Fault emergency safety protection control system for injection molding machine

Abstract

The invention discloses an injection molding machine fault emergency safety protection control system, which belongs to the technical field of fault emergency processing, and comprises a sensor system, an intelligent data analysis module, a self-adaptive emergency control unit, an alarm and display module and a safety protection module. The sensor system collects operation data of key components of the injection molding machine in real time; the intelligent data analysis module performs data analysis and fault diagnosis by using machine learning; the self-adaptive emergency control unit automatically adjusts parameters according to a diagnosis result, and emergently stops or recovers equipment operation; the alarm and display module sends an alarm signal in time and displays related information; the safety protection module guarantees the safety of equipment and personnel through an emergency braking sub-module, a protective fence sub-module, a fire alarm sub-module, a fire extinguishing sub-module and the like. All the modules work cooperatively, faults are effectively monitored and diagnosed, safe and stable operation of the injection molding machine is ensured, the production efficiency is improved, and the occurrence probability of safety accidents is reduced.

Description

Technical Field

[0001] The invention relates to the technical field of fault emergency processing, and in particular to a fault emergency safety protection control system for an injection molding machine. Background Art

[0002] In the existing automobile parts production system, due to the advancement of new material technology, the cylinder head guard, intake manifold and other parts on the engine are generally injection molded. The use of injection molding to produce cylinder head guards and intake manifolds has significant advantages, including high cost-effectiveness, strong design flexibility, weight reduction, corrosion resistance, good insulation performance, fast production speed, high surface finish, high material utilization, easy integration, recyclability, color and texture diversity, and reduced maintenance. These characteristics make injection molded parts not only excellent in economy, aesthetics and environmental protection, but also improve the performance and durability of the product, while reducing production and maintenance costs, which is very suitable for the large-scale production of automobile parts.

[0003] As a key equipment in the plastic processing industry, injection molding machines play an important role in production. In order to ensure the normal operation and safety protection of injection molding machines, they are equipped with hardware composed of some sensors and actuators, as well as corresponding control software, thus forming an emergency safety protection control system. However, the existing system is not perfect and cannot handle the failures that occur during the operation of injection molding machines well, which brings many troubles and safety hazards to production.

[0004] For example, in the prior art, the number of sensors usually equipped on injection molding machines is limited and their functions are single, which makes it impossible to comprehensively and accurately monitor the working status of each key part of the injection molding machine. For example, for the clamping, injection, hydraulic and electrical systems, there is often a lack of specialized and targeted sensors, which makes it impossible to obtain detailed operating data of these systems in a timely and accurate manner.

[0005] In terms of data processing and analysis, most existing systems do not have intelligent data analysis capabilities, which means that it is impossible to conduct in-depth mining and real-time analysis of the limited data collected, and it is difficult to quickly identify fault modes and characteristics, so that effective early warning and diagnosis cannot be carried out in the early stages of faults.

[0006] In addition, the existing system emergency control mechanism is not flexible and adaptive enough. When a fault occurs, it is often impossible to automatically adjust the equipment operating parameters according to the specific type, location and severity of the fault, or quickly start the emergency shutdown procedure, nor can it automatically restore the normal operation of the equipment after the fault is eliminated. It is even more difficult to support remote monitoring and control, which greatly affects production efficiency and equipment reliability. Summary of the invention

[0007] The main purpose of the present invention is to provide an injection molding machine fault emergency safety protection control system, which can effectively solve the problems mentioned in the background technology.

[0008] To achieve the above object, the technical solution adopted by the present invention is: An injection molding machine fault emergency safety protection control system, the system consists of a sensor system, an intelligent data analysis module, an adaptive emergency control unit, an alarm and display module and a safety protection module, specifically: The sensor system includes a clamping system sensor, an injection system sensor, a hydraulic system sensor and an electrical system sensor, which collects the operating data of the clamping system, injection system, hydraulic system and electrical system of the injection molding machine in real time and transmits it to the intelligent data analysis module.

[0009] The intelligent data analysis module includes a data acquisition submodule, a data analysis submodule, and a fault diagnosis submodule, wherein the data acquisition submodule receives data from the sensor system, the data analysis submodule uses machine learning to process and analyze data, and the fault diagnosis submodule makes a diagnosis based on the analysis results and transmits the diagnosis results to the adaptive emergency control unit.

[0010] The adaptive emergency control unit includes a parameter adjustment submodule, an emergency shutdown submodule, a fault recovery submodule, and a remote control submodule, wherein the parameter adjustment submodule adjusts the operating parameters according to the diagnosis results; the emergency shutdown submodule stops the equipment operation and triggers an alarm in the event of a serious fault; the fault recovery submodule restores the equipment operation after the fault is eliminated; and the remote control submodule realizes remote monitoring and operation through the network.

[0011] The alarm and display module includes an alarm submodule and a display submodule, wherein the alarm submodule sends an alarm signal according to the instruction of the adaptive emergency control unit; the display submodule displays relevant information by interacting with the data of the intelligent data analysis module and the adaptive emergency control unit.

[0012] The safety protection module includes an emergency braking submodule, a protective fence and light curtain sensor submodule, and a fire alarm and fire extinguishing submodule, wherein the emergency braking submodule directly stops the operation of the injection molding machine in an emergency; the protective fence and light curtain sensor submodule monitors the dangerous area and sends a signal to the adaptive emergency control unit to control the injection molding machine to stop; the fire alarm and fire extinguishing submodule sounds an alarm and initiates fire extinguishing measures when a fire is detected.

[0013] Preferably, the locking system sensors include: a position sensor for monitoring the position of the locking mechanism to ensure accurate locking; a pressure sensor for measuring the size of the locking force to ensure that the mold is tightly closed to prevent problems such as flash on the product.

[0014] The injection system sensors include: a pressure sensor, which monitors the injection pressure to ensure the pressure stability during the injection process; a flow sensor, which measures the injection speed and controls the injection volume and injection time.

[0015] The hydraulic system sensors include: a pressure sensor, which detects the pressure of the hydraulic system to ensure that the system pressure is normal; a temperature sensor, which monitors the temperature of the hydraulic oil to prevent the oil temperature from being too high and affecting the system performance; a liquid level sensor, which detects the liquid level of the hydraulic oil and replenishes the hydraulic oil in time; and an oil quality sensor, which monitors the contamination degree and quality of the hydraulic oil to ensure the cleanliness of the oil.

[0016] The electrical system sensors include: a current sensor, which measures the current of the electrical system and monitors the current changes in the circuit to promptly detect problems such as short circuits and overloads; a voltage sensor, which detects the voltage of the electrical system to ensure voltage stability; and an insulation resistance sensor, which monitors the insulation resistance of the electrical system to prevent leakage and short circuit accidents.

[0017] Preferably, the function implementation of the data acquisition submodule includes: Data reception: Through the TCP / IP network communication protocol and the communication interface of the sensor system, the data sent by the sensor is obtained in real time.

[0018] Data preprocessing: Perform preliminary processing on the received data, using 3p The outlier detection method is used and then the data is normalized.

[0019] Data storage: Store the preprocessed data in a database for subsequent analysis.

[0020] The outlier detection includes the following steps: First, calculate the mean of the data ( m ) and standard deviation ( s ). Set the upper and lower thresholds: the lower limit is m - 3s , the upper limit is μ + 3σ . Iterate over each data point in the data set, and if the value of the data point is less than the lower limit or greater than the upper limit, mark it as an outlier; choose to delete the outlier directly, or replace it with an adjacent value, mean, etc.

[0021] Data normalization uses minimum-maximum normalization. Specifically, the data is mapped to the interval [0, 1], and the expression is:

[0022] in is the original data, is the minimum value in the data, is the maximum value in the data.

[0023] Preferably, the function implementation of the data analysis submodule includes: Feature extraction: Extract meaningful features from preprocessed data, use fast Fourier transform (FFT) to extract frequency domain features for time series data, and calculate the mean, variance, and standard deviation statistical features of the data.

[0024] Data modeling: Use random forest algorithm to build the model.

[0025] Model training: Use mean square error (MSE) as the loss function, use historical data to train the model, and minimize the prediction error of the model on the training data by continuously adjusting the model parameters.

[0026] The extraction of frequency domain features includes the following steps: Perform FFT transformation on time series data to convert time domain signals into frequency domain signals.

[0027] After obtaining the spectrum, the peak frequency and the amplitude of the main frequency components of the spectrum can be extracted as features.

[0028] Calculate statistical characteristics (mean, variance, standard deviation), specifically: Mean: Calculated by adding the values ​​of all data points and then dividing by the total number of data points. The expression is:

[0029] in is the value of the data point, is the number of data points.

[0030] Variance: measures the degree of dispersion of data, expressed as:

[0031] Standard Deviation: The square root of the variance, expressed as: .

[0032] Preferably, the function implementation of the fault diagnosis submodule includes: Fault pattern matching: Match the analyzed features with pre-defined fault patterns through rule-based reasoning. The rules refer to specific and clear matching rules formulated based on the mechanical structure, working principle, common fault types and historical fault data of large injection molding machines.

[0033] Fault location and severity assessment: Determine the location and severity of the fault based on the model output and feature analysis results. If the measurement value of a sensor exceeds the normal range to a large extent, the fault at that location is judged to be more serious.

[0034] Diagnostic result output: The diagnostic result is transmitted to the adaptive emergency control unit in the form of digital code.

[0035] The specific matching steps of fault mode matching are as follows: Establish a fault mode library: Based on the mechanical structure, working principle, common fault types and historical fault data of large injection molding machines, list and describe various possible fault modes in detail; define key features for each fault mode, including the range of sensor measurement values, change trends, and the relationship between multiple related sensor data.

[0036] Formulate matching rules and weights: According to the key features of the fault mode, formulate specific and clear matching rules; for example, for the fault of the mold locking system, it is stipulated that when the value of the mold locking pressure sensor is lower than a certain threshold and lasts for a certain period of time, or the deviation of the mold locking displacement sensor exceeds a certain amplitude, it is considered a possible fault mode. Assign a corresponding weight to each rule to reflect its importance in judging the fault mode; the weight can be determined based on the contribution of the rule to the accurate diagnosis of the fault in the historical data.

[0037] Real-time feature extraction and integration: Get the latest sensor data features from the data analysis submodule, integrate and standardize these features for comparison with the definitions in the fault mode library.

[0038] Pattern matching calculation: For each possible fault mode, the matching degree between the currently extracted features and the fault mode is calculated according to the established rules and weights. This can be achieved through weighted summation or other comprehensive calculation methods to obtain a numerical value representing the matching degree.

[0039] Determine the preliminary matching result: compare the calculated matching degree value with the preset threshold value, and if the matching degree exceeds the threshold value, list the fault mode as a candidate for preliminary matching.

[0040] Confirmation in conjunction with subsequent analysis: Refer to the subsequent fault location and severity assessment for information such as the degree to which the sensor measurement value exceeds the normal range. If the measurement value of a sensor exceeds the normal range by a large amount and is consistent with the initially matched fault mode, further confirm the match of the fault mode.

[0041] Output matching results: Finally determine the matching fault mode and output the results in a clear and explicit manner, providing an accurate basis for subsequent diagnosis and processing.

[0042] Preferably, the function implementation of the parameter adjustment submodule includes: Receive diagnostic results: Get detailed information about the fault type and extent from the intelligent data analysis module; Determine the adjustment strategy: According to the pre-set rules and strategies, combined with the diagnosis results, determine the operating parameters that need to be adjusted and the adjustment range; Execution parameter adjustment: send adjustment instructions through the control system interface of the equipment to modify the operating parameters; Among them, when determining the adjustment range, the proportional-integral-differential (PID) control algorithm is used to calculate the appropriate adjustment amount, specifically: first, determine the target value and the current actual measurement value; secondly, calculate the deviation between the two, that is, the target value minus the actual measurement value; then, calculate the integral term, which is obtained by continuously accumulating the deviation obtained from each calculation, and the initial integral term is 0; next, calculate the differential term, which is the current deviation minus the previous deviation, and then divided by the sampling time interval; finally, the adjustment amount is equal to the sum of the proportional term, the integral term, and the differential term; the proportional term is the deviation multiplied by the proportional coefficient, the integral term is the integral multiplied by the integral coefficient, and the differential term is the differential multiplied by the differential coefficient. The formula is as follows: deviation:

[0043] Integral Item: , initially

[0044] Differential term:

[0045] Adjustment amount:

[0046] Indicates at time The target value at time , that is, the ideal state value that the system is expected to achieve.

[0047] Indicates at time The actual measured value at that time, that is, the current actual operating status value of the system.

[0048] Indicates at time The deviation is the difference between the target value and the actual measured value.

[0049] Represents the sampling time interval, that is, the time interval between two measurements or calculations.

[0050] The proportional coefficient is used to adjust the controller's immediate response to deviations.

[0051] Integral coefficient, used to eliminate the steady-state error of the system.

[0052] The differential coefficient is used to make adjustments in advance according to the changing trend of the deviation to reduce overshoot.

[0053] Indicates at time The integral term at , takes into account the impact of historical errors by accumulating the deviations.

[0054] Indicates at time The differential term at time , reflecting the rate of change of the deviation.

[0055] Indicates at time The adjustment amount at that time, that is, the output of the controller, is used to adjust the system to reduce the deviation.

[0056] Preferably, the function implementation of the emergency shutdown submodule includes: Receiving serious fault signals: When the intelligent data analysis module diagnoses a serious fault, it sends a corresponding signal to the emergency shutdown submodule.

[0057] Trigger shutdown action: immediately send a shutdown command to the equipment's actuator to stop all related moving parts.

[0058] Trigger alarm: The sound and light alarm devices are activated simultaneously to alert the operator to a serious fault.

[0059] Preferably, the function implementation of the fault recovery submodule includes: Monitor the troubleshooting status: Maintain communication with the fault detection system to obtain real-time information on whether the fault has been eliminated.

[0060] Restore the device to its initial settings: After confirming that the fault has been eliminated, restore the device's operating parameters to the normal state before the fault or the preset initial state.

[0061] Start device operation: Send a startup command to make the device resume normal operation.

[0062] Preferably, the function implementation of the remote control submodule includes: Network connection establishment: Establish a stable connection with the remote monitoring terminal through the network protocol.

[0063] Data transmission: Transmit the operating status and sensor data information of the equipment to the remote terminal in real time, and receive the control instructions sent by the remote terminal.

[0064] Command execution: Execute corresponding operations according to the received remote control commands.

[0065] Preferably, the emergency brake submodule in the safety protection module includes an emergency brake button installed on the operation panel of the injection molding machine, and the button is connected to the main control system of the injection molding machine through a hard line. A program is written in the main control system, and when the emergency brake button is pressed, a stop command is immediately sent to each moving part of the injection molding machine to stop operation.

[0066] The protective fence and light curtain sensor submodule includes a protective fence installed around the dangerous area of ​​the injection molding machine, and a light curtain sensor is installed on the protective fence to ensure that the sensor can cover the entire dangerous area; when the light curtain sensor detects that an object or a person passes through, a signal is sent to the adaptive emergency control unit, and after the control unit receives the signal, the operation of the injection molding machine is immediately stopped.

[0067] The fire alarm and fire extinguishing submodule includes fire detectors installed at key positions of the injection molding machine. When the detectors detect signs of fire, they send alarm signals to the alarm and display module. In addition, the fire alarm and fire extinguishing submodule also includes automatic fire extinguishing equipment. When the fire alarm is triggered, the fire extinguishing is automatically started, and the operation of the injection molding machine is stopped at the same time, and the relevant power and energy supply are cut off.

[0068] Compared with the prior art, the present invention has the following beneficial effects: The present invention re-targets the possible failures of injection molding machines that are different from other equipment. A set of fault emergency safety protection control system is redesigned. In this system: first, special sensors are set for the clamping, injection, hydraulic and electrical systems to achieve all-round and high-precision monitoring. The clamping system sensor ensures the clamping accuracy, the injection system sensor optimizes the injection process, the hydraulic system sensor ensures hydraulic stability, and the electrical system sensor maintains electrical safety, providing a rich and accurate data basis for fault diagnosis.

[0069] Secondly, the intelligent data analysis module uses advanced data processing and analysis technologies, such as statistical analysis and feature engineering, combined with machine learning algorithms such as decision trees and support vector machines to build and train models. It can accurately and quickly diagnose faults, provide early warnings, and effectively avoid fault deterioration and production losses.

[0070] In addition, the adaptive emergency control unit can automatically adjust the equipment operating parameters to repair minor faults based on the results of intelligent data analysis, quickly shut down the equipment to ensure safety in the event of a serious fault, and automatically resume equipment operation after the fault is eliminated, greatly reducing downtime and improving production efficiency. At the same time, it supports remote monitoring and control, which improves the convenience of equipment management and the reliability of operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 It is a schematic diagram of the composition architecture flow of the present invention; Figure 2 It is a schematic diagram of the composition and flow of the intelligent data analysis module of the present invention; Figure 3 It is a schematic diagram of the composition and flow of the adaptive emergency control unit of the present invention. DETAILED DESCRIPTION

[0072] In order to make the technical means, creative features, objectives and effects achieved by the present invention easy to understand, the present invention is further explained below in conjunction with specific implementation methods.

[0073] like Figure 1-3 As shown, this embodiment aims to explain in detail the operation process and effect of the injection molding machine fault emergency safety protection control system in actual production.

[0074] 1. System installation and configuration Sensor system installation Install corresponding sensor sub-modules on key components such as the clamping system, injection system, hydraulic system and electrical system of large injection molding machines.

[0075] For example, in the clamping system, a position sensor is installed at the clamping mechanism, such as the Omron E2E-X10ME1 position sensor, to accurately monitor the position of the clamping mechanism; a pressure sensor is installed, such as the Honeywell HPT600 pressure sensor, to measure the clamping force.

[0076] In the injection system, install a pressure sensor, such as Siemens SITRANSFS pressure sensor, to monitor the injection pressure; install a flow sensor, such as Danfoss MBS3050 flow sensor, to measure the injection speed.

[0077] In the hydraulic system, install pressure sensors, such as Danfoss MBS3050 hydraulic pressure sensor, to detect the pressure of the hydraulic system; install temperature sensors, such as Bosch PT100 temperature sensor, to monitor the temperature of the hydraulic oil; install level sensors, such as Endress+Hauser FMI51 level sensor, to detect the level of the hydraulic oil; install oil quality sensors, such as Vaisala Oil Quality Sensor, to monitor the contamination level and quality of the hydraulic oil.

[0078] In the electrical system, install current sensors, such as LEM LEMIT current sensors, to measure the current of the electrical system; install voltage sensors, such as ABBESB voltage sensors, to detect the voltage of the electrical system; install insulation resistance sensors, such as MeggerMIT410 insulation resistance sensors, to monitor the insulation resistance of the electrical system.

[0079] Intelligent data analysis module configuration The data acquisition submodule is connected to the communication interface of the sensor system through the TCP / IP network communication protocol to obtain the data sent by the sensor in real time.

[0080] The data analysis submodule uses machine learning for data processing and analysis, such as using the random forest algorithm to build a model, using the mean square error (MSE) as the loss function, and using historical data to train the model.

[0081] The fault diagnosis submodule establishes a fault mode library, comprehensively lists and describes various possible fault modes based on the mechanical structure, working principle, common fault types and historical fault data of large injection molding machines. For example, for the fault of the clamping system, it is defined that when the value of the clamping pressure sensor is lower than 10MPa and lasts for 5 seconds, or the deviation of the clamping displacement sensor exceeds 2mm, it is considered as a possible fault mode, and a corresponding weight is assigned to each rule.

[0082] Adaptive emergency control unit configuration The parameter adjustment submodule adjusts the operating parameters according to the diagnosis results. For example, when a minor fault of insufficient pressure is detected, the proportional-integral-differential (PID) control algorithm is used to calculate the appropriate adjustment amount. Assuming that the current target pressure is 15MPa, the actual measured pressure is 12MPa, the deviation is 3MPa, the sampling time interval is 0.1 seconds, the proportional coefficient is 0.5, the integral coefficient is 0.2, and the differential coefficient is 0.1, the adjustment amount is calculated according to the formula.

[0083] When the emergency stop submodule receives the serious fault signal diagnosed by the intelligent data analysis module, it immediately sends a stop command to the equipment's actuator, stops all related moving parts, and simultaneously activates the sound and light alarm device.

[0084] After confirming that the fault has been eliminated, the fault recovery submodule restores the operating parameters of the device to the normal state before the fault or the preset initial state, and then sends a startup command to enable the device to resume normal operation.

[0085] The remote control submodule establishes a stable connection with the remote monitoring terminal through a network protocol (such as TCP / IP), transmits the device's operating status, sensor data and other information to the remote terminal in real time, and receives control instructions sent by the remote terminal. During data transmission, encryption algorithms (such as AES encryption algorithms) are used to ensure data security.

[0086] Alarm and display module configuration The alarm submodule establishes a communication connection with the adaptive emergency control unit. When receiving the alarm command, it starts the sound alarm device (such as a buzzer or speaker) to emit an alarm with a specific frequency (such as 500Hz) and loudness (such as 80dB), and at the same time controls the light alarm device (such as a flashing red light) to prompt the operator with a striking visual effect.

[0087] The display submodule is designed with a clear and intuitive display interface, including a real-time parameter display area for the equipment's operating status, a fault information prompt area, etc. The data on the display interface is updated in real time at a certain frequency (such as once per second), and when a fault is detected, the fault information is displayed in a striking manner (such as changing color to red, flashing, bolding, etc.).

[0088] Security protection module configuration The emergency brake submodule is installed on the operation panel of the injection molding machine or in an easily accessible position. The button is connected to the main control system of the injection molding machine through a hard line. When the emergency brake button is pressed, the main control system immediately sends a stop command to each moving part of the injection molding machine to stop it quickly.

[0089] Protective fence and light curtain sensor submodule Install a solid protective fence around the dangerous area of ​​the injection molding machine with a height of 1.5 meters and strength that meets the requirements of relevant safety standards. Install light curtain sensors at the entrance of the protective fence or where intrusion may occur to ensure that the sensors can cover the entire dangerous area. When the light curtain sensor detects an object or person passing through, it sends a signal to the adaptive emergency control unit, which immediately stops the injection molding machine.

[0090] Fire alarm and fire extinguishing submodules are installed in key parts of the injection molding machine, such as the electrical control cabinet, heating area, etc., with smoke detectors (such as Bosch ISN-SB1), temperature detectors (such as Siemens QAM2120.040) and other fire detectors. When the detector detects signs of fire, it triggers the sound and light alarm device, and sends the alarm signal to the operator and related monitoring systems. The fire extinguishing system is configured as a dry powder fire extinguisher (such as a portable ABC dry powder fire extinguisher) or a gas fire extinguishing system (such as a heptafluoropropane fire extinguishing system), and ensures that the fire extinguishing system can cover areas where fires may occur. When the fire alarm is triggered, the fire extinguishing system is automatically started, and the operation of the injection molding machine is stopped at the same time, and the relevant power and energy supply are cut off.

[0091] 2. System operation process Real-time monitoring and data collection Each sensor submodule in the sensor system collects the operating data of key components such as the clamping system, injection system, hydraulic system and electrical system of the large injection molding machine in real time, and transmits the data to the data acquisition submodule of the intelligent data analysis module.

[0092] For example, the position sensor monitors the position of the clamping mechanism in real time, the pressure sensor measures the clamping force and injection pressure in real time, and the temperature sensor monitors the temperature of the hydraulic oil in real time.

[0093] Data preprocessing and analysis The data acquisition submodule performs preliminary processing on the received data and uses the 3σ rule to detect outliers. First, the mean (μ) and standard deviation (σ) of the data are calculated, and the upper and lower thresholds are set (the lower limit is μ-3σ, and the upper limit is μ+3σ). Each data point in the data set is traversed, and if the value of the data point is less than the lower limit or greater than the upper limit, it is marked as an outlier. You can choose to delete the outlier directly, or replace it with an adjacent value, mean, etc.

[0094] Then, the data is normalized using the min-max normalization or Z-score normalization method. For example, for temperature data, the min-max normalization is used to map it to the [0,1] interval.

[0095] Fault Diagnosis and Pattern Matching The fault diagnosis submodule matches the analyzed features with predefined fault modes through rule-based reasoning. The latest sensor data features, including values, rates of change, and correlations with other related sensor data, are obtained from the data analysis submodule, and these features are integrated and standardized for comparison with the definitions in the fault mode library.

[0096] For each possible failure mode, the matching degree between the currently extracted features and the failure mode is calculated according to the established rules and weights. For example, for the failure mode of the clamping system, when the value of the clamping pressure sensor is lower than 10MPa and lasts for 5 seconds, or the deviation of the clamping displacement sensor exceeds 2mm, the matching degree is calculated according to the weight.

[0097] The calculated matching degree value is compared with the preset threshold to determine the preliminary matching result. If the matching degree exceeds the threshold, the fault mode is listed as a candidate for preliminary matching. Combined with the subsequent fault location and severity assessment information about the degree to which the sensor measurement value exceeds the normal range, the matching of the fault mode is further confirmed. Finally, the matching fault mode is determined and the results are output in a clear and unambiguous manner, providing an accurate basis for subsequent diagnosis and processing.

[0098] Emergency response and control The adaptive emergency control unit automatically adjusts the equipment operating parameters to try to fix minor faults based on the diagnosis results of the intelligent data analysis module. For example, when a minor fault of insufficient injection pressure is diagnosed, the parameter adjustment submodule calculates the adjustment amount based on the PID control algorithm and increases the pressure setting value of the hydraulic system.

[0099] In case of serious faults, the emergency stop submodule quickly starts the emergency stop procedure, stops the equipment and triggers an alarm. For example, when a serious fault such as a short circuit in the electrical system is detected, a shutdown command is immediately sent to the equipment's actuator and the sound and light alarm device is activated at the same time.

[0100] After the fault is eliminated, the fault recovery submodule automatically restores the normal operation of the equipment. It monitors the fault elimination status, maintains communication with the fault detection system, and after confirming that the fault has been eliminated, restores the operating parameters of the equipment to the normal state before the fault or the preset initial state, and then sends a startup command to make the equipment resume normal operation.

[0101] The remote control submodule realizes remote monitoring and control through the network, transmits the equipment's operating status, sensor data and other information to the remote terminal in real time, and receives control commands sent by the remote terminal. For example, the operator can view the operating status of the injection molding machine in real time through the remote monitoring terminal, and send control commands to adjust the equipment's operating parameters or shut down the machine when an abnormality is found.

[0102] Alarm and display The alarm submodule sends out sound and light alarm signals according to the instructions of the adaptive emergency control unit. When the equipment fails, the alarm submodule receives the instructions, starts the sound alarm device to sound the alarm, and controls the light alarm device to flash red to remind the operator to deal with it in time.

[0103] The display submodule displays the operating status and fault information of the equipment in real time. Through data interaction with other modules, the operating status data and fault information of the equipment are obtained and displayed to the operator in a clear and intuitive manner on the display interface. For example, the clamping force, injection pressure, hydraulic oil temperature and other operating parameters are displayed in real time on the display interface. When a fault is detected, the fault information is displayed in a striking manner, including the fault type and fault location.

[0104] Safety protection Emergency brake submodule In an emergency, the operator presses the emergency brake button, and the main control system of the injection molding machine immediately sends a stop command to each moving part to stop running quickly to ensure the safety of equipment and personnel.

[0105] During normal operation of the protective fence and light curtain sensor submodule, the protective fence blocks people or objects from entering the dangerous area. When the light curtain sensor detects that an object or person has passed through, it immediately sends a signal to the adaptive emergency control unit, and the control unit stops the operation of the injection molding machine to avoid accidental injuries.

[0106] The fire alarm and fire extinguishing submodule monitors the key parts of the injection molding machine in real time. When the smoke detector or temperature detector detects signs of fire, the sound and light alarm device is triggered, and the alarm signal is sent to the operator and related monitoring systems. The fire extinguishing system is automatically activated to put out possible fires in time.

[0107] The electrical protection submodule monitors the electrical system in real time. When an overload or short circuit is detected in the electrical system, it takes protective actions, such as cutting off the circuit or starting the backup power supply, to prevent safety accidents caused by electrical failures.

[0108] 3. System Effect Evaluation In actual production applications, this injection molding machine fault emergency safety protection control system can effectively monitor and diagnose faults of large injection molding machines, take emergency measures in time, and ensure the safe and stable operation of the equipment. For example, during a certain production process, the sensor system detected that the value of the clamping pressure sensor was continuously lower than 10MPa for 5 seconds. The intelligent data analysis module diagnosed it as a clamping system failure, and the adaptive emergency control unit automatically adjusted the clamping parameters to avoid defects such as flash and dimensional instability in the product. For another example, when the temperature sensor of the hydraulic system detects that the oil temperature is too high and exceeds 60°C, the system promptly issues an alarm signal, and the operator takes measures to lower the oil temperature, avoiding the risk of hydraulic system performance degradation and equipment damage.

[0109] Through the application of this system, production efficiency has been improved, the impact of equipment failures on production has been reduced, the probability of safety accidents has been reduced, and significant economic and social benefits have been brought to the enterprise.

[0110] The above embodiments describe in detail the installation, configuration, operation process and effect evaluation of the injection molding machine fault emergency safety protection control system, and demonstrate the effectiveness and reliability of the system in actual production.

[0111] Please note that the above embodiments are only examples, and can be adjusted and optimized according to specific circumstances in actual applications. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. An injection molding machine fault emergency safety protection control system, characterized in that: The system consists of a sensor system, an intelligent data analysis module, an adaptive emergency control unit, an alarm and display module, and a safety protection module. Specifically: The sensor system includes a clamping system sensor, an injection system sensor, a hydraulic system sensor and an electrical system sensor, which collects the operating data of the clamping system, injection system, hydraulic system and electrical system of the injection molding machine in real time and transmits it to the intelligent data analysis module; The intelligent data analysis module includes a data acquisition submodule, a data analysis submodule, and a fault diagnosis submodule, wherein the data acquisition submodule receives data from the sensor system, the data analysis submodule uses machine learning to process and analyze data, and the fault diagnosis submodule makes a diagnosis based on the analysis results and transmits the diagnosis results to the adaptive emergency control unit; The adaptive emergency control unit includes a parameter adjustment submodule, an emergency stop submodule, a fault recovery submodule, and a remote control submodule, wherein the parameter adjustment submodule adjusts the operating parameters according to the diagnosis results; the emergency stop submodule stops the equipment from running and triggers an alarm in case of a serious fault; the fault recovery submodule restores the equipment from running after the fault is eliminated; and the remote control submodule realizes remote monitoring and operation through the network; The alarm and display module includes an alarm submodule and a display submodule, wherein the alarm submodule sends an alarm signal according to the instruction of the adaptive emergency control unit; the display submodule displays relevant information by interacting with the data of the intelligent data analysis module and the adaptive emergency control unit; The safety protection module includes an emergency braking submodule, a protective fence and light curtain sensor submodule, and a fire alarm and fire extinguishing submodule, wherein the emergency braking submodule directly stops the operation of the injection molding machine in an emergency; the protective fence and light curtain sensor submodule monitors the dangerous area and sends a signal to the adaptive emergency control unit to control the injection molding machine to stop; the fire alarm and fire extinguishing submodule sounds an alarm and initiates fire extinguishing measures when a fire is detected.

2. According to claim 1, an injection molding machine fault emergency safety protection control system is characterized in that: The clamping system sensors include: a position sensor for monitoring the position of the clamping mechanism; a pressure sensor for measuring the clamping force; The injection system sensors include: a pressure sensor to monitor the injection pressure; a flow sensor to measure the injection speed; The hydraulic system sensors include: a pressure sensor to detect the pressure of the hydraulic system; a temperature sensor to monitor the temperature of the hydraulic oil; a level sensor to detect the level of the hydraulic oil; and an oil quality sensor to monitor the contamination level and quality of the hydraulic oil. The electrical system sensors include: a current sensor for measuring the current of the electrical system and monitoring the current change in the circuit; a voltage sensor for detecting the voltage of the electrical system; and an insulation resistance sensor for monitoring the insulation resistance of the electrical system.

3. According to claim 1, an injection molding machine fault emergency safety protection control system is characterized in that: The functional implementation of the data acquisition submodule includes: Data reception: Through the TCP / IP network communication protocol and the communication interface of the sensor system, the data sent by the sensor is obtained in real time; Data preprocessing: Perform preliminary processing on the received data, using 3σ The law is used to detect outliers and then normalize the data; Data storage: Store the preprocessed data in the database for subsequent analysis; The outlier detection includes the following steps: First, calculate the mean of the data ( μ ) and standard deviation ( σ ); Set upper and lower thresholds: the lower limit is μ - 3σ , the upper limit is μ + 3σ ; Iterate over each data point in the dataset and mark it as an outlier if its value is less than the lower limit or greater than the upper limit. Choose to directly delete outliers, or replace them with adjacent values, means, etc.; Data normalization uses minimum-maximum normalization. Specifically, the data is mapped to the interval [0, 1], and the expression is: in is the original data, is the minimum value in the data, is the maximum value in the data.

4. The injection molding machine fault emergency safety protection control system according to claim 3, characterized in that: The functional implementation of the data analysis submodule includes: Feature extraction: Extract meaningful features from preprocessed data, use fast Fourier transform (FFT) to extract frequency domain features for time series data, and calculate the mean, variance, and standard deviation statistical features of the data; Data modeling: Use random forest algorithm to build models; Model training: Use mean square error (MSE) as the loss function, use historical data to train the model, and continuously adjust the model parameters to minimize the prediction error of the model on the training data; The extraction of frequency domain features includes the following steps: Perform FFT transformation on time series data to convert time domain signals into frequency domain signals; After obtaining the spectrum, the peak frequency and amplitude of the main frequency components of the spectrum can be extracted as features; Calculate statistical characteristics (mean, variance, standard deviation), specifically: Mean: Calculated by adding the values ​​of all data points and then dividing by the total number of data points. The expression is: in is the value of the data point, is the number of data points; Variance: measures the degree of dispersion of data, expressed as: ； Standard Deviation: The square root of the variance, expressed as: 。 5. The injection molding machine fault emergency safety protection control system according to claim 4, characterized in that: The functional implementation of the fault diagnosis submodule includes: Fault pattern matching: Matching the analyzed features with pre-defined fault patterns through rule-based reasoning; Fault location and severity assessment: Determine the location and severity of the fault based on the model output and feature analysis results; Diagnostic result output: The diagnostic result is transmitted to the adaptive emergency control unit in the form of digital code; The specific matching steps of fault mode matching are as follows: Establish a failure mode library: Comprehensively consider the mechanical structure, working principle, common failure types and historical failure data of large injection molding machines, list and describe various possible failure modes in detail; define key features for each failure mode; Formulate matching rules and weights: formulate specific and clear matching rules based on the key characteristics of the failure mode; assign corresponding weights to each rule to reflect its importance in judging the failure mode; Real-time feature extraction and integration: Get the latest sensor data features from the data analysis submodule, integrate and standardize these features for comparison with the definitions in the fault mode library; Pattern matching calculation: For each possible fault mode, the matching degree between the currently extracted features and the fault mode is calculated according to the established rules and weights, and a numerical value indicating the matching degree is obtained; Determine the preliminary matching result: compare the calculated matching degree value with the preset threshold value, and if the matching degree exceeds the threshold value, list the fault mode as a candidate for preliminary matching; Confirmation in combination with subsequent analysis: Refer to the information about the degree to which the sensor measurement value exceeds the normal range in the subsequent fault location and severity assessment. If the measurement value of a certain sensor exceeds the normal range by a large amount and is consistent with the initially matched fault mode, further confirm the matching of the fault mode; Output matching results: Finally determine the matching fault mode and output the results in a clear and explicit manner to provide a basis for subsequent diagnosis and processing.

6. The injection molding machine fault emergency safety protection control system according to claim 5, characterized in that: The function implementation of the parameter adjustment submodule includes: Receive diagnostic results: Get detailed information about the fault type and extent from the intelligent data analysis module; Determine the adjustment strategy: According to the pre-set rules and strategies, combined with the diagnosis results, determine the operating parameters that need to be adjusted and the adjustment range; Execution parameter adjustment: send adjustment instructions through the control system interface of the equipment to modify the operating parameters; Among them, when determining the adjustment range, the proportional-integral-differential (PID) control algorithm is used to calculate the appropriate adjustment amount, specifically: first, determine the target value and the current actual measurement value; secondly, calculate the deviation between the two, that is, the target value minus the actual measurement value; then, calculate the integral term, which is obtained by continuously accumulating the deviation obtained from each calculation, and the initial integral term is 0; next, calculate the differential term, which is the current deviation minus the previous deviation, divided by the sampling time interval; finally, the adjustment amount is equal to the sum of the proportional term, the integral term and the differential term; the proportional term is the deviation multiplied by the proportional coefficient, the integral term is the integral multiplied by the integral coefficient, and the differential term is the differential multiplied by the differential coefficient.

7. The injection molding machine fault emergency safety protection control system according to claim 6, characterized in that: The function implementation method of the emergency stop submodule includes: Receiving serious fault signals: When the intelligent data analysis module diagnoses a serious fault, it sends a corresponding signal to the emergency shutdown submodule; Trigger shutdown action: immediately send a shutdown command to the equipment's actuator to stop all related moving parts; Trigger alarm: The sound and light alarm devices are activated simultaneously to alert the operator to a serious fault.

8. The injection molding machine fault emergency safety protection control system according to claim 7, characterized in that: The functional implementation of the fault recovery submodule includes: Monitor the troubleshooting status: maintain communication with the fault detection system to obtain real-time information on whether the fault has been eliminated; Restore the equipment to its initial settings: After confirming that the fault has been eliminated, restore the equipment's operating parameters to the normal state before the fault or the preset initial state; Start device operation: Send a startup command to make the device resume normal operation.

9. The injection molding machine fault emergency safety protection control system according to claim 8, characterized in that: The function implementation method of the remote control submodule includes: Network connection establishment: Establish a stable connection with the remote monitoring terminal through the network protocol; Data transmission: Transmit the operating status and sensor data information of the equipment to the remote terminal in real time, and receive the control instructions sent by the remote terminal; Command execution: Execute corresponding operations according to the received remote control commands.

10. The injection molding machine fault emergency safety protection control system according to claim 9, characterized in that: The emergency brake submodule in the safety protection module includes an emergency brake button installed on the operation panel of the injection molding machine, the button is connected to the main control system of the injection molding machine through a hard line, and a program is written in the main control system. When the emergency brake button is pressed, a stop command is immediately sent to each moving part of the injection molding machine to stop running; The protective fence and light curtain sensor submodule includes a protective fence installed around the dangerous area of ​​the injection molding machine, and a light curtain sensor is installed on the protective fence to ensure that the sensor can cover the entire dangerous area; when the light curtain sensor detects that an object or a person passes through, a signal is sent to the adaptive emergency control unit, and after the control unit receives the signal, the action of the injection molding machine is immediately stopped; The fire alarm and fire extinguishing submodule includes fire detectors installed at key positions of the injection molding machine. When the detectors detect signs of fire, they send alarm signals to the alarm and display module. In addition, the fire alarm and fire extinguishing submodule also includes automatic fire extinguishing equipment. When the fire alarm is triggered, the fire extinguishing is automatically started, and the operation of the injection molding machine is stopped at the same time, and the relevant power and energy supply are cut off.

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