Bending machine remote operation and maintenance diagnosis method and system, storage medium and equipment

Abstract

The invention discloses a remote operation and maintenance diagnosis method and system for a bending machine, a storage medium and equipment, and is applied to the technical field of metal forming equipment manufacturing. The method comprises the steps that load inertia distribution is calculated according to machining process parameters, a dynamic torque pulse instruction is generated, transmission chain electromechanical disturbance is actively excited, and torque fluctuation response data is collected; torque ripple characteristics are analyzed to recognize the transmission chain clearance state, and gear backlash quantization parameters and bearing clearance evaluation data are generated; a structure connection state evaluation parameter and a transmission chain flexibility index are generated in combination with a rack springback waveform in the sudden stop process and response lag data of frequency sweep excitation; integrating multiple parameters to perform fusion analysis to generate a health state evaluation report, and calculating servo system compensation parameters based on the report; and the compensation effect is verified through low-load trial folding, and the health reference interval is updated to form an operation and maintenance diagnosis closed loop. According to the invention, the conversion from passive maintenance to predictive maintenance is realized, the equipment reliability is improved, and the service life is prolonged.

Description

Technical Field

[0001] This application relates to the field of metal forming equipment manufacturing technology, and in particular to a remote operation and maintenance diagnosis method, system, storage medium and equipment for bending machines. Background Technology

[0002] As a core piece of equipment in sheet metal processing, the health of the transmission chain and frame structure of a bending machine directly determines the processing accuracy and stability. Traditional maintenance mainly relies on fixed-cycle maintenance and repair after failure, lacking the ability to predict early performance degradation of mechanical components. Problems such as tooth backlash, bearing clearance, and loose frame connections caused by wear of the transmission sprocket are difficult to detect in the early stages, but will gradually lead to bending angle errors, workpiece surface scratches, and even sudden equipment shutdown.

[0003] Current technologies for monitoring the condition of bending machines largely rely on monitoring single parameters, resulting in limited diagnostic dimensions. For example, they cannot effectively distinguish between electrical response and mechanical transmission characteristics, nor can they accurately pinpoint whether backlash originates in the gearbox or bearing housing. Conventional methods struggle to effectively elicit and capture weak characteristic signals representing early-stage faults under static or uniform-speed conditions. Furthermore, there are technological blind spots in the quantitative assessment of key health indicators such as frame structural connection stiffness and overall transmission chain flexibility, leading to an inability to comprehensively grasp the overall degradation status of the equipment and insufficient early warning capabilities.

[0004] Currently, the operation and maintenance control and process adjustment of bending machines are disconnected. Even if some performance degradation is detected, most of the adjustments are made mechanically based on manual experience or simple servo gain tuning. There is a lack of a closed-loop system that automatically converts multi-source diagnostic results into precise compensation commands. This results in the equipment operating in a suboptimal, defective state for a long time, and the processing accuracy is guaranteed by repeated trial molding, which is inefficient. Summary of the Invention

[0005] This application provides a remote operation and maintenance diagnosis method, system, storage medium, and device for a bending machine. Through dynamic torque pulse excitation, multi-source data acquisition, and parameter fusion analysis, it achieves accurate assessment and proactive maintenance of the bending machine's health status, effectively improving equipment reliability and service life. To achieve the above objectives, this application adopts the following technical solution:

[0006] A remote operation and maintenance diagnostic method for a bending machine, the method comprising:

[0007] The load inertia distribution is calculated based on the processing thickness, bending length and cycle time requirements of the bending task, and dynamic torque pulse commands are generated.

[0008] The dynamic torque pulse command is executed to excite electromechanical disturbances in the transmission chain and generate electromechanical response data including torque fluctuation characteristics.

[0009] The torque fluctuation characteristics in the electromechanical response data are collected to identify the transmission chain clearance status and generate quantification parameters of tooth flank clearance and bearing clearance evaluation data.

[0010] During the emergency stop of the bending machine, the vibration response of the frame is acquired, the springback waveform data of the frame is collected, and structural connection status evaluation parameters are generated.

[0011] Apply a micro-amplitude frequency sweep excitation to the servo system, collect the response hysteresis data of the transmission system, and generate a transmission chain compliance degradation index.

[0012] By combining the aforementioned tooth flank clearance quantification parameters, bearing clearance assessment data, structural connection status assessment parameters, and transmission chain compliance degradation index, a multi-parameter fusion analysis is performed to generate a comprehensive health status assessment report.

[0013] Based on the comprehensive health status assessment report, the compensation parameters of the servo drive system are calculated, and a set of torque compensation parameters, dynamic response gain adjustment amount and mechanical preload correction value are generated.

[0014] The torque compensation parameter set, dynamic response gain adjustment amount and mechanical preload correction value are executed to perform low-load bending test verification, and force feedback data and structural response data are collected during the bending test.

[0015] Analyze the force feedback data and structural response data to verify the effect of parameter correction and generate health status verification results;

[0016] The health reference range of the bending machine is updated based on the health status verification results, forming a closed-loop record for operation and maintenance diagnosis.

[0017] In some possible implementations, the step of calculating the load inertia distribution based on the processing thickness, bending length, and cycle time requirements of the bending task, and generating dynamic torque pulse commands, includes:

[0018] Obtain the process parameters for the current bending task;

[0019] The process parameters include sheet thickness, bending length, and processing cycle time;

[0020] Based on the sheet thickness and bending length, calculate the theoretical load torque required for the bending process;

[0021] Based on the processing cycle time, determine the dynamic response frequency required by the servo system;

[0022] By combining the theoretical load torque and the dynamic response frequency, the equivalent load inertia at the servo axis end is calculated.

[0023] Based on the equivalent load inertia, a dynamic torque pulse waveform including the fundamental wave and multiple harmonics is constructed;

[0024] The dynamic torque pulse waveform serves as the dynamic torque pulse command.

[0025] In some possible implementations, executing the dynamic torque pulse command to excite electromechanical disturbances in the drivetrain and generate electromechanical response data including torque fluctuation characteristics includes:

[0026] Execute the dynamic torque pulse command to excite wideband electromechanical disturbances in the transmission chain;

[0027] Based on the aforementioned wideband electromechanical disturbance, torque feedback data and position feedback data of the servo motor, as well as vibration acceleration data of the transmission chain, are collected.

[0028] The rotational speed fluctuation information is calculated based on the collected position feedback data;

[0029] Torque fluctuation information is extracted based on the collected torque feedback data;

[0030] The speed fluctuation information, torque fluctuation information, and acceleration data are time-aligned and fused to output electromechanical response data.

[0031] In some possible implementations, the acquisition of frame vibration response, collection of frame springback waveform data, and generation of structural connection status evaluation parameters during the emergency stop of the bending machine include:

[0032] The system acquires the operating status of the bending machine. Upon detecting an emergency stop signal, it triggers and collects the acceleration signal at the frame connection point.

[0033] Extract the raw vibration data packet from the start of the emergency stop to the vibration decay period from the acquired acceleration signal;

[0034] The original vibration data packet is filtered to obtain a clean frame rebound waveform;

[0035] The time-domain attenuation characteristics of the vibration energy of the frame rebound waveform are calculated to obtain the attenuation time constant;

[0036] The frequency domain modal characteristics of the frame rebound waveform vibration mode are identified to obtain the inherent frequency characteristics of the structure;

[0037] Based on the decay time constant and the inherent frequency characteristics of the structure, calculate its overall offset relative to the reference value;

[0038] Based on the comprehensive offset, the structural connection status evaluation parameters are generated using a preset evaluation parameter calculation formula.

[0039] In some possible implementations, the method involves integrating the tooth flank clearance quantification parameters, bearing clearance assessment data, structural connection status assessment parameters, and transmission chain compliance degradation indicators to perform multi-parameter fusion analysis and generate a comprehensive health status assessment report, including:

[0040] The tooth flank clearance quantification parameters are standardized to obtain the first standard feature;

[0041] The bearing clearance evaluation data is standardized to obtain a second standard feature;

[0042] The structural connection state evaluation parameters are standardized to obtain the third standard feature;

[0043] The transmission chain compliance degradation index is standardized to obtain the fourth standard feature;

[0044] The first standard feature, the second standard feature, the third standard feature, and the fourth standard feature are combined in sequence to form a multi-parameter feature vector;

[0045] Weighted fuzzy inference analysis is performed on the multi-parameter feature vectors to generate fusion analysis results;

[0046] The fusion analysis results are then matched with a historical health database to generate a comprehensive health status assessment report, including health scores and warning levels.

[0047] In some possible implementations, the calculation of servo drive system compensation parameters based on the comprehensive health status assessment report, generating a torque compensation parameter set, dynamic response gain adjustment, and mechanical preload correction value, includes:

[0048] Extract the quantification parameters of tooth flank clearance from the comprehensive health status assessment report;

[0049] The periodic torque compensation amount is calculated based on the aforementioned backlash quantification parameters.

[0050] Extract bearing clearance assessment data from the comprehensive health status assessment report;

[0051] The speed loop feedforward gain adjustment amount is determined based on the bearing clearance evaluation data.

[0052] Extract structural connectivity status assessment parameters from the comprehensive health status assessment report;

[0053] Optimize the position loop control parameters based on the structural connection status evaluation parameters;

[0054] Extract the drivetrain compliance degradation index from the comprehensive health status assessment report;

[0055] The mechanical preload setting value is adjusted based on the aforementioned transmission chain compliance degradation index.

[0056] In some possible implementations, the execution of the torque compensation parameter set, dynamic response gain adjustment, and mechanical preload correction value, along with low-load bending test verification, and the collection of force feedback data and structural response data during the bending test, includes:

[0057] The torque compensation parameter set, dynamic response gain adjustment amount, and mechanical preload correction value are configured collaboratively, and a low-load test bending verification process is triggered.

[0058] During the verification process, pressure feedback sequences from force sensors, as well as vibration and deformation sequences from accelerometers and displacement sensors, are collected.

[0059] The pressure feedback sequence is used as the force feedback data;

[0060] The vibration and deformation sequence is used as the structural response data.

[0061] A remote operation and maintenance diagnostic system for a bending machine, the system comprising:

[0062] The load analysis module is used to calculate the load inertia distribution based on the processing thickness, bending length and cycle time requirements of the bending task, and generate dynamic torque pulse commands.

[0063] The disturbance excitation module is used to execute the dynamic torque pulse command, excite electromechanical disturbances in the transmission chain, and generate electromechanical response data including torque fluctuation characteristics;

[0064] The clearance diagnosis module is used to collect torque fluctuation characteristics in the electromechanical response data, identify the transmission chain clearance status, and generate tooth flank clearance quantification parameters and bearing clearance evaluation data.

[0065] The structural monitoring module is used to acquire the frame vibration response during the emergency stop of the bending machine, collect frame springback waveform data, and generate structural connection status evaluation parameters.

[0066] The stiffness testing module is used to apply micro-amplitude frequency sweep excitation to the servo system, collect the response hysteresis data of the transmission system, and generate the transmission chain compliance degradation index.

[0067] The health assessment module is used to integrate the tooth flank clearance quantification parameters, bearing clearance assessment data, structural connection status assessment parameters, and transmission chain compliance degradation index to perform multi-parameter fusion analysis and generate a comprehensive health status assessment report.

[0068] The compensation calculation module is used to calculate the compensation parameters of the servo drive system based on the comprehensive health status assessment report, and generate a torque compensation parameter set, dynamic response gain adjustment amount and mechanical preload correction value;

[0069] The verification execution module is used to execute the torque compensation parameter set, dynamic response gain adjustment amount and mechanical preload correction value to perform low-load bending test verification, and collect force feedback data and structural response data during the bending test;

[0070] The effect verification module is used to analyze the force feedback data and structural response data, verify the effect of parameter correction, and generate health status verification results.

[0071] The closed-loop management module is used to update the health reference range of the bending machine based on the health status verification results, forming a closed-loop record for operation and maintenance diagnosis.

[0072] In a second aspect, this application provides a computer storage medium including computer instructions that, when executed on a mobile terminal, cause the electronic device to perform the method described in any one of the first aspects.

[0073] Thirdly, this application provides an electronic device, including: a processor and a memory;

[0074] The memory stores one or more computer programs, the one or more computer programs including instructions; when the instructions are executed by the processor, the electronic device performs the method described in any one of the first aspects.

[0075] As can be seen from the above technical solution, this application has the following beneficial effects:

[0076] 1. This method actively excites electromechanical disturbances in the transmission chain by generating dynamic torque pulse commands based on load inertia analysis and collects multi-source response data such as torque and vibration. It can sensitively detect slight degradation of mechanical parameters such as tooth backlash and bearing clearance in the early stages. Based on the health status assessment report, it automatically generates and executes compensation parameters, such as periodic torque compensation and speed loop gain adjustment. When the equipment performance deteriorates but has not yet completely failed, the system actively compensates for the negative impact of mechanical wear through adaptive adjustment of the control system, effectively extending the service life of the equipment, advancing the timing of operation and maintenance intervention, and reducing unplanned downtime and major failures from the source.

[0077] 2. This method integrates four key mechanical and electrical coupling indicators—tooth flank clearance, bearing clearance, structural connection status, and transmission chain compliance—through multi-parameter fusion analysis. This results in more comprehensive and reliable diagnostic conclusions. The entire process constructs a complete closed loop: by verifying the collected pressure feedback and structural response data through low-load bending tests, the actual effect of parameter corrections is quantitatively evaluated, and the equipment's health reference range is updated accordingly. This data-driven closed-loop learning mechanism enables the system to continuously accumulate experience, adaptively optimize diagnostic thresholds and compensation strategies, and ultimately form an increasingly accurate and intelligent operation and maintenance ecosystem, providing a solid technical foundation for predictive maintenance. Attached Figure Description

[0078] The invention will now be further described with reference to the accompanying drawings.

[0079] Figure 1 A first flowchart provided for an embodiment of this application;

[0080] Figure 2 A second flowchart provided for embodiments of this application;

[0081] Figure 3 A third flowchart provided for embodiments of this application;

[0082] Figure 4 The fourth flowchart provided for the embodiments of this application;

[0083] Figure 5 The fifth flowchart provided for the embodiments of this application. Detailed Implementation

[0084] The terms "first," "second," and "third," etc., used in this application specification, claims, and drawings are for distinguishing different objects, not for specifying a particular order.

[0085] In the embodiments of this application, the words "exemplary" or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the words "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0086] Research has found that traditional operation and maintenance methods mainly rely on periodic inspections and post-fault repairs, which have problems such as insufficient early warning capabilities, limited diagnostic accuracy, and inability to assess equipment status in real time.

[0087] To address the aforementioned issues, this application provides a remote operation and maintenance diagnostic method, system, storage medium, and device for bending machines:

[0088] Example 1

[0089] To solve the above problems, such as Figure 1 - Figure 5 As shown, this embodiment uses a large CNC bending machine as an application scenario. This equipment includes a servo drive system, a transmission chain containing core components such as a gearbox, coupling, drive shaft, slide mechanism, and frame structure. This method is implemented through a remote operation and maintenance platform, comprising four core stages: data acquisition, status assessment, parameter compensation, and verification and update, forming a complete diagnostic closed loop. The remote operation and maintenance platform connects to the bending machine control system via industrial Ethernet to achieve data acquisition and command issuance.

[0090] Step 1: Calculate the load inertia distribution based on the processing thickness, bending length, and cycle time requirements of the bending task, and generate dynamic torque pulse commands.

[0091] Implementation details: First, detailed process parameters for the current bending task are obtained from the production management system. These parameters include the material type of the sheet metal to be processed, sheet thickness, bending length, bending angle, and processing cycle requirements, i.e., the number of bends per unit time. Based on the principles of materials mechanics and the theory of sheet metal plastic deformation, the theoretical torque value required for the bending process is calculated according to the sheet thickness, bending length, and material properties. This calculated value comprehensively considers factors such as the material's yield strength, strain hardening effect, and deformation work.

[0092] The frequency range of torque variation is determined based on the processing cycle requirements. High-speed cycles correspond to higher test frequency components to ensure that the test signal can cover the dynamic range of the equipment's actual operation. Based on the calculated torque value and the determined frequency range, combined with the rotational inertia calculation model of each shaft in the transmission system, the load inertia distribution characteristics on the transmission shaft are accurately calculated. This calculation takes into account the mass distribution and geometric characteristics of each component of the transmission system.

[0093] A dynamic torque pulse waveform with multiple harmonic components is generated based on the load inertia distribution characteristics. This waveform is based on the fundamental signal with multiple higher harmonics of specific frequencies superimposed on it. The selection of these harmonic frequencies is based on prior knowledge of the inherent characteristics of the transmission system, ensuring that the important vibration modes of the transmission chain can be effectively excited, providing rich feature information for subsequent state identification.

[0094] Definitions:

[0095] Load inertia distribution: refers to the dynamic characteristics of the rotational inertia of each component of the transmission system as the position of the transmission shaft changes during the bending process. It reflects the distribution of inertial load in the system during motion.

[0096] Dynamic torque pulse command: A special control signal that includes rapidly changing torque components. It is characterized by rich frequency components and is used to stimulate the dynamic response of the system to obtain state information.

[0097] Harmonic components: Sine wave components whose frequency is an integer multiple of the fundamental frequency. In testing, they are used to examine the response characteristics of the system in different frequency ranges and to reveal the state characteristics of different mechanical components.

[0098] Beneficial effects:

[0099] By accurately calculating the load inertia distribution based on actual process parameters, the dynamic torque pulse test signal generated in this application can be optimized and customized for the current processing conditions, ensuring that the excited electromechanical disturbances have sufficient detection sensitivity without posing an overload risk to the equipment. Compared with traditional test signals using fixed amplitude and fixed frequency, this method fully considers the influence of actual load conditions, making the diagnostic process closer to the actual operating conditions of the equipment and significantly improving the accuracy and reliability of condition assessment. At the same time, this test method based on actual process parameters can be implemented during normal production, minimizing the impact on production schedule.

[0100] Step 2: Execute the dynamic torque pulse command to excite electromechanical disturbances in the transmission chain and generate electromechanical response data including torque fluctuation characteristics.

[0101] Implementation details: Carefully designed dynamic torque pulse commands are sent to the servo drive system via the servo driver interface, precisely controlling the servo motor to operate according to preset test commands. During this process, multiple key signals are simultaneously acquired: including motor torque feedback signals from the high-precision torque sensor built into the servo driver, and vibration acceleration sensor signals installed at key locations such as the gearbox housing, bearing housing, and drive shaft support.

[0102] A high-speed data acquisition system was used to record all signal data throughout the entire test cycle, ensuring a sufficiently high sampling frequency to meet subsequent analysis requirements. Then, specialized signal processing algorithms were employed to extract time-domain features of torque fluctuations from the recorded raw signals. These features included statistical parameters such as peak values, valley values, root mean square values, waveform factors, and impulse indices. Simultaneously, frequency-domain analysis methods, such as Fast Fourier Transform, were used to obtain the signal's spectral characteristics, including amplitude and phase information for each characteristic frequency. Finally, these extracted time-domain and frequency-domain features were combined according to a standard format to form a structured electromechanical response dataset, providing a complete data foundation for further in-depth analysis.

[0103] Definitions:

[0104] Electromechanical disturbance: The dynamic response process of a mechanical system triggered by a specially designed electrical control command. By analyzing this response, the state characteristics of the mechanical system can be inferred.

[0105] Torque ripple characteristics: The periodic or non-periodic variation patterns exhibited in the torque signal, which are closely related to the state of the mechanical transmission system.

[0106] Time-domain characteristics: The various statistical features exhibited by a signal in the time dimension, reflecting the pattern of signal change over time.

[0107] Frequency domain characteristics: The distribution characteristics of a signal in the frequency dimension, revealing the composition of various frequencies in the signal.

[0108] Beneficial effects:

[0109] By executing carefully designed dynamic torque pulses and simultaneously acquiring multi-source response data, this application enables the testing process to be completed during normal production intervals without disassembling equipment or prolonged production interruptions, achieving seamless integration of testing and production. The torque feedback signal directly reflects the torque transmission state of the transmission system, while the mechanical vibration signal provides detailed information on the vibration of local components. The organic combination of these two provides a comprehensive and three-dimensional diagnostic basis for equipment condition assessment. Compared with traditional single-signal acquisition methods, this multi-sensor, multi-physical-quantity data fusion method significantly improves the identification of fault characteristics and the reliability of diagnosis, laying a solid data foundation for subsequent accurate identification of specific fault types and locations.

[0110] Step 3: Collect torque fluctuation characteristics from the electromechanical response data, identify the transmission chain clearance status, and generate quantified parameters for tooth flank clearance and bearing clearance evaluation data.

[0111] Implementation details: First, high-frequency components in the torque fluctuation signal are extracted from the structured electromechanical response dataset. These high-frequency components usually correspond to the gear meshing frequency and its harmonic region. By analyzing the amplitude variation law of these high-frequency components, especially focusing on the amplitude change characteristics of the torque signal near the zero crossing point, when there is tooth backlash in the transmission chain, the torque will show obvious amplitude jump phenomenon when the direction changes. Based on this characteristic physical phenomenon, the backlash state of the gear transmission can be accurately identified.

[0112] Based on a pre-established calibration model relating abrupt change amplitude to actual clearance size, the detected signal characteristics are converted into precise quantized values ​​of tooth flank clearance. Mid-frequency components of torque fluctuations are extracted from the electromechanical response dataset; these components typically correspond to the bearing's characteristic frequency range. By analyzing the phase shift characteristics of these mid-frequency components, it is found that increased bearing clearance leads to a significant phase lag between the torque signal and the position signal. The bearing clearance condition is assessed based on the magnitude and variation of the phase lag angle.

[0113] By combining the results of high-frequency and mid-frequency analysis, a complete transmission chain clearance status report is generated, which includes quantified parameters of tooth flank clearance and bearing clearance assessment data. This report includes not only numerical results but also confidence assessments and quality indicators.

[0114] Definitions:

[0115] Tooth flank clearance quantification parameter: A numerical description of the size of the tooth flank clearance of a gear pair, including the clearance value and related reliability indicators.

[0116] Bearing clearance assessment data: A comprehensive assessment of the internal clearance condition of a bearing, including clearance size, trend of change, and health status classification.

[0117] High-frequency components: The higher-frequency components in a signal, which usually reflect the gear meshing state and tooth surface contact characteristics.

[0118] Mid-frequency component: The component with a moderate frequency in the signal, which usually reflects the bearing's operating status and the contact characteristics between the rolling elements and the raceway.

[0119] Beneficial effects:

[0120] By deeply analyzing the characteristics of torque fluctuation signals across different frequency bands, this application effectively distinguishes between two common but fundamentally different mechanical problems: gear backlash and bearing clearance. High-frequency components are particularly sensitive to gear backlash, accurately reflecting the meshing state of the gear pair; mid-frequency components exhibit excellent specificity for bearing clearance, accurately assessing the bearing's operating condition. This frequency domain feature separation-based analysis method significantly improves the specificity and accuracy of diagnosis, enabling maintenance personnel not only to identify whether a problem exists in the equipment but also to accurately determine whether the problem originates from the gear drive or the bearing components, providing a clear direction for subsequent precise maintenance. This method overcomes the problem of various fault characteristics being confused in traditional vibration analysis.

[0121] Step 4: Acquire the frame vibration response during the emergency stop of the bending machine, collect the frame springback waveform data, and generate structural connection status assessment parameters.

[0122] Implementation details: First, the operating status of the bending machine is monitored, and the triggering timing of the emergency stop signal is identified through the control system interface. Upon triggering the emergency stop signal, the high-speed data acquisition system is immediately activated to ensure the complete dynamic response during the emergency stop process is captured. Multiple triaxial accelerometers are simultaneously collected from key locations such as the foundation connection, beam-column joint surfaces, and slide rail mounting surfaces, all pre-positioned at various connection points on the frame.

[0123] Vibration waveform data during the rebound decay phase of the frame was specifically extracted from the collected vibration signals. The characteristics of this phase can well reflect the stiffness characteristics of the structural connections. Professional time-domain analysis was performed on the extracted rebound vibration waveform to obtain the time constant of vibration decay, which directly reflects the damping characteristics of the structure. At the same time, precise frequency-domain analysis was performed to obtain the offset of the structure's natural frequency through spectrum analysis, which reflects the change in the structure's stiffness.

[0124] Based on the decay time constant and natural frequency offset, the connection stiffness coefficient is calculated using a pre-established structural dynamics model. Finally, structural connection status evaluation parameters are generated based on the connection stiffness coefficients. These parameters comprehensively reflect the fastening status and structural integrity of each connection part of the frame.

[0125] Definitions:

[0126] Frame springback waveform data: Records of the elastic vibration and springback process of the frame structure caused by inertia during the emergency stop of the bending machine.

[0127] The decay time constant is the time required for the vibration amplitude to decay to a specific proportion of its initial value, reflecting the damping characteristics of the structure.

[0128] Natural frequency offset: The difference between the actual measured natural frequency of the structure and the designed natural frequency, reflecting the change in structural stiffness.

[0129] Connection stiffness coefficient: A comprehensive parameter that quantifies the fastening state and connection stiffness of structural connection parts.

[0130] Beneficial effects:

[0131] This application utilizes the natural excitation generated by the sudden stop of a bending machine to assess structural condition. Without requiring additional vibration excitation equipment, it achieves accurate assessment of structural connection status under real-world working conditions. The impact load generated during the sudden stop effectively excites the structure's inherent characteristics, and the rebound waveform contains rich information about the structural condition. By analyzing vibration attenuation characteristics and frequency features, potential faults such as loosening and cracks in frame connections can be detected in a timely manner, providing crucial information for preventing structural damage. This method is particularly suitable for assessing the integrity of large welded structures and bolted connections, overcoming the limitations of traditional visual inspection and non-destructive testing, which can only detect surface defects.

[0132] Step 5: Apply a small-amplitude frequency sweep excitation to the servo system, collect the response hysteresis data of the transmission system, and generate a transmission chain compliance degradation index.

[0133] Implementation details: First, a linear sweep frequency signal with amplitude strictly controlled within a specific ratio range of the rated torque is generated to ensure the safety of the testing process and prevent damage to the equipment. The generated linear sweep frequency signal is cleverly superimposed on the normal torque command of the servo system to form a composite torque command, achieving a smooth transition between testing and normal operation.

[0134] The servo drive is controlled to precisely execute composite torque commands, while simultaneously acquiring position feedback data from the motor (from a high-resolution encoder built into the servo motor) and load (from a position sensor mounted on the slider or drive shaft). Based on the acquired position data from the motor and load, the phase difference sequence between them is calculated, and a complete response hysteresis curve is plotted.

[0135] The area integral value of the response hysteresis curve within the characteristic frequency band is calculated using professional signal analysis methods. This integral value comprehensively reflects the compliance characteristics of the transmission system throughout the entire test frequency band. Finally, a transmission chain compliance degradation index is generated based on the area integral value. This index quantifies the degree of change in the overall compliance of the transmission system relative to its initial state.

[0136] Definitions:

[0137] Micro-amplitude frequency sweep excitation: A test signal with a small amplitude and continuously varying frequency is used to examine the system's response characteristics at different frequencies without affecting normal operation.

[0138] Response lag data: The phase delay and amplitude variation of the system output relative to the input, reflecting the dynamic characteristics of the system.

[0139] Phase difference sequence: The continuous variation of the phase difference between the system input and output at different frequencies.

[0140] Transmission chain compliance degradation index: A comprehensive index that quantifies the degree of degradation of the overall compliance characteristics of a transmission system over time.

[0141] Beneficial effects:

[0142] By employing micro-amplitude frequency sweep excitation testing, this application can accurately assess the overall compliance characteristics of a transmission system. This is a crucial parameter that is difficult to obtain through traditional vibration analysis. The degradation of transmission chain compliance often precedes a significant increase in clearance or aggravation of vibration; therefore, this indicator has excellent early warning value. By analyzing the phase hysteresis characteristics at different frequencies, the state changes of flexible components such as elastic couplings and drive shafts in the transmission system can be comprehensively evaluated, providing a forward-looking indicator for predictive maintenance. This method is particularly suitable for detecting progressive faults in transmission systems, such as gradual material fatigue and stiffness reduction, enabling a more comprehensive understanding of the equipment's condition.

[0143] Step 6: Integrate the aforementioned tooth flank clearance quantification parameters, bearing clearance assessment data, structural connection status assessment parameters, and transmission chain compliance degradation index to perform multi-parameter fusion analysis and generate a comprehensive health status assessment report.

[0144] Implementation details: Multi-parameter information fusion technology is adopted to comprehensively analyze and evaluate the four core status parameters from different testing methods. First, each parameter is standardized and normalized to eliminate dimensional differences and ensure data comparability. Then, based on the pre-established equipment health status assessment model, the correlation and mutual influence between the parameters are analyzed.

[0145] By using an expert system rule base and fuzzy inference algorithms, the deviation of each parameter and its weight in the overall health status of the equipment are evaluated. The coupling effect between parameters is considered, such as the increased tooth flank clearance which will aggravate vibration and thus affect the structural connection status. Finally, a structured comprehensive health status assessment report is generated. This report includes the health status score of each component, the overall health index, the fault risk level, maintenance priority recommendations, etc., forming a comprehensive and three-dimensional evaluation of the equipment status.

[0146] Definitions:

[0147] Multi-parameter fusion analysis: a technical method for comprehensively processing and correlating multiple parameters from different sources and of different types.

[0148] Comprehensive Health Status Assessment Report: A comprehensive and structured assessment of the overall health status of the equipment, including quantitative indicators and qualitative analysis.

[0149] Health status assessment model: A mathematical model based on equipment mechanism and operational data used to assess the health status of equipment.

[0150] Failure Risk Level: A risk classification index that predicts the likelihood of future failures based on the current state.

[0151] Beneficial effects:

[0152] By employing multi-parameter fusion analysis, this application overcomes the limitations and biases of single-parameter assessments, achieving a comprehensive and accurate evaluation of equipment health status. Different parameters reflect the equipment condition from different perspectives, and their organic combination can uncover problems that cannot be identified by a single parameter. For example, decreased transmission chain compliance may indicate early fatigue in the shaft system, while deterioration of structural connection conditions may stem from long-term vibration. This multi-dimensional correlation analysis significantly improves the depth and reliability of diagnosis. The comprehensive assessment report provides a scientific basis for maintenance decisions, helping users prioritize problems and optimize the allocation of maintenance resources.

[0153] Step 7: Calculate the servo drive system compensation parameters based on the comprehensive health status assessment report, and generate the torque compensation parameter set, dynamic response gain adjustment amount, and mechanical preload correction value.

[0154] Implementation details: First, extract the tooth backlash quantification parameter from the comprehensive health status assessment report. Calculate the corresponding torque feedforward compensation based on the backlash size and distribution characteristics. This compensation is used to offset the control lag and nonlinear characteristics caused by the backlash. Extract the bearing clearance assessment data from the assessment report. Determine the speed loop gain adjustment coefficient based on the size and trend of the clearance to optimize the dynamic response characteristics of the system.

[0155] Structural connection status evaluation parameters are extracted from the evaluation report. The position loop gain correction value is calculated based on the connection stiffness coefficient to ensure that the stiffness characteristics of the control system match those of the mechanical structure. The transmission chain compliance degradation index is extracted from the evaluation report. The mechanical preload adjustment amount is calculated based on the compliance change. Performance degradation caused by component aging is partially compensated by adjusting bearing preload and other measures.

[0156] Finally, all compensation and adjustment parameters are integrated into a complete set of servo drive system compensation parameters, including specific parameter values, timing of action, and execution conditions, forming a set of compensation instructions that can be directly issued to the control system.

[0157] Definitions:

[0158] Torque compensation parameter set: A set of torque control parameters used to compensate for the nonlinear characteristics of mechanical transmission.

[0159] Dynamic response gain adjustment: Optimized adjustment value for the gain of the speed loop and position loop of the control system.

[0160] Mechanical preload correction value: A recommended value for adjusting the preload state of mechanical components to optimize mechanical transmission performance.

[0161] Torque feedforward compensation: Feedforward control parameters based on the system model, used to improve system response speed and control accuracy.

[0162] Beneficial effects:

[0163] Based on the health status assessment results, targeted compensation parameters are generated. This application realizes a complete closed loop from condition diagnosis to performance compensation. Through adaptive adjustment of control parameters, the processing accuracy and dynamic performance of the equipment can still be maintained even when mechanical parts have degraded to a certain extent, effectively extending the service life of the equipment. This software compensation method, combined with hardware maintenance, forms a more economical and efficient equipment maintenance strategy. Especially in the early stage of mechanical part wear, the optimization of control parameters can often significantly improve equipment performance, postpone the overhaul time, and save users a lot of maintenance costs.

[0164] Step 8: Execute the torque compensation parameter set, dynamic response gain adjustment, and mechanical preload correction value to perform a low-load bending test verification, and collect force feedback data and structural response data during the bending test.

[0165] Implementation details: The generated set of compensation parameters is sent to the bending machine control system through a secure communication protocol to ensure the reliability and integrity of parameter transmission. First, the mechanical preload correction value is executed. The preload of key bearings and guide rails is precisely adjusted through hydraulic or mechanical adjustment devices. Then, the control parameters of the servo drive are updated, including torque compensation parameters and dynamic response gain adjustment.

[0166] After the parameters are updated, a lower load condition is selected for trial bending verification, typically using thinner sheet metal and a smaller bending angle. During the trial bending, multiple key signals are simultaneously collected, including servo motor torque feedback, slider position trajectory, vibration acceleration at each measuring point, and hydraulic system pressure, ensuring that the collected data covers the entire bending cycle, including rapid descent, feed bending, pressure holding, and return. A preliminary quality check is performed on the collected data to ensure its integrity and reliability.

[0167] Definitions:

[0168] Low-load bending test verification: A bending test conducted under low load conditions to verify the effect of parameter compensation without putting a burden on the equipment.

[0169] Force feedback data: The general term for various signal data that reflect the actual stress state during the bending process.

[0170] Structural response data: Vibration, strain, and other response data generated by the equipment structure during bending.

[0171] Parameter transmission security protocol: A communication protocol that ensures data integrity and security during parameter transmission.

[0172] Beneficial effects:

[0173] Through low-load bending tests, this application can verify the actual effect of parameter compensation under safe conditions, ensuring the effectiveness of maintenance measures. The bending test simulates real working conditions, but with a low load, so even if the compensation effect is not ideal, it will not cause damage to the equipment or product. The synchronous acquisition of multi-sensor data provides an objective basis for evaluating the compensation effect, avoiding the limitations of relying on subjective feelings in traditional maintenance. This rigorous verification process ensures the scientific nature and reliability of maintenance measures, providing real and effective data support for subsequent status updates.

[0174] Step 9: Analyze the force feedback data and structural response data, verify the effect of parameter correction, and generate health status verification results.

[0175] Implementation details: In-depth analysis was conducted on the force feedback data and structural response data collected during the trial bending process. First, the torque fluctuation characteristics before and after parameter adjustment were compared to evaluate the effect of tooth backlash compensation; the smoothness of the slider motion trajectory was analyzed to verify the rationality of the dynamic response gain adjustment; the spectral characteristics of the vibration signal were examined to evaluate the impact of mechanical preload adjustment on structural vibration.

[0176] The degree of improvement of various performance indicators is quantified through professional data analysis algorithms, including the reduction ratio of torque fluctuation amplitude, the improvement of trajectory tracking accuracy, and the degree of reduction in vibration level. Based on these quantitative indicators, a health status verification result is generated. The result clearly records the actual effect of each compensation measure, including successful items, items to be improved, and ineffective items, and includes specific performance indicator data and improvement suggestions.

[0177] Definitions:

[0178] Verification of parameter correction effects: A systematic evaluation process of the actual effects of implemented parameter adjustment and compensation measures.

[0179] Health status verification results: a structured assessment of the effectiveness of maintenance measures, including effectiveness evaluation and improvement recommendations.

[0180] Performance improvement rate: Various indicators and parameters that quantify the degree of performance improvement.

[0181] Trajectory tracking accuracy: The degree of consistency between the actual motion trajectory and the desired motion trajectory.

[0182] Beneficial effects:

[0183] Through systematic effect verification analysis, this application can objectively evaluate the actual effectiveness of maintenance measures, providing a basis for continuous improvement. Quantified performance indicators make the effect evaluation more scientific and accurate, avoiding subjective judgments such as "it feels much better" in traditional maintenance. The verification results not only reflect the effectiveness of the current maintenance measures, but also provide valuable experience to guide future maintenance decisions, forming a virtuous cycle of knowledge accumulation. This method ensures that every maintenance activity is a data-driven scientific decision, significantly improving the professional level of equipment management.

[0184] Step 10: Update the bending machine's health reference range based on the health status verification results to form a closed-loop record for operation and maintenance diagnosis.

[0185] Implementation details: Based on the health status verification results and equipment operation data, update the health reference range of the bending machine. For effective compensation measures, adjust the normal range threshold of the status parameters accordingly to reflect the health benchmark of the equipment under the new control parameters. For aspects that need further improvement, update the early warning threshold and alarm rules to optimize the sensitivity of status monitoring.

[0186] The entire diagnostic process, including data, analysis results, compensation measures, and verification effects, was compiled into a complete closed-loop record for operation and maintenance diagnosis, encompassing raw data, intermediate results, final conclusions, and knowledge summaries. This record is stored using a standardized data structure for easy retrieval, analysis, and reference later. Simultaneously, the equipment health status database and expert knowledge base are updated to provide stronger data and knowledge support for future diagnostics and maintenance.

[0187] Definitions:

[0188] Health reference range: Defines the upper and lower limit thresholds of the normal state parameters of the device.

[0189] Operation and maintenance diagnostic closed-loop record: including a complete maintenance record document covering the entire process from detection to verification.

[0190] State parameter threshold: Boundary values ​​used to determine whether a state parameter is normal.

[0191] Expert knowledge base: An intelligent database that stores diagnostic rules and maintenance experience.

[0192] Beneficial effects:

[0193] By dynamically updating the health reference range and forming a complete closed-loop record, continuous optimization and knowledge accumulation in equipment operation and maintenance management are achieved. The adaptive adjustment of the health reference range ensures that the status assessment always matches the actual condition of the equipment, avoiding false alarms or missed alarms caused by equipment aging. The complete closed-loop record not only meets the traceability requirements of equipment management, but more importantly, it forms a valuable equipment operation and maintenance knowledge base, laying a solid foundation for building a more intelligent operation and maintenance system. This continuous learning and optimization mechanism enables the operation and maintenance system to continuously evolve with changes in equipment status, always maintaining optimal working condition.

[0194] This embodiment details a remote operation and maintenance diagnostic method for a bending machine. Through dynamic torque pulse excitation, multi-source data acquisition, parameter fusion analysis, and intelligent compensation adjustment, it achieves accurate assessment and proactive maintenance of the bending machine's health status. The core value of this method lies in transforming traditional periodic maintenance and post-fault maintenance into condition-based predictive maintenance. Through electromechanical coupling characteristic analysis, multi-parameter information fusion, and closed-loop verification optimization, a complete equipment health management system is established. In practical applications, this method can detect potential faults early, optimize equipment performance, extend service life, and significantly improve the reliability and efficiency of the bending machine, providing an innovative technical solution for equipment operation and maintenance in the era of intelligent manufacturing.

[0195] The foregoing has shown and described the basic principles, main features, and advantages of this application. Those skilled in the art should understand that this application is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of this application. Various changes and modifications can be made to this application without departing from the spirit and scope thereof, and all such changes and modifications fall within the scope of this application as claimed. The scope of protection of this application is defined by the appended claims and their equivalents.

Claims

1. A remote operation and maintenance diagnostic method for a bending machine, characterized in that, The method includes: The load inertia distribution is calculated based on the processing thickness, bending length and cycle time requirements of the bending task, and dynamic torque pulse commands are generated. The dynamic torque pulse command is executed to excite electromechanical disturbances in the transmission chain and generate electromechanical response data including torque fluctuation characteristics. The torque fluctuation characteristics in the electromechanical response data are collected to identify the transmission chain clearance status and generate quantification parameters of tooth flank clearance and bearing clearance evaluation data. During the emergency stop of the bending machine, the vibration response of the frame is acquired, the springback waveform data of the frame is collected, and structural connection status evaluation parameters are generated. Apply a micro-amplitude frequency sweep excitation to the servo system, collect the response hysteresis data of the transmission system, and generate a transmission chain compliance degradation index. By combining the aforementioned tooth flank clearance quantification parameters, bearing clearance assessment data, structural connection status assessment parameters, and transmission chain compliance degradation index, a multi-parameter fusion analysis is performed to generate a comprehensive health status assessment report. Based on the comprehensive health status assessment report, the compensation parameters of the servo drive system are calculated, and a set of torque compensation parameters, dynamic response gain adjustment amount and mechanical preload correction value are generated. The torque compensation parameter set, dynamic response gain adjustment amount and mechanical preload correction value are executed to perform low-load bending test verification, and force feedback data and structural response data are collected during the bending test. Analyze the force feedback data and structural response data to verify the effect of parameter correction and generate health status verification results; The health reference range of the bending machine is updated based on the health status verification results, forming a closed-loop record for operation and maintenance diagnosis.

2. The method according to claim 1, characterized in that, The step of calculating the load inertia distribution based on the processing thickness, bending length, and cycle time requirements of the bending task, and generating dynamic torque pulse commands, includes: Obtain the process parameters for the current bending task; The process parameters include sheet thickness, bending length, and processing cycle time; Based on the sheet thickness and bending length, calculate the theoretical load torque required for the bending process; Based on the processing cycle time, determine the dynamic response frequency required by the servo system; By combining the theoretical load torque and the dynamic response frequency, the equivalent load inertia at the servo axis end is calculated. Based on the equivalent load inertia, a dynamic torque pulse waveform including the fundamental wave and multiple harmonics is constructed; The dynamic torque pulse waveform serves as the dynamic torque pulse command.

3. The method according to claim 2, characterized in that, The execution of the dynamic torque pulse command, which excites electromechanical disturbances in the transmission chain and generates electromechanical response data including torque fluctuation characteristics, includes: Execute the dynamic torque pulse command to excite wideband electromechanical disturbances in the transmission chain; Based on the aforementioned wideband electromechanical disturbance, torque feedback data and position feedback data of the servo motor, as well as vibration acceleration data of the transmission chain, are collected. The rotational speed fluctuation information is calculated based on the collected position feedback data; Torque fluctuation information is extracted based on the collected torque feedback data; The speed fluctuation information, torque fluctuation information, and acceleration data are time-aligned and fused to output electromechanical response data.

4. The method according to claim 1, characterized in that, The process of acquiring the frame vibration response during the emergency stop of the bending machine, collecting frame springback waveform data, and generating structural connection status evaluation parameters includes: The system acquires the operating status of the bending machine. Upon detecting an emergency stop signal, it triggers and collects the acceleration signal at the frame connection point. Extract the raw vibration data packet from the start of the emergency stop to the vibration decay period from the acquired acceleration signal; The original vibration data packet is filtered to obtain a clean frame rebound waveform; The time-domain attenuation characteristics of the vibration energy of the frame rebound waveform are calculated to obtain the attenuation time constant; The frequency domain modal characteristics of the frame rebound waveform vibration mode are identified to obtain the inherent frequency characteristics of the structure; Based on the decay time constant and the inherent frequency characteristics of the structure, calculate its overall offset relative to the reference value; Based on the comprehensive offset, the structural connection status evaluation parameters are generated using a preset evaluation parameter calculation formula.

5. The method according to claim 1, characterized in that, The aforementioned tooth flank clearance quantification parameters, bearing clearance assessment data, structural connection status assessment parameters, and transmission chain compliance degradation indicators are integrated into a multi-parameter fusion analysis to generate a comprehensive health status assessment report, including: The tooth flank clearance quantification parameters are standardized to obtain the first standard feature; The bearing clearance evaluation data is standardized to obtain a second standard feature; The structural connection state evaluation parameters are standardized to obtain the third standard feature; The transmission chain compliance degradation index is standardized to obtain the fourth standard feature; The first standard feature, the second standard feature, the third standard feature, and the fourth standard feature are combined in sequence to form a multi-parameter feature vector; Weighted fuzzy inference analysis is performed on the multi-parameter feature vectors to generate fusion analysis results; The fusion analysis results are then matched with a historical health database to generate a comprehensive health status assessment report, including health scores and warning levels.

6. The method according to claim 1, characterized in that, The calculation of servo drive system compensation parameters based on the comprehensive health status assessment report, generating a torque compensation parameter set, dynamic response gain adjustment, and mechanical preload correction value, includes: Extract the quantification parameters of tooth flank clearance from the comprehensive health status assessment report; The periodic torque compensation amount is calculated based on the aforementioned backlash quantification parameters. Extract bearing clearance assessment data from the comprehensive health status assessment report; The speed loop feedforward gain adjustment amount is determined based on the bearing clearance evaluation data. Extract structural connectivity status assessment parameters from the comprehensive health status assessment report; Optimize the position loop control parameters based on the structural connection status evaluation parameters; Extract the drivetrain compliance degradation index from the comprehensive health status assessment report; The mechanical preload setting value is adjusted based on the aforementioned transmission chain compliance degradation index.

7. The method according to claim 1, characterized in that, The process involves executing the torque compensation parameter set, dynamic response gain adjustment, and mechanical preload correction value to perform low-load bending test verification, collecting force feedback data and structural response data during the bending test, including: The torque compensation parameter set, dynamic response gain adjustment amount, and mechanical preload correction value are configured collaboratively, and a low-load test bending verification process is triggered. During the verification process, pressure feedback sequences from force sensors, as well as vibration and deformation sequences from accelerometers and displacement sensors, are collected. The pressure feedback sequence is used as the force feedback data; The vibration and deformation sequence is used as the structural response data.

8. A remote operation and maintenance diagnostic system for a bending machine, characterized in that, The system includes: The load analysis module is used to calculate the load inertia distribution based on the processing thickness, bending length and cycle time requirements of the bending task, and generate dynamic torque pulse commands. The disturbance excitation module is used to execute the dynamic torque pulse command, excite electromechanical disturbances in the transmission chain, and generate electromechanical response data including torque fluctuation characteristics; The clearance diagnosis module is used to collect torque fluctuation characteristics in the electromechanical response data, identify the transmission chain clearance status, and generate tooth flank clearance quantification parameters and bearing clearance evaluation data. The structural monitoring module is used to acquire the frame vibration response during the emergency stop of the bending machine, collect frame springback waveform data, and generate structural connection status evaluation parameters. The stiffness testing module is used to apply micro-amplitude frequency sweep excitation to the servo system, collect the response hysteresis data of the transmission system, and generate the transmission chain compliance degradation index. The health assessment module is used to integrate the tooth flank clearance quantification parameters, bearing clearance assessment data, structural connection status assessment parameters, and transmission chain compliance degradation index to perform multi-parameter fusion analysis and generate a comprehensive health status assessment report. The compensation calculation module is used to calculate the compensation parameters of the servo drive system based on the comprehensive health status assessment report, and generate a torque compensation parameter set, dynamic response gain adjustment amount and mechanical preload correction value; The verification execution module is used to execute the torque compensation parameter set, dynamic response gain adjustment amount and mechanical preload correction value to perform low-load bending test verification, and collect force feedback data and structural response data during the bending test; The effect verification module is used to analyze the force feedback data and structural response data, verify the effect of parameter correction, and generate health status verification results. The closed-loop management module is used to update the health reference range of the bending machine based on the health status verification results, forming a closed-loop record for operation and maintenance diagnosis.

9. A computer storage medium, characterized in that, Includes computer instructions, which, when executed on an electronic device, cause the electronic device to perform the method as described in any one of claims 1-8.

10. An electronic device, characterized in that, include: Processor and memory; The memory stores one or more computer programs, the one or more computer programs including instructions; when the instructions are executed by the processor, the electronic device performs the method as described in any one of claims 1-8.

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