenance personnel can carry out troubleshooting and maintenance in time.

Description

Technical Field

[0001] The present invention belongs to the field of logistics warehousing, and specifically relates to a method for abnormal vibration monitoring and early warning of a stacker based on digital twin. Background Technique

[0002] Nowadays, the automated warehousing system is developing rapidly. As the core equipment in the automated stereoscopic warehouse, the performance and stability of the stacker are crucial for the efficiency of the entire logistics system. During the process of high-speed operation and frequent operation, the stacker may malfunction for various reasons, and vibration is considered one of the main faults of the stacker. Therefore, real-time monitoring and early warning of abnormal vibration at the key positions of the stacker, timely detecting and handling faults, have become the key to improving the reliability and efficiency of the logistics system.

[0003] In recent years, as an emerging digital means, digital twin technology has been widely applied to the condition monitoring, fault diagnosis, and operation and maintenance management of industrial equipment. Digital twin technology constructs a virtual model of the equipment, which can reflect the operating state and performance of the equipment in real time, providing strong support for the maintenance and management of the equipment. In the field of stackers, applying digital twin technology to condition monitoring and early warning can achieve real-time tracking and analysis of the operating state of the stacker, improving the accuracy and timeliness of fault early warning.

[0004] Currently, the traditional vibration detection of stackers is mainly based on the monitoring method of traditional sensors. In domestic and foreign research, the traditional vibration monitoring method of stackers mainly relies on vibration sensors and acceleration sensors installed on the stacker. These sensors can collect the operating data of the stacker in real time and transmit it to the central control system for analysis and processing through wired or wireless methods.

[0005] Chinese Patent with Publication No. CN116735199A introduces a method and device for fault diagnosis of the transmission system of a stacker based on digital twin. In this patent, the vibration parameters and noise change parameters generated by the transmission system during the actual operation of the stacker are obtained and transmitted to the digital twin system; the fault monitoring model of the transmission system established in the digital twin system is compared with the obtained working state signals to conduct fault diagnosis and fault type determination of the stacker. However, this method has the following deficiencies: the sensor layout position is single, only the transmission system is monitored, and it cannot comprehensively cover the key components and operating states of the stacker; there is no early warning mechanism, and it is impossible to obtain the information that the stacker has a fault in time and take corresponding measures according to different degrees of faults. Summary of the Invention

[0006] The objective of the present invention is to overcome the deficiencies of the prior art and propose a method for abnormal vibration monitoring and early warning of a stacker crane based on digital twin, which can cover the key components of the stacker crane and monitor the operating state, and give an early warning before or in the initial stage of abnormal vibration of the stacker crane, enabling maintenance personnel to promptly conduct fault troubleshooting and maintenance.

[0007] To achieve the above objectives, the present invention proposes the following technical solutions: A method for abnormal vibration monitoring and early warning of a stacker crane based on digital twin, comprising the following steps: S1. Install multiple sensors on the column, traveling mechanism, and fork motor of the stacker crane to collect vibration parameters in real time, including but not limited to amplitude and vibration frequency; S2. Establish a digital twin model of the stacker crane, and transmit the collected vibration parameter data to the digital twin model of the stacker crane; S3. Establish a vibration fault service system in the platform of the digital twin model to diagnose the collected vibration parameter data; S4. Set up an early warning system in the digital twin model, set an early warning threshold, and when the vibration parameters of the stacker crane reach or exceed the early warning threshold, send out an early warning signal.

[0008] Preferably, in step S1, sensors are respectively installed on the top, middle, and bottom of the column of the stacker crane, the outer shell of the driving motor of the traveling mechanism, and the housing of the fork motor.

[0009] Preferably, in step S1, the sensors on the top, middle, and bottom of the column of the stacker crane and the outer shell of the driving motor of the traveling mechanism use a communication method of laying network cables and signal cables, and use the 485 communication protocol for communication between devices; the sensors on the housing of the fork motor use a wireless data transmission communication method; the sensors are vibration sensors and acceleration sensors.

[0010] The fork of the stacker crane is a component that moves up and down. Using the traditional signal cable wiring method will face problems such as the signal cable being unable to stretch, inconvenient to retract and tidy up, and even the line may be broken during the movement of the fork, resulting in safety accidents. Therefore, it is more reasonable for the sensors on the housing of the fork motor to use a wireless data transmission communication method.

[0011] Preferably, in step S1, sensors are installed at the connection between the top of the column and the pallet truck and the slide rail, at the 1 / 2 of the total height of the column, at the rigid support point near the bottom of the column and the base connection, in the middle of the outer shell of the driving motor of the traveling mechanism, and on the housing of the fork motor; the rigid support point near the bottom of the column and the base connection is 20 - 50 mm from the ground; at these five key positions, by setting sensors at five different key positions of the stacker crane, comprehensive and reasonable coverage monitoring of the stacker crane is realized.

[0012] Preferably, step S2 is specifically as follows: First, use Python to read the collected data, and process the data through OPC software, including noise reduction using mean filtering and wavelet denoising methods, filtering using Kalman filtering and adaptive filtering methods, normalizing or standardizing the real-time data, extracting feature information according to actual needs, calculating frequency and amplitude, and finally visualizing and saving the processed data through WINCC control, and drawing the corresponding curve change diagram.

[0013] Preferably, in the digital twin model in step S2, define a physical mechanism that drives the interaction between the physical entity and the virtual entity, and use Internet of Things technology to drive the virtual entity to perform real-time information interaction to ensure that the state of the digital twin model is consistent with the physical entity; in the physical mechanism of virtual-real interaction, complete the model design of the stacker through SolidWorks software, and then import it into the digital twin platform Unity to complete the settings of the nodes, behaviors, attributes and characteristics of the stacker, so as to realize the real-time communication and data interaction between the physical model and the digital model of the stacker, and the digital twin model reflects the operating state of the stacker in real time, including but not limited to speed, acceleration, vibration frequency and amplitude.

[0014] The stacker is built-in with a PLC, which can obtain the real-time position coordinate data of the stacker in real time, and communicate and interact with the digital twin model in real time, so that the digital twin model can reflect the operating position of the stacker. This is the prior art.

[0015] Preferably, the vibration fault service system in step S3 diagnoses the collected data based on the machine learning support vector machine algorithm and artificial intelligence technology.

[0016] Preferably, in the warning system in step S4, the warning rules and thresholds are adjusted and optimized dynamically in multiple dimensions according to the actual situation of the stacker, and verified and tested to ensure the accuracy and reliability of the warning rules.

[0017] Preferably, in step S4, the setting of the warning rules and thresholds respectively considers the amplitude and vibration frequency of the stacker.

[0018] Preferably, in step S4, in the warning system, different warning levels are set according to the severity of the warning conditions, namely primary warning, secondary warning and tertiary warning, and each level corresponds to different vibration parameter ranges, warning methods and response measures.

[0019] The primary warning is that the amplitude or vibration frequency exceeds within 20% of the normal threshold. The corresponding warning method is to give a warning prompt on the administrator's desktop, and give the fault occurrence location according to the sensors at different positions. After the administrator checks, they can understand the warning content and propose a treatment plan, and arrange to deal with the hidden faults of the equipment during the maintenance stage.

[0020] The secondary warning is that the amplitude or vibration frequency exceeds the normal threshold by 20%-50%. The corresponding warning methods include speed-limiting the stacker, sound alarm, sending text messages or emails for notification, etc. When it is confirmed that a transportation failure has occurred, the machine should be stopped for maintenance in a timely manner and a fault log should be recorded.

[0021] The tertiary warning is that the amplitude or vibration frequency exceeds the normal threshold by 50%. The corresponding warning methods are audible and visual alarms and synchronized warnings on the administrator's desktop. When necessary, the device is stopped from working through linkage control. After waiting for the fault to be repaired and the warning to be processed through the management authority, the device can resume normal operation.

[0022] Preferably, in step S4, the warning system has fault thresholds set for different positions of the stacker, which are associated with typical faults. The fault threshold is greater than the warning threshold. When the amplitude or vibration frequency at a certain position of the stacker exceeds the fault threshold, a fault alarm is given to indicate the type of fault, and the administrator conducts corresponding processing for the specific type of fault. The setting of the fault threshold can facilitate the identification of various faults to achieve rapid diagnosis and warning of faults and optimize the warning system.

[0023] Preferably, it further includes step S5. By using the simulation results of the digital twin model and combining the historical data of abnormal vibration warnings, the maintenance cycle of the equipment is optimized, the allocation of maintenance resources is optimized, and the risks caused by over-maintenance and non-maintenance are avoided; after each fault analysis, the data is fed back into the digital twin system to optimize the fault diagnosis model and improve the diagnostic accuracy of the model; at the same time, the maintenance strategy is updated regularly to adapt to the changes in the equipment state; through the remote monitoring function of the digital twin system, maintenance personnel can view the status of the stacker in real time and conduct remote debugging and diagnosis when necessary to improve the maintenance efficiency.

[0024] The beneficial effects of the present invention are as follows: By installing sensors at each specified position point of the stacker, the present invention measures the vibration data at the specified position points during the working process of the stacker, enabling comprehensive and rational coverage monitoring and protection of the stacker, and improving the motion control consistency between the virtual stacker and the actual stacker in the digital twin system and the authenticity of digital twin simulation.

[0025] The stacker digital twin system in the present invention, by simulating the operating state of the stacker and combining the real-time collected data, monitors the traveling mechanism, lifting mechanism, and column of the stacker in real time, can more reliably monitor potential abnormal vibrations, improve the accuracy of abnormal vibration warning of the stacker, provide a scientific basis for maintenance and repair, and reduce the unplanned downtime.

[0026] In the present invention, through the warning system, an alarm can be issued before or at the initial stage when the stacker crane has abnormal vibration, enabling the operator to have sufficient time for troubleshooting and maintenance, avoiding damage to the stacker crane due to excessive vibration, reducing production losses caused by shutdown for maintenance, and improving the reliability and service life of the equipment.

[0027] The warning system in the present invention can monitor the maintenance requirements of the stacker crane, helping the staff to formulate a more reasonable maintenance plan. By real-time monitoring and analyzing the vibration state of the equipment, the warning system can accurately judge the health status of the equipment and timely detect possible fault points, which enables the staff to arrange maintenance resources in advance and avoid the situation where the equipment cannot be repaired in time when a fault occurs.

[0028] Adopting the above solution, the present invention can cover the key components of the stacker crane and monitor the operating state, and give a warning before or at the initial stage when the stacker crane has abnormal vibration, enabling the maintenance personnel to conduct troubleshooting and maintenance in time. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0030] Figure 1 It is a front layout schematic diagram of each sensor on the stacker crane.

[0031] Figure 2 It is a side layout schematic diagram of each sensor on the stacker crane.

[0032] Figure 3 It is a layout schematic diagram of the second sensor, the fourth sensor and the fifth sensor on the stacker crane.

[0033] Figure 4 It is a curve graph of the change of vibration frequency and amplitude at the top of the column of the stacker crane.

[0034] In the figure, 1 - slide rail, 2 - guide wheel, 3 - lifting mechanism, 4 - electric control box, 5 - driving motor, 6 - ground rail, 7 - column, 8 - loading platform, 9 - fork, 10 - steel wire rope, 11 - first sensor, 12 - second sensor, 13 - third sensor, 14 - fourth sensor, 15 - fifth sensor. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0035] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0036] It should be noted that when an element is referred to as being "fixed to" or "disposed on" another element, it can be directly on the other element or indirectly on the other element. When an element is referred to as being "connected to" another element, it can be directly connected to the other element or indirectly connected to the other element.

[0037] It should be understood that the orientation or positional relationship indicated by the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation to the present invention.

[0038] In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly indicating the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the present invention, "a plurality of" means two or more, unless otherwise specifically defined. Embodiment

[0039] A method for abnormal vibration monitoring and early warning of a stacker based on digital twin includes the following steps: S1. As Figures 1 to 3 shown, a first sensor 11 is disposed near the connection of the top of the column 7 to the pallet truck and the slide rail; a second sensor 12 is disposed at the middle part of the height of the column 7, i.e., at the 1 / 2 of the total height of the column 7; a third sensor 13 is disposed near the rigid support point where the column 7 is connected to the base, within a range of 20 - 50 mm from the ground; a fourth sensor 14 is disposed at the middle part of the housing of the drive motor 5; and a fifth sensor 15 is disposed on the motor housing of the stacker fork 9.

[0040] The top of the column 7 is a free end, which is the area most affected by external loads such as pallet movement and cargo acceleration. The dynamic response is significant. Installing the first sensor 11 at the top of the column 7 can detect the amplitude and vibration frequency changes at the top of the column; the middle area of the column 7 is one of the maximum response areas of the bending mode vibration shape, suitable for capturing the overall vibration characteristics. The slender characteristics of the column make the middle prone to local resonance and significant displacement. Arranging the second sensor 12 in the middle of the column 7 can monitor the bending vibration characteristics and evaluate the vibration situation in the middle area; the bottom of the column 7 is a fixed end, which is the starting point of vibration transmission. The vibration of the base directly affects the overall response of the column. Arranging the third sensor 13 at the bottom of the column 7 facilitates the analysis of the vibration input characteristics at the bottom; a fourth sensor is installed outside the drive motor 5 of the traveling mechanism, which can not only monitor the vibration state of the transmission components of the traveling mechanism to judge the looseness or wear of the connection structure, but also monitor the overall vibration state of the motor to judge whether the motor is operating stably; installing the fifth sensor 15 on the motor housing of the forklift 9 can detect the vibration state of the forklift during operation and judge whether there are resonance, looseness or imbalance faults.

[0041] By installing sensors at five different key positions of the stacker, comprehensive and rational coverage monitoring of the stacker can be achieved.

[0042] The first sensor 11, the second sensor 12, the third sensor 13, the fourth sensor 14 and the fifth sensor 15 all include vibration sensors and acceleration sensors. The first sensor 11, the second sensor 12 and the third sensor 13 collect the amplitude change of the stacker column 7 in real time; the fourth sensor 14 and the fifth sensor 15 are responsible for collecting the vibration frequency change of the stacker traveling mechanism and the forklift 9.

[0043] The first sensor 11, the second sensor 12, the third sensor 13 and the fourth sensor 14 use the communication method of laying network cables and signal cables and use the 485 communication protocol for communication between devices; the sensor on the motor housing of the forklift uses the communication method of wireless data transmission.

[0044] The vibration sensors of the first sensor 11, the second sensor 12 and the third sensor 13 collect the amplitude change of the stacker column in real time.

[0045] The vibration monitoring of the stacker relies on the acceleration sensor to collect time-domain signals and converts them into displacement amplitudes through the frequency-domain integration method.

[0046] The vibration sensor uses a piezoelectric accelerometer, which converts mechanical vibration into an electrical signal through the piezoelectric effect and outputs a voltage signal proportional to the acceleration , and its mathematical relationship is:

[0047] Where: : The time-domain acceleration signal (unit: m / s²), which is directly measured by a vibration sensor.

[0048] : The time-domain displacement signal (unit: m), representing the vibration displacement of the monitoring point of the stacker crane.

[0049] is the sensor sensitivity (unit: mV / (m / s²)) is the environmental noise.

[0050] To eliminate noise and accurately extract the amplitude, the following steps are taken: First, perform preprocessing (denoising), apply wavelet threshold denoising (select the sym5 wavelet basis) to remove high-frequency noise:

[0051] Then, use the Fourier transform to convert the time-domain signal to the frequency domain:

[0052] : Frequency (unit: Hz), representing the frequency components of the vibration signal.

[0053] : The frequency-domain representation of acceleration (unit: m / s² / Hz).

[0054] Next, calculate the displacement spectrum through double integration: (High-pass filter cut-off frequency 0.5Hz) : The frequency-domain representation of displacement (unit: m / Hz), obtained by integrating the acceleration spectrum.

[0055] : The imaginary unit, satisfying .

[0056] : The transfer function of the high-pass filter, used to eliminate low-frequency noise (such as sensor drift), usually set the cut-off frequency to 0.5Hz.

[0057] Then, use the inverse Fourier transform: to obtain the time-domain displacement signal:

[0058] : The inverse Fourier transform operator, which converts the frequency-domain signal to the time-domain signal.

[0059] : The time-domain displacement signal obtained after integration (unit: m).

[0060] Finally, calculate the peak amplitude and extract the amplitude:

[0061] : Peak displacement amplitude (unit: m), representing the maximum amplitude of vibration.

[0062] The absolute value of the displacement signal is used to calculate the amplitude envelope.

[0063] When the vibration sensor measures the vibration frequency of the motor in the stacker system, through methods such as Fourier transform or fast Fourier transform (FFT), the time-domain signal of the vibration is converted to the frequency domain to obtain the vibration frequency of the motor.

[0064] The vibration sensor collects acceleration data in real time , and the sampling rate is higher than twice the maximum frequency of the signal, meeting the Nyquist theorem; when the vibration sensor processes the acceleration data, first remove the DC component of the signal to ensure that it fluctuates around zero:

[0065] Among them, is the mean value of the signal, is the processed acceleration.

[0066] Then use a band-pass filter to remove the high-frequency noise and low-frequency drift that are not of interest.

[0067] For the preprocessed vibration signal , perform a fast Fourier transform to obtain the spectrum. The Fourier transform formula is as follows:

[0068] Where: is the frequency-domain representation, is the time-domain signal, is the frequency.

[0069] After obtaining the frequency-domain signal, perform spectrum analysis to identify the main frequency components and the spectral characteristics of the vibration; the spectrogram can display the amplitude of the vibration signal at different frequencies and provide information about the vibration frequency, intensity, and distribution.

[0070] S2. Establish a digital twin model of the stacker, and transmit the collected vibration parameter data to the digital twin model of the stacker.

[0071] In the digital twin model, a physical mechanism that drives the interaction between the physical entity and the virtual entity is defined. The Internet of Things technology is used to drive the virtual entity to perform real-time information interaction, ensuring that the state of the digital twin model is consistent with that of the physical entity. In the physical mechanism of virtual-real interaction, the model design of the stacker is completed through SolidWorks software, and then imported into the digital twin platform Unity to complete the settings of the nodes, behaviors, attributes, and characteristics of the stacker, thereby realizing the real-time communication and data interaction between the physical model and the digital model of the stacker. The digital twin model reflects the operating state of the stacker in real time, including but not limited to speed, acceleration, vibration frequency, and amplitude.

[0072] The stacker is built-in with a PLC, which can obtain the real-time position coordinate data of the stacker in real time, and communicate and interact with the digital twin model in real time, enabling the digital twin model to reflect the operating position of the stacker. This is the existing technology.

[0073] First, Python is used to read the collected data, and the data is processed through OPC software, including noise reduction using mean filtering and wavelet denoising methods, filtering using Kalman filtering and adaptive filtering methods, normalizing or standardizing the real-time data, extracting feature information according to actual needs, calculating frequency and amplitude, and finally visualizing and saving the processed data through the WINCC control.

[0074] S3. Establish a vibration fault service system within the digital twin model platform, and diagnose the collected vibration parameter data based on the machine learning support vector machine algorithm and artificial intelligence technology.

[0075] According to the processed data and feature information, the state of the digital twin model is updated in real time. By comparing the state differences between the digital twin model and the physical entity, the operating state of the stacker is evaluated. In comparing the state differences between the digital twin model and the physical entity, machine learning algorithms are applied in the digital twin model to analyze the real-time data and the associated digital twin model, detect and identify signs of potential faults, and conduct analysis and diagnosis.

[0076] As one of the surrogate model methods, the support vector machine method can achieve fast calculation and ensure the accuracy and precision of the calculation, and has been well applied: Step 1: Load the original training set and standardize the data. For each attribute value, the formula after standardization is:

[0077] where is the value after standardization, is the value before standardization, is the maximum value in this dataset, is the minimum value in this data.

[0078] Step 2: Divide the standardized dataset into a training sample set and a prediction sample set according to a certain ratio. The training set is used for model training and parameter optimization, and the test set is used to verify the generalization ability of the model and avoid overfitting.

[0079] Step 3: Input the feature data in the training set into the SVM model to obtain the fitness of the population. Then perform selection, crossover, and mutation operations, and select the parameters C and g under the best fitness.

[0080] C is the regularization parameter, which controls the tolerance of the model to classification errors and balances the model complexity and training error; g is the kernel function parameter, which determines the influence range of a single sample on the classification boundary and controls the "width" of the kernel function. By adjusting C and g, the SVM can achieve high accuracy on the training set and maintain good generalization ability on the test set.

[0081] S4. Set up an early warning system in the digital twin model and set an early warning threshold. When the vibration parameters of the stacker reach or exceed the early warning threshold, an early warning signal is issued.

[0082] The setting of the early warning rules and thresholds respectively considers the amplitude and vibration frequency of the stacker. The early warning threshold is initially set according to international standards and stacker industry standards, and the early warning rules and thresholds are dynamically adjusted and optimized in multiple dimensions according to the actual situation of the stacker, and verified and tested.

[0083] In the early warning system, different early warning levels are set according to the severity of the early warning conditions, namely level 1 early warning, level 2 early warning, and level 3 early warning. Each level corresponds to different vibration parameter ranges, early warning methods, and response measures.

[0084] Level 1 early warning means that the amplitude or vibration frequency exceeds within 20% of the normal threshold. The corresponding early warning method is to give an early warning prompt on the administrator's desktop, and give the fault location according to the sensors at different positions. After the administrator checks, they can understand the early warning content and propose a treatment plan, and arrange to deal with the hidden faults of the equipment during the maintenance stage.

[0085] Level 2 early warning means that the amplitude or vibration frequency exceeds 20%-50% of the normal threshold. The corresponding early warning methods include speed limiting the stacker, sound alarm, sending text messages or emails, etc. When it is confirmed that a transportation fault has occurred, the machine should be stopped for maintenance in time and the fault log should be recorded.

[0086] Level 3 early warning means that the amplitude or vibration frequency exceeds 50% of the normal threshold. The corresponding early warning method is a combination of sound and light alarm and synchronous early warning on the administrator's desktop. When necessary, the device is stopped from working through linkage control. After waiting for the fault to be repaired and the early warning to be processed through management permissions, the device can resume normal operation.

[0087] Due to the high position of the top of the column of the stacker crane, the amplitude is large, but the frequency is low; the amplitude at the bottom may be small, but the frequency is high, reflecting the foundation stability; the operating conditions of the traveling mechanism and the forklift are also different. Therefore, different warning thresholds for amplitude and frequency need to be set separately for different positions of the stacker crane, as shown in Table 1.

[0088] In the warning system, fault thresholds are set for different positions of the stacker crane, which are associated with typical faults. The fault threshold is greater than the warning threshold. When the amplitude or vibration frequency at a certain position of the stacker crane exceeds the fault threshold, a fault alarm is issued to prompt the fault type, and the administrator conducts corresponding processing for the specific fault type. The setting of the fault threshold can facilitate the identification of various faults to achieve fault diagnosis and warning.

[0089] According to international standards and stacker crane industry standards, the initial warning thresholds and fault thresholds are set as follows: the warning threshold for the amplitude at the top of the column is 0.08 mm, the fault threshold is 0.15 mm, the warning threshold for the vibration frequency is 8 Hz, and the fault threshold is 12 Hz; the warning threshold for the amplitude in the middle of the column is 0.05 mm, the fault threshold is 0.1 mm, the warning threshold for the vibration frequency is 15 Hz, and the fault threshold is 25 Hz; the warning threshold for the amplitude at the bottom of the column is 0.03 mm, the fault threshold is 0.06 mm, the warning threshold for the vibration frequency is 30 Hz, and the fault threshold is 45 Hz; the warning threshold for the amplitude at the traveling mechanism is 0.1 mm, the fault threshold is 0.25 mm, the warning threshold for the vibration frequency is 20 Hz, and the fault threshold is 30 Hz; the warning threshold for the amplitude at the forklift is 0.04 mm, the fault threshold is 0.12 mm, the warning threshold for the vibration frequency is 30 Hz, and the fault threshold is 45 Hz, as shown in Table 1.

[0090] For example, at the top of the column, initially, the fault threshold for the amplitude is 0.15 mm, and the fault threshold for the vibration frequency is 12 Hz. When the amplitude at the top of the column exceeds 0.15 mm or the vibration frequency exceeds 12 Hz, a fault alarm will be triggered, and the fault type will be prompted, such as top pulley wear, eccentric load, or structural resonance, and loose connections; for other positions of the stacker crane, it is the same as above, as shown in Table 1.

[0091] Table 1 Initial Threshold Setting Table for Amplitude and Vibration Frequency at Different Positions of the Stacker Crane

[0092] Among them, the amplitude and vibration frequency change curves at the top of the column of the stacker crane are as Figure 4As shown, a part of the amplitude and vibration frequency curves at the top of the column exceed the warning threshold but do not exceed the failure threshold. The present invention can initiate a first-level warning when the amplitude at the top of the column is within the range of 0.08 - 0.096 mm, a second-level warning when the amplitude at the top of the column is within the range of 0.096 - 0.12 mm, and a third-level warning when the amplitude at the top of the column is greater than 0.12 mm. The same applies to the amplitude and vibration frequency at other positions.

[0093] During the subsequent experimental process, the warning rules and thresholds are dynamically adjusted and optimized in multiple dimensions according to the actual situation of the stacker crane and verified and tested.

[0094] Using the simulation results of the digital twin model and combining with the historical data of abnormal vibration warning, it is possible to optimize the maintenance cycle of the equipment, optimize the allocation of maintenance resources, and avoid the risks caused by over-maintenance and non-maintenance; after each failure analysis, the data is fed back into the digital twin system to optimize the fault diagnosis model and improve the diagnostic accuracy of the model; at the same time, the maintenance strategy is updated regularly to adapt to the changes in the equipment state; through the remote monitoring function of the digital twin system, maintenance personnel can view the status of the stacker crane in real time and perform remote debugging and diagnosis when necessary, improving the maintenance efficiency.

[0095] Experiments show that through a method for abnormal vibration monitoring and warning of a stacker crane based on digital twin included in the present invention, it is possible to reduce the unplanned downtime, shorten the fault troubleshooting time, optimize the resource scheduling, and thus greatly improve the maintenance efficiency; reduce the downtime events caused by sudden failures, improve the equipment stability, indirectly improve the production beat consistency, reduce the defective product rate or process interruption, and plan the maintenance window period during non-production peak periods, such as night shifts or low-load periods, thereby reducing the production losses caused by downtime for maintenance; since vibration abnormality is an early signal of mechanical wear, through the method included in the present invention, it is possible to intervene in time to avoid chain damage, optimize the maintenance effect through long-term data accumulation, and thus improve the reliability and service life of the equipment.

[0096] Compared with the stacker crane that does not use the method included in the present invention, after using the method included in the present invention, the maintenance efficiency of the stacker crane is increased by 26 - 30%, the service life of the equipment is extended by 18 - 24%, and the production losses are reduced by 40 - 50%, with remarkable effects.

Claims

1. A method for monitoring and early warning abnormal vibration of a stacker based on digital twins, characterized in that: The following steps are involved: S1. Multiple sensors are installed on the stacker's column, walking mechanism and fork motor to collect vibration parameters in real time, including but not limited to amplitude and frequency; S2. Establish a digital twin model of the stacker, and transmit the collected vibration parameter data to the digital twin model of the stacker; S3. Establish a vibration fault service system within the digital twin model platform to diagnose the collected vibration parameter data; S4. Set up an early warning system in the digital twin model and set the early warning threshold. When the vibration parameters of the stacker reach or exceed the early warning threshold, a early warning signal is issued.

2. According to the method for monitoring and early warning abnormal vibration of a stacker based on digital twins according to claim 1, it is characterized in that: In step S1, sensors are respectively installed on the top, middle and bottom of the column of the stacker, the driving motor housing of the walking mechanism and the motor housing of the fork.

3. According to the method for monitoring and early warning abnormal vibration of a stacker based on digital twins according to claim 1, it is characterized in that: In step S1, sensors are installed at the top of the column near the connection between the pallet truck and the slide rail, at 1 / 2 of the total height of the column, at the rigid support point at the bottom of the column near the connection with the base, in the middle of the housing of the walking mechanism drive motor, and on the fork motor housing; the rigid support point at the bottom of the column near the connection with the base is 20 to 50 mm above the ground.

4. According to the method for abnormal vibration monitoring and early warning of a stacker based on digital twins according to claim 1, it is characterized in that: Specifically, step S2 is to first use Python to read the collected data, process the data through OPC software, including using mean filtering and wavelet denoising methods for noise reduction, using Kalman filtering and adaptive filtering methods for filtering, normalizing or standardizing real-time data, extracting feature information according to actual needs, calculating frequency and amplitude, and finally visualizing and saving the processed data through WINCC controls, and drawing the corresponding curve change graph.

5. According to the method for monitoring and early warning abnormal vibration of a stacker based on digital twins according to claim 1, it is characterized in that: In the digital twin model in step S2, a physical mechanism is defined to drive the interaction between the physical entity and the virtual entity, and the Internet of Things technology is used to drive the virtual entity to interact with information in real time, ensuring that the state of the digital twin model is consistent with the physical entity; in the physical mechanism of virtual-real interaction, the model design of the stacker is completed through SolidWorks software, and then imported into the digital twin platform Unity to complete the node, behavior, attribute and feature setting of the stacker, thereby realizing real-time communication and data interaction between the physical model of the stacker and the digital model; the digital twin model reflects the operating status of the stacker in real time, including but not limited to position, speed, acceleration, vibration frequency and amplitude.

6. The method for monitoring and early warning abnormal vibration of a stacker based on digital twin according to claim 1 is characterized in that: The vibration fault service system in step S3 diagnoses the collected data based on machine learning support vector machine algorithm and artificial intelligence technology.

7. The method for monitoring and early warning abnormal vibration of a stacker based on digital twin according to claim 1 is characterized in that: In step S4, the setting of the warning rules and thresholds takes into account the amplitude and frequency of the stacker respectively, and the warning rules and thresholds are dynamically adjusted and optimized in multiple dimensions according to the actual situation of the stacker, and are verified and tested.

8. The method for monitoring and early warning abnormal vibration of a stacker based on digital twin according to claim 1 is characterized in that: In step S4, the early warning system sets fault thresholds for different positions of the stacker, which are associated with typical faults. The fault threshold is greater than the early warning threshold. When the amplitude or frequency of a certain position of the stacker exceeds the fault threshold, a fault alarm is issued to indicate the fault type. The administrator handles the specific fault type.

9. The method for monitoring and early warning abnormal vibration of a stacker based on digital twin according to claim 1 is characterized in that: The method further includes step S5, using the simulation results of the digital twin model in combination with the abnormal vibration warning historical data to optimize the maintenance cycle of the equipment and the configuration of maintenance resources; After each fault analysis, the data is fed back to the digital twin system to optimize the fault diagnosis model. At the same time, the maintenance strategy is updated regularly to adapt to changes in equipment status. Through the remote monitoring function of the digital twin system, maintenance personnel can view the status of the stacker in real time and perform remote debugging and diagnosis when necessary.

10. The method for monitoring and early warning abnormal vibration of a stacker based on digital twin according to claim 7, characterized in that: Different warning levels are set in the early warning system according to the severity of the warning conditions, namely, level one warning, level two warning and level three warning. Each level corresponds to a different vibration parameter range, warning method and response measure. Level one warning means that the amplitude or frequency exceeds the normal threshold by less than 20%. The corresponding warning method is to give a warning prompt on the administrator's desktop, and give the fault location according to the sensors in different positions. The administrator checks and understands the warning content and proposes a solution, and arranges to deal with the hidden faults of the equipment during the maintenance stage. Level two warning means that the amplitude or frequency exceeds the normal threshold by 20%-50%. The corresponding warning methods include speed limiting the stacker, sound alarm, sending SMS or email notifications, etc. When a transportation fault is confirmed, it should be stopped for maintenance in time and the fault log should be recorded. Level three warning means that the amplitude or frequency exceeds the normal threshold by 50%. The corresponding warning methods are sound and light alarm and synchronous warning on the administrator's desktop. If necessary, the equipment can be stopped through linkage control to wait for the fault to be repaired and the warning to be processed through management authority before it can resume normal work.

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