A method and system for controlling a PLC device for industrial automation

Abstract

The embodiment of the application provides a PLC device control method and system for industrial automation, relates to the technical field of PLC device control for industrial automation, and comprises the following steps: establishing a normal behavior benchmark of device operation parameters as an abnormal behavior mode identification reference; the operation parameters comprise driving motor current, driving motor voltage, torque, trajectory deviation and completion time of related processes; the operation parameters are monitored in real time, and when an auxiliary safety sensor reports a no-obstacle signal, at least two parameters in the driving motor current, the driving motor voltage, the torque, the trajectory deviation and the completion time of the related processes are analyzed based on the normal behavior benchmark, an abnormal behavior mode conforming to slight physical interference is identified, and a hierarchical safety response is triggered according to the abnormal behavior mode. The application can improve the stability and safety of an industrial automation production line.

Description

Technical Field

[0001] This application relates to the field of PLC equipment control technology for industrial automation, and in particular to a PLC equipment control method and system for industrial automation. Background Technology

[0002] In modern industrial automation, programmable logic controllers (PLCs) are the core control units, responsible for coordinating the actions of various devices to ensure smooth production processes and stable product quality. However, with the increasing complexity of industrial environments and the continuous introduction of new equipment, unforeseen challenges may arise, particularly in maintaining the accuracy of control signals and the reliability of safety mechanisms. For example, newly installed high-frequency welding equipment generates significant electromagnetic radiation during operation. These electromagnetic fields may intermittently interfere with the PLC's input signal acquisition module and the signal cables connecting sensors through spatial radiation, capacitive or inductive coupling between cables. This interference typically manifests as instantaneous voltage or current spikes superimposed on the signal cables, with extremely short durations and amplitudes that may be below the conventional fault detection threshold, insufficient to trigger the PLC system's built-in conventional fault alarm mechanisms. This subtle interference may cause momentary signal distortion in the input data of a critical position sensor. However, if the signal distortion caused by the aforementioned electromagnetic interference has a specific waveform and duration, it may be able to bypass or be misjudged by these digital filtering programs. In this situation, the digital filtering program might misinterpret a brief signal distortion from a critical position sensor as a valid "positioned" signal. Although this misinterpretation may only last for one PLC scan cycle, for a main control process that relies on precise timing and status judgment, an error in this one scan cycle is enough to affect its subsequent logical judgments. Summary of the Invention

[0003] This application aims to address at least one of the technical problems existing in the prior art. To this end, this application proposes a PLC equipment control method and system for industrial automation, aiming to improve the stability and safety of industrial automated production lines.

[0004] In a first aspect, embodiments of this application provide a PLC equipment control method for industrial automation, comprising: Establish normal behavior benchmarks for equipment operating parameters as a reference for identifying abnormal behavior patterns; the operating parameters include drive motor current, drive motor voltage, torque, trajectory deviation, and completion time of related processes; The system monitors the operating parameters in real time, and when the auxiliary safety sensor reports an obstacle-free signal, it analyzes at least two parameters from the drive motor current, drive motor voltage, torque, trajectory deviation, and completion time of the relevant process, as well as the normal behavior benchmark, to identify abnormal behavior patterns that correspond to slight physical interference. Based on the described abnormal behavior pattern, a tiered security response is triggered.

[0005] According to some embodiments of this application, the step of real-time monitoring of the operating parameters and, when the auxiliary safety sensor reports an obstacle-free signal, identifying abnormal behavior patterns consistent with slight physical interference by analyzing at least two parameters among the drive motor current, the drive motor voltage, the torque, the trajectory deviation, and the completion time of the relevant process, and the normal behavior benchmark, includes: The drive motor current and drive motor voltage are collected when the device performs each standard operation cycle; When the auxiliary safety sensor reports a signal of no obstacle, the total energy consumption of the action cycle is calculated based on the drive motor current and the drive motor voltage. Based on the total energy consumption of the action cycle and the energy benchmark in the normal behavior benchmark, calculate the deviation of the total energy consumption of the current action cycle relative to the energy benchmark; The deviation from the standard action cycle is accumulated over nearly N cycles to obtain a component health trend index, where N is a natural constant and 0. <N<100; Based on the changing trends of the component health trend indicators, abnormal behavior patterns consistent with minor physical interference are identified.

[0006] According to some embodiments of this application, the step of accumulating the deviation over a recent period to obtain the component health trend index includes: Identify the type of production task currently being performed by the device, including load type and speed type; For each identified production task type, maintain an independent deviation accumulator and corresponding component health trend indicators; When the equipment switches production task types, it selects the deviation accumulator corresponding to the current production task type and updates the corresponding component health trend index based on the data of the deviation accumulator.

[0007] According to some embodiments of this application, the step following the identification of abnormal behavior patterns consistent with minor physical interference based on the changing trend of the component health trend index includes: When the health trend index of the component increases, the key components of the equipment are identified. For each critical component, collect detailed operational parameters related to the component's health status; For each critical component, calculate the individual critical component energy deviation based on the detailed operating parameters; For each critical component, maintain an independent critical component health trend indicator; Based on the changing trends of critical component health trend indicators and preset degradation thresholds, identify abnormal behavior patterns and abnormal critical components that correspond to minor physical interference.

[0008] According to some embodiments of this application, the step of identifying abnormal behavior patterns and abnormal critical components consistent with minor physical interference based on the changing trend of the critical component's health trend index includes: Identify the priority of the current production task; The preset degradation threshold is adjusted based on the priority of the production task, the cumulative operating time of the critical components, and the current ambient temperature. Based on the changes in the adjusted degradation threshold and the health trend indicators of the critical components, abnormal behavior patterns and abnormal critical components consistent with minor physical interference are identified.

[0009] According to some embodiments of this application, the step of identifying abnormal behavior patterns and abnormal critical components consistent with minor physical interference based on the changing trends of the adjusted degradation threshold and the health trend indicators of the critical components includes: When the change trend of the health trend index of the key component is less than the adjusted degradation threshold, the rate of change of the health trend index of the key component is continuously monitored when the equipment performs high-precision or high-load tasks. The rate of change of the health trend index of the key component is compared with the degradation rate threshold preset for the high-precision or high-load task. By combining the positioning error and instantaneous stress peak of the current task, the changing trend of the health trend index of the key component is further verified; When the rate of change of the health trend index of the critical component exceeds the preset degradation rate threshold, abnormal behavior patterns and abnormal critical components that are consistent with slight physical interference are identified.

[0010] According to some embodiments of this application, the step of assisting in verifying the changing trend of the health trend index of the key component by combining the positioning error of the current task and the instantaneous stress peak includes: Collect the positioning error and instantaneous stress peak of the current task; The positioning error and the instantaneous stress peak value are subjected to multi-source heterogeneous data fusion processing; Based on the positioning error and instantaneous stress peak value after the multi-source heterogeneous data fusion processing, combined with time series filtering and outlier detection, the influence of external environmental noise is removed to obtain the denoised real-time performance data. The changing trends of the component health trend indicators are verified based on the denoised real-time performance data.

[0011] According to some embodiments of this application, the step of performing multi-source heterogeneous data fusion processing on the positioning error and the instantaneous stress peak includes: The data from different sensors in the positioning error and the instantaneous stress peak are timestamped to obtain the timestamped sensor data; The format of the timestamp-calibrated sensor data is converted to obtain the converted sensor data. Frequency matching is performed on the converted sensor data to obtain frequency-matched sensor data. The frequency-matched sensor data is subjected to data fusion processing to obtain sensor data after consistency check; Based on the sensor data after the consistency check, multi-source heterogeneous data fusion processing is performed.

[0012] According to some embodiments of this application, the step of performing data fusion processing on the frequency-matched sensor data to obtain consistent sensor data includes: For each sensor, its output data is continuously collected during the stable operation of the robotic arm, and the short-term average and long-term average of the output data are calculated. Continuously monitor the difference between the short-term average and the long-term average; When the difference continues to exceed the preset low-amplitude drift threshold, it is determined that the sensor has a hidden drift caused by slow aging. Based on the sensor data with hidden drift, the data is corrected to obtain the corrected sensor data; Based on the corrected sensor data, data fusion processing is performed to obtain sensor data after consistency check.

[0013] Secondly, embodiments of this application provide a PLC equipment control system for industrial automation, comprising: The benchmark establishment module is used to establish normal behavior benchmarks for equipment operating parameters, including the current or torque of the drive motor, the deviation between the actual motion trajectory and the command trajectory, and the completion time of related processes. An anomaly detection module is used to monitor the operating parameters in real time, and when the auxiliary safety sensor reports a signal of no obstacle, it identifies abnormal behavior patterns that are consistent with slight physical interference by performing correlation analysis on the combination patterns of the operating parameters. The response triggering module is used to trigger a graded security response based on the abnormal behavior pattern.

[0014] The technical solution according to the embodiments of this application has at least the following beneficial effects: The PLC equipment control method for industrial automation disclosed in this application establishes a normal behavior benchmark for equipment operating parameters and monitors operating parameters such as drive motor current, drive motor voltage, torque, trajectory deviation, and completion time of related processes in real time. When the auxiliary safety sensor reports an obstacle-free signal, by analyzing at least two parameters and the normal behavior benchmark, it can identify abnormal behavior patterns that correspond to slight physical interference and trigger graded safety responses based on these patterns. This method effectively solves the technical problem in the prior art where PLC signal misjudgment caused by electromagnetic interference leads to slight physical interference in the equipment, resulting in the accumulation of hidden damage, ultimately causing unexpected equipment shutdown that is difficult to diagnose and repair. Through comprehensive analysis of multi-dimensional operating parameters, this application can capture subtle anomalies that are difficult to detect by traditional fault detection mechanisms, such as slight scratches between the robotic arm and the workpiece caused by signal misjudgment, thereby identifying potential equipment damage risks at an early stage. This proactive anomaly detection capability avoids the long-term accumulation of hidden damage, effectively prevents sudden equipment failures and production line downtime, significantly improves the stability and safety of industrial automated production lines, and reduces maintenance costs and downtime.

[0015] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0016] The accompanying drawings are used to provide a further understanding of the technical solutions of this application and constitute a part of the specification. They are used together with the embodiments of this application to explain the technical solutions of this application and do not constitute a limitation on the technical solutions of this application.

[0017] Figure 1 A schematic flowchart of a PLC equipment control method for industrial automation provided in one embodiment of this application; Figure 2 This is a schematic diagram of a PLC equipment control system for industrial automation provided as an embodiment of this application. Detailed Implementation

[0018] To make the objectives, technical methods, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application. It should be noted that the meaning of "multiple" (or "more than") in the description of the embodiments of this application refers to two or more, and "greater than," "less than," "exceeding," etc. are understood to exclude the number itself, while "above," "below," "within," etc. are understood to include the number itself. If "first," "second," etc. are used in the description, they are only for the purpose of distinguishing technical features and should not be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated or the order of the technical features indicated. In this application embodiment, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent the existence of A alone, the simultaneous existence of A and B, or the existence of B alone. A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of singular or plural items. For example, at least one of a, b, and c can represent: the existence of a alone, the existence of b alone, the existence of c alone, the simultaneous existence of a and b, the simultaneous existence of a and c, the simultaneous existence of b and c, or the simultaneous existence of a, b, and c, where a, b, and c can be single or multiple.

[0019] In the description of this application, unless otherwise expressly defined, terms such as "setup," "installation," and "connection" should be interpreted broadly, and those skilled in the art can reasonably determine the specific meaning of the above terms in this application in conjunction with the specific content of the technical solution.

[0020] Based on the above, this application proposes a PLC equipment control method and system for industrial automation, aiming to improve the stability and safety of industrial automated production lines.

[0021] The PLC equipment control method for industrial automation provided in this application can be applied to a terminal, a server, or software running on either a terminal or a server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, etc.; the server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDN), and big data and artificial intelligence platforms; the software can be an application that implements the PLC equipment control method for industrial automation, but is not limited to the above forms. This application can be applied to numerous general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics devices, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via communication networks. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices. It should be noted that in various specific embodiments of this invention, when processing is required based on data related to the characteristics of an object (e.g., user attributes or sets of attribute information), permission or consent from the corresponding object is obtained first, and the collection, use, and processing of this data comply with relevant laws and standards. Furthermore, when the embodiments of the present invention need to obtain the attribute information of an object, they will obtain the separate permission or separate consent of the corresponding object through pop-up windows or redirection to a confirmation page. After obtaining the separate permission or separate consent of the corresponding object, they will then obtain the relevant data of the object necessary for the embodiments of the present invention to operate normally.

[0022] See Figure 1 , Figure 1This is a flowchart illustrating a PLC device control method for industrial automation provided in one embodiment of this application. The PLC device control method for industrial automation provided in this embodiment includes, but is not limited to, steps S110 to S150, which are described in detail below.

[0023] Step S110: Establish normal behavior benchmarks for equipment operating parameters as a reference for identifying abnormal behavior patterns; operating parameters include drive motor current, drive motor voltage, torque, trajectory deviation, and completion time of related processes; Step S120: Monitor operating parameters in real time, and when the auxiliary safety sensor reports an obstacle-free signal, analyze at least two parameters from the drive motor current, drive motor voltage, torque, trajectory deviation, and completion time of related processes, as well as the normal behavior benchmark, to identify abnormal behavior patterns that are consistent with slight physical interference. Step S130: Trigger a graded security response based on the abnormal behavior pattern.

[0024] It should be noted that "equipment operating parameters" refer to various physical quantities that reflect the working status and performance of industrial automation equipment during operation, such as drive motor current, drive motor voltage, torque, trajectory deviation, and the completion time of related processes. Real-time data on these parameters are the basis for assessing equipment health and identifying abnormal behavior. "Normal behavior baseline" refers to the typical range or variation pattern of equipment operating parameters established through long-term monitoring and data analysis when the equipment is operating normally and without faults. This baseline serves as a reference standard for identifying abnormal behavior; any operating mode that significantly deviates from this baseline may indicate a potential problem with the equipment. "Auxiliary safety sensors" refer to sensors used to provide additional safety information or verify environmental conditions, in addition to the critical position sensors relied upon by the main control system. For example, it could be a proximity sensor or photoelectric sensor used to detect the presence of obstacles in the work area. The "obstacle-free signal" it reports is an important prerequisite for determining whether the equipment can safely perform its actions. "Minor physical interference" refers to a slight contact, scratch, or collision between the equipment and the workpiece or environment during operation due to some reason (such as control timing error, component wear, etc.). This interference is usually insufficient to trigger an emergency shutdown, but it can cumulatively cause damage to equipment components. "Abnormal behavior pattern" refers to a specific data pattern or trend in which the equipment's operating parameters deviate from the normal behavior baseline, and this deviation is correlated with minor physical interference. Identifying this pattern is one of the core objectives of this application. "Graded safety response" refers to taking different levels of safety measures based on the severity and potential risk of the identified abnormal behavior pattern. For example, for minor anomalies, only a warning or adjustment of operating parameters may be issued; for more serious anomalies, deceleration, pause, or emergency shutdown may be triggered.

[0025] In one embodiment, it is first necessary to establish a normal behavior benchmark for the equipment's operating parameters. This benchmark can be established in various ways. For example, during the initial operation of the equipment, under standard operating conditions, operating parameters such as drive motor current, drive motor voltage, torque, trajectory deviation, and the completion time of related processes can be continuously collected over a long period. The collected data, after statistical analysis, can yield statistical characteristics such as the average value, standard deviation, and fluctuation range of these parameters under normal operating conditions, thereby constructing a normal behavior benchmark. Alternatively, the normal behavior benchmark for these parameters can be manually set or adjusted using an expert experience system, combined with the equipment's design specifications and historical operating data. For example, for a specific grasping action, the normal range of its drive motor current can be set to X amperes to Y amperes, and the completion time to T1 seconds to T2 seconds. After establishing the normal behavior benchmark, the method of this application will monitor the aforementioned operating parameters in real time. Real-time monitoring can be achieved by integrating a data acquisition module into the PLC system. This module can continuously acquire data such as drive motor current, drive motor voltage, torque, trajectory deviation, and the completion time of related processes at a preset sampling frequency. These data can be stored in the PLC's memory or transmitted to a host computer or industrial IoT platform via communication protocols such as Industrial Ethernet for further processing. For example, drive motor current and voltage data can be collected every 10 milliseconds, torque and trajectory deviation data every 50 milliseconds, and the completion time of each process can be recorded. While monitoring operating parameters in real time, the method of this application also requires auxiliary safety sensors to report obstacle-free signals. These auxiliary safety sensors can be photoelectric sensors, proximity sensors, or lidar installed within the equipment's working area. When these sensors detect no obstacles in the working area, they output an "obstacle-free" signal. For example, a photoelectric sensor can be configured to output a high-level signal when its beam is not blocked, indicating that the working area is safe. When the auxiliary safety sensor reports an obstacle-free signal, the method of this application will analyze at least two parameters—drive motor current, drive motor voltage, torque, trajectory deviation, and the completion time of the relevant process—and a normal behavior benchmark to identify abnormal behavior patterns consistent with slight physical interference. This analysis can employ various technical means. For example, a threshold comparison method can be used, comparing the real-time monitored operating parameters with corresponding thresholds in the normal behavior baseline. If a parameter continuously or momentarily exceeds the preset normal range, it may indicate an anomaly. As another implementation, a statistical process control (SPC) method can be employed. By calculating control charts (such as X-bar charts or R-charts) of the operating parameters, the mean and volatility of the parameters can be monitored to see if they are within the statistical control range. Any pattern exceeding the control limits or exhibiting a specific trend may be identified as an anomaly. Finally, based on the identified abnormal behavior patterns, the method of this application will trigger a tiered safety response.The purpose of tiered safety response is to take appropriate measures based on the severity and potential risk of the anomaly to prevent equipment damage or production accidents. For example, if a minor, intermittent anomaly pattern is identified, the system can trigger a warning message to prompt the operator to check and may slightly reduce the equipment speed in the next operation cycle. If a persistent, increasingly severe anomaly pattern is identified, the system can trigger a higher-level response, such as suspending the current process and initiating diagnostic procedures, or even triggering an emergency shutdown if necessary.

[0026] It should be noted that a "standard action cycle" can be understood as a series of continuous actions performed by a device to complete a preset task or sub-task, such as an industrial robotic arm performing a grasping-and-place operation. During each standard action cycle, the drive motor current and voltage are continuously collected. These drive motor current and voltage are key parameters reflecting the device's workload and energy consumption. Furthermore, when the auxiliary safety sensor reports an obstacle-free signal, the total energy consumption of the action cycle can be calculated based on the collected drive motor current and voltage. Total energy consumption is typically obtained by integrating the product of current and voltage over the action cycle time, reflecting the actual energy consumed by the device when completing that action cycle. The "energy benchmark in normal behavior" refers to the average or typical energy consumption value and its fluctuation range obtained through statistical analysis of a large amount of standard action cycle data under normal, interference-free operation. By comparing the total energy consumption of the current action cycle with this energy benchmark, the deviation of the current action cycle's total energy consumption from the energy benchmark can be calculated. This deviation quantifies the difference between the current cycle's energy consumption and normal energy consumption. To more effectively identify cumulative minor physical interference, this application proposes accumulating the deviation over nearly N standard operating cycles to obtain a component health trend index. N is a natural number between 0 and 100, and its specific value can be adjusted according to the equipment's operating characteristics, the frequency of task repetition, and the required detection sensitivity. For example, N can be set to 10, 20, or 50 to ensure that the cumulative effect over a sufficiently long period can be captured. By accumulating the deviation, the component health trend index can smooth out instantaneous fluctuations and more clearly demonstrate the long-term performance change trend of equipment components. Therefore, based on the changing trend of the component health trend index, abnormal behavior patterns consistent with minor physical interference can be identified. For example, when the component health trend index continues to rise or its rate of rise exceeds a preset threshold, it may indicate that the equipment is experiencing gradually increasing physical interference such as friction, wear, or minor jamming.

[0027] In one embodiment, consider a robotic arm on an industrial automated production line, whose PLC control system is configured to perform repetitive gripping-placement tasks. The drive motor current and voltage of the robotic arm are continuously monitored. First, the system establishes an energy baseline for the robotic arm under normal, interference-free operation. For example, by analyzing drive motor current and voltage data from thousands of normal gripping-placement cycles, the average total energy consumption per cycle is calculated to be 100 joules, with fluctuations within ±5 joules. When the robotic arm begins performing a production task, the system collects drive motor current and voltage in real time during each standard cycle (e.g., completing one gripping-placement). Suppose that during a certain cycle, an auxiliary safety sensor reports an obstacle-free signal, and the system calculates the total energy consumption for that cycle to be 108 joules based on the collected current and voltage. At this point, the deviation from the 100 joule energy baseline is +8 joules. The system records this deviation. Assuming N is set to 20, the system accumulates the deviations from the last 20 standard cycle operations. For example, in the initial few cycles, the deviation may fluctuate within the normal range (e.g., +2 joules, -1 joule, etc.). However, as a bearing in a joint of the robotic arm begins to show slight wear, its friction gradually increases, causing the energy consumption of subsequent motion cycles to consistently be slightly higher than the normal baseline, for example, the deviation stabilizes between +6 joules and +10 joules. By accumulating these 20 deviations, the component health trend index will gradually show an upward trend. For example, if the average deviation of the first 10 cycles is +2 joules, and the average deviation of the next 10 cycles rises to +7 joules, then the change trend of the component health trend index will show a significant improvement. When this upward trend continues and exceeds a preset threshold (e.g., the average deviation of 5 consecutive cycles exceeds +5 joules, or the sum of the 20 accumulated deviations exceeds a certain threshold), the system will identify an abnormal behavior pattern consistent with slight physical interference, such as determining that a component of the robotic arm has early wear or slight jamming. At this point, the PLC equipment control system can trigger a graded safety response, such as issuing an early warning to maintenance personnel for inspection, or adjusting the operating parameters of the robotic arm to slow down the wear process without affecting production.

[0028] It should be noted that production task type can be understood as the specific operating mode performed by the equipment under different working scenarios, which is usually determined by both load type and speed type. For example, load type can include light load, medium load, heavy load, etc., while speed type can include low speed, medium speed, high speed, etc. These different task types will have different impacts on the operating parameters of the equipment, resulting in different baselines and fluctuation ranges for energy consumption deviation under different task types. The independent deviation accumulator refers to setting up a separate data storage unit for each identified production task type to independently accumulate deviation data under that task type. The corresponding component health trend index refers to the independently calculated and maintained health assessment index associated with each deviation accumulator. Its purpose is to ensure that the health status of the equipment can be independently and accurately assessed under different task types. When the equipment switches from one production task type to another, the system automatically identifies the new task type and selects the pre-set deviation accumulator for that task type. Subsequently, based on the historical deviation data stored in the selected deviation accumulator, the corresponding component health trend index is updated. For example, if the device switches from a "light load, low speed" task to a "heavy load, high speed" task, the system will stop updating the accumulators and metrics for "light load, low speed" and instead activate and update the accumulators and metrics for "heavy load, high speed". The purpose is to enable component health trend indicators to reflect the actual operating status of the device in real time under the current specific task type.

[0029] In one embodiment, it is assumed that an industrial robotic arm performs two main tasks on a production line: a "high-precision assembly task," involving light loads and medium speeds; and a "heavy-load handling task," involving heavy loads and low speeds. Prior to the solution proposed in this application, if only a single uniform deviation accumulator is used, when the robotic arm switches from the "high-precision assembly task" to the "heavy-load handling task," the deviation may naturally increase because the "heavy-load handling task" itself requires greater energy consumption. In this case, the uniform health trend indicator may incorrectly show a deterioration in component health, thereby triggering unnecessary safety responses. However, according to the solution proposed in this application, the system first identifies the two production task types: "high-precision assembly task" and "heavy-load handling task." For each task type, the system maintains an independent deviation accumulator. For example, when the robotic arm performs a "high-precision assembly task," the resulting energy consumption deviation is accumulated in the accumulator corresponding to the "high-precision assembly task" and used to update the component health trend indicator for that task type. When the robotic arm switches to the "heavy object handling task," the system recognizes the task type change and automatically selects the deviation accumulator corresponding to the "heavy object handling task." Subsequent deviation data is then accumulated in this accumulator, and the corresponding component health trend index is updated. In this way, even if the deviation of the "heavy object handling task" is generally higher than that of the "high-precision assembly task," as long as it fluctuates within the normal range of the "heavy object handling task," the corresponding component health trend index will not show any abnormalities. Only when the deviation consistently exceeds the normal range of a specific task type will it be identified as an abnormal behavior pattern.

[0030] It should be noted that when the aforementioned component health trend indicators show a continuous upward trend, it indicates that there may be potential minor physical interference or performance degradation in the overall equipment or a certain area. At this point, the system will be triggered to identify critical components of the equipment. Critical components refer to parts that have a decisive impact on the overall performance, safety, or production efficiency of the equipment during operation, such as joints of a robotic arm, spindles, important gears or bearings in the transmission mechanism, etc. The identification of these critical components can be based on pre-set equipment structure diagrams, historical fault data analysis, or expert experience. Furthermore, for each identified critical component, more detailed operating parameters more relevant to its health status will be collected. These detailed operating parameters may include, but are not limited to, the vibration frequency of a specific bearing, the meshing noise of a specific gear, the response time of a specific actuator, local temperature, lubricating oil pressure, etc. These parameters can more directly and sensitively reflect the operating status of individual critical components. Based on the collected detailed operating parameters, the energy deviation of individual critical components will be calculated. For example, the energy deviation can be obtained by analyzing the local energy consumption of a critical component in a specific motion cycle and comparing it with the energy consumption of the critical component under normal behavior benchmarks. This deviation quantifies the difference between a critical component's current operating state and its normal state. For each critical component, an independent critical component health trend indicator is maintained. This indicator is constructed by accumulating the energy deviation of that critical component and combining it with time series analysis methods (such as moving average, exponential smoothing, etc.). Each critical component has an independent health trend indicator, making the assessment of the health status of individual critical components more accurate and avoiding the ambiguity of the overall indicator. Therefore, based on the changing trend of the critical component health trend indicator and the preset degradation threshold, abnormal behavior patterns and abnormal critical components consistent with minor physical interference can be identified. The preset degradation threshold is pre-set for different critical components and different operating conditions. When the changing trend of the critical component health trend indicator exceeds this threshold, it is determined that there is abnormal behavior consistent with minor physical interference, and it can clearly indicate which critical component has become abnormal.

[0031] It's important to note that prioritizing the current production task refers to the system's assessment of the importance or urgency of the task currently being performed by the equipment. For example, high-priority tasks may require higher precision and stability, thus having a lower tolerance for degradation of critical components; while low-priority tasks may allow for a wider range of degradation. Priorities can be manually entered by the operator or automatically obtained by the system based on the production plan. Adjusting the preset degradation threshold based on the production task priority, the cumulative operating time of critical components, and the current ambient temperature can be understood as the system dynamically revising the threshold used to determine whether a critical component is abnormal, taking into account multiple factors. The cumulative operating time of a critical component refers to the total working time of the component since it was put into use or since the last maintenance, directly reflecting its wear and aging status. The current ambient temperature refers to the real-time temperature of the equipment's operating environment, as temperature has a significant impact on the performance and lifespan of mechanical components; for example, high temperatures can accelerate component aging. These parameters can be input into a predefined model (e.g., a rule-based expert system, a machine learning model, or a lookup table method) to calculate a degradation threshold that better reflects the current situation. For example, when production tasks have high priority, critical components have long cumulative operating times, or ambient temperatures are high, the degradation threshold may be lowered to detect potential anomalies earlier. Identifying anomalous behavior patterns and anomalous critical components consistent with minor physical interference involves comparing the dynamically adjusted degradation threshold with the changing trends of the critical component's health trend indicators. If the changing trend of the critical component's health trend indicators exceeds the adjusted degradation threshold, an anomalous behavior pattern consistent with minor physical interference is identified, and the specific anomalous critical component is identified. This dynamic adjustment mechanism makes anomaly identification more accurate and timely.

[0032] It should be noted that "continuously monitoring the rate of change of critical component health trend indicators" means that even when the health status of critical components has not yet reached the preset degradation threshold, the system will still track the rate of change of their health trend indicators over time in real time. This rate of change can be understood as the derivative of the health trend indicator with respect to time or the average change within a certain time window. For example, it can be obtained by calculating the ratio of the difference in health trend indicators between adjacent time points or time periods to the time interval. Its purpose is to capture dynamic changes in health status, especially potential accelerated degradation trends. The "preset degradation rate threshold" is a critical value pre-set for specific high-precision or high-load tasks to determine whether the rate of change of critical component health trend indicators is abnormal. This threshold can be set based on historical data, expert experience, or equipment manufacturer recommendations, and can be adjusted according to different task types, load conditions, and accuracy requirements. For example, for tasks requiring extremely high precision, even a small degradation rate may be considered abnormal, so the degradation rate threshold will be set lower. In practical applications, "using the positioning error and instantaneous stress peak value of the current task to assist in verifying the changing trend of the health trend indicators of critical components" means that when judging whether a critical component is abnormal, in addition to considering the rate of change of the health trend indicators, other parameters directly related to the equipment performance and stress condition are introduced for cross-verification. Positioning error refers to the deviation between the actual position and the target position of the equipment when performing a task, and instantaneous stress peak value refers to the maximum instantaneous stress that the critical component bears during operation. These parameters can reflect the operating status of the equipment and the damage to critical components from different dimensions, thereby improving the accuracy and reliability of anomaly identification. For example, if the rate of change of the health trend indicators increases slightly, but at the same time the positioning error and instantaneous stress peak value also show abnormal fluctuations, it can be more confidently determined that a slight physical interference exists.

[0033] In one embodiment, assume an industrial robotic arm is performing a high-precision assembly task. A joint bearing of the robotic arm is identified as a critical component, and its health trend indicator (e.g., based on cumulative energy consumption deviation) is continuously monitored. According to the method described above, the task priority, the bearing's cumulative operating time, and the ambient temperature are used to adjust the degradation threshold. Specifically, when the change trend of the bearing's health trend indicator has not yet reached the adjusted degradation threshold, the system continuously monitors the rate of change of the indicator. For example, the system calculates the instantaneous rate of change of the health trend indicator every 10 seconds. If the rate of change is found to be continuously increasing over several consecutive monitoring cycles and exceeding the preset degradation rate threshold for the high-precision assembly task (e.g., the health trend indicator increases by more than 0.05% per hour), even if the absolute value of the health trend indicator is still within a safe range, the system will initially determine that an anomaly may exist. The system simultaneously collects the positioning error of the current assembly task and the instantaneous stress peak of the bearing during its movement. If a slight but continuous increase in positioning error is detected, and the instantaneous stress peak is also slightly higher than normal, these data will serve as supporting evidence to confirm that the bearing may have minor physical interference, such as poor lubrication or early signs of minor wear. When the rate of change of the critical component's health trend indicators exceeds the preset deterioration rate threshold, and the supporting verification data also supports the anomaly judgment, the system will immediately identify the abnormal behavior pattern consistent with minor physical interference and indicate that the anomalous critical component is the joint bearing, thereby triggering the corresponding graded safety response, such as issuing a maintenance alarm or recommending a reduction in task speed to avoid potential serious failures.

[0034] It should be noted that positioning error refers to the deviation between the actual position or trajectory of the equipment and the preset commanded position or trajectory when performing a specific task. This error can be collected in real time using high-precision encoders, laser sensors, or vision systems. Instantaneous stress peak refers to the phenomenon where the stress on critical components of the equipment reaches its maximum value within a short period of time due to factors such as load changes, impacts, or vibrations during operation. Instantaneous stress peak is typically monitored using strain gauges or force sensors installed on critical stress-bearing parts. The collection of these parameters aims to obtain the physical performance of the equipment in actual operation, providing direct evidence for subsequent health trend verification. Furthermore, the collected positioning error and instantaneous stress peak undergo multi-source heterogeneous data fusion processing. Multi-source heterogeneous data fusion refers to the process of integrating and unifying data from different types of sensors with different data formats, sampling frequencies, and accuracies. For example, positioning error may come from high-frequency encoder data, while instantaneous stress peak may come from low-frequency strain gauge data. Based on the positioning error and instantaneous stress peak after multi-source heterogeneous data fusion processing, combined with time series filtering and outlier detection, the influence of external environmental noise is removed to obtain denoised real-time performance data. Time series filtering techniques, such as Kalman filtering, moving average filtering, or exponential smoothing, can effectively smooth data fluctuations and reduce the interference of random noise on data analysis. Outlier detection techniques, such as statistical methods (e.g., Z-score, IQR) or machine learning methods (e.g., Isolation Forest, LOF), are used to identify and remove outliers in the data caused by occasional interference or sensor malfunctions, ensuring data purity. Through these processes, more realistic and reliable real-time equipment performance data can be obtained, avoiding misjudgments. Finally, the changes in component health trend indicators are further validated based on the denoised real-time performance data. This means that, in addition to relying on the changes in the component health trend indicators themselves, the system also uses finely processed positioning error and instantaneous stress peak data as independent references to cross-validate or enhance the judgment of component health status.

[0035] In one embodiment, multi-source heterogeneous data fusion processing refers to the process of integrating and unifying positioning error and instantaneous stress peak data from different sensors, different data formats, and different sampling frequencies. Its purpose is to eliminate differences between data sources, ensure data consistency and reliability, and provide high-quality input for subsequent analysis. Specifically, timestamp calibration refers to synchronizing the positioning error and instantaneous stress peak data from different sensors to ensure that all data points accurately correspond to the same point in time. This can be achieved using a global clock, Network Time Protocol (NTP), or the sensor's internal time synchronization mechanism, aiming to solve the problem of asynchronous data acquisition from different sensors and ensure data consistency in the time dimension. Further, format conversion refers to converting the timestamp-calibrated sensor data from its original format to a unified data format. For example, binary data, text data, or protocol-specific data output from different sensors can be converted to standardized JSON, CSV, or database record formats, aiming to eliminate data format differences and facilitate subsequent unified processing and analysis. Frequency matching refers to adjusting the format-converted sensor data to the same sampling frequency. For example, if some sensors sample at 100Hz while others sample at 50Hz, techniques such as interpolation, downsampling, or resampling are needed to unify all data to a preset sampling frequency. The purpose is to resolve the inconsistency in sampling frequencies between different sensors and ensure data consistency across the frequency dimension. Based on this, data fusion processing refers to the initial integration and consistency checks performed on the frequency-matched sensor data. This may include preliminary data cleaning, noise reduction, missing value imputation, and redundant data elimination, aiming to improve data quality and reliability and lay the foundation for final multi-source heterogeneous data fusion.

[0036] It should be noted that for each sensor, during the stable operation of the robotic arm, its output data is continuously collected, and short-term and long-term average values ​​are calculated. The short-term average can be understood as the average output value of the sensor over a recent period (e.g., minutes or hours), reflecting the sensor's current immediate performance status. The long-term average can be understood as the average output value of the sensor over a longer period (e.g., days, weeks, or longer), typically obtained after the sensor is in a known stable state or has been calibrated, serving as a benchmark for its normal behavior. The purpose is to establish a dynamic and comparable performance reference. Furthermore, the difference between the short-term and long-term averages is continuously monitored. This difference reflects the degree of deviation between the sensor's current performance and its historical stable performance. When the difference consistently exceeds a preset low-amplitude drift threshold, it is determined that the sensor exhibits latent drift due to slow aging. The low-amplitude drift threshold is a pre-set, small value designed to capture small but persistent systematic deviations in the sensor output. These deviations are usually caused by aging or wear of internal sensor components or environmental factors (such as temperature changes), rather than actual changes in the monitored physical quantity. Latent drift refers to the slow and continuous deviation of a sensor's output value from its true or calibrated value in the absence of significant external interference or changes in the measured physical quantity. Therefore, sensor data exhibiting latent drift is corrected to obtain corrected sensor data. Correction processing may include, but is not limited to, applying a fixed offset, adjusting a scaling factor, or using adaptive correction algorithms based on machine learning models to compensate for biases caused by sensor aging, making the output data closer to the true value. The aim is to eliminate or reduce the impact of latent drift on data accuracy. Finally, based on the corrected sensor data, data fusion processing is performed to obtain sensor data after consistency checks.

[0037] In one embodiment, consider an industrial robotic arm equipped with multiple force / torque sensors to monitor the forces acting on its end effector. Over extended periods of operation, one of the force sensors may, due to internal component aging, begin to slowly and continuously deviate from its initial calibration value, even when the arm is unloaded or performing standard light-load tasks. For example, the sensor may consistently report a force 0.05 Newtons higher than the actual value. To detect this latent drift, the system continuously collects the force sensor's output data during stable operation of the robotic arm (e.g., during the self-test phase after each startup or while performing a known no-load standard action cycle). The system calculates a short-term average (e.g., the average over the past 10 minutes) and a long-term average (e.g., the average over the past 24 hours or longer, representing its historical stability baseline) of the sensor's output data. The system continuously monitors the difference between these two averages. If the short-term average is consistently found to be 0.05 Newtons higher than the long-term average, and this difference consistently exceeds a preset low-amplitude drift threshold (e.g., 0.02 Newtons), the system determines that the force sensor exhibits latent drift due to slow aging. Once latent drift is identified, the system corrects subsequent output data from that sensor. For example, the system automatically subtracts a 0.05 Newton offset from all real-time readings of the sensor. The corrected sensor data regains its accuracy and is then used for frequency matching and data fusion, ensuring that the "consistency-checked sensor data" accurately reflects the forces acting on the robotic arm, rather than being misled by sensor degradation. This method effectively prevents errors in identifying anomalous behavior patterns caused by latent sensor drift, such as misinterpreting minute force changes caused by sensor drift as slight physical interference.

[0038] See Figure 2 , Figure 2 This is a schematic diagram of a PLC equipment control system for industrial automation provided in one embodiment of this application. The PLC equipment control system 200 for industrial automation includes: The baseline establishment module 210 is used to establish a normal behavior baseline for the equipment operating parameters, including the current or torque of the drive motor, the deviation between the actual motion trajectory and the command trajectory, and the completion time of related processes. The anomaly identification module 220 is used to monitor operating parameters in real time and, when the auxiliary safety sensor reports a signal of no obstacle, to identify abnormal behavior patterns that are consistent with slight physical interference by performing correlation analysis on the combination patterns of operating parameters. The response triggering module 230 is used to trigger a graded security response based on the abnormal behavior pattern.

[0039] It should be noted that the information interaction and execution process between the above modules are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, which will not be repeated here.

[0040] It will be understood by those skilled in the art that all or some of the steps and systems in the methods disclosed above can be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components can be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, which can include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, as is known to those skilled in the art, communication media typically include computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium. The foregoing has provided a detailed description of the preferred embodiments of this application. However, this application is not limited to the above-described embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of this application. All such equivalent modifications or substitutions are included within the scope defined in this application.

Claims

1. A PLC equipment control method for industrial automation, characterized in that, include: Establish normal behavior benchmarks for equipment operating parameters as a reference for identifying abnormal behavior patterns; The operating parameters include drive motor current, drive motor voltage, torque, trajectory deviation, and the completion time of related processes; The system monitors the operating parameters in real time, and when the auxiliary safety sensor reports an obstacle-free signal, it analyzes at least two parameters from the drive motor current, drive motor voltage, torque, trajectory deviation, and completion time of the relevant process, as well as the normal behavior benchmark, to identify abnormal behavior patterns that correspond to slight physical interference. Based on the described abnormal behavior pattern, a tiered security response is triggered. The step of real-time monitoring of the operating parameters, and identifying abnormal behavior patterns consistent with slight physical interference by analyzing at least two parameters among the drive motor current, drive motor voltage, torque, trajectory deviation, and completion time of the relevant process, and the normal behavior benchmark when the auxiliary safety sensor reports an obstacle-free signal, includes: The drive motor current and drive motor voltage are collected when the device performs each standard operation cycle; When the auxiliary safety sensor reports a signal of no obstacle, the total energy consumption of the action cycle is calculated based on the drive motor current and the drive motor voltage. Based on the total energy consumption of the action cycle and the energy benchmark in the normal behavior benchmark, calculate the deviation of the total energy consumption of the current action cycle relative to the energy benchmark; The deviation from the standard action cycle is accumulated over nearly N cycles to obtain a component health trend index, where N is a natural number and 0 < N < 0. <N<100; Based on the changing trends of the component health trend indicators, identify abnormal behavior patterns that correspond to minor physical interference; The step of obtaining the component health trend index by accumulating the deviation of nearly N standard action cycles includes: Identify the type of production task currently being performed by the device, including load type and speed type; For each identified production task type, maintain an independent deviation accumulator and corresponding component health trend indicators; When the equipment switches production task types, it selects the deviation accumulator corresponding to the current production task type and updates the corresponding component health trend index based on the data of the deviation accumulator.

2. The method according to claim 1, characterized in that, The step following the identification of abnormal behavior patterns consistent with minor physical interference based on the changing trends of the component's health trend indicators includes: When the health trend index of the component increases, the key components of the equipment are identified. For each critical component, collect detailed operational parameters related to the health status of the critical component; For each critical component, calculate the individual critical component energy deviation based on the detailed operating parameters; For each critical component, maintain an independent critical component health trend indicator; Based on the changing trends of critical component health trend indicators and preset degradation thresholds, identify abnormal behavior patterns and abnormal critical components that correspond to minor physical interference.

3. The method according to claim 2, characterized in that, The step of identifying abnormal behavior patterns and abnormal critical components consistent with minor physical interference based on the changing trend of the health trend index of the critical component includes: Identify the priority of the current production task; The preset degradation threshold is adjusted based on the priority of the production task, the cumulative operating time of the critical components, and the current ambient temperature. Based on the changes in the adjusted degradation threshold and the health trend indicators of the critical components, abnormal behavior patterns and abnormal critical components consistent with minor physical interference are identified.

4. The method according to claim 3, characterized in that, The step of identifying abnormal behavior patterns and abnormal critical components consistent with minor physical interference based on the changes in the adjusted degradation threshold and the health trend indicators of the critical components includes: When the change trend of the health trend index of the key component is less than the adjusted degradation threshold, the rate of change of the health trend index of the key component is continuously monitored when the equipment performs high-precision or high-load tasks. The rate of change of the health trend index of the key component is compared with the degradation rate threshold preset for the high-precision or high-load task. By combining the positioning error and instantaneous stress peak of the current task, the changing trend of the health trend index of the key component is further verified; When the rate of change of the health trend index of the critical component exceeds the preset degradation rate threshold, abnormal behavior patterns and abnormal critical components that are consistent with slight physical interference are identified.

5. The method according to claim 4, characterized in that, The step of combining the positioning error and instantaneous stress peak of the current task to assist in verifying the changing trend of the health trend index of the key component includes: Collect the positioning error and instantaneous stress peak of the current task; The positioning error and the instantaneous stress peak value are subjected to multi-source heterogeneous data fusion processing; Based on the positioning error and instantaneous stress peak value after the multi-source heterogeneous data fusion processing, combined with time series filtering and outlier detection, the influence of external environmental noise is removed to obtain the denoised real-time performance data. The changing trend of the component health trend index is verified based on the denoised real-time performance data.

6. The method according to claim 5, characterized in that, The step of performing multi-source heterogeneous data fusion processing on the positioning error and the instantaneous stress peak includes: The data from different sensors in the positioning error and the instantaneous stress peak are timestamped to obtain the timestamped sensor data; The format of the timestamp-calibrated sensor data is converted to obtain the converted sensor data. Frequency matching is performed on the converted sensor data to obtain frequency-matched sensor data. The frequency-matched sensor data is subjected to data fusion processing to obtain sensor data after consistency check; Based on the sensor data after the consistency check, multi-source heterogeneous data fusion processing is performed.

7. The method according to claim 6, characterized in that, The step of performing data fusion processing on the frequency-matched sensor data to obtain consistent sensor data includes: For each sensor, its output data is continuously collected during the stable operation of the robotic arm, and the short-term average and long-term average of the output data are calculated. Continuously monitor the difference between the short-term average and the long-term average; When the difference continues to exceed the preset low-amplitude drift threshold, it is determined that the sensor has a hidden drift caused by slow aging. Based on the sensor data with hidden drift, the data is corrected to obtain the corrected sensor data; Based on the corrected sensor data, data fusion processing is performed to obtain sensor data after consistency check.

8. A PLC equipment control system for industrial automation, characterized in that, include: The benchmark establishment module is used to establish normal behavior benchmarks for equipment operating parameters, including the current or torque of the drive motor, the deviation between the actual motion trajectory and the command trajectory, and the completion time of related processes. An anomaly detection module is used to monitor the operating parameters in real time, and when the auxiliary safety sensor reports a signal of no obstacle, it identifies abnormal behavior patterns that are consistent with slight physical interference by performing correlation analysis on the combination patterns of the operating parameters. The step of real-time monitoring of the operating parameters and, when the auxiliary safety sensor reports an obstacle-free signal, identifying abnormal behavior patterns consistent with minor physical interference through correlation analysis of the combination patterns of the operating parameters includes: The drive motor current and drive motor voltage are collected when the device performs each standard operation cycle; When the auxiliary safety sensor reports a signal of no obstacle, the total energy consumption of the action cycle is calculated based on the drive motor current and the drive motor voltage. Based on the total energy consumption of the action cycle and the energy benchmark in the normal behavior benchmark, calculate the deviation of the total energy consumption of the current action cycle relative to the energy benchmark; The deviation from the standard action cycle is accumulated over nearly N cycles to obtain a component health trend index, where N is a natural number and 0 < N < 0. <N<100; Based on the changing trends of the component health trend indicators, identify abnormal behavior patterns that correspond to minor physical interference; The step of obtaining the component health trend index by accumulating the deviation of nearly N standard action cycles includes: Identify the type of production task currently being performed by the device, including load type and speed type; For each identified production task type, maintain an independent deviation accumulator and corresponding component health trend indicators; When the equipment switches production task types, it selects the deviation accumulator corresponding to the current production task type and updates the corresponding component health trend index according to the data of the deviation accumulator. The response triggering module is used to trigger a graded security response based on the abnormal behavior pattern.

Images