Intelligent data monitoring method and system for enameled wire production line

Abstract

The invention discloses an intelligent data monitoring method and system for an enameled wire production line, and belongs to the technical field of industrial automatic monitoring. The system comprises a master controller, a plurality of slave control modules and a far-end alarm device, the master controller is connected with the far-end alarm device and all the slave control modules. Each slave control module comprises a slave controller, a sensor unit, an execution unit and a field alarm device; wherein the slave controller is respectively connected with the master controller, the sensor unit, the execution unit and the field alarm device. According to the invention, intelligent data monitoring of the enameled wire production line equipment is realized, so that abnormity early warning of the enameled wire production line equipment is realized.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of industrial automation monitoring, and particularly relates to a method and system for intelligent data monitoring of an enameled wire production line. BACKGROUND

[0002] With the development of intelligent technology, industrial intelligent construction needs to be continuously promoted, among which, the Internet of Things technology is applied more and more widely in large industrial enterprises. Real-time data acquisition and analysis of equipment through sensors, computers and network communication technologies are the key to ensuring stable, efficient and safe production. The existing industrial monitoring technology has the following problems:

[0003] (1) The deployment cost of monitoring equipment is high, and small and medium-sized enterprises cannot afford it.

[0004] (2) The running state of the equipment depends on manual inspection, and the response is lagging.

[0005] (3) The monitoring equipment often analyzes the collected data in isolation, which is only suitable for single equipment state judgment, and there is a lack of correlation between data, which leads to an inability to reasonably assess the state of the entire production line.

[0006] (4) The abnormal early warning mechanism is single, and lacks multi-level response strategy.

[0007] Therefore, the present application provides a method and system for intelligent data monitoring of an enameled wire production line. SUMMARY

[0008] The present application aims to overcome the shortcomings of the prior art and provides a method and system for intelligent data monitoring of an enameled wire production line to achieve the following purposes: intelligent data monitoring of enameled wire production line equipment, thereby realizing abnormal early warning.

[0009] In order to achieve the above purpose, the technical solution adopted by the present application is as follows: a system for intelligent data monitoring of an enameled wire production line, comprising a main controller, a plurality of slave control modules, and a remote alarm device; the main controller is connected with the remote alarm device and all slave control modules; each slave control module comprises a slave controller, a sensor unit, an execution unit, and a local alarm device; wherein the slave controller is connected with the main controller, the sensor unit, the execution unit, and the local alarm device.

[0010] Preferably, one slave control module is used to control the cutter motor, and in the corresponding slave control module, the sensor unit comprises two proximity switches, both of which are connected with the slave controller under the current slave control module, one of which is used to receive the signal of the cutter opening position, and the other is used to receive the signal of the cutter closing position; the execution unit comprises a time relay and a cutter motor, and the slave controller under the current slave control module is connected with the cutter motor through the time relay.

[0011] Preferably, the sensor unit includes a temperature sensor, a device power detection sensor, and a production line output detection sensor.

[0012] Preferably, all the slave controllers adopt the ESP32 embedded system.

[0013] Preferably, the on-site alarm device includes a buzzer and an alarm light, both of which are connected to the slave controller under the corresponding slave control module.

[0014] Preferably, the remote alarm device includes a human-machine interface display screen, which is connected to the main controller.

[0015] This application also proposes an intelligent data monitoring method for enameled wire production lines, using the aforementioned intelligent data monitoring system for enameled wire production lines. The method includes the following steps:

[0016] Step S1: The main controller obtains the enameled wire production line status data through each slave controller, including the power, temperature, and output of the production line equipment;

[0017] Step S2: The main controller performs anomaly analysis on the enameled wire production line status data. After detecting an anomaly, it sends an alarm signal to the remote alarm device and the corresponding on-site alarm device.

[0018] Step S3: The remote alarm device and the corresponding on-site alarm device start alarming after receiving the alarm signal.

[0019] Preferably, the anomaly analysis in step S2 includes:

[0020] Set dynamic thresholds for various enameled wire production line status data; the method for setting the dynamic thresholds is as follows:

[0021] Obtain historical data on the status of the corresponding enameled wire production line within a preset time window, and calculate its mean and median;

[0022] The baseline threshold is calculated using the following formula:

[0023] Baseline = a * mean + b * median;

[0024] Where a and b represent the preset weights of the mean and median, and their sum is 1;

[0025] Finally, the dynamic threshold Y is obtained, that is, Y = c * Baseline, where c represents the preset empirical coefficient.

[0026] Preferably, the anomaly analysis in step S2 further includes:

[0027] A production line health index model is established for anomaly analysis. The production line health index model is expressed by the following formula:

[0028] health_index=d*(power / baseline_power)+e*(temp / baseline_temp)+f*(baseline_production / production);

[0029] Wherein, health_index represents the health index; d, e, and f represent the preset weighting coefficients for power, temperature, and output, respectively; power, temp, and production represent the real-time power, temperature, and output data, respectively; and baseline_power, baseline_temp, and baseline_production represent the dynamic thresholds for power, temperature, and output data, respectively.

[0030] Preferably, the anomaly analysis in step S2 further includes: tiered alarms based on the magnitude of the health index, i.e.:

[0031] When the preset first health index threshold < health index ≤ preset second health index threshold, it indicates a slight abnormality and a primary alarm signal is issued.

[0032] When the preset second health index threshold < health index ≤ preset third health index threshold, it indicates a medium-level abnormality and a medium-level alarm signal is issued.

[0033] When the preset third health index threshold is less than the health index, it indicates a serious abnormality and a high-level alarm signal is issued.

[0034] The technical effects of this invention are as follows:

[0035] (1) The system structure of the present invention is simple and the deployment is based on it, which reduces the cost. At the same time, the data acquisition and control functions are delegated to the slave controller, and the master controller is only used for data analysis, which improves the system's working efficiency and ensures timely detection and alarm of anomalies.

[0036] (2) The present invention is equipped with an on-site alarm device and a remote alarm device to ensure that both remote monitoring personnel and on-site monitoring personnel can receive alarms in a timely manner.

[0037] (3) The anomaly analysis of this invention, through the setting of dynamic thresholds, makes the judgment of anomalies more consistent with the real-time production line status, thereby improving the accuracy of subsequent anomaly analysis. At the same time, through the application of a health index model established by parameters, a comprehensive assessment of the health of the entire production line is achieved.

[0038] (4) The present invention also includes a graded alarm mechanism, which provides convenience for users. Attached Figure Description

[0039] Figure 1 This is a structural block diagram of an intelligent data monitoring system for an enameled wire production line provided in an embodiment of the present invention. Detailed Implementation

[0040] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. This is to help those skilled in the art to have a more complete, accurate, and in-depth understanding of the inventive concept and technical solutions of the present invention, and to facilitate its implementation. It should be noted that the terms "first," "second," etc., used in this application are only for the convenience of describing the technical solutions and to distinguish components; the corresponding component configurations may be the same or different, and are not intended to limit the scope of this application. To make the technical solutions of the present invention clearer, the present invention will be explained and illustrated through the following embodiments.

[0041] This embodiment provides an intelligent data monitoring system for enameled wire production lines, such as... Figure 1 As shown, the system includes a main controller, multiple slave control modules, and a remote alarm device. The main controller is connected to the remote alarm device and all slave control modules. Each slave control module includes a slave controller, a sensor unit, an execution unit, and a local alarm device. The slave controller is connected to the main controller, the sensor unit, the execution unit, and the local alarm device. This embodiment features a simple system structure, is easy to deploy, and reduces costs. Furthermore, by delegating data acquisition and control functions to the slave controllers, while the main controller is only used for data analysis, system efficiency is improved, ensuring timely detection and alarm of anomalies.

[0042] In this embodiment, all slave controllers employ the ESP32 embedded system. The ESP32 embedded system is small in size, easy to deploy, and reduces costs; it also has multiple general-purpose input / output interfaces, providing strong expandability; furthermore, the ESP32 possesses highly integrated wireless communication capabilities, enabling wireless communication with the main controller. In this embodiment, each ESP32 embedded system communicates with the main controller via the TCP protocol, thereby uploading data acquired by the slave controller to the main controller.

[0043] In this embodiment, each slave controller can connect to multiple sensor units simultaneously, enabling the simultaneous acquisition of various data. Commonly used sensor units include temperature sensors, equipment power detection sensors, and production line output detection sensors, which can be flexibly configured according to actual needs during implementation. Temperature sensors are used to collect the temperatures of key components in the enameled wire production line equipment (such as motor housings and bearing housings), ambient temperature, and coating curing oven temperature. Temperature is a core factor affecting the quality of enameled wire; excessively high temperatures can lead to enamel aging, while excessively low temperatures can affect the curing effect; overheating of equipment components is also a precursor to malfunctions. Equipment power detection sensors are used to collect the power of the enameled wire production equipment, i.e., the electrical load status. Abnormal power fluctuations may indicate problems such as mechanical jamming or motor failure. Production line output detection sensors are used to collect output data from the enameled wire production line, which is directly related to production efficiency. All data collected by the various sensor units is sent to the main controller via the slave controller for data analysis.

[0044] In this embodiment, a slave control module is used to control the cutter motor. Within this module, the sensor unit includes two proximity switches, both connected to a slave controller under the current slave control module. One switch receives a signal indicating the cutter is in the open position, and the other receives a signal indicating the cutter is in the closed position. Both signals are sent to the slave controller. The execution unit includes a time relay and a cutter motor. The slave controller under the current slave control module is connected to the cutter motor via the time relay; that is, the slave controller is connected to the control terminal of the time relay to set the time of the time relay. A circuit is formed between the normally open contact of the time relay, the power supply, and the cutter motor. In existing technology, the cutter motor is only de-energized when it receives a proximity switch signal indicating the cutter is in the closed position. However, in actual operation, due to equipment aging or damage, the cutter may not stop at the correct position, preventing the proximity switch from sending a signal indicating the cutter is in the closed position. This results in the cutter motor remaining energized, which could damage itself or surrounding electrical components. Therefore, if the main controller detects that the proximity switch signal uploaded from the slave controller does not contain a signal indicating the cutter is in the closed position for an extended period, it can send an alarm signal to remote and on-site alarm devices. Meanwhile, in order to overcome the above problems, the slave controller in this embodiment controls the power supply of the cutter motor through a time relay. That is, the slave controller presets a time interval for the time relay. When the cutter motor runs for more than the time interval, the time relay automatically disconnects, cutting off the power supply to the cutter motor, thereby protecting the equipment.

[0045] An on-site alarm device is used to sound an alarm at the equipment location to alert on-site personnel. In this embodiment, the on-site alarm device includes a buzzer and an alarm light. Both the buzzer and the alarm light are connected to a slave controller under a corresponding slave control module. The slave controller receives an alarm signal from the master controller and then drives the on-site alarm device to sound the alarm. The on-site alarm device can be driven to sound the alarm in different ways depending on the level of the alarm signal, including different brightness and colors of the alarm light, and continuous frequency of the buzzer, etc.

[0046] Remote alarm devices are typically installed in the central control room, along with the main controller, to alert personnel in the control room. In this embodiment, the remote alarm device includes a human-machine interface display screen connected to the main controller. The display screen provides alarms to the user through data visualization and also supports user command input, such as setting various threshold parameters.

[0047] The system in this embodiment enables real-time monitoring, analysis, and early warning of equipment status during the enameled wire production process, thereby ensuring product quality, improving production efficiency, and reducing losses.

[0048] This embodiment also proposes an intelligent data monitoring method for enameled wire production lines. Using the aforementioned intelligent data monitoring system for enameled wire production lines, the method includes the following steps:

[0049] Step S1: The main controller obtains the enameled wire production line status data through each slave controller, including the power, temperature, and output of the production line equipment;

[0050] Step S2: The main controller performs anomaly analysis on the enameled wire production line status data. After detecting an anomaly, it sends an alarm signal to the remote alarm device and the corresponding on-site alarm device.

[0051] Step S3: The remote alarm device and the corresponding on-site alarm device start alarming after receiving the alarm signal.

[0052] Specifically, in step S1, each slave controller acquires various enameled wire production line status data through sensor units and sends them to the main controller for aggregation. The slave controllers also preprocess the collected data before sending it to the main controller. The preprocessing includes:

[0053] (1) Noise Reduction: The moving average filtering method is used to smooth high-frequency fluctuation data such as vibration and temperature. The formula is as follows:

[0054]

[0055] Among them, X filtered The input data is represented by X; N represents the size of the selected sliding serial port; t represents the time point.

[0056] (2) Outlier removal, i.e., based on the 3σ principle, identify data that exceeds the reasonable range and fill the gap by interpolating data from previous and subsequent time points.

[0057] (3) Data alignment, that is, for a slave controller that collects data from multiple sensors at the same time, the multi-source data is aligned by timestamps to ensure the consistency of different sensor data in the time dimension, laying the foundation for subsequent correlation analysis.

[0058] Then, referring to step S2, the main controller performs anomaly analysis on the enameled wire production line status data, the anomaly analysis including:

[0059] Set dynamic thresholds for various enameled wire production line status data; the method for setting the dynamic thresholds is as follows:

[0060] For any type of enameled wire production line status data, first obtain the historical data of the corresponding enameled wire production line status within a preset time window, and calculate its mean and median;

[0061] Then, the baseline threshold is calculated, expressed by the following formula:

[0062] Baseline = a * mean + b * median;

[0063] Where a and b represent the preset weights of the mean and median, and their sum is 1. In this embodiment, a = 0.6 and b = 0.4. In specific implementation, they can be flexibly selected according to actual needs. The baseline threshold obtained in this way can reflect the overall trend of the data (mean) and resist the interference of individual outliers (median).

[0064] Finally, the dynamic threshold Y is obtained, i.e., Y = c * Baseline, where c represents a preset empirical coefficient, which is related to historical data, process understanding, and practical experience. It is used to correct the baseline threshold to make it more consistent with actual working conditions. In this embodiment, c = 1.25, but in specific implementations, it can be flexibly selected according to actual needs. The dynamic threshold will adaptively adjust with changes in production conditions, making it more accurate and intelligent than the traditional fixed threshold alarm method.

[0065] After obtaining the dynamic thresholds for various enameled wire production line status data, anomaly analysis can be performed. This involves comparing the real-time collected enameled wire production line status data with the corresponding dynamic thresholds; any deviations exceeding the thresholds are considered anomalies and trigger an alarm. In addition, this embodiment establishes a production line health index model using power, temperature, and output data to assess the production line's health status, thereby achieving more accurate anomaly analysis through data correlation between multiple parameters. Specifically, the production line health index model is expressed by the following formula:

[0066] health_index=d*(power / baseline_power)+e*(temp / baseline_temp)+f*(baseline_production / production);

[0067] Wherein, `health_index` represents the health index; the smaller the health index, the healthier the production line. `d`, `e`, and `f` represent the preset weighting coefficients for power, temperature, and output, respectively. In this embodiment, `d` = 0.5, `e` = 0.3, and `f` = 0.2. In specific implementations, these can be flexibly selected according to actual needs. `power`, `temp`, and `production` represent the real-time power, temperature, and output data, respectively. `baseline_power`, `baseline_temp`, and `baseline_production` represent the dynamic thresholds for power, temperature, and output data, respectively. `power / baseline_power` represents the multiple of the current power relative to the dynamic power threshold; `temp / baseline_temp` represents the multiple of the current temperature relative to the dynamic temperature threshold; `baseline_production / production` represents the multiple of the current output relative to the dynamic output threshold. The larger the current output, the smaller `baseline_production / production`, and the smaller the health index, meaning a healthier production line.

[0068] The health index in this embodiment provides a single, quantitative indicator to quickly determine whether the production line as a whole is in a healthy state, which is far more intuitive and reliable than monitoring multiple data points individually.

[0069] Furthermore, this embodiment can also provide tiered alarms based on the magnitude of the health index, namely:

[0070] When the preset first health index threshold < health index ≤ preset second health index threshold, it indicates a slight abnormality and a primary alarm signal is issued.

[0071] When the preset second health index threshold < health index ≤ preset third health index threshold, it indicates a medium-level abnormality and a medium-level alarm signal is issued.

[0072] When the preset third health index threshold is less than the health index, it indicates a serious abnormality and a high-level alarm signal is issued. In this embodiment, the first health index threshold is 1, the second health index threshold is 1.2, and the third health index threshold is 1.5. In specific implementation, the thresholds can be flexibly selected according to actual needs.

[0073] By using alarm signals that characterize different degrees of abnormality, the corresponding on-site alarm devices and remote alarm devices can adopt different alarm methods to indicate the severity of the abnormality to the user, thereby enabling timely solutions to ensure normal production on the production line.

[0074] The present invention has been described above by way of example with reference to the accompanying drawings. Obviously, the specific implementation of the present invention is not limited to the above-described manner. Any non-substantial improvements made using the inventive concept and technical solution; or the direct application of the inventive concept and technical solution to other situations without modification, are all within the protection scope of the present invention.

Claims

1. An intelligent data monitoring system for enameled wire production lines, characterized in that: The system comprises a master controller, a plurality of slave modules, and a remote alarm device; the master controller is connected with the remote alarm device and all slave modules; each slave module comprises a slave controller, a sensor unit, an execution unit, and a local alarm device; wherein the slave controller is connected with the master controller, the sensor unit, the execution unit, and the local alarm device.

2. The enameled wire production line intelligent data monitoring system according to claim 1, characterized in that: One of the slave modules is used for controlling a cutter motor; in the corresponding slave module, the sensor unit comprises two proximity switches, both of which are connected with the slave controller under the current slave module, one of which is used for receiving a signal of a cutter opening position, and the other of which is used for receiving a signal of a cutter closing position; the execution unit comprises a time relay and a cutter motor, and the slave controller under the current slave module is connected with the cutter motor through the time relay.

3. The enameled wire production line intelligent data monitoring system according to claim 1, characterized in that: The sensor unit comprises a temperature sensor, a device power detection sensor, and a production line yield detection sensor.

4. The enameled wire production line intelligent data monitoring system according to claim 1, characterized in that: The slave controller adopts an ESP32 embedded system.

5. The enameled wire production line intelligent data monitoring system according to claim 1, characterized in that: The local alarm device comprises a buzzer and an alarm lamp, both of which are connected with the slave controller under the corresponding slave module.

6. The enameled wire production line intelligent data monitoring system according to claim 1, characterized in that: The remote alarm device comprises a man-machine interactive display screen, which is connected with the master controller.

7. A method for monitoring intelligent data of an enameled wire production line, using an enameled wire production line intelligent data monitoring system according to any one of claims 1-6, characterized in that: The method comprises the following steps: Step S1: the master controller acquires enameled wire production line state data through each slave controller, including the power, temperature, and yield of the production line device; Step S2: the master controller performs abnormality analysis on the enameled wire production line state data, and after detecting an abnormality, sends an alarm signal to the remote alarm device and the corresponding local alarm device; Step S3: after receiving the alarm signal, the remote alarm device and the corresponding local alarm device start alarming.

8. The enameled wire production line intelligent data monitoring method according to claim 7, characterized in that: The abnormality analysis of step S2 comprises: setting dynamic thresholds of various enameled wire production line state data; the setting method of the dynamic thresholds is as follows: acquiring historical data of the corresponding enameled wire production line state within a preset time window, calculating the mean and median thereof; calculating a baseline threshold Baseline, which is expressed by the following formula: Baseline=a*mean+b*median; wherein a and b represent preset weights of the mean and median, and their sum is 1; finally obtaining a dynamic threshold Y, i.e. Y=c*Baseline, wherein c represents a preset empirical coefficient.

9. The enameled wire production line intelligent data monitoring method according to claim 8, characterized in that: The abnormality analysis of step S2 further comprises: establishing a production line health index model for abnormality analysis, which is expressed by the following formula: health_index=d*(power / baseline_power)+e*(temp / baseline_temp)+f*(baseline_production / production). Wherein, the health_index represents the health index; d, e, f respectively represent preset weight coefficients of power, temperature, yield; power, temp, production respectively represent real-time acquired power, temperature, yield data; baseline_power, baseline_temp, baseline_production respectively represent dynamic thresholds of power, temperature, yield data.

10. The enameled wire production line intelligent data monitoring method according to claim 9, characterized in that: The step S2 further comprises: grading alarm according to the health index, namely: When the preset first health index threshold < health index ≤ preset second health index threshold, it represents slight abnormality, and a primary alarm signal is sent out; When the preset second health index threshold < health index ≤ preset third health index threshold, it represents intermediate abnormality, and an intermediate alarm signal is sent out; When the preset third health index threshold < health index, it represents serious abnormality, and a high-level alarm signal is sent out.

Images