Linear module with early warning protection function and use method

Abstract

This invention relates to the field of linear module technology and provides a linear module with early warning and protection functions and its usage method. The module includes a base, a slide block slidably mounted on the base, and a drive mechanism for driving the slide block's reciprocating motion. It also includes a sensing unit comprising: a torque sensor located at the power output end of the drive mechanism for real-time acquisition of drive torque data; a vibration sensor located on the slide block or base for real-time acquisition of high-frequency vibration data; a temperature sensor for real-time acquisition of temperature field data from the drive mechanism or guide rail pair; and an early warning and protection controller. This invention, through multi-source sensors combined with a built-in self-learning unit and trend prediction algorithm, achieves comprehensive real-time perception and quantitative health assessment of the linear module's operating status, effectively avoiding production losses caused by sudden downtime and reducing maintenance costs.

Description

Technical Field

[0001] This invention relates to the field of linear module technology, and more specifically, to a linear module with early warning and protection functions and its usage method. Background Technology

[0002] Linear transmission modules, as core transmission components of automated equipment, are widely used in industrial robots, CNC machine tools, electronic manufacturing equipment, and other fields. Their operational stability directly affects the efficiency and safety of the entire production line.

[0003] Currently, existing linear modules are typically equipped with basic safety devices such as limit switches and overload protection, which can prevent overtravel or severe overload to a certain extent. However, these protection mechanisms are mostly reactive, meaning that alarms or shutdowns are only triggered when a fault has already occurred or parameters have been severely exceeded. By this time, damage to components (such as lead screw wear, guide rail jamming, and motor burnout) has often already occurred, or even production accidents may have occurred.

[0004] Furthermore, traditional monitoring methods are relatively limited, mostly monitoring only a single parameter such as motor current or temperature, lacking a comprehensive understanding of multiple physical quantities. Because a single parameter cannot fully reflect the module's health status (for example, slight lubrication problems may initially manifest as only changes in vibration characteristics, without significant current fluctuations), the accuracy and timeliness of early warnings are insufficient. More importantly, existing technologies generally lack trend prediction capabilities, failing to anticipate potential future failures based on the evolution of operational data, thus hindering true predictive maintenance.

[0005] Therefore, how to achieve real-time comprehensive perception of the operating status of linear modules, early fault trend prediction, and graded early warning protection to avoid sudden shutdowns and extend equipment life has become a technical problem that urgently needs to be solved in this field. Summary of the Invention

[0006] The problem solved by this invention is: how to achieve real-time comprehensive perception of the operating status of linear modules, early fault trend prediction, and hierarchical early warning protection.

[0007] To address the aforementioned problems, this invention provides a linear module with early warning and protection functions, a method of use, an electronic device, and a storage medium.

[0008] In a first aspect, the present invention provides a linear module with an early warning protection function, comprising a base, a slide block slidably disposed on the base, and a drive mechanism for driving the slide block to reciprocate, and further comprising: The sensing unit includes: A torque sensor is installed at the power output end of the drive mechanism to collect drive torque data in real time. A vibration sensor is mounted on the slide or base for real-time acquisition of high-frequency vibration data; Temperature sensors are used to collect temperature field data of the drive mechanism or guide rail pair in real time; and Early warning protection controller, the early warning protection controller includes: The data acquisition module is electrically connected to the torque sensor, vibration sensor and temperature sensor, and is used to receive and process the driving torque data, high-frequency vibration data and temperature field data. The trend analysis module, connected to the data acquisition module, is used to construct a trend curve based on the received data and predict the probability that the data will exceed a preset safety threshold at a future time point. A health assessment module, connected to the trend analysis module, is used to generate a current health score based on the predicted probability. The execution module, connected to the health assessment module, is used to issue a warning signal when the health score is lower than a first preset threshold, and to send a deceleration or stop command to the drive mechanism when the health score is lower than a second preset threshold.

[0009] Optionally, the temperature sensor is an infrared thermal imaging sensor, with its lens facing the position of the lead screw and nut pair or the linear motor mover of the drive mechanism, for non-contact acquisition of the two-dimensional temperature field distribution.

[0010] Optionally, the drive mechanism includes a servo motor and a lead screw driven by the servo motor, and the torque sensor is integrated in the coupling between the servo motor and the lead screw, or integrated in the internal encoder circuit of the servo motor.

[0011] Optionally, the early warning protection controller further includes a self-learning unit, which is used to record the baseline data of the linear module during initial no-load operation. The trend analysis module performs differential comparison between the real-time collected data and the baseline data to eliminate installation errors and environmental noise.

[0012] Optionally, it also includes a braking module, which is a normally closed electromagnetic brake, located at the input end of the drive mechanism and connected to the execution module; when the health score is lower than a third preset threshold, the execution module triggers the braking module to urgently lock the drive mechanism.

[0013] Optionally, the sensing unit further includes a position sensor for real-time monitoring of the position and speed of the slide; the trend analysis module combines the position and speed information to perform spatiotemporal synchronous analysis of the driving torque data, high-frequency vibration data, and temperature field data.

[0014] Optionally, the early warning protection controller further includes a communication module for sending the health score and early warning signal to a remote monitoring terminal or industrial cloud platform in real time.

[0015] Secondly, the present invention provides a method for using a linear module with early warning protection function, comprising the following steps: S1, Reference Acquisition: Start the linear module and run a complete stroke under no-load or light-load conditions. The reference characteristic values ​​of torque, vibration and temperature are acquired and stored through the self-learning unit of the early warning and protection controller. S2. Real-time monitoring: During normal operation, operating data is collected in real time through torque sensors, vibration sensors, and temperature sensors; S3. Trend Prediction: The trend analysis module compares the real-time data with the baseline feature values ​​in step S1, extracts the feature change amount, and uses a time series prediction algorithm to calculate the feature value change trend at future times. S4. Health Assessment and Grading Warning: The health assessment module calculates a health score based on the changing trend. When the score falls into different preset level ranges, the execution module will issue a warning, limit the maximum speed, or perform an emergency shutdown operation.

[0016] Optionally, the time series prediction algorithm in step S3 is a prediction model based on a long short-term memory neural network or an autoregressive integral moving average model, used to predict feature values ​​within the next N sampling periods.

[0017] Optionally, it also includes step S5, fault self-check and recording: after the emergency stop operation is performed, the early warning protection controller automatically locks the high-frequency sensor data for M seconds before and after the stop time, packages them to generate a fault diagnosis report, and outputs it through the communication module.

[0018] Thirdly, embodiments of the present invention provide an electronic device, including a processor, a communication interface, a memory, and a bus, wherein the processor, the communication interface, and the memory communicate with each other through the bus, and the processor can call logical instructions in the memory to execute the steps of the method provided in the second aspect.

[0019] Fourthly, embodiments of the present invention provide a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method provided in the second aspect.

[0020] Compared with the prior art, the linear module with early warning protection function of the present invention has the following beneficial effects: This invention integrates multiple sensors, including torque, vibration, infrared thermal imaging, and position sensors, and combines them with a built-in self-learning unit and trend prediction algorithm to achieve comprehensive real-time perception and quantitative health assessment of the linear module's operating status. It can provide accurate early warnings based on data change trends before faults occur, and automatically execute graded protection measures from alerts and speed reduction to emergency braking based on health scores. Simultaneously, it locks data before and after faults to generate diagnostic reports, thereby upgrading traditional passive overload protection to proactive predictive maintenance. This significantly improves the reliability, safety, and service life of the equipment, effectively avoids production losses caused by sudden shutdowns, and reduces operation and maintenance costs. Attached Figure Description

[0021] Figure 1 This is a structural block diagram of a linear module with early warning and protection functions in an embodiment of the present invention; Figure 2 This is a structural block diagram of the drive mechanism in an embodiment of the present invention; Figure 3 This is a flowchart illustrating the usage method of the linear module with early warning protection function in an embodiment of the present invention; Figure 4 This is a structural block diagram of the electronic device in an embodiment of the present invention.

[0022] Explanation of reference numerals in the attached figures: 1. Base; 2. Slide; 3. Drive mechanism; 4. Torque sensor; 5. Vibration sensor; 6. Temperature sensor; 7. Early warning and protection controller; 8. Braking module; 9. Position sensor; 31. Servo motor; 32. Lead screw; 33. Coupling; 71. Data acquisition module; 72. Trend analysis module; 73. Health assessment module; 74. Execution module; 75. Self-learning unit; 76. Communication module. Detailed Implementation

[0023] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0024] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in sequences other than those illustrated or described herein.

[0025] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "set," "install," "connect," and "link" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal communication between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0026] In the description of this specification, references to terms such as "embodiment," "one embodiment," and "one implementation" indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or implementation is included in at least one embodiment or illustrative implementation of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or implementation. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or implementations.

[0027] like Figure 1 and Figure 2 As shown, this embodiment of the invention provides a linear module with early warning and protection functions, including a base 1, a slide block 2 slidably disposed on the base 1, and a drive mechanism 3 for driving the slide block 2 to reciprocate, and further including: The sensing unit includes: Torque sensor 4 is installed at the power output end of drive mechanism 3 to collect drive torque data in real time; Vibration sensor 5 is mounted on slide 2 or base 1 and is used to collect high-frequency vibration data in real time; Temperature sensor 6 is used to collect temperature field data of drive mechanism 3 or guide rail pair in real time; and Early warning protection controller 7, which includes: The data acquisition module 71 is electrically connected to the torque sensor 4, vibration sensor 5 and temperature sensor 6, and is used to receive and process drive torque data, high-frequency vibration data and temperature field data. The trend analysis module 72, connected to the data acquisition module 71, is used to construct a trend curve based on the received data and predict the probability that the data will exceed a preset safety threshold at a future time point. The health assessment module 73, connected to the trend analysis module 72, is used to generate the current health score based on the predicted probability. The execution module 74 is connected to the health assessment module 73 and is used to issue a warning signal when the health score is lower than the first preset threshold; and to send a deceleration or stop command to the drive mechanism 3 when the health score is lower than the second preset threshold.

[0028] Specifically, the working principle is as follows: Data acquisition: Torque sensor 4 acquires the driving torque data of drive mechanism 3 in real time; vibration sensor 5 acquires the high-frequency vibration data of slide 2 or base 1 in real time; temperature sensor 6 acquires the temperature field data of drive mechanism or guide rail pair in real time. These data are sent to data acquisition module 71 of early warning protection controller 7.

[0029] Trend Analysis: The data acquisition module 71 transmits the processed data to the trend analysis module 72. The trend analysis module 72 constructs real-time trend curves for each parameter based on historical data and uses algorithms to predict the probability that these data will exceed a preset safety threshold at a certain future moment.

[0030] Health assessment: The health assessment module 73 generates a health score (e.g., 0-100 points) for the current linear module based on the predicted probability output by the trend analysis module 72 and the weight model.

[0031] Tiered execution: Execution module 74 triggers different levels of actions based on health scores: When the health score is lower than the first preset threshold (e.g., 80 points), a warning signal (such as an audible and visual alarm or a prompt from the host computer) will be issued. When the health score is lower than the second preset threshold (e.g., 60 points), a deceleration or shutdown command is sent directly to the drive mechanism 3 to prevent the fault from worsening.

[0032] By monitoring the operating status of the linear module in real time through multiple source sensors (torque, vibration, temperature), and utilizing data fusion and trend prediction technologies, the traditional "post-event alarm" is transformed into "pre-event early warning".

[0033] The controller employs a modular design, with each module working collaboratively to achieve closed-loop control from data perception to decision execution. It proactively detects potential faults (such as lead screw wear, motor overload, and poor lubrication) to prevent sudden shutdowns and production losses. Through tiered early warning and automatic protection, it ensures equipment safety while avoiding frequent erroneous shutdowns, thereby improving equipment utilization.

[0034] Temperature sensor 6 is an infrared thermal imaging sensor, with its lens facing the position of the lead screw and nut pair or the linear motor mover of the drive mechanism 3, for non-contact acquisition of two-dimensional temperature field distribution.

[0035] Infrared thermal imaging sensor 6 acts as a temperature sensor, with its lens continuously aimed at the position of the lead screw and nut pair or the mover of the linear motor in the drive mechanism. The sensor collects two-dimensional temperature field distribution data and transmits it to the data acquisition module 71. The controller can obtain a temperature distribution map of the entire target area, rather than a single point temperature value. Infrared thermal imaging technology uses the infrared energy radiated by an object to form a thermal image. For linear modules, the lead screw and nut pair or the mover is the area where frictional heat is most concentrated. Through thermal imaging, it is possible to visually see whether the temperature distribution is uniform and whether the hot spot positions are abnormal.

[0036] The drive mechanism 3 includes a servo motor 31 and a lead screw 32 driven by the servo motor. The torque sensor 4 is integrated in the coupling 33 between the servo motor 31 and the lead screw 32, or integrated in the internal encoder circuit of the servo motor 31.

[0037] The torque sensor 4 is integrated in the coupling 33 between the servo motor 31 and the lead screw 32, or in the internal encoder circuit of the servo motor 31. When the drive mechanism is running, the sensor detects the torque value on the transmission shaft in real time and converts it into an electrical signal that is transmitted to the controller.

[0038] Specifically, strain gauges or magnetoelastic torque sensors are installed inside the coupling to directly measure the transmitted torque. Alternatively, the motor output torque can be indirectly calculated using algorithms based on the current or speed loop feedback of the servo motor encoder (i.e., a sensorless method). This approach is less expensive and does not alter the mechanical structure.

[0039] It can monitor drive torque in real time and reflect conditions such as load changes, lead screw jamming, and increased guide rail resistance. The method of integration into the coupling provides high measurement accuracy and directly reflects the mechanical transmission torque; the method of integration into the encoder circuit requires no additional hardware, has a compact structure, and is easy to implement.

[0040] The early warning protection controller 7 also includes a self-learning unit 75, which is used to record the baseline data of the linear module during initial no-load operation. The trend analysis module 72 performs differential comparison between the real-time collected data and the baseline data to eliminate installation errors and environmental noise.

[0041] During the initial installation, commissioning, or periodic calibration of the linear module, the self-learning unit 75 is activated. The module completes a full stroke under no-load or light-load conditions, and the self-learning unit records and stores the baseline characteristic values ​​(such as average values ​​and fluctuation ranges) of torque, vibration, and temperature. During subsequent normal operation, the trend analysis module 72 performs differential comparison between the real-time collected data and these baseline data to obtain the characteristic changes, which are then input into the prediction model.

[0042] The self-learning unit establishes a personalized "digital twin baseline" by collecting operational data of the device in a healthy state. The difference between the real-time data and the baseline reflects the degradation or abnormal changes in device performance, and differential processing can effectively eliminate common-mode interference caused by installation errors, changes in ambient temperature, etc.

[0043] The above approach improves the accuracy of trend analysis and avoids false alarms caused by initial installation differences or environmental changes. It adapts to different equipment and operating conditions, making warning thresholds more reasonable and reducing the complexity and subjectivity of manually setting thresholds. The self-learning process can be repeated periodically to adapt to performance drift after long-term equipment operation, maintaining the effectiveness of the warning system.

[0044] It should be noted that the system also includes a braking module 8, which is a normally closed electromagnetic brake. It is located at the input end of the drive mechanism 3 and connected to the execution module 74. When the health score is lower than the third preset threshold, the execution module 74 triggers the braking module 8 to lock the drive mechanism 3 in an emergency.

[0045] Normally closed electromagnetic brake 8 is installed at the input end of drive mechanism 3 (such as the rear end of motor shaft). When the health score calculated by health assessment module 73 is lower than the third preset threshold (e.g., 30 points, representing extremely high risk), execution module 74 immediately triggers brake module 8 to act, and brake instantly locks drive mechanism to achieve emergency stop.

[0046] Normally closed electromagnetic brakes rely on spring force for braking when power is off and release when power is on. When the controller issues an emergency lock signal, the power supply to the brake is cut off, and the brake immediately engages, quickly stopping the slide movement regardless of the current state of the drive mechanism.

[0047] The sensing unit also includes a position sensor 9, which is used to monitor the position and speed of the slide 2 in real time; the trend analysis module 72 combines the position and speed information to perform spatiotemporal synchronous analysis of the driving torque data, high-frequency vibration data and temperature field data.

[0048] Position sensor 9 monitors the position and velocity of slide 2 in real time. Trend analysis module 72 receives data from the position sensor, as well as torque, vibration, and temperature sensors, and aligns all data using position and time information for spatiotemporal synchronous analysis. For example, it can analyze vibration characteristic changes at different positions or the torque-position curve. Using position information as a coordinate axis, other sensor data are mapped onto the slide's motion trajectory to form a "position-feature" map. This allows analysis of the correlation between faults and spatial location (e.g., a certain section of the guide rail always causes increased vibration) and the impact of velocity changes on temperature.

[0049] The above method can accurately locate the source of the fault, for example, by identifying the damage point on the guide rail based on the location of the vibration peak. It improves the accuracy of trend prediction because data at different locations and speeds have different statistical characteristics, and spatiotemporal synchronous analysis can model them separately. It provides precise location information for subsequent maintenance, shortening repair time.

[0050] The early warning and protection controller 7 also includes a communication module 76, which is used to send the health score and early warning signal to the remote monitoring terminal or industrial cloud platform in real time.

[0051] The communication module 76 within the early warning and protection controller 7 transmits health scores, early warning signals, and real-time data to a remote monitoring terminal or industrial cloud platform via industrial Ethernet, Wi-Fi, 4G / 5G interfaces. Administrators can view the equipment status in real time and receive alarm notifications through host computer software or a mobile app.

[0052] The communication module enables interconnection between the device and the upper-layer information system, supporting standard industrial communication protocols (such as Modbus TCP and OPC UA). The controller, acting as an IoT node, uploads data processing results for centralized monitoring and big data analysis.

[0053] Secondly, embodiments of the present invention disclose a method for using a linear module with early warning and protection functions, such as... Figure 3 As shown, it includes the following steps: S1, Reference Acquisition: Start the linear module and run a complete stroke under no-load or light-load conditions. The reference characteristic values ​​of torque, vibration and temperature are acquired and stored through the self-learning unit 75 of the early warning protection controller 7. S2. Real-time monitoring: During normal operation, operating data is collected in real time through torque sensor 4, vibration sensor 5 and temperature sensor 6; S3. Trend Prediction: The trend analysis module 72 compares the real-time data with the baseline feature value in step S1, extracts the feature change amount, and uses a time series prediction algorithm to calculate the feature value change trend at future times. S4. Health assessment and graded early warning: The health assessment module 73 calculates the health score based on the changing trend. When the score falls into different preset level ranges, the execution module 74 will execute an early warning prompt, limit the maximum speed, or perform an emergency shutdown operation.

[0054] The time series prediction algorithm in step S3 is a prediction model based on a long short-term memory neural network or an autoregressive integral moving average model, used to predict feature values ​​within the next N sampling periods.

[0055] It also includes step S5, fault self-check and recording: after the emergency stop operation is performed, the early warning protection controller 7 automatically locks the high-frequency sensor data M seconds before and after the stop time, packages them to generate a fault diagnosis report, and outputs it through the communication module 76.

[0056] Figure 4 A structural block diagram of the electronic device provided in the embodiments of the present invention, such as... Figure 4 As shown, the electronic device may include a processor 610, a communications interface 620, a memory 630, and a communication bus 640, wherein the processor 610, the communications interface 620, and the memory 630 communicate with each other via the communication bus 640. The processor 610 can call logical instructions in the memory 630 to execute the following methods: S1, Reference Acquisition: Start the linear module and run a complete stroke under no-load or light-load conditions. The reference characteristic values ​​of torque, vibration and temperature are acquired and stored through the self-learning unit 75 of the early warning protection controller 7. S2. Real-time monitoring: During normal operation, operating data is collected in real time through torque sensor 4, vibration sensor 5 and temperature sensor 6; S3. Trend Prediction: The trend analysis module 72 compares the real-time data with the baseline feature value in step S1, extracts the feature change amount, and uses a time series prediction algorithm to calculate the feature value change trend at future times. S4. Health assessment and graded early warning: The health assessment module 73 calculates the health score based on the changing trend. When the score falls into different preset level ranges, the execution module 74 will execute an early warning prompt, limit the maximum speed, or perform an emergency shutdown operation.

[0057] Furthermore, the logical instructions in the aforementioned memory 630 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0058] This invention also provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, is implemented to perform the methods provided in the above embodiments.

[0059] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of various embodiments or some parts of embodiments.

[0060] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A linear module with early warning protection function, characterized in that, The system includes a base (1), a slide (2) slidably mounted on the base (1), and a drive mechanism (3) for driving the slide (2) to reciprocate. It also includes: The sensing unit includes: A torque sensor (4) is installed at the power output end of the drive mechanism (3) to collect drive torque data in real time. A vibration sensor (5) is installed on the slide (2) or the base (1) for real-time acquisition of high-frequency vibration data; Temperature sensor (6) is used to collect temperature field data of the drive mechanism (3) or guide rail pair in real time; and Early warning protection controller (7), the early warning protection controller (7) includes: The data acquisition module (71) is electrically connected to the torque sensor (4), vibration sensor (5) and temperature sensor (6) and is used to receive and process the driving torque data, high-frequency vibration data and temperature field data. The trend analysis module (72) is connected to the data acquisition module (71) and is used to construct a trend curve based on the received data and predict the probability that the data will exceed a preset safety threshold at a future time point. The health assessment module (73), connected to the trend analysis module (72), is used to generate the current health score based on the predicted probability; The execution module (74) is connected to the health assessment module (73) and is used to issue a warning signal when the health score is lower than the first preset threshold; and to send a deceleration or stop command to the drive mechanism (3) when the health score is lower than the second preset threshold.

2. The linear module with early warning protection function according to claim 1, characterized in that, The temperature sensor (6) is an infrared thermal imaging sensor, with its lens facing the position of the lead screw and nut pair or the linear motor mover of the drive mechanism (3), for non-contact acquisition of two-dimensional temperature field distribution.

3. A linear module with early warning and protection function according to claim 1, characterized in that, The drive mechanism (3) includes a servo motor (31) and a lead screw (32) driven by the servo motor. The torque sensor (4) is integrated in the coupling (33) between the servo motor (31) and the lead screw (32), or integrated in the internal encoder circuit of the servo motor (31).

4. A linear module with early warning and protection function according to claim 1, characterized in that, The early warning protection controller (7) also includes a self-learning unit (75), which is used to record the baseline data of the linear module during initial no-load operation. The trend analysis module (72) performs differential comparison between the real-time collected data and the baseline data to eliminate installation errors and environmental noise.

5. A linear module with early warning and protection function according to claim 1, characterized in that, It also includes a braking module (8), which is a normally closed electromagnetic brake, located at the input end of the drive mechanism (3) and connected to the execution module (74); when the health score is lower than the third preset threshold, the execution module (74) triggers the braking module (8) to lock the drive mechanism (3) in an emergency.

6. A linear module with early warning and protection function according to claim 1, characterized in that, The sensing unit also includes a position sensor (9) for real-time monitoring of the position and speed of the slide (2); the trend analysis module (72) combines the position and speed information to perform spatiotemporal synchronous analysis of the driving torque data, high-frequency vibration data and temperature field data.

7. A linear module with early warning and protection function according to claim 1, characterized in that, The early warning protection controller (7) also includes a communication module (76) for sending the health score and early warning signal to a remote monitoring terminal or industrial cloud platform in real time.

8. A method of using a linear module with early warning protection function as described in any one of claims 1 to 7, characterized in that, Includes the following steps: S1, Reference Acquisition: Start the linear module and run a complete stroke under no-load or light-load conditions. The reference characteristic values ​​of torque, vibration and temperature are acquired and stored through the self-learning unit (75) of the early warning protection controller (7). S2. Real-time monitoring: During normal operation, operating data is collected in real time through torque sensor (4), vibration sensor (5) and temperature sensor (6); S3, Trend Prediction: The trend analysis module (72) compares the real-time data with the baseline feature value in step S1, extracts the feature change amount, and uses a time series prediction algorithm to calculate the feature value change trend at future moments; S4. Health assessment and graded early warning: The health assessment module (73) calculates the health score based on the changing trend. When the score falls into different preset level ranges, the execution module (74) performs an early warning prompt, limits the maximum speed, or performs an emergency shutdown operation.

9. The method of use according to claim 8, characterized in that, The time series prediction algorithm in step S3 is a prediction model based on a long short-term memory neural network or an autoregressive integral moving average model, used to predict feature values ​​within the next N sampling periods.

10. The method of use according to claim 8, characterized in that, It also includes step S5, fault self-check and recording: after the emergency stop operation is performed, the early warning protection controller (7) automatically locks the high-frequency sensor data for each M seconds before and after the stop time, packages it to generate a fault diagnosis report, and outputs it through the communication module (76).

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