An emergency stop and alarm control system and method for unwinding an oversized roll

Abstract

This application provides an emergency stop and alarm control system and method for unwinding an oversized roll, which is applied to the field of automation technology. Through the coordinated operation of the emergency stop control module and the alarm module, it solves the problems of timely detection and processing of end surface abnormalities during the unwinding process of oversized rolls, as well as accurate identification and alarm of the cause of equipment stoppage, thereby improving production safety and efficiency. Therefore, this application has the advantages of being able to monitor unwinding end surface abnormalities in real time and implement timely emergency stop, while also being able to distinguish the cause of equipment stoppage and issue corresponding alarms, thereby improving production safety and fault response efficiency.

Description

Technical Field

[0001] The present application relates to the field of automation technology, and in particular to a system and method for controlling an emergency stop and alarm for unwinding an oversized roll. Background Art

[0002] In the strapping tape production industry, the application of oversized roll unwinding equipment is crucial. During the strapping tape production process, oversized roll unwinding equipment is responsible for gradually unwinding the rolled strapping tape for subsequent processing, packaging, and other steps. However, its long-term operation presents a series of technical challenges that require urgent resolution.

[0003] Currently, the control systems for most equipment used to unwind oversized rolls of strapping tape are relatively basic. Existing technologies have significant shortcomings when it comes to monitoring the condition of the unwinding end face. Traditional monitoring methods can only obtain a relatively rough unwinding position, making it difficult to accurately determine whether the unwinding end face has any abnormalities such as tape bending, deviation, breakage, or wrinkles. During the unwinding process, if an abnormality occurs on the unwinding end face, it can lead to uneven tension, uneven winding, and even the shedding of entire layers of tape, which in turn affects the quality and efficiency of packaging. Because the abnormality on the unwinding end face cannot be detected in a timely manner, the unwinding equipment continues to operate, which can eventually lead to a series of production quality issues such as damaged strapping tape, unstable packaging, and unwinding safety issues. In severe cases, it can even damage subsequent processing equipment, causing huge economic losses and production delays.

[0004] Furthermore, existing strapping unwinding equipment also has flaws in identifying the cause of the stop when it stops. When the unwinder stops, it's impossible to accurately distinguish whether the stop was caused by an emergency stop or a normal stop, such as the end of normal production or a planned short shutdown. This makes it difficult for staff to quickly take targeted measures after the equipment stops. For example, in the case of an emergency stop, the cause of the fault must be promptly investigated and repairs and maintenance must be performed, while a normal stop may only require routine record keeping and preparation for the next start-up. This inability to accurately identify the cause of the stop not only reduces production efficiency but can also lead to safety hazards, as some potential faults cannot be discovered and addressed in a timely manner.

[0005] Therefore, the existing technology is in urgent need of improvement. Summary of the Invention

[0006] In view of the above-mentioned shortcomings of the existing technology, the present application provides an emergency stop and alarm control system and method for unwinding of extra-large rolls, which has the advantages of being able to monitor abnormalities of the unwinding end face in real time and make emergency stops in time, while being able to distinguish the reasons for equipment stoppage and issue corresponding alarms, thereby improving production safety and fault response efficiency.

[0007] In a first aspect, an emergency stop and alarm control system for unwinding an oversized roll is provided, wherein the system monitors the unwinding equipment through an emergency stop control module and an alarm module;

[0008] The emergency stop control module includes:

[0009] End face monitoring module: used to obtain the unwinding position of the strapping tape and monitor whether the unwinding end face of the unwinding machine is abnormal based on the unwinding position;

[0010] Abnormal control module: used for controlling the unwinding device to shut down when an abnormality occurs on the unwinding end surface;

[0011] The alarm module includes:

[0012] Emergency stop alarm module: when there is no abnormality on the unwinding end face and the unwinder stops running, it executes the stop detection process to detect whether the stop reason is an emergency stop or a normal stop, and issues a corresponding emergency stop alarm or normal stop alarm according to the stop reason.

[0013] This application proposes an emergency stop and alarm control system for unwinding an oversized roll. By using an emergency stop control module and an alarm module to work together, this system solves the problems of timely detection and processing of end surface anomalies during the unwinding process of an oversized roll, as well as accurate identification and alarm of the cause of equipment stoppage, thereby improving production safety and efficiency. Therefore, this application has the advantages of being able to monitor unwinding end surface anomalies in real time and initiate an emergency stop in a timely manner, while also being able to distinguish the cause of equipment stoppage and issue a corresponding alarm, thereby improving production safety and fault response efficiency.

[0014] Furthermore, the alarm module further includes:

[0015] Alarm control module: used to control the baler to stop running according to the emergency stop alarm;

[0016] Reset module: used to receive alarm reset information and turn off the emergency stop alarm according to the alarm reset information.

[0017] The present application proposes an emergency stop and alarm control system for unwinding an extra-large roll, which improves the system's response and processing mechanism after the emergency stop alarm is issued by adding an alarm control module and a reset module, thereby improving the system's safety and operability.

[0018] Furthermore, the emergency stop alarm module includes:

[0019] Stop detection module: used to determine whether the emergency stop button is pressed, whether the upper sensor switch is sensed, whether the upper anti-collision switch is sensed, and whether the remote control switch is stopped;

[0020] First alarm module: when any one or more of the following occurs: the emergency stop button is pressed, the upper sensor switch is sensed, the upper anti-collision switch is sensed, and the remote control switch stops, the stop reason is an emergency stop, and an emergency stop alarm is issued;

[0021] Second alarm module: When the emergency stop button is not pressed, the upper sensor switch is not sensing, the upper anti-collision switch is not sensing, and the remote control switch is not stopped, the stop reason is normal stop, and a normal stop alarm is issued.

[0022] The present application proposes an emergency stop and alarm control system for unwinding of extra-large rolls, which provides a specific stop cause detection and classification alarm mechanism by refining the functions of the emergency stop alarm module, thereby solving the problem in the prior art of being unable to accurately identify the cause of equipment stoppage.

[0023] Furthermore, the end face monitoring module includes:

[0024] Photoelectric counting module, used to collect the number of unwinding turns of the strapping tape;

[0025] The end face warning module is used to issue an end face monitoring alarm when the number of unwinding turns reaches a preset turn threshold to remind the user to monitor the unwinding of the strapping tape.

[0026] Furthermore, the end face monitoring module also includes:

[0027] Acquisition module: used to collect vibration data of the unwinder during operation to obtain vibration frequency and vibration amplitude;

[0028] Filter processing module: used for filtering the unwinding position using a Kalman filter algorithm according to the vibration frequency and the vibration amplitude to obtain a filtered unwinding position;

[0029] Abnormality judgment module: Based on the filtered unwinding position, the flatness parameter and edge integrity parameter of the unwinding end surface are calculated. If the flatness parameter exceeds the preset range or the edge integrity parameter is lower than the threshold, it is determined that there is an abnormality in the unwinding end surface.

[0030] The present application proposes an emergency stop and alarm control system for unwinding an extra-large roll, which can more accurately judge the flatness and edge integrity of the unwinding end face by collecting vibration data and performing filtering processing, thereby improving the accuracy of abnormality detection.

[0031] Furthermore, the filtering processing module includes:

[0032] Compensation module: constructing an autoregressive sliding average model according to the vibration frequency to obtain frequency compensation parameters;

[0033] The first adjustment module: determines the measurement noise variance according to the vibration amplitude and obtains the amplitude adjustment parameter;

[0034] The first filtering module adjusts the system noise covariance matrix and the measurement noise covariance matrix of the Kalman filter algorithm according to the frequency compensation parameter and the amplitude adjustment parameter, and uses the adjusted Kalman filter algorithm to filter the unwinding position to obtain the unwinding position after filtering.

[0035] Furthermore, the compensation module includes:

[0036] Extraction module: used to analyze historical vibration data, extract multiple vibration frequency components using fast Fourier transform to obtain a frequency set;

[0037] Construction module: used to construct an autoregressive sliding average model for each vibration frequency component in the frequency set, and obtain multiple frequency compensation parameters corresponding to each vibration frequency component;

[0038] The first calculation module is used to perform weighted averaging on multiple frequency compensation parameters according to the proportion of each vibration frequency component in the frequency set to obtain the final frequency compensation parameter.

[0039] Furthermore, the first adjustment module includes:

[0040] Normalization processing module: acquires the vibration amplitude data of the unwinding device in real time, and performs normalization processing on the vibration amplitude data to obtain a normalized vibration amplitude value;

[0041] Mapping establishment module: establishes a mapping relationship between the normalized vibration amplitude value and the measurement noise variance, wherein the mapping relationship is a nonlinear function;

[0042] The second calculation module maps the normalized vibration amplitude value to a corresponding measurement noise variance according to the mapping relationship, and uses the measurement noise variance as an amplitude adjustment parameter.

[0043] Furthermore, the first filtering module includes:

[0044] A second adjustment module: adjusting the Kalman filter algorithm state transfer matrix and the system noise covariance matrix according to the frequency compensation parameters;

[0045] A third adjustment module: calculating an adjusted measurement noise covariance matrix according to the amplitude adjustment parameter and a preset noise adjustment factor;

[0046] The second filtering module uses the adjusted Kalman filtering algorithm, combined with the modified state transfer matrix and the measurement noise covariance matrix, to iteratively filter the unwinding position, dynamically adjust the state estimation value according to the Kalman gain, and obtain the unwinding position after filtering.

[0047] In a second aspect, a method for controlling the emergency stop and alarm of unwinding of an oversized roll is applied to any of the above-mentioned systems, the method comprising the steps of:

[0048] S1: Obtain the unwinding position of the strapping tape, and monitor whether the unwinding end surface of the unwinding machine is abnormal based on the unwinding position;

[0049] S2: When an abnormality occurs on the unwinding end surface, controlling the unwinding device to shut down;

[0050] S3: When no abnormality occurs on the unwinding end face and the unwinder stops running, a stop detection process is executed, wherein the stop detection process is used to detect whether the stop cause is an emergency stop or a normal stop, and a corresponding emergency stop alarm or normal stop alarm is issued according to the stop cause.

[0051] Beneficial Effects: This application proposes an emergency stop and alarm control system and method for unwinding an oversized roll. Through the coordinated operation of the emergency stop control module and the alarm module, this system solves the problems of timely detection and processing of end surface anomalies during the unwinding process of an oversized roll, as well as accurate identification and alarm of the cause of equipment stoppage, thereby improving production safety and efficiency. Therefore, this application has the advantages of being able to monitor unwinding end surface anomalies in real time and initiate a timely emergency stop, while also being able to distinguish the cause of the equipment stoppage and issue a corresponding alarm, thereby improving production safety and fault response efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 This is a workflow diagram of the emergency stop and alarm control system for unwinding of oversized rolls proposed in this application.

[0053] Figure 2 This is another workflow block diagram of the oversized roll unwinding emergency stop and alarm control system proposed in this application.

[0054] Figure 3 This is a structural diagram of the emergency stop and alarm control system for unwinding an oversized roll proposed in this application.

[0055] Figure 4 This is a flow chart of the unwinding emergency stop and alarm control method for an oversized roll proposed in this application.

[0056] Explanation of reference numerals: 101, emergency stop control module; 102, alarm module; 103, end face monitoring module; 104, abnormality control module; 105, emergency stop alarm module. DETAILED DESCRIPTION

[0057] The technical solutions 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. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application generally described and marked in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work fall within the scope of protection of the present application.

[0058] It should be noted that similar reference numerals and letters represent similar items in the following figures. Therefore, once an item is defined in one figure, it does not need to be further defined or explained in subsequent figures. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and should not be understood as indicating or implying relative importance.

[0059] In the traditional production of strapping tape, the use of oversized unwinding equipment is crucial. However, in actual operation, oversized unwinding equipment faces technical challenges: difficulty accurately monitoring and promptly addressing abnormalities at the unwinding end, as well as an inability to accurately identify the cause of the equipment stoppage after it stops. Currently, the control systems of most equipment are inadequate in monitoring the condition of the unwinding end, making it difficult to accurately determine the unwinding position and, consequently, whether the unwinding end exhibits abnormalities such as deviation, damage, or wrinkles. Furthermore, when existing equipment stops, it is unable to accurately identify whether the stop was due to an emergency stop or a normal stop.

[0060] To solve the above problems, please refer to Figures 1 to 3 ,This application proposes an emergency stop and alarm control system for unwinding of an oversized roll, the system monitors the unwinding equipment through an emergency stop control module 101 and an alarm module 102;

[0061] The emergency stop control module 101 includes:

[0062] End surface monitoring module 103: used to obtain the unwinding position of the strapping tape and detect whether there is any abnormality in the unwinding end surface of the unwinding machine based on the unwinding position;

[0063] Abnormal control module 104: used to control the unwinding device to shut down when an abnormality occurs on the unwinding end surface;

[0064] The alarm module 102 includes:

[0065] Emergency stop alarm module 105: used to execute the stop detection process when there is no abnormality on the unwinding end surface and the unwinding machine stops running. The stop detection process is used to detect whether the stop reason is an emergency stop or a normal stop, and issue a corresponding emergency stop alarm or normal stop alarm according to the stop reason.

[0066] The end face monitoring module 103 is used to obtain the unwinding position of the strapping tape and, based on this position, monitor the unwinding end face of the unwinder for any abnormalities. The unwinding position can be acquired using encoders, sensors, or other methods. Monitoring for end face abnormalities can be achieved using technologies such as visual recognition, laser scanning, and vibration analysis. This provides a data basis for determining the condition of the unwinding end face.

[0067] The abnormality control module 104 is used to control the unwinding equipment to shut down when an abnormality occurs on the unwinding end surface. This can be achieved by cutting off the power supply, activating the brake device, sending a stop command, etc. Its purpose is to immediately stop the equipment operation when an abnormality is detected to prevent further damage.

[0068] The emergency stop alarm module 105 is designed to execute a stop detection process when the unwinder stops operating without any abnormalities on the unwinding end face. This process detects whether the stop was caused by an emergency stop or a normal stop, and issues an emergency stop alarm or a normal stop alarm based on the cause. The stop detection process may include checking the status of the emergency stop button, safety sensors, and control signals. The alarm may be issued using audio, visual signals, or informational prompts. Its purpose is to distinguish the nature of the equipment stop and provide targeted information.

[0069] The core innovation of this application is that the state of the unwinding end face is judged by introducing the end face monitoring module 103, and the abnormal control module 104 immediately shuts down the equipment in case of abnormality. At the same time, when the equipment stops and the end face is normal, the emergency stop alarm module 105 executes the stop detection process and distinguishes the cause of the stop and issues an alarm, thereby solving the problem that the abnormality of the unwinding end face is difficult to handle in time and the cause of the equipment stop cannot be accurately identified, thereby improving production safety and management efficiency.

[0070] As a preferred embodiment, the solution of this application is specifically implemented as follows:

[0071] The unwinder is turned on manually or automatically by program control. The end surface monitoring module 103 then checks whether the initial position is normal and whether the emergency stop alarm module 105 has issued an alarm. If the initial position is normal and there is no alarm, a start signal is issued to control the motor, which drives the turntable to unwind the oversized roll. During the unwinding process, the unwinding position of the strapping tape is monitored in real time to determine whether it has reached the near-end surface (i.e., whether the number of unwinding turns has reached a preset threshold). If so, the end surface monitoring module 103 continuously monitors the unwinding end surface for any abnormalities. If an abnormality is detected, the abnormality control module 104 stops the unwinder. Alternatively, the unwinder can be manually stopped and its associated upstream and downstream devices shut down.

[0072] If the near end surface is not reached, the photoelectric counting module will continue to record the number of unwinding turns until the unwinding position reaches the near end surface.

[0073] If no abnormality occurs at the unwinding end, the unwinder is determined to have stopped. If not, the unwinding is proceeding normally. If the unwinder has stopped, the emergency stop alarm module 105 executes a stop detection process to detect whether the stop was an emergency stop or a normal stop. A normal stop indicates that the unwinding of an oversized roll has been completed and the unwinder has stopped unwinding normally.

[0074] The stop detection process is executed to determine whether the emergency stop button has been pressed, the upper sensor switch has been activated, the upper anti-collision switch has been activated, and the remote control switch has been deactivated. If any one or more of these four emergency stop conditions are met, the cause of the stop is determined to be an emergency stop, an emergency stop alarm is issued, and the unwinder's upstream equipment is stopped. After the emergency stop alarm is issued, if the emergency stop alarm module detects an operation reset request, the alarm signal is cleared and the alarm cause continues to be detected. If no operation reset request is detected, the process ends immediately.

[0075] The end face monitoring module 103 can be configured with a visual sensor to collect images of the unwinding end face of the strapping tape in real time, and analyze the flatness, edge shape and other features of the end face through image processing algorithms to determine whether there is any abnormality.

[0076] The abnormality control module 104 may be a relay or a contactor, which immediately cuts off the power supply of the main motor of the unwinding device after receiving the abnormality signal sent by the end surface monitoring module.

[0077] The emergency stop alarm module 105 can be integrated into a programmable logic controller (PLC). When it receives a stop signal from the unwinding device and no end surface abnormality signal, the PLC executes a stop detection program. This program reads the status of input signals such as the emergency stop button, safety light curtain, and host computer control instructions, and determines the cause of the stop based on these status combinations. For example, if the emergency stop button is pressed, it is determined to be an emergency stop. Based on the determination result, the PLC activates an audible and visual alarm to sound an emergency stop alarm, or displays a normal stop message on the user interface.

[0078] Through the above solution, this application solves the problem in the existing technology that it is difficult for ultra-large roll unwinding equipment to accurately monitor and promptly handle abnormalities at the unwinding end face. Through end face monitoring and abnormality control, the equipment can be quickly stopped when an abnormality occurs, avoiding damage to the strapping tape and equipment failure. At the same time, it solves the problem of being unable to accurately distinguish the cause of the stop after the equipment stops. Through the stop detection process and differentiated alarms, operators can quickly understand the equipment status and take appropriate subsequent treatment measures, improving the efficiency of fault diagnosis and the convenience of production management.

[0079] In some of the above-mentioned embodiments of the present application, an alarm module is proposed. When the unwinder stops running, the alarm module can determine whether it is an emergency stop or a normal stop by detecting the reason for the stop, such as whether the emergency stop button is pressed or whether the sensor senses an abnormality, and accordingly issue an emergency stop alarm or a normal stop alarm in the form of a sound or light signal. In this way, the operator can be informed of the stop status and reason of the equipment in a timely manner. However, in this process, simply issuing an emergency stop alarm is not enough to deal with emergency situations. Corresponding control measures are required to stop the relevant equipment, and the issued alarm also needs to have a reset mechanism to release it. In order to solve the above problems, the present application further improves the function of the alarm module, which also includes an alarm control module and a reset module.

[0080] Alarm control module: used to control the baler to stop running according to the emergency stop alarm;

[0081] Reset module: used to receive alarm reset information and turn off the emergency stop alarm according to the alarm reset information.

[0082] Among them, the alarm control module refers to a functional unit used to receive the emergency stop alarm signal and perform control operations according to the signal. It can be implemented by using the output module of the programmable logic controller (PLC), the relay control circuit or the control interface of the industrial computer.

[0083] Among them, the reset module refers to a functional unit for receiving an external reset instruction and releasing the alarm state according to the instruction, which can be implemented by a physical button, a software button on a touch screen interface, a communication interface or a remote control signal receiver.

[0084] This solution builds upon the existing alarm module by further adding an alarm control module and a reset module, thereby improving the system's response and handling mechanisms after an emergency stop alarm is issued. Specifically, when the emergency stop alarm module detects a situation requiring an emergency stop alarm, the alarm control module receives this emergency stop alarm signal and, as a trigger, immediately stops the baling machine associated with the unwinding equipment. This coordinated control ensures that if an abnormality occurs at the unwinding end or an emergency stop occurs, the entire production process is promptly interrupted, preventing further damage or danger from inertia or subsequent equipment operation.

[0085] After the cause of the emergency stop is eliminated or processed, the system needs to recover from the emergency stop state. The reset module receives the alarm reset command from the external input, and according to the command, releases or closes the emergency stop alarm state previously issued by the emergency stop alarm module. This enables the system to smoothly recover from the emergency stop state to the normal standby or ready-to-run state, improving the recovery efficiency of the equipment and the overall availability of the system. By introducing the alarm control module and the reset module, this solution adds linkage control of subsequent equipment and effective management of the alarm status on the basis of the original alarm issuance. This combination enables the system to achieve rapid, effective response and safe and reliable recovery when dealing with emergency stop situations, significantly improving the safety and ease of operation of the entire system.

[0086] In some of the aforementioned solutions of this application, an emergency stop alarm module is proposed for executing a stop detection process to detect whether the stop cause is an emergency stop or a normal stop when no abnormality occurs on the unwinding end surface and the unwinder stops running, and to issue a corresponding emergency stop alarm or normal stop alarm based on the stop cause. However, in its implementation process, only the functions of the emergency stop alarm module are described, without specifying how to detect the stop cause and how to distinguish between emergency stops and normal stops based on the detection results and issue corresponding alarms. As a result, the existing technology is unable to accurately and effectively identify the specific cause of the equipment stop, affecting the efficiency and safety of subsequent troubleshooting and processing.

[0087] Furthermore, the emergency stop alarm module includes:

[0088] Stop detection module: used to determine whether the emergency stop button is pressed, whether the upper sensor switch is sensed, whether the upper anti-collision switch is sensed, and whether the remote control switch is stopped;

[0089] First alarm module: when any one or more of the following occurs: the emergency stop button is pressed, the upper sensor switch is sensed, the upper anti-collision switch is sensed, and the remote control switch stops, the stop reason is an emergency stop, and an emergency stop alarm is issued;

[0090] Second alarm module: When the emergency stop button is not pressed, the upper sensor switch is not sensing, the upper anti-collision switch is not sensing, and the remote control switch is not stopped, the stop reason is normal stop, and a normal stop alarm is issued.

[0091] The stop detection module is responsible for collecting and analyzing multiple signal inputs related to equipment stoppage. These inputs can come from physical switches, sensors, or control system commands. Its function is to combine the status of these signals to determine the specific cause of the equipment stoppage. This can be achieved by, but is not limited to, reading the status of digital input ports, receiving specific messages on the communication bus, or monitoring flags within the control system.

[0092] The first alarm module generates and outputs an emergency stop alarm signal when it detects that any one or more preset emergency stop trigger conditions are met. This signal can be used to drive the sound and light alarm device, display information on the operation interface, or notify other control systems.

[0093] The second alarm module determines a normal stop if all preset emergency stop trigger conditions are not met, and generates and outputs a normal stop alarm signal. This signal indicates that the equipment is shutting down according to plan or normal process, as opposed to an abnormal shutdown.

[0094] This solution refines the functions of the emergency stop alarm module and provides a specific stop cause detection and classification alarm mechanism, thus solving the problem of the existing technology that cannot accurately identify the cause of equipment stop. Specifically:

[0095] The stop detection module determines the specific cause of the stop by evaluating multiple possible stop triggers, including whether the emergency stop button is pressed, whether the upper sensor switch is activated, whether the upper anti-collision switch is activated, and whether the remote control switch is activated. This multi-factor evaluation allows the system to comprehensively consider all possible causes of equipment stoppage, improving the accuracy of stop cause determination.

[0096] Based on the stop detection module's results, the first alarm module is designed to detect any one or more emergency stop-related triggering factors, namely, the emergency stop button being pressed, the upper sensor switch being sensed, the upper anti-collision switch being sensed, or the remote control switch being stopped, and determine that the stop cause is an emergency stop, and issue an emergency stop alarm. This ensures that in the event of an abnormal stop, the system can issue a timely and clear warning signal, prompting the operator to take immediate emergency measures.

[0097] Correspondingly, the second alarm module is used to determine that the stop cause is a normal stop and issue a normal stop alarm if the stop detection module determines that all triggering factors related to the emergency stop have not occurred (i.e., the emergency stop button has not been pressed, the upper sensor switch has not sensed, the upper anti-collision switch has not sensed, and the remote control switch has not stopped). This mechanism of distinguishing between normal stops and emergency stops allows operators to take targeted follow-up actions based on different alarm types, avoiding unnecessary troubleshooting during normal shutdowns, improving work efficiency, and ensuring a timely response when emergency measures are truly needed.

[0098] Therefore, by introducing a specific stop detection module and the first and second alarm modules that distinguish between emergency stops and normal stops, a clear and effective stop cause identification and classification alarm solution is provided, which significantly improves the system's ability to judge the equipment's operating status and the practicality of the alarm.

[0099] Furthermore, the end face monitoring module includes:

[0100] Photoelectric counting module, used to collect the number of unwinding turns of the strapping tape;

[0101] The end face warning module is used to issue an end face monitoring alarm when the number of unwinding turns reaches a preset threshold, so as to remind the user to monitor the unwinding of the strapping tape.

[0102] Among them, the photoelectric counting module refers to a functional unit that uses the principle of photoelectric conversion to count the unwinding process of the strapping tape. It can use, for example, a through-type photoelectric sensor, a reflective photoelectric sensor in conjunction with a mark on the strapping tape, or estimate the number of unwinding turns by detecting the rotation of the unwinding shaft and combining the thickness of the strapping tape.

[0103] The end face warning module refers to a functional unit that compares the collected number of unwinding turns with preset conditions and triggers an alarm output when the conditions are met. It can be implemented by a microcontroller, a programmable logic controller (PLC) or a dedicated logic circuit.

[0104] The preset winding threshold is a pre-set number of windings required for the strapping tape to be unwound. When the actual winding number reaches or exceeds this threshold, the system deems the strapping tape to be nearing completion or entering a high-risk phase, requiring attention. Issuing an end face monitoring alarm means sending a warning signal to the operator or higher-level system through some method. This can be in the form of an audible sound, flashing light, text or graphic prompts on the display, or a specific alarm signal sent to the control system.

[0105] In a specific embodiment, a photoelectric counting module can be used in conjunction with a single-chip microcomputer counting module. The photoelectric counting module is used to detect whether the unwinding position has reached the near-end surface. If so, the single-chip microcomputer counter continues to monitor the number of unwinding turns at the unwinding end surface, allowing technicians to determine whether an abnormality has occurred at the unwinding end surface. Assuming that a vertical oversized roll of strapping tape has 300 turns from top to bottom, and assuming that the preset turn thresholds are 10 and 290, meaning there are 10 turns of strapping tape remaining from the upper and lower end surfaces, respectively, when the photoelectric counter detects the position corresponding to the preset turn threshold, it triggers the end surface warning module, issuing an end surface monitoring alarm to remind the user to monitor the unwinding of the strapping tape.

[0106] Furthermore, the end face monitoring module also includes:

[0107] Acquisition module: used to collect vibration data during the operation of the unwinder to obtain vibration frequency and vibration amplitude;

[0108] Filter processing module: used to filter the unwinding position according to the vibration frequency and vibration amplitude using the Kalman filter algorithm to obtain the unwinding position after filtering;

[0109] Abnormality judgment module: Based on the filtered unwinding position, the flatness parameter and edge integrity parameter of the unwinding end surface are calculated. If the flatness parameter exceeds the preset range or the edge integrity parameter is lower than the threshold, it is determined that there is an abnormality in the unwinding end surface.

[0110] The acquisition module refers to a unit configured to acquire vibration measurement signals generated during the operation of the unwinding device, and can be implemented using an acceleration sensor, a vibration sensor, or other suitable measurement devices.

[0111] Vibration data refers to the raw measurement signals acquired by the acquisition module, reflecting the dynamic motion state of the equipment. Vibration frequency and amplitude are quantities extracted from the vibration data that characterize the vibration, representing the repetition rate and intensity of the vibration, respectively.

[0112] The filtering processing module refers to a unit configured to apply a filtering algorithm to data to reduce noise and improve data accuracy, which can be implemented using a dedicated hardware circuit or a software program running on a general-purpose processor.

[0113] The Kalman filter algorithm is a recursive estimation algorithm suitable for processing data from dynamic systems with random noise. The unwinding position refers to data indicating the current unwinding point or area of ​​the strapping tape, which can be provided by an encoder, vision system, or other position sensor.

[0114] The filtered unwinding position refers to the unwinding position data after filtering and with noise effectively suppressed.

[0115] The abnormality judgment module refers to a unit configured to evaluate the unwinding section state based on processed data, which can be implemented as processing logic or software function.

[0116] Flatness parameters and edge integrity parameters are indices calculated from the unwinding position after filtering and used to quantitatively describe the geometry and edge condition of the unwinding end face.

[0117] In some specific implementations, it is assumed that the collected vibration data is a discrete time series signal , the vibration frequency can be calculated by fast Fourier transform (FFT).

[0118] Assume the sampling frequency is , the number of FFT points is N, then the frequency resolution for: .

[0119] For the complex result after FFT transformation , the corresponding frequency is for:

[0120] , is the index of point number N, The vibration frequency is the frequency component corresponding to the signal energy concentration or the larger amplitude. value.

[0121] For the collected vibration data time series signal , its root mean square (RMS) value can be used to indicate an indicator of the vibration amplitude. The calculation formula is: Where M is the number of sampling points. The RMS value can reflect the effective amplitude of the vibration signal, thereby obtaining the vibration amplitude.

[0122] During the unwinding process of an oversized roll, as the strapping tape is continuously unwound, the remaining weight, diameter, and center of mass of the roll continuously change. These changes in physical parameters directly affect the dynamic characteristics of the unwinding system, manifesting as changes in the system's natural vibration frequency and vibration transmission characteristics. Specifically, as the unwinding position advances from a full roll to an empty roll, the system's mass decreases, causing the natural frequency to increase. Simultaneously, changes in the roll's tension and stiffness affect the attenuation of the vibration amplitude. Therefore, the vibration frequency and amplitude become characteristic indicators of the unwinding position, and a definite functional mapping relationship exists between the two.

[0123] By establishing an empirical database of vibration characteristics and unwinding positions during the equipment commissioning phase, a nonlinear mapping model was constructed using statistical regression methods. ,in , , ..., is the main vibration frequency component extracted, is the effective value of the vibration amplitude, and p is the corresponding unwinding position.

[0124] During actual operation, vibration signals are collected in real time and frequency and amplitude features are extracted. These features are input into a pre-trained mapping model to infer the current unwinding position. This inferred position is then fused with position data directly measured by encoders through the Kalman filter algorithm to obtain a more accurate and reliable final unwinding position.

[0125] Among them, the Fourier transform theory, a fast algorithm proposed by Cooley-Tukey in 1965, can be used for Nyquist sampling theorem and frequency domain analysis theory. Based on this existing technology, this application applies it to the field of unwinding technology of unwinding machines, and collects vibration data of the unwinding machine during unwinding, and extracts vibration frequency and vibration amplitude, which is practical.

[0126] According to the vibration frequency and vibration amplitude, the Kalman filter algorithm is used to filter the unwinding position to obtain the filtered unwinding position, which is specifically:

[0127] Assume that the state vector is In the unwinding system of this application, the state vector of the Kalman filter algorithm is It is composed of three state components: unwinding position, unwinding speed and unwinding acceleration. At time k, the unwinding position The unwinding axis angle is measured by the rotary encoder and the speed is calculated based on the roll diameter. It is calculated by numerically differentiating the continuous position measurements ( ), is the unwinding position at time k-1, is the time interval between time k and time k-1, acceleration It is obtained by numerical differentiation of the speed or by measuring with an acceleration sensor directly mounted on the unwinder.

[0128] The state transition matrix is In the unwinding system proposed in this application, The kinematic relationship between the unwinding position, speed and acceleration is described.

[0129] The system noise covariance matrix is , represents the influence of uncertainty or external disturbance, and considers the influence of load change, mechanical wear, environmental interference and other factors on the system dynamics during the unwinding process. The larger the value, the higher the uncertainty of the system model, and the lower the filter's confidence in the prediction.

[0130] Prediction Status for: . Forecast status It represents the predicted value of the system state at time k based on the state estimation and system model at time k-1. It is used to estimate the current unwinding position, speed, and other states according to the motion law before obtaining the actual measurement value at time k, providing prior information for subsequent state updates.

[0131] At the same time, the prediction error covariance matrix is : . Represents the state estimate at the previous moment. represents the error covariance matrix at the previous moment. Represents the transposed matrix of the state transition matrix. Indicates the predicted status The error covariance matrix relative to the true state quantifies the uncertainty of the prediction.

[0132] Assume that the measurement matrix is , The role of the state vector is to The elements in are mapped directly to the measurement space, allowing the Kalman filter algorithm to utilize these measurements To update the state estimate and error covariance matrix. Then the Kalman gain for: , is the measurement noise covariance matrix.

[0133] Kalman gain Is a weight coefficient used to determine the prediction value when the state is updated and measured values relative level of trust.

[0134] Updated state estimate for: ,in, is the measured value at time k, i.e. the unwinding position, is the updated state estimate, including the filtered unwinding position .

[0135] Through the above equations, the present application completes the correction of the noise parameters in the Kalman filter algorithm according to the vibration amplitude and vibration frequency. This is the innovative process of the present application in the original Kalman filter algorithm.

[0136] In the prior art, the unwinding position of the strapping tape can be obtained by an encoder, but the encoder itself has measurement errors, which can lead to inaccurate monitoring of end face abnormalities. In order to improve accuracy, the Kalman filter algorithm can be used to filter the unwinding position collected by the encoder (the Kalman filter algorithm is an optimal estimation algorithm proposed by Rudolf Kalman in 1960, which can be used in this application to achieve accurate estimation of the unwinding position). However, direct application cannot solve the problem in the application technology field of this application: that is, when the unwinding machine is running, the turntable rotates, which will cause the unwinding shaft to vibrate, and this vibration will also affect the monitoring of end face abnormalities. Therefore, in order to solve this problem, this application innovatively proposes to use vibration frequency and vibration frequency to filter the system noise covariance matrix in the Kalman filter algorithm. and the measurement noise covariance matrix The unwinding position is estimated by using the modified Kalman filter algorithm, which solves the technical problems existing in this field and makes the end face abnormality monitoring more accurate.

[0137] At the same time, the updated error covariance matrix for:

[0138] .in, is the identity matrix.

[0139] Through the updated The value of can be used to determine the reliability of the adjusted Kalman filter algorithm in estimating the unwinding position. Under normal circumstances, , indicating that the estimated unwinding position is reliable. This indicates that the estimated unwinding position is unreliable. You can collect vibration data again and estimate the unwinding position again.

[0140] Assume that the unwinding position after filtering forms a series of points on the end face , the plane fitting degree of these points can be calculated as the flatness parameter. For example, the least squares method is used to fit the plane.

[0141] When the end surface of the strapping tape is flat, all points are basically on the same plane, so the general form of the plane equation can be set as (Three-dimensional coordinates are used here, but adjustments can be made based on specific circumstances.) This plane equation fits an optimal plane using multiple measured end face locations. This optimal plane represents the ideal end face position and provides a reference for determining whether the end face is actually flat. a, b, and c are the coefficients of the plane equation, which together form the plane's normal vector.

[0142] For each point , its distance to the fitting plane for: The flatness parameter can be defined as the standard deviation of the distances from all points to the fitted plane. : .in, is the average distance from all points to the fitted plane, is the number of points, when When it exceeds the set range, it is judged as flatness abnormal.

[0143] In addition, for the edge of the unwinding end face, the edge pixel points can be extracted (assuming that the edge point coordinates are obtained by image processing The edge integrity parameter can be calculated based on the edge continuity. For example: calculate the distance between adjacent edge points : .

[0144] The edge integrity parameter can be defined as the standard deviation of the distances between adjacent edge points :

[0145] .in, is the average distance between adjacent edge points, is the number of edge points (minus 1). When it is lower than the set threshold, the edge integrity is judged to be abnormal; otherwise, the edge is judged to be good. Where j is the serial index of the edge pixel point.

[0146] The original sources of the flatness calculation and edge integrity parameters are Euclidean distance and statistical basis: standard deviation measures the degree of data dispersion. Applying the existing algorithms to this application, it is practical to deduce the calculation of flatness and edge integrity.

[0147] Furthermore, the filtering processing module includes:

[0148] Compensation module: According to the vibration frequency, an autoregressive sliding average model is constructed to obtain the frequency compensation parameters;

[0149] The first adjustment module: determines the Kalman filter noise variance according to the vibration amplitude and obtains the amplitude adjustment parameter;

[0150] The first filtering module: adjusts the system noise covariance matrix and the measurement noise covariance matrix of the Kalman filter algorithm according to the frequency compensation parameters and the amplitude adjustment parameters, and uses the adjusted Kalman filter algorithm to filter the unwinding position to obtain the filtered unwinding position.

[0151] The compensation module refers to a functional unit for analyzing vibration frequency and generating compensation parameters, which can be implemented by a processor executing software instructions, a dedicated hardware circuit, or a combination of software and hardware.

[0152] An autoregressive moving average model is a time series model used to describe and predict data with autocorrelation and moving average characteristics. It can be constructed using standard statistical modeling algorithms or machine learning methods. A frequency compensation parameter is a numerical value or vector that characterizes the effect of vibration frequency on the system's dynamics.

[0153] The first adjustment module is a functional unit used to analyze the vibration amplitude and generate adjustment parameters. It can be implemented using a processor executing software instructions, dedicated hardware circuits, or a combination of software and hardware. The measurement noise variance is a statistic used in the Kalman filter algorithm to describe the uncertainty of measurement noise. It is typically the diagonal elements of the measurement noise covariance matrix or its related values. The amplitude adjustment parameter is a numerical value or vector calculated based on the vibration amplitude that characterizes its effect on measurement noise.

[0154] The first filtering module is a functional unit used to execute the Kalman filter algorithm and output the filtering results. It can be implemented using a processor executing software instructions, dedicated hardware circuits, or a combination of software and hardware. The system noise covariance matrix of the Kalman filter algorithm refers to the matrix used in the Kalman filter algorithm to describe the uncertainty of the system model. The measurement noise covariance matrix of the Kalman filter algorithm refers to the matrix used in the Kalman filter algorithm to describe the uncertainty of the measurement value.

[0155] By constructing an autoregressive sliding average model based on the vibration frequency to obtain frequency compensation parameters, and determining the Kalman filter noise variance based on the vibration amplitude to obtain amplitude adjustment parameters, and then using these parameters to dynamically adjust the system noise covariance matrix and measurement noise covariance matrix of the Kalman filter algorithm, this solution can enable the Kalman filter algorithm to better adapt to the actual vibration environment of the unwinding equipment under different operating conditions. This adaptive adjustment improves the accuracy of the Kalman filter algorithm's filtering of the unwinding position, thereby obtaining a more accurate and reliable filtered unwinding position. More accurate position information helps to improve the accuracy of subsequent calculations of the unwinding end face flatness parameters and edge integrity parameters, ultimately improving the reliability of unwinding end face abnormality judgments, and effectively solving the problem of insufficient filtering accuracy in the existing technology due to insufficient consideration of the impact of vibration characteristics on the filtering effect.

[0156] Furthermore, the compensation module includes:

[0157] Extraction module: used to analyze historical vibration data, extract multiple vibration frequency components using fast Fourier transform to obtain a frequency set;

[0158] Construction module: used to construct an autoregressive sliding average model for each vibration frequency component in the frequency set, and obtain multiple frequency compensation parameters corresponding to each vibration frequency component;

[0159] The first calculation module is used to perform weighted averaging on multiple frequency compensation parameters according to the proportion of each vibration frequency component in the frequency set to obtain the final frequency compensation parameter.

[0160] The historical vibration data refers to a vibration signal sequence collected during a period of time when the unwinder is in operation, which can be collected using devices such as an acceleration sensor, a velocity sensor, or a displacement sensor.

[0161] Fast Fourier transform is an algorithm that converts a time domain signal into a frequency domain signal, which can be implemented by using a digital signal processor or a general-purpose processor to execute a corresponding calculation program.

[0162] Vibration frequency components refer to the individual frequency components that constitute a complex vibration signal obtained through frequency domain analysis.

[0163] The frequency set refers to a set of multiple vibration frequency components extracted by fast Fourier transform.

[0164] The frequency compensation parameter refers to a parameter obtained through an autoregressive sliding average model and is used to correct or compensate for the influence of specific frequency vibration on the unwinding position.

[0165] The proportion in a frequency set refers to the ratio of the energy or amplitude of a certain vibration frequency component to other frequency components in the frequency set, which can be calculated based on the square of the energy or amplitude of each frequency component in the fast Fourier transform result.

[0166] Weighted averaging refers to a method of comprehensively calculating the corresponding frequency compensation parameters according to the importance of each frequency component.

[0167] The final frequency compensation parameter refers to a parameter obtained by comprehensively considering the influence of multiple vibration frequency components and used to compensate for the influence of vibration on the unwinding position as a whole.

[0168] In one embodiment, the extraction module may be configured to receive a vibration acceleration signal of the unwinder frame within a period of time collected by an acceleration sensor as historical vibration data.

[0169] The extraction module can call the fast Fourier transform function in the signal processing library to perform frequency domain analysis on the vibration acceleration signal, identify multiple vibration frequency components with significant energy, and store these frequency components and their corresponding energy or amplitude information in a frequency set.

[0170] The construction module builds an autoregressive moving average model for each major vibration frequency component in the frequency set. The model order is selected based on the historical vibration data segment corresponding to that frequency component using an information criterion (such as AIC or BIC). The autoregressive moving average model can be used to obtain the frequency compensation parameters associated with that frequency component.

[0171] The first calculation module can calculate the contribution of each vibration frequency component in the total energy or total amplitude based on the energy or amplitude squared corresponding to each vibration frequency component in the frequency set, and use these contributions as weights. The first calculation module then applies these weights to the corresponding frequency compensation parameters, performing a weighted summation to obtain the final frequency compensation parameters. For example, if the frequency set includes frequencies f1 and f2, whose energy contributions are p1 and p2 respectively, and the corresponding compensation parameters are c1 and c2, then the final frequency compensation parameters can be calculated as p1*c1+p2*c2.

[0172] Furthermore, the first adjustment module includes:

[0173] Normalization processing module: acquires the vibration amplitude data of the unwinding equipment in real time, and performs normalization processing on the vibration amplitude data to obtain the normalized vibration amplitude value;

[0174] Mapping establishment module: establishes a mapping relationship between the normalized vibration amplitude value and the measurement noise variance, and the mapping relationship is a nonlinear function;

[0175] The second calculation module maps the normalized vibration amplitude value to the corresponding measurement noise variance according to the mapping relationship, and uses the measurement noise variance as the amplitude adjustment parameter.

[0176] Among them, the normalization processing module refers to the linear or nonlinear scaling processing of the original vibration amplitude data, converting its numerical range to a preset standard range, such as [0, 1] or [-1, 1], which can be achieved by using the minimum-maximum normalization method.

[0177] The mapping establishment module refers to constructing a mathematical model or lookup table to describe the correspondence between input values ​​(normalized vibration amplitude values) and output values ​​(measurement noise variance). It can be implemented by function fitting, data interpolation, or a lookup table based on empirical data.

[0178] A nonlinear function refers to a functional relationship between input and output that does not satisfy the linear superposition principle. It can be represented by polynomial functions, exponential functions, logarithmic functions, piecewise functions or neural network models.

[0179] The second calculation refers to the process of substituting or looking up the input normalized vibration amplitude value according to the established mapping relationship to obtain the corresponding Kalman filter noise variance, which can be achieved by the processor executing a specific algorithm or table lookup operation.

[0180] Specifically, the technical solution can be implemented in the following manner: the normalization processing module can be a software program segment that receives the original amplitude signal from the vibration sensor, executes the minimum-maximum normalization algorithm, and scales the amplitude value to the [0, 1] interval.

[0181] The mapping relationship may be a preset polynomial function, the coefficients of which are obtained by fitting the training data.

[0182] The second calculation module is a set of instructions executed by a processor. Based on the real-time normalized vibration amplitude value, it queries a lookup table or calculates the value of a polynomial function to obtain the corresponding measurement noise variance. For example, when the normalized vibration amplitude value is 0.2, the measurement noise variance calculated by the lookup table or function is a small value; when the normalized vibration amplitude value is 0.8, the measurement noise variance is a large value. This calculated variance value serves as the amplitude adjustment parameter for subsequent parameter updates in the Kalman filter algorithm.

[0183] Furthermore, the first filtering module includes:

[0184] The second adjustment module: adjusts the Kalman filter algorithm state transfer matrix and the system noise covariance matrix according to the frequency compensation parameters;

[0185] The third adjustment module calculates the adjusted measurement noise covariance matrix according to the amplitude adjustment parameter and the preset noise adjustment factor;

[0186] The second filtering module uses the adjusted Kalman filtering algorithm, combined with the corrected state transfer matrix and measurement noise covariance matrix, to iteratively filter the unwinding position, dynamically adjust the state estimation value according to the Kalman gain, and obtain the filtered unwinding position.

[0187] The frequency compensation parameter refers to a parameter that reflects the change in the dynamic characteristics of the unwinding device at a specific vibration frequency, and can be obtained by constructing an autoregressive sliding average model based on historical vibration data.

[0188] The Kalman filter algorithm state transition matrix refers to the matrix that describes how the system state changes from one moment to the next.

[0189] The amplitude adjustment parameter refers to a parameter reflecting the measured noise level of the unwinding device at a specific vibration amplitude, which can be obtained by converting the normalized vibration amplitude value into the Kalman filter noise variance through a nonlinear mapping relationship.

[0190] The preset noise adjustment factor refers to a fixed or variable coefficient used to further correct the measurement noise covariance matrix calculated according to the amplitude adjustment parameter, and can be an empirical value or a value determined through offline optimization.

[0191] The measurement noise covariance matrix refers to a matrix that describes the uncertainty of the measurement error, which can be a diagonal matrix, and the diagonal elements represent the variance of each measurement component.

[0192] Kalman gain refers to the coefficient used to determine the weights of the predicted state and the measured value in the update step of the Kalman filter algorithm. It can be dynamically calculated based on the prediction error covariance and the measurement error covariance.

[0193] The state estimate refers to the optimal estimate of the system's true state (such as position and speed) by the Kalman filter algorithm.

[0194] In one embodiment, the second adjustment module may receive a frequency compensation matrix as a frequency compensation parameter, and add the frequency compensation matrix to a standard state transfer matrix of the Kalman filter algorithm to form a modified state transfer matrix.

[0195] The second adjustment module may also use a frequency compensation matrix as a frequency compensation parameter, and add the compensated frequency compensation matrix to the system noise covariance matrix of the Kalman filter algorithm to form a modified system noise covariance matrix.

[0196] The third adjustment module can receive an amplitude adjustment variance as an amplitude adjustment parameter and multiply it with a preset noise adjustment factor (for example, a multiplicative factor greater than 1) to obtain an adjusted measurement noise variance, which constitutes an adjusted measurement noise covariance matrix (for one-dimensional position measurement, it is a 1x1 matrix).

[0197] The second filtering module follows the standard Kalman filter algorithm iteration process: First, the state prediction and prediction error covariance prediction are performed using the modified state transition matrix and the system noise covariance matrix. Then, the Kalman gain is calculated based on the prediction error covariance and the adjusted measurement noise covariance matrix. Next, the predicted state is updated using the Kalman gain and the real-time unwinding position measurement value to obtain the current filtered state estimate. The position component of this state estimate is the filtered unwinding position information. Finally, the error covariance matrix is ​​updated to prepare for the next iteration. This entire process continues, filtering the continuously input unwinding position information in real time.

[0198] By adjusting the Kalman filter algorithm's state transition matrix and the system noise covariance matrix based on frequency compensation parameters, the filter can be better adapted to the dynamic characteristics of the unwinding equipment at different vibration frequencies. By calculating the adjusted measurement noise covariance matrix based on the amplitude adjustment parameters and the preset noise adjustment factor, the magnitude of the measurement noise can be more accurately reflected, making the filter more reliable when integrating measurement information. Using the adjusted Kalman filter algorithm for iterative filtering and dynamically adjusting the state estimate based on the Kalman gain effectively integrates predicted and measured information, eliminating interference from factors such as vibration on the unwinding position, thereby obtaining a more accurate and smoother filtered unwinding position. This provides high-quality data input for subsequent unwinding end face anomaly detection, improving the accuracy and reliability of anomaly detection.

[0199] Please refer to Figure 4 A method for controlling the emergency stop and alarm of unwinding of an oversized roll, applied to any of the above-mentioned systems, comprises the steps of:

[0200] S1: Obtain the unwinding position of the strapping tape and monitor whether the unwinding end surface of the unwinding machine is abnormal based on the unwinding position;

[0201] S2: When an abnormality occurs on the unwinding end surface, the unwinding device is controlled to shut down;

[0202] S3: When there is no abnormality on the unwinding end face and the unwinder stops running, the stop detection process is executed. The stop detection process is used to detect whether the stop reason is an emergency stop or a normal stop, and issue a corresponding emergency stop alarm or normal stop alarm according to the stop reason.

[0203] Among them, obtaining the unwinding position of the strapping tape refers to obtaining the spatial position information or unwinding length / number of turns information of the strapping tape in the unwinding process in real time through a sensor or counting device, which can be achieved by using technologies such as photoelectric counting, encoder measurement, and visual positioning.

[0204] Monitoring whether there is any abnormality on the unwinding end face of the unwinding machine means judging whether there is any abnormal condition such as deflection, unevenness, edge damage, wrinkles, etc. on the unwinding end face of the strapping tape based on the obtained unwinding position or other auxiliary information through data analysis or image processing. This can be achieved by using technologies such as end face profile analysis based on image recognition, flatness calculation based on sensor data, and abnormal pattern recognition based on vibration signals.

[0205] The execution of the stop detection process is used to detect whether the stop reason is an emergency stop or a normal stop. This means that after the unwinder stops running, by checking specific control signals, sensor status or system flags, it is determined whether the stop is due to an emergency stop triggered by the operator, a stop triggered by a safety device, or a normal stop executed by the system as planned. This can be achieved by detecting the emergency stop button status, safety switch signal, control system instruction type, etc.

[0206] This method provides a specific operating process for an emergency stop and alarm control system for oversized unwinding. By executing a series of steps in an orderly manner, it achieves effective monitoring of the unwinding end face, timely emergency stop under abnormal conditions, and accurate identification and alarm of the cause of equipment stoppage.

[0207] This method continuously obtains the unwinding position and monitors the end face status through step S1, providing basic information for subsequent processing. When S1 determines that the end face is abnormal, the process immediately enters step S2, triggering the shutdown operation of the unwinding equipment, thereby quickly responding to the abnormal situation and preventing the problem from escalating. When S1 determines that there is no abnormality in the end face, but the unwinder stops running, the process enters step S3, executes a special stop detection process, distinguishes between emergency stops and normal stops, and issues a corresponding alarm. This process design enables the system to monitor potential risks in real time during the unwinding process and provide clear status feedback after the equipment stops.

[0208] By applying this method to the above-mentioned system, the method steps guide the specific execution logic of the end face monitoring module, abnormality control module and emergency stop alarm module in the system, so that the modules can work together to achieve refined control and abnormality management of the unwinding process, effectively improving the overall performance and reliability of the system.

[0209] In one embodiment, the method can be implemented as follows:

[0210] In step S1, a photoelectric counting module collects the number of unwound strap turns as the unwinding position, while also collecting vibration data from the unwinder during operation. A filtering module, such as one employing a Kalman filter algorithm, filters the unwinding position to obtain a filtered unwinding position. Based on the filtered unwinding position, an abnormality determination module calculates the flatness and edge integrity parameters of the unwinding end surface. If the flatness parameter exceeds a preset range or the edge integrity parameter falls below a preset threshold, an abnormality is determined for the unwinding end surface.

[0211] In step S2, when the abnormality judgment module determines that there is an abnormality on the unwinding end surface, the abnormality control module receives an abnormality signal and immediately sends a shutdown command to the unwinding device to make the unwinding device stop suddenly.

[0212] In step S3, if the abnormality determination module does not determine that the unwinding end face is abnormal and the unwinder stops operating, the stop detection module checks the status of the emergency stop button, upper sensor switch, upper anti-collision switch, and remote control switch. If any one or more of the following occurs: the emergency stop button is pressed, the upper sensor switch is sensed, the upper anti-collision switch is sensed, or the remote control switch is stopped, the first alarm module issues an emergency stop alarm. If none of the above emergency stop triggering conditions occur, the second alarm module issues a normal stop alarm.

[0213] By implementing this method, it is possible to accurately monitor whether the unwinding end face has any abnormalities, thereby promptly controlling the unwinding equipment to shut down when an abnormality occurs, avoiding damage to the strapping tape and equipment. At the same time, when the unwinder stops running, it can accurately identify whether the stop reason is an emergency stop or a normal stop, and issue a corresponding alarm, allowing staff to quickly understand the equipment status and take targeted follow-up measures, thereby improving production efficiency and eliminating potential safety hazards caused by unknown stop reasons.

[0214] In this document, relational terms such as first and second, etc. are used merely to distinguish one entity or operation from another entity or operation, but do not necessarily require or imply any actual relationship or order between these entities or operations.

[0215] The foregoing is merely an embodiment of the present application and is not intended to limit the scope of protection of the present application. Persons skilled in the art will readily appreciate that the present application may be modified and altered in various ways. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present application shall be included within the scope of protection of the present application.

Claims

1. An emergency stop and alarm control system for unwinding of an oversized roll, characterized in that: The system monitors the unwinding equipment through the emergency stop control module and the alarm module; The emergency stop control module includes: End face monitoring module: used to obtain the unwinding position of the strapping tape and monitor whether the unwinding end face of the unwinding machine is abnormal based on the unwinding position; The end face monitoring module includes: Photoelectric counting module, used to collect the number of unwinding turns of the strapping tape; An end face warning module is used to issue an end face monitoring alarm when the number of unwinding turns reaches a preset turn threshold, so as to remind the user to monitor the unwinding of the strapping tape; The end face monitoring module also includes: Acquisition module: used to collect vibration data of the unwinder during operation to obtain vibration frequency and vibration amplitude; Filter processing module: used for filtering the unwinding position using a Kalman filter algorithm according to the vibration frequency and the vibration amplitude to obtain a filtered unwinding position; Abnormality judgment module: Calculates the flatness and edge integrity parameters of the unwinding end surface based on the filtered unwinding position. If the flatness parameter exceeds the preset range or the edge integrity parameter is lower than the threshold, it is determined that there is an abnormality on the unwinding end surface. Abnormal control module: used for controlling the unwinding device to shut down when an abnormality occurs on the unwinding end surface; The alarm module includes: Emergency stop alarm module: used for executing a stop detection process when there is no abnormality on the unwinding end surface and the unwinder stops running. The stop detection process is used to detect whether the stop reason is an emergency stop or a normal stop, and issue a corresponding emergency stop alarm or normal stop alarm according to the stop reason; The emergency stop alarm module includes: Stop detection module: used to determine whether the emergency stop button is pressed, whether the upper sensor switch is sensed, whether the upper anti-collision switch is sensed, and whether the remote control switch is stopped; First alarm module: when any one or more of the following occurs: the emergency stop button is pressed, the upper sensor switch is sensed, the upper anti-collision switch is sensed, and the remote control switch stops, the stop reason is an emergency stop, and an emergency stop alarm is issued; Second alarm module: When the emergency stop button is not pressed, the upper sensing switch is not sensing, the upper anti-collision switch is not sensing, and the remote control switch is not stopped, the stop reason is normal stop, and a normal stop alarm is issued.

2. The unwinding emergency stop and alarm control system for an oversized roll according to claim 1 is characterized in that: The alarm module also includes: Alarm control module: used to control the baler to stop running according to the emergency stop alarm; Reset module: used to receive alarm reset information and turn off the emergency stop alarm according to the alarm reset information.

3. The unwinding emergency stop and alarm control system for an oversized roll according to claim 1, characterized in that: The filtering processing module includes: Compensation module: constructing an autoregressive sliding average model according to the vibration frequency to obtain frequency compensation parameters; The first adjustment module: determines the measurement noise variance according to the vibration amplitude and obtains the amplitude adjustment parameter; The first filtering module adjusts the system noise covariance matrix and the measurement noise covariance matrix of the Kalman filter algorithm according to the frequency compensation parameter and the amplitude adjustment parameter, and uses the adjusted Kalman filter algorithm to filter the unwinding position to obtain the unwinding position after filtering.

4. The oversized roll unwinding emergency stop and alarm control system according to claim 3, characterized in that: The compensation module includes: Extraction module: used to analyze historical vibration data, extract multiple vibration frequency components using fast Fourier transform to obtain a frequency set; Construction module: used to construct an autoregressive sliding average model for each vibration frequency component in the frequency set, and obtain multiple frequency compensation parameters corresponding to each vibration frequency component; The first calculation module is used to perform weighted averaging on multiple frequency compensation parameters according to the proportion of each vibration frequency component in the frequency set to obtain the final frequency compensation parameter.

5. The unwinding emergency stop and alarm control system for an oversized roll according to claim 3 is characterized in that: The first adjustment module includes: Normalization processing module: acquires the vibration amplitude data of the unwinding device in real time, and performs normalization processing on the vibration amplitude data to obtain a normalized vibration amplitude value; Mapping establishment module: establishes a mapping relationship between the normalized vibration amplitude value and the measurement noise variance, wherein the mapping relationship is a nonlinear function; The second calculation module maps the normalized vibration amplitude value to a corresponding measurement noise variance according to the mapping relationship, and uses the measurement noise variance as an amplitude adjustment parameter.

6. The unwinding emergency stop and alarm control system for an oversized roll according to claim 3, characterized in that: The first filtering module includes: A second adjustment module: adjusting the Kalman filter algorithm state transfer matrix and the system noise covariance matrix according to the frequency compensation parameters; A third adjustment module: calculating an adjusted measurement noise covariance matrix according to the amplitude adjustment parameter and a preset noise adjustment factor; The second filtering module uses the adjusted Kalman filtering algorithm, combined with the corrected state transfer matrix and the measurement noise covariance matrix, to iteratively filter the unwinding position, dynamically adjust the state estimation value according to the Kalman gain, and obtain the unwinding position after filtering.

7. A method for controlling the emergency stop and alarm of unwinding of an oversized roll, characterized in that: Applied to the system according to any one of claims 1 to 6 above, the method comprises the steps of: S1: Obtain the unwinding position of the strapping tape, and monitor whether the unwinding end surface of the unwinding machine is abnormal based on the unwinding position; S2: When an abnormality occurs on the unwinding end surface, controlling the unwinding device to shut down; S3: When no abnormality occurs on the unwinding end face and the unwinder stops running, a stop detection process is executed, wherein the stop detection process is used to detect whether the stop cause is an emergency stop or a normal stop, and a corresponding emergency stop alarm or normal stop alarm is issued according to the stop cause.

Images