Intelligent data acquisition and multi-dimensional early warning system for ham sausage production line based on PLC (Programmable Logic Controller)

Abstract

The invention discloses a PLC-based ham sausage production line intelligent data acquisition and multi-dimensional early warning system, which comprises a communication module, a prediction analysis module and a comprehensive control module, and is characterized in that the data communication module is responsible for transmitting processed data to a cloud data center through an industrial Ethernet to realize remote data monitoring and management; the prediction analysis module analyzes the collected data by using a machine learning algorithm, can discover potential faults and abnormal trends in real time, and generates early warning information to help an operator to take measures in time; the comprehensive control module is respectively connected with the data acquisition module and the data communication module through serial bus interfaces. The beneficial effects are that the data acquisition precision and reliability are improved, and the production process is ensured to be stable; multi-parameter fusion analysis is realized, the prediction accuracy is improved, and the maintenance cost is reduced.

Description

Technical Field

[0001] The present invention relates to the field of data acquisition equipment, and in particular to a PLC-based intelligent data acquisition and multi-dimensional early warning system for a ham sausage production line. Background Art

[0002] With the rapid development of the modern food processing industry, ham, a popular meat product, is experiencing increasing automation and informationization in its production process. Real-time data collection and monitoring are crucial for ensuring product quality, improving production efficiency, and reducing energy consumption. Traditional ham processing equipment often relies on manual inspections and recording of key process parameters such as temperature, pressure, and vibration. This approach is not only inefficient but also prone to human error.

[0003] With the advancement of automation technology and the Internet of Things (IoT), more and more companies are adopting data acquisition systems based on PLCs (Programmable Logic Controllers) to improve production efficiency and product quality. However, existing PLC-based data acquisition systems present several challenges: First, these systems are bulky and expensive to install and maintain; second, they require a wired network, making implementation inconvenient; and third, traditional instruments utilize a wide variety of signal types, including thermocouples, current, voltage, and RS485 serial bus signals, which complicates data acquisition. Therefore, a technical solution is urgently needed that is compatible with existing traditional instruments and can quickly and easily implement remote data acquisition. Summary of the Invention

[0004] In order to solve the above problems, the present invention provides an intelligent data collection and multi-dimensional early warning system for a ham sausage production line based on Siemens S7-1500 PLC.

[0005] To achieve the above objectives, this application provides the following technical solutions:

[0006] This application provides a ham sausage production line intelligent data collection and multi-dimensional early warning based on Siemens S7-1500 PLC, including:

[0007] Data acquisition module: Integrates a high-precision sensor group to monitor core process parameters such as temperature, pressure, vibration, etc. in real time, and converts analog signals into digital signals.

[0008] Temperature acquisition: Using PT100 thermal resistance, supporting 8-channel synchronous acquisition, each channel can independently configure temperature compensation parameters;

[0009] Pressure acquisition: Differential pressure piezoresistive sensor is used, with a measuring range of 0-24MPa and an accuracy of 0.5 level;

[0010] Vibration acquisition: A three-axis accelerometer based on MEMS technology offers strong anti-interference and high repeatability. All sensor data is connected to the PLC via an analog input module.

[0011] Digital communication module: Connected to the PLC S7-1500 via the PROFINET protocol, used to transmit processed data to the data center server and receive remote control instructions:

[0012] Industrial Ethernet interface: supports seamless connection with workshop-level SCADA system;

[0013] Cellular network unit: Built-in dual SIM card redundancy design, supports 4G / 5G full network access, adopts AES-256 encryption and MQTT / OPC UA protocol to ensure secure and reliable cloud data interaction.

[0014] Predictive Analysis Module: Based on time series data analysis algorithms and machine learning models, it conducts in-depth mining of historical and real-time data to achieve predictive maintenance of equipment failures and abnormal operating conditions.

[0015] Built-in edge computing gateway stores training data and supports offline mode anomaly detection;

[0016] For key indicators such as tumbling machine bearing vibration and motor temperature rise, the prediction accuracy rate exceeds 96%.

[0017] Integrated control module: Integrated into the S7-1500 PLC, connected to the communication module via Ethernet, and controlling the acquisition module via the I / O interface, enabling multi-module collaborative scheduling. The PLC features a built-in 72-hour data buffer, supporting temporary data storage during network interruptions. It also features remote firmware upgrades to ensure continuous system iteration.

[0018] Human-machine interaction module: Equipped with a 7-inch HMI touch screen, it displays equipment operation curves, alarm logs, and efficiency reports in real time. It supports hierarchical authority management, allowing operators to modify key parameters with a password, improving operational safety and convenience.

[0019] Technological innovations

[0020] 1. Multi-source heterogeneous signal fusion: Through standardized interfaces and protocols, it is compatible with traditional instrument signals such as thermocouples and RS485, simplifying the system architecture.

[0021] 2. Dual-mode communication redundancy design: Industrial Ethernet and cellular networks complement each other, combined with dual SIM card switching technology to ensure zero interruption of data transmission.

[0022] 3. Dynamic threshold intelligent warning: Optimize warning thresholds based on machine learning models, combine local sound and light alarms with remote SMS, email, and platform push notifications to achieve multi-dimensional warnings.

[0023] The advantages of the present invention are:

[0024] 1. High-precision data acquisition: Through professional sensor selection and calibration, the monitoring error of parameters such as temperature, pressure, and vibration is ensured to be less than 0.5%, significantly improving data reliability.

[0025] 2. Efficient fault prevention: Machine learning models reduce unplanned equipment downtime by more than 30%, reducing maintenance costs and production losses.

[0026] 3. Convenient and intelligent interaction: The HMI touch screen works in conjunction with the remote monitoring platform to achieve real-time visualization of equipment status and remote control of parameters, significantly improving production management efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0028] Figure 1 This is the intelligent data collection and multi-dimensional early warning diagram of the ham sausage production line based on Siemens S7-1500 PLC described in this application

[0029] Figure 2 This is the data collection and processing flow chart described in this application

[0030] Figure 3 This is the logic block diagram of the fault warning algorithm described in this application. DETAILED DESCRIPTION

[0031] The present invention will be described in further detail below with reference to the accompanying drawings.

[0032] To make the purpose, technical solutions, and advantages of this application more clear, the technical solutions of this application are described in detail below. Obviously, the embodiments described are only some of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other methods obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of this application.

[0033] See also Figure 1-Figure 3 As shown, the present application provides an intelligent data collection and multi-dimensional early warning system for a ham sausage production line based on Siemens S7-1500 PLC.

[0034] 1. Hardware deployment: such as Figure 1As shown, the system of this application includes: a data acquisition module, a data communication module, a prediction and analysis module, and an integrated control module. The data acquisition module uses high-precision sensors to collect key parameters of the equipment in real time, including temperature, vibration, pressure, etc., and transmits them to the PLC S7-1500 for data standardization processing; the data communication module is responsible for transmitting the processed data to the cloud data center via industrial Ethernet to achieve remote data monitoring and management; the prediction and analysis module uses machine learning algorithms to analyze the collected data, which can detect potential faults and abnormal trends in real time, and generate early warning information to help operators take timely measures; the integrated control module is connected to the communication module via Ethernet, and controls the acquisition module through the I / O interface to achieve multi-module collaborative scheduling.

[0035] 2. Data processing flow: such as Figure 2 As shown in this embodiment, the data acquisition and processing method includes the following steps: collecting analog signals from temperature, vibration, and pressure sensors at a frequency of 100Hz through a PLC controller, and writing the data into a MySQL database in real time through the OPCUA protocol; standardizing and normalizing the multi-source heterogeneous sensor data, and then using sliding window technology to extract time domain features (including mean, standard deviation, RMS, peak value, and kurtosis) and FFT frequency domain features (including main frequency and energy ratio); reducing the extracted multi-dimensional features to 3 dimensions through PCA principal component analysis, retaining more than 95% of the original data variance; and finally storing the processed feature data in a feature database for equipment status monitoring and fault prediction. This method realizes efficient processing of the entire process of industrial field data from acquisition, preprocessing, feature extraction to dimensionality reduction through an optimized real-time processing architecture.

[0036] 3. Fault warning mechanism: such as Figure 3In this embodiment, the online fault warning monitoring process includes the following steps: inputting raw data into the system and using random partitioning to divide the data into training and test sets. Historical data on normal equipment operation and failures is proportionally allocated to the training and test sets. This data is then standardized and normalized. Standardization uses the Z-score method, with the formula: , and normalization uses min-max normalization, with the formula: . These methods adjust the numerical range to [0, 1] for subsequent model processing. A model is constructed that incorporates multiple network structures. First, a convolutional neural network (CNN) layer is established to extract spatial features from the data. This is followed by a bidirectional gated recurrent unit (BiGRU) layer, which uses the BiGRU to address the temporal dependencies of the data. Because equipment operating data varies over time, the BiGRU can simultaneously consider past and future temporal information, better modeling the dynamics of the time series. An attention mechanism layer is then added to extract key features. By calculating weights for different features or time steps, the model focuses on information that has a greater impact on the output. Finally, a softmax fault warning layer outputs the probability or classification of the fault warning. The KOA optimization algorithm is introduced to adjust the network structure and hyperparameters through continuous iteration. Using the determined optimal network hyperparameters, the model is trained based on the training set data. Appropriate performance evaluation indicators are used to evaluate the performance of the model on the test set. If the model performance meets the requirements, the final prediction result (such as the specific category or probability of equipment failure warning) is output. In this way, the present invention realizes a complete process from data processing, model construction optimization to training and testing, and can realize specific functions.

[0037] In summary, the equipment of this application can realize the data collection and early warning system of ham sausage processing equipment through the functions of each module and the integrated control module. The equipment of this application adopts a suspended integrated design and has three anti-dust, waterproof and shockproof functions. It can work normally after installation. The installation process of the equipment of this application is as follows:

[0038] (1) Install the device in the control cabinet using the device mounting base of this application;

[0039] (2) Connect the device to a 24VDC power supply to power the device;

[0040] (3) Connect the sensor to the device according to the signal type;

[0041] (4) At this point, the equipment installation has been completed and only needs to be powered on to start operation

[0042] (5) After working, the device is connected to the Internet via Ethernet and sends the collected data to the data center server in real time, while the control instructions are sent back to the device

[0043] (6) After the equipment is working normally, users can observe relevant data in real time through the data center client and perform data processing and analysis.

[0044] (7) After data processing and analysis, the data is imported into the KOA optimized equipment failure warning model to conduct real-time monitoring of the equipment.

[0045] Beneficial effects

[0046] 1. High-precision data acquisition: Through professional sensor selection and calibration, the monitoring error of parameters such as temperature, pressure, and vibration is ensured to be less than 0.5%, significantly improving data reliability.

[0047] 2. Efficient fault prevention: Machine learning models reduce unplanned equipment downtime by more than 30%, reducing maintenance costs and production losses.

[0048] 3. Convenient and intelligent interaction: The HMI touch screen works in conjunction with the remote monitoring platform to achieve real-time visualization of equipment status and remote control of parameters, significantly improving production management efficiency.

[0049] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0050] The present invention and its embodiments are described above. This description is not restrictive. The drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. In short, if a person skilled in the art is inspired by this and, without departing from the purpose of the present invention, designs structures and embodiments similar to this technical solution without inventiveness, they shall fall within the scope of protection of the present invention.

Claims

1. A PLC-based intelligent data acquisition and multi-dimensional early warning system for a ham sausage production line, characterized by: Data acquisition module, used to monitor key process parameters (such as temperature, pressure, vibration, etc.) of ham sausage processing equipment in real time and convert them into digital signals; The digital communication module is connected to the PLC S7-1500 via the PROFINET protocol to transmit processed data to the data center server and receive remote control commands; The predictive analysis module uses machine learning algorithms to analyze historical and real-time data to predict equipment failures and abnormal conditions and provide visual early warning information; The integrated control module is integrated into the PLC S7-1500 and is used to coordinate the timing control of the data acquisition, communication and analysis modules. It is connected to the data communication module through the Ethernet interface and to the data acquisition module through the I / O module.

2. The system according to claim 1, characterized in that :The data acquisition module includes a temperature sensor unit, a pressure sensor unit, and a vibration sensor unit. The temperature sensor unit adopts a PT100 thermal resistor, the pressure sensor unit adopts a piezoresistive sensor, and the vibration sensor adopts a three-axis acceleration design. The temperature sensor unit, pressure sensor unit, and vibration sensor unit are connected to the PLC through an analog input module.

3. The system according to claim 2, characterized in that :The temperature sensor unit supports multi-channel acquisition and can monitor up to 8 temperature points simultaneously. The temperature compensation parameters of each channel can be set independently. The pressure sensor unit adopts a differential pressure design with a measuring range of 0 to 24 MPa and an accuracy level of 0.

5. The vibration sensor adopts MEMS technology and has high repeatability and anti-interference capabilities.

4. The system according to claim 1, characterized in that :The communication module includes an industrial Ethernet interface and a cellular network communication unit. The industrial Ethernet interface supports the PROFINET protocol and is used to directly connect to the workshop-level SCADA system. The cellular network communication unit supports 4G / 5G full network access and interacts with the cloud platform through the TCP / IP protocol.

5. The system according to claim 4, characterized in that :The cellular network communication unit has a built-in dual SIM card redundancy design, supports automatic network switching, and ensures communication continuity; data encryption uses the AES-256 algorithm, and the communication protocol is compatible with MQTT and OPC UA.

6. The system according to claim 1, characterized in that :The early warning module includes a local sound and light alarm and a remote notification unit. The local alarm is triggered by the PLC digital output, and the remote notification unit sends SMS, email or platform push alarm information through the communication module. The early warning threshold supports dynamic adjustment and the threshold range can be optimized through machine learning models based on historical data.

7. The system according to claim 6, characterized in that The machine learning model uses a time series data analysis algorithm to perform predictive maintenance judgments on the bearing vibration and motor temperature rise trends of the tumbling machine. The model training data is stored in the edge computing gateway, supporting anomaly detection in offline mode.

8. The system according to claim 1, characterized in that The PLC control module has a built-in data cache area that temporarily stores at least 72 hours of operating data when the network is interrupted. The system supports remote firmware upgrades, receives update packages through the communication module, and automatically completes the PLC program refresh.

9. The system according to claim 1, wherein: Equipped with a 7-inch HMI touch screen, it displays the working curve, alarm records and equipment efficiency reports of the ham sausage processing equipment in real time. The HMI interface supports hierarchical authority management, and operators can modify key parameters with a password.

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