Bucket wheel machine unattended control method based on PLC data regional storage

Abstract

The invention provides a bucket wheel machine unattended control method based on PLC data regional storage, and aims to improve the real-time performance, the reliability and the automation level of a bucket wheel machine system. According to the method, the control function of the bucket wheel machine is divided into a plurality of independent areas including bucket wheel machine rotation and pitching control, belt conveying control, equipment state monitoring, remote monitoring and the like, and each area is responsible for data collection, control instruction execution and local data storage through an independent PLC unit. Real-time data synchronization of the PLCs in all the areas is achieved through an industrial Ethernet or a field bus, an intelligent fault diagnosis model is adopted to analyze and predict the equipment operation state, and a graded alarm mechanism ensures that equipment faults can be processed in time. And the remote monitoring platform can realize real-time data display, alarm notification and remote control of the equipment, and ensures that the system efficiently operates under the unattended condition. According to the method, the equipment maintenance and operation cost is greatly reduced, and the operation stability and safety of the system are improved.

Description

Technical Field

[0001] The invention relates to the technical field of industrial automation control, and in particular to an unattended control method for a bucket wheel machine based on regional storage of PLC data. Background Art

[0002] At present, bucket wheel excavators are widely used in large mines, ports, power plants and other places for efficient processing and handling of large amounts of bulk materials. However, traditional bucket wheel excavator control systems usually rely on manual operation or partial automation control, which has many problems. First, the bucket wheel excavator system is complex, and operators need to monitor the operating status of the equipment in real time and manually adjust the operating parameters, resulting in high labor intensity and low operating efficiency. With the increase in unmanned demand, traditional control methods are difficult to meet the requirements of long-term continuous operation. Therefore, realizing unmanned automatic control of bucket wheel excavators is an urgent need to improve equipment utilization and reduce operating costs.

[0003] Existing bucket wheel excavator control systems generally use PLC (programmable logic controller) for equipment control, but a common problem is that data storage is concentrated in a single control center, resulting in large data transmission delays, especially under complex working conditions, where real-time performance and response speed cannot be guaranteed. In addition, single-point data storage brings a higher risk of failure. Once the central control PLC fails, it may cause the entire system to be paralyzed, causing serious production interruptions. At the same time, the fault diagnosis of existing systems relies on manual experience or simple alarm systems, making it difficult to identify potential faults or predict the operating status of equipment in a timely manner, increasing the difficulty of equipment maintenance and the time of fault downtime.

[0004] In order to improve the operating stability, real-time performance and safety of bucket wheel machines and solve the bottleneck problem in traditional control systems, the present invention proposes an unmanned control method for bucket wheel machines based on regional storage of PLC data. By dividing the control functions of the bucket wheel machine into multiple areas and introducing intelligent fault diagnosis and remote monitoring mechanisms, the real-time performance and safety of the system can be significantly improved, and more efficient fault handling and automatic control can be achieved. This method not only reduces the system response time and improves fault tolerance through distributed data storage and synchronization mechanisms, but also effectively manages the operation of the bucket wheel machine in an unmanned situation. Summary of the invention

[0006] The present invention relates to an unattended control method for bucket wheel machine based on PLC data regional storage, aiming to solve the problems of insufficient real-time performance, poor data security and difficulty in fault diagnosis in the existing bucket wheel machine system. By dividing the control function into multiple areas and combining the distributed control strategy, the efficient operation and safety of the system are guaranteed, and through intelligent fault diagnosis and remote monitoring, real unattended operation is achieved.

[0007] 1. Regional storage and control architecture

[0008] The bucket wheel control system of the present invention adopts a design concept of regional storage and control. The main functions of the bucket wheel system are divided into multiple independent control areas, such as the rotation and pitch control of the bucket wheel, belt transmission speed control, equipment status monitoring, etc. Each area is managed by an independent PLC unit, whose tasks include data acquisition, control instruction execution and local storage. This distributed architecture not only reduces the load of a single PLC, but also effectively improves the security of data and reduces the risk of data loss due to single point failure.

[0009] 2. Data storage and real-time synchronization in different regions

[0010] The PLC unit in each area independently stores the operating data of the area and synchronizes with the PLCs in other areas in real time through industrial Ethernet or fieldbus. The data of the bucket wheel machine, such as the rotation angle, belt speed, equipment temperature, etc., are collected and stored locally according to the predetermined sampling period. The data synchronization mechanism ensures that different parts of the system can coordinate work based on the latest equipment status. Through this real-time data synchronization method, even if the equipment in different areas operates independently, the unified coordinated control of the entire bucket wheel machine system can be maintained.

[0011] 3. Fault diagnosis and alarm mechanism

[0012] The present invention introduces an intelligent fault diagnosis model based on historical data. By collecting and analyzing the operating status parameters of the equipment (such as temperature, vibration, load, etc.), the system can automatically identify potential fault problems during operation. Using machine learning algorithms, the operating data of bucket wheel excavators is analyzed and classified to achieve automatic identification of common equipment faults (such as motor overload, belt wear, etc.). The system alarms according to the severity of the fault and initiates the emergency shutdown procedure when necessary to ensure the safety of equipment and personnel.

[0013] 4.Automated control and fault recovery

[0014] In the unattended operation mode, the bucket wheel machine performs operation control autonomously through the PLC. When the system is started, the PLC controls the key parameters of the bucket wheel machine such as rotation, pitch and belt transmission according to the predetermined operation plan to ensure efficient and stable operation of the equipment. The system is equipped with a PID control algorithm to automatically adjust the motion parameters of the bucket wheel machine, such as rotation angle and speed, according to real-time operation requirements and sensor data. When the equipment fails, the system automatically shuts down and records the fault information. After the fault is eliminated, the system can automatically resume operation.

[0015] 5. Remote monitoring and data management

[0016] In order to achieve true unattended operation, the present invention designs a remote monitoring and management platform. Through the network communication module, various operating data and alarm information of the bucket wheel machine can be transmitted to the remote monitoring center in real time for the duty personnel to view and analyze. The monitoring platform provides functions such as real-time status display, alarm management, and remote control of the equipment. Even if there is no one on duty, the duty personnel can still understand the operating status of the equipment through the monitoring platform, and remotely adjust the operating parameters or troubleshoot when necessary.

[0017] 6. Parameter setting and optimization

[0018] In order to ensure the efficient operation of the system, the present invention reasonably sets the relevant parameters during the implementation process. For example, the sampling period is usually set to 1 second, and the delay of data synchronization is controlled within hundreds of milliseconds. For control accuracy, the parameters of the PID controller are determined through debugging to ensure the smoothness and efficiency of equipment operation. In addition, based on the historical data of system operation, the remote monitoring platform can regularly generate equipment operation reports to provide data support for production optimization and equipment maintenance.

[0019] 7. Interaction between systems

[0020] In the present invention, the PLC systems in each area communicate and exchange data through fieldbus or industrial Ethernet. The mutual cooperation between different PLC units ensures the overall coordination of the system. For example, the linkage between the rotation control of the bucket wheel machine and the belt transmission speed needs to be coordinated through a real-time data synchronization mechanism to ensure the coordination of system actions. The fault diagnosis and alarm mechanism in the system also maintain close contact with the remote monitoring platform. Once an alarm occurs, the remote monitoring system can immediately receive the alarm signal and notify the on-duty personnel to handle it.

[0021] Beneficial effects:

[0022] The present invention significantly improves the real-time performance and reliability of the system by dividing the bucket wheel control system into multiple independent PLC areas and performing distributed data storage and synchronous control. The regional control architecture reduces the delay in data transmission and ensures that the linkage between equipment in each area is more coordinated; at the same time, the decentralized data storage avoids single point failures and improves the security of the system. In addition, the intelligent fault diagnosis model effectively identifies and warns of potential faults, reduces equipment downtime, and combines with remote monitoring and operation platforms to achieve unmanned automatic control of the bucket wheel, greatly reducing the need for manual intervention and maintenance costs. Overall, the present invention provides a more efficient, stable and safe bucket wheel control solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0024] Figure 1 It is a schematic diagram of the overall architecture of the system of the present invention;

[0025] Figure 2 This is a schematic diagram of the linkage control between the bucket wheel machine rotation and the belt transmission of the present invention;

[0026] Figure 3 This is a schematic diagram of the fault diagnosis and alarm process of the present invention;

[0027] Figure 4 It is a schematic diagram of the remote monitoring and operation interface flow of the present invention. DETAILED DESCRIPTION

[0028] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0029] It should be noted that the references to "one embodiment", "an embodiment", "an exemplary embodiment", "some embodiments" and the like in the specification indicate that the embodiments described may include specific features, structures or characteristics, but not every embodiment may include the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in conjunction with an embodiment, it should be within the knowledge of a person skilled in the art to implement such feature, structure or characteristic in conjunction with other embodiments (whether or not explicitly described).

[0030] In general, a term can be understood, at least in part, from its use in context. For example, depending, at least in part, on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending, at least in part, on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0031] Example 1

[0032] The present invention aims to provide an unattended control method for bucket wheel machine based on regional storage of PLC data, so as to solve the problems existing in the existing bucket wheel machine control system, such as insufficient real-time performance, system reliability problems caused by centralized data storage, and difficulties in fault diagnosis and maintenance. By introducing regional storage and distributed control in the PLC system, combined with intelligent fault diagnosis algorithm and remote monitoring platform, unattended operation of bucket wheel machine can be realized.

[0033] In order to ensure the real-time performance and data security of the system, this method divides the control functions of the bucket wheel machine into multiple areas. The PLC in each area processes the relevant data independently and stores it locally. Through distributed communication, the information between the areas can be synchronized in real time to ensure the normal operation of the bucket wheel machine. At the same time, an intelligent fault diagnosis model based on historical data is adopted to realize automatic fault detection and alarm through machine learning algorithms, minimizing manual intervention.

[0035] 1. System architecture design

[0036] The bucket wheel excavator system consists of multiple subsystems, and the control functions are divided into the following four main parts by area:

[0037] Area A: Bucket wheel excavator rotation and pitch control

[0038] Area B: Belt transmission and speed regulation control

[0039] Area C: Equipment status monitoring and sensor data collection

[0040] Area D: Remote monitoring and communication module

[0041] The PLC system in each area operates independently, responsible for collecting and storing the corresponding equipment status data and sensor information, and making real-time decisions based on the control logic. The PLCs in each area achieve data synchronization through industrial Ethernet or fieldbus (such as PROFIBUS, CAN, etc.).

[0042] 2.PLC regional storage design

[0043] In order to improve the stability of the system, data is stored by region. The data type and storage content of each region are as follows: In order to improve the stability of the system, data is stored by region. The data type and storage content of each region are as follows:

[0044] Area A (bucket wheel machine motion control): The real-time data of the bucket wheel machine's rotation angle θ and pitch angle φ are stored as an array [θ(t), φ(t)], where t is time. The PLC stores the collected angle data locally, and the data collection frequency is set to sample once per second, that is, the sampling period T s =1 second.

[0045] Area B (belt conveyor control): belt speed v b and power P b The data is stored as [v b (t),P b (t)], where v b The unit is meter / second, P b The unit is kilowatt (kW). The data storage period is also set to T s =1 second.

[0046] Area C (device status monitoring): data from various sensors, including device temperature T e , vibration amplitude A v and running load L, stored as [T e (t),A v (t),L(t)]. Each sensor data is stored and updated in a cycle of 1 second, and a warning signal is issued when the data exceeds the limit.

[0047] Area D (remote monitoring and data communication): The PLC in this area is mainly used for communication and data aggregation. The core data of each sub-area will be regularly sent to the remote monitoring platform for storage and analysis. The data synchronization interval is set to once per minute.

[0048] 3. Real-time data synchronization mechanism

[0049] Each regional PLC realizes data synchronization through field bus or Ethernet switch. The specific process is as follows:

[0050] The main PLC acts as the master controller and is responsible for coordinating data synchronization between regions. Each regional PLC acts as a slave and regularly reports current status data to the main PLC.

[0051] Data update logic: When a control parameter in any area (such as bucket wheel rotation angle or belt speed) changes, the PLC sends a data update request to the main PLC. After receiving the data, the main PLC broadcasts it to other related PLC units to ensure that the entire system remains consistent.

[0052] 3. Data synchronization formula: Assume x A (t) is the angle data of area A, x B (t) is the speed data of area B, then the data synchronization logic can be expressed as:

[0053] x A (t) = f(x B (t-Δt))

[0054] Here, Δt is the data transmission delay, which is determined through actual testing and is usually hundreds of milliseconds.

[0055] 4. Fault diagnosis and alarm mechanism

[0056] The system introduces an intelligent fault diagnosis model based on historical data to analyze equipment status and detect potential faults in real time. The key implementation steps of fault diagnosis include:

[0057] 1. Fault detection model: Use machine learning algorithms to train and analyze equipment operation data to extract feature vectors of equipment faults.

[0058] Assume that X is a fault feature set, which includes n equipment status parameters (such as temperature, vibration, etc.):

[0059] X=[T e ,A v ,L,…]

[0060] By training classifiers (such as support vector machines (SVMs), neural networks, etc.), the real-time running data X t Classify and determine whether there is an abnormality.

[0061] 2. Fault classification formula: Assuming that the output of the fault classifier is y(t), the fault judgment formula of the system is:

[0062] y(t)=sign(W T ·X t +b)

[0063] Where W is the model parameter and b is the bias term. If y(t) = 1, it means the device is in normal condition; if y(t) = -1, it means a fault is detected.

[0064] 3. Grading alarm mechanism: According to the fault diagnosis results, the system sets different alarm levels:

[0065] Low-level alarm: If the temperature is slightly high or the vibration fluctuates abnormally, the system will send out a low-level alarm signal to alert the on-duty personnel.

[0066] Advanced alarm: If a critical device fails, the system immediately executes the emergency shutdown procedure and sends an alarm to the remote monitoring center.

[0067] 5. Unattended automatic control process

[0068] 1. Automatic startup and self-test: Every day before the system starts, the PLC automatically performs a self-test procedure. The test content includes the motor status, sensor signal, communication status, etc. of the bucket wheel excavator. After the self-test passes, the system enters the normal operation mode.

[0069] Operation control logic: The system automatically adjusts the bucket wheel machine's motion state and belt speed according to the preset operation plan and sensor data. For example, the bucket wheel machine needs to rotate at a specific angle to stack materials. At this time, the PLC calculates the required rotation angle θ and pitch angle φ, and adjusts them according to the following control formula:

[0070]

[0071] Among them, K p , K i , K d is the PID control parameter, and e(t) is the deviation between the current angle and the target angle.

[0072] 3. Fault handling and automatic recovery: When the system detects a fault, it automatically shuts down and records the fault information. After the fault is repaired, the system automatically restarts and returns to the operating state before the fault, and continues to execute the unfinished work.

[0073] 6. Remote monitoring and data management

[0074] The system realizes real-time monitoring and management of bucket wheel excavators through the remote monitoring platform. Specifically:

[0075] Real-time data display: The remote monitoring platform displays the bucket wheel excavator's operating status, parameters of each area, and alarm information in real time.

[0076] Data analysis and optimization: The platform regularly generates equipment operation reports and analyzes historical data to provide data support for equipment maintenance and production optimization.

[0077] Remote control: When the system fails or parameters need to be adjusted, the on-duty personnel can remotely debug the bucket wheel excavator or reconfigure the PLC program through the remote platform.

[0078] 3. Parameter setting and optimization

[0079] The main control parameters involved in the system include:

[0080] Sampling period: T s = 1 second

[0081] PID control parameters: According to actual test, the initial setting is K p =0.5, K i =0.1, K d =0.01, which can be adjusted according to the actual operating conditions.

[0082] Data synchronization delay: Δt = 200 milliseconds.

[0083] The present invention realizes unattended control of bucket wheel machine by storing PLC data of bucket wheel machine control system in different regions and combining intelligent fault diagnosis and remote monitoring technology. This method not only improves the real-time performance and safety of the system, but also greatly reduces the manual maintenance cost and has good application prospects.

[0084] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0085] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for controlling an unmanned bucket wheel machine based on regional storage of PLC data, characterized in that: The following steps are involved: The control system of the bucket wheel machine is divided into multiple independent areas, each of which is managed by an independent PLC unit. The areas include: Bucket wheel machine rotation and pitch control area; Belt conveyor speed control area; Equipment status monitoring area; Remote monitoring and communication area; Each PLC unit independently performs local data collection, control and storage tasks, including collecting equipment status data and control parameters in their respective areas and storing them in local memory according to the set sampling period; Realize real-time data synchronization between PLC units in each area through industrial Ethernet or fieldbus to ensure information sharing and status consistency between different areas; Use intelligent fault diagnosis models to analyze the operating status of equipment in real time, automatically identify potential faults, and issue alarm signals based on the severity of the faults; During the operation of the system, the PLC automatically executes the scheduled operation plan, adjusts the key parameters of the bucket wheel machine's rotation angle, pitch angle, and belt transmission speed, and realizes unattended operation; When the system detects a fault, it automatically stops and records the fault information. After the fault is resolved, the system automatically returns to the operating state before the fault and continues to operate; The operating status of the bucket wheel excavator can be monitored in real time through the remote monitoring platform, and the remote duty personnel can adjust the system parameters or handle faults remotely through the network.

2. The unattended control method for bucket wheel excavator based on PLC data regional storage according to claim 1 is characterized in that: The data sampling period of the PLC unit is set to 1 second, and the data is synchronized in real time via industrial Ethernet or PROFIBUS field bus.

3. The unattended control method for bucket wheel machine based on PLC data regional storage according to claim 1 or 2, characterized in that: The intelligent fault diagnosis model adopts a classification algorithm based on machine learning to perform real-time analysis on the temperature, vibration, and load operating parameters of the equipment and output fault judgment results.

4. The unattended control method for bucket wheel machine based on PLC data regional storage according to any one of claims 1 to 3, characterized in that: The fault judgment formula of the fault diagnosis model is: y(t)=sign(W T ·X t +b) Among them, W is the model parameter and b is the bias term.

5. The unattended control method for bucket wheel machine based on PLC data regional storage according to any one of claims 1 to 4, characterized in that: The control system introduces a PID control algorithm to control the rotation angle and pitch angle of the bucket wheel machine. The PID control formula is: Among them, K p , K i , K d is the PID control parameter, and e(t) is the deviation between the current angle and the target angle.

6. The unattended control method for bucket wheel machine based on PLC data regional storage according to any one of claims 1 to 5, characterized in that: The remote monitoring platform has data visualization, alarm notification and remote control functions, and the on-duty personnel can use the platform to adjust the operating parameters of the bucket wheel machine or remotely debug system failures.

7. The unattended control method for bucket wheel machine based on PLC data regional storage according to any one of claims 1 to 6, characterized in that: The system performs a self-test before starting up every day. The test content includes the motor status, sensor signal and communication status of the bucket wheel excavator. The system can enter the normal operation mode only after passing the self-test.

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