An unmanned control method for bucket wheel excavator based on PLC data storage in different regions

Abstract

The present invention provides an unmanned control method for a bucket wheel excavator based on regional storage of PLC data, aiming to improve the real-time performance, reliability, and automation level of the bucket wheel excavator system. This method divides the control functions of the bucket wheel excavator into multiple independent areas, including bucket wheel excavator rotation and pitch control, belt conveyor control, equipment status monitoring, and remote monitoring. Each area is managed by an independent PLC unit responsible for data acquisition, control instruction execution, and local data storage. The PLCs in each area achieve real-time data synchronization through industrial Ethernet or fieldbus, and use intelligent fault diagnosis models to analyze and predict the equipment's operating status. A hierarchical alarm mechanism ensures that equipment failures can be handled in a timely manner. The remote monitoring platform can realize real-time data display, alarm notification, and remote control of the equipment, ensuring that the system operates efficiently in an unmanned environment. This method significantly reduces equipment maintenance and operating costs and improves the system's operational stability and safety.

Description

Technical Field

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

[0002] Bucket-wheel excavators are currently widely used in large mines, ports, power plants, and other locations for efficiently processing and moving large quantities of bulk materials. However, traditional bucket-wheel excavator control systems typically rely on manual operation or partial automation, which presents numerous challenges. First, the complex systems of bucket-wheel excavators require operators to monitor the equipment's operating status in real time and manually adjust operating parameters, resulting in high labor intensity and low operating efficiency. With the increasing demand for unmanned operation, traditional control methods are unable to meet the requirements of long-term continuous operation. Therefore, achieving 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 PLCs (Programmable Logic Controllers) for equipment control. However, a common problem is that data storage is centralized in a single control center, resulting in significant data transmission delays. This makes real-time performance and response speed uncertain, especially under complex operating conditions. Furthermore, single-point data storage carries a high risk of failure. A failure in the central control PLC can paralyze the entire system, causing severe production disruptions. Furthermore, fault diagnosis in existing systems relies on manual experience or simple alarm systems, making it difficult to promptly identify potential faults or predict equipment operating conditions. This increases the complexity of equipment maintenance and the duration of downtime.

[0004] To improve the operational stability, real-time performance, and safety of bucket wheel excavators and address bottlenecks in traditional control systems, this paper proposes an unmanned control method for bucket wheel excavators based on regionalized PLC data storage. By dividing the control functions of the bucket wheel excavator into multiple zones and introducing intelligent fault diagnosis and remote monitoring mechanisms, the system's real-time performance and safety can be significantly improved, enabling more efficient fault handling and automated control. This method, through distributed data storage and synchronization, not only reduces system response time and improves fault tolerance, but also effectively manages bucket wheel excavator operations in unattended conditions. Summary of the Invention

[0005] This invention relates to an unmanned bucket wheel excavator control method based on regional storage of PLC data. The method aims to address existing problems in bucket wheel excavator systems, such as insufficient real-time performance, poor data security, and difficulty diagnosing faults. By dividing control functions into multiple regions and incorporating a distributed control strategy, the system's efficient and secure operation is ensured. Furthermore, through intelligent fault diagnosis and remote monitoring, truly unmanned operation is achieved.

[0006] 1. Regional storage and control architecture

[0007] The bucket wheel excavator control system of this invention utilizes a zoned storage and control design. The system's main functions are divided into multiple independent control areas, such as rotation and pitch control, belt conveyor speed control, and equipment status monitoring. Each area is managed by an independent PLC unit, responsible for data acquisition, control instruction execution, and local storage. This distributed architecture not only reduces the load on individual PLCs but also effectively improves data security, mitigating the risk of data loss due to single points of failure.

[0008] 2. Data storage and real-time synchronization in different regions

[0009] Each zone's PLC independently stores its operating data and synchronizes it in real time with the PLCs in other zones via Industrial Ethernet or fieldbus. Data such as the bucket wheel's rotation angle, belt speed, and equipment temperature are collected and stored locally according to a predetermined sampling cycle. This data synchronization mechanism ensures that different parts of the system can coordinate their operations based on the latest equipment status. This real-time data synchronization ensures unified and coordinated control of the entire bucket wheel system, even when equipment in different zones operates independently.

[0010] 3. Fault diagnosis and alarm mechanism

[0011] This invention introduces an intelligent fault diagnosis model based on historical data. By collecting and analyzing equipment operating parameters (such as temperature, vibration, and load), the system can automatically identify potential faults during operation. Using machine learning algorithms, it analyzes and classifies bucket wheel excavator operating data, enabling automatic identification of common equipment faults (such as motor overload and belt wear). The system generates alarms based on fault severity and, when necessary, initiates emergency shutdown procedures to ensure the safety of equipment and personnel.

[0012] 4.Automated control and fault recovery

[0013] In unattended operation, the bucket wheel excavator autonomously controls operations via a programmable logic controller (PLC). Once the system is activated, the PLC controls key parameters such as rotation, pitch, and belt transmission according to the predetermined operating plan, ensuring efficient and stable operation. The system is equipped with a PID control algorithm that automatically adjusts the excavator's motion parameters, such as rotation angle and speed, based on real-time operational requirements and sensor data. In the event of a malfunction, the system automatically shuts down and records the fault information. Once the fault is resolved, the system automatically resumes operation.

[0014] 5. Remote monitoring and data management

[0015] To achieve true unmanned operation, this invention incorporates a remote monitoring and management platform. Through a network communication module, various bucket wheel excavator operating data and alarm information are transmitted in real time to a remote monitoring center for on-duty personnel to review and analyze. The monitoring platform provides real-time equipment status display, alarm management, and remote control. Even when unattended, on-duty personnel can monitor equipment operating conditions through the monitoring platform and remotely adjust operating parameters or troubleshoot problems as necessary.

[0016] 6. Parameter setting and optimization

[0017] To ensure efficient system operation, the present invention rationally sets relevant parameters during implementation. For example, the sampling period is typically set to 1 second, and data synchronization delays are controlled within hundreds of milliseconds. Regarding control accuracy, the parameters of the PID controller are determined through debugging to ensure smooth and efficient equipment operation. Furthermore, based on historical system operation data, the remote monitoring platform can regularly generate equipment operation reports, providing data support for production optimization and equipment maintenance.

[0018] 7. Interaction between systems

[0019] In this invention, the PLC systems in each area communicate and exchange data via a fieldbus or industrial Ethernet. The interoperability between the different PLC units ensures overall system coordination. For example, the linkage between bucket wheel excavator rotation control and belt conveyor speed requires real-time data synchronization to ensure coordinated system operation. The system's fault diagnosis and alarm mechanisms are also closely linked to the remote monitoring platform. In the event of an alarm, the remote monitoring system immediately receives the alarm signal and notifies the on-duty personnel for action.

[0020] Beneficial effects:

[0021] The present invention significantly improves the real-time performance and reliability of the bucket wheel excavator control system by dividing it into multiple independent PLC areas and performing distributed data storage and synchronous control. The regional control architecture reduces data transmission delays and ensures more coordinated linkage between equipment in each area; at the same time, decentralized data storage avoids single points of failure and improves system security. In addition, the intelligent fault diagnosis model effectively identifies and warns of potential faults, reducing equipment downtime. Combined with the remote monitoring and operation platform, it realizes unmanned automated control of the bucket wheel excavator, significantly reducing the need for manual intervention and maintenance costs. Overall, the present invention provides a more efficient, stable and secure bucket wheel excavator control solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the present invention or 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 for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0023] Figure 1 Schematic diagram of the overall architecture of the system of the present invention;

[0024] Figure 2 This is a schematic diagram of the linkage control between the bucket wheel excavator rotation and the belt transmission of the present invention;

[0025] Figure 3 This is a schematic diagram of the fault diagnosis and alarm process of the present invention;

[0026] Figure 4 It is a flow chart of the remote monitoring and operation interface of the present invention. DETAILED DESCRIPTION

[0027] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments. It is also noted that, to provide a more detailed description, the following embodiments are best and preferred embodiments, and those skilled in the art may employ alternative methods for implementing certain known technologies. Furthermore, the accompanying drawings are intended only to provide a more detailed description of the embodiments and are not intended to limit the present invention.

[0028] It should be noted that references in the specification to "one embodiment," "an embodiment," "an exemplary embodiment," "some embodiments," etc. indicate that the described embodiments may include specific features, structures, or characteristics, but not every embodiment necessarily includes such specific features, structures, or characteristics. In addition, when specific features, structures, or characteristics are described in conjunction with an embodiment, it is within the knowledge of persons skilled in the relevant art to implement such features, structures, or characteristics in conjunction with other embodiments (whether or not explicitly described).

[0029] In general, terms can be understood, at least in part, from their 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.

[0030] Example 1

[0031] This invention aims to provide an unattended bucket wheel excavator control method based on regional PLC data storage. This method addresses existing bucket wheel excavator control systems, including issues such as insufficient real-time performance, system reliability issues caused by centralized data storage, and difficulties in fault diagnosis and maintenance. By introducing regional storage and distributed control into the PLC system, combined with intelligent fault diagnosis algorithms and a remote monitoring platform, unattended operation of the bucket wheel excavator is achieved.

[0032] To ensure real-time system performance and data security, this method divides the bucket wheel excavator's control functions into multiple zones. Each zone's PLC independently processes and stores relevant data locally. Distributed communication ensures real-time synchronization of information between zones, ensuring the proper operation of the bucket wheel excavator. Furthermore, an intelligent fault diagnosis model based on historical data and machine learning algorithms enable automated fault detection and alarms, minimizing manual intervention. DETAILED DESCRIPTION

[0034] 1. System architecture design

[0035] The bucket wheel excavator system consists of multiple subsystems, and the control functions are divided into the following four main parts by area:

[0036] Area A: Bucket wheel excavator rotation and pitch control

[0037] Area B: Belt transmission and speed regulation control

[0038] Area C: Equipment status monitoring and sensor data collection

[0039] Area D: Remote monitoring and communication module

[0040] Each zone's PLC system operates independently, collecting and storing relevant equipment status data and sensor information, and making real-time decisions based on control logic. Data synchronization is achieved across the zones via industrial Ethernet or a fieldbus (e.g., PROFIBUS, CAN, etc.).

[0041] 2.PLC regional storage design

[0042] To improve the stability of the system, data is stored by region. The data type and storage content of each region are as follows: To improve the stability of the system, data is stored by region. The data type and storage content of each region are as follows:

[0043] 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 1 sampling per second, that is, the sampling period T s =1 second.

[0044] Region 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.

[0045] Area C (equipment status monitoring): data from various sensors, including equipment 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.

[0046] 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.

[0047] 3. Real-time data synchronization mechanism

[0048] Each area PLC realizes data synchronization through field bus or Ethernet switch. The specific process is as follows:

[0049] The master PLC acts as the master controller, responsible for coordinating data synchronization between zones. Each zone PLC acts as a slave, regularly reporting current status data to the master PLC.

[0050] Data update logic: When a control parameter in any zone (such as bucket wheel rotation angle or belt speed) changes, the PLC sends a data update request to the master PLC. The master PLC receives the data and broadcasts it to other relevant PLC units to ensure consistency across the entire system.

[0051] 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, and the data synchronization logic can be expressed as:

[0052] x A (t) = f(x B (t-Δt))

[0053] Here, Δt is the data transmission delay, which is determined through actual testing and is usually hundreds of milliseconds.

[0054] 4. Fault diagnosis and alarm mechanism

[0055] 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 steps for fault diagnosis include:

[0056] 1. Fault detection model: Use machine learning algorithms to train and analyze equipment operation data to extract feature vectors of equipment faults.

[0057] Assume that X is a fault feature set, which includes n equipment status parameters (such as temperature, vibration, etc.):

[0058] X=[T e ,A v ,L,…]

[0059] By training classifiers (such as support vector machines SVM, neural networks, etc.), real-time running data X t Classify and determine whether there is an abnormality.

[0060] 2. Fault classification formula: Assuming that the output of the fault classifier is y(t), the fault judgment formula of the system is:

[0061] y(t)=sign(W T ·X t +b)

[0062] 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 has been detected.

[0063] 3. Grading alarm mechanism: According to the fault diagnosis results, the system sets different alarm levels:

[0064] Low-level alarm: If the temperature is slightly high or the vibration fluctuates abnormally, the system will issue a low-level alarm signal to alert the on-duty personnel.

[0065] Advanced alarm: If a critical device fails, the system will immediately execute the emergency shutdown procedure and send an alarm to the remote monitoring center.

[0066] 5. Unattended automatic control process

[0067] 1. Automatic startup and self-test: Every day before the system starts, the PLC automatically performs a self-test. This test checks the motor status, sensor signals, and communication status of the bucket wheel excavator. Once the self-test passes, the system enters normal operation mode.

[0068] Operational control logic: The system automatically adjusts the bucket wheel's motion and belt speed based on the preset operation plan and sensor data. For example, if the bucket wheel needs to rotate at a specific angle to stack material, the PLC calculates the required rotation angle θ and pitch angle φ and adjusts them according to the following control formula:

[0069]

[0070] 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.

[0071] 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 its pre-fault operating state, continuing the unfinished work.

[0072] 6. Remote monitoring and data management

[0073] The system realizes real-time monitoring and management of bucket wheel excavators through the remote monitoring platform. Specifically including:

[0074] 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.

[0075] Data analysis and optimization: The platform regularly generates equipment operation reports and analyzes them in combination with historical data to provide data support for equipment maintenance and production optimization.

[0076] 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.

[0077] 3. Parameter Setting and Optimization

[0078] The main control parameters involved in the system include:

[0079] Sampling period: T s =1 second

[0080] 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 actual operating conditions.

[0081] Data synchronization delay: Δt = 200 milliseconds.

[0082] This invention achieves unmanned control of bucket wheel machinery by storing the PLC data of the bucket wheel machinery 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 significantly reduces the manual maintenance cost and has good application prospects.

[0083] The present invention encompasses any alternatives, modifications, equivalents, and solutions that fall within the spirit and scope of the present invention. To provide a thorough understanding of the present invention, specific details are described in detail below in connection with the preferred embodiments of the present invention, but those skilled in the art will be able to fully understand the present invention without these detailed descriptions. Furthermore, to avoid unnecessary confusion regarding the essence of the present invention, well-known methods, processes, procedures, components, and circuits have not been described in detail.

[0084] 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 principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A method for controlling an unmanned bucket wheel excavator based on regional storage of PLC data, characterized in that: The following steps are involved: The control system of the bucket wheel excavator is divided into multiple independent areas, each of which is managed by an independent PLC unit. The areas include: Bucket wheel excavator 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 acquisition, control and storage tasks, including collecting equipment status data and control parameters in its own area and storing them in local memory according to the set sampling period; Real-time data synchronization between PLC units in each area is achieved through industrial Ethernet or fieldbus, ensuring information sharing and status consistency between different areas; Utilize 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 system operation, the PLC automatically executes the scheduled operation plan and adjusts the key parameters of the bucket wheel excavator, such as rotation angle, pitch angle, and belt transmission speed, to achieve 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 is monitored in real time through the remote monitoring platform. The remote on-duty personnel can adjust the system parameters or handle the fault remotely through the network. The fault judgment formula of the intelligent fault diagnosis model is: ; Among them, W is the model parameter, b is the bias term, Refers to the real-time operating parameter feature vector collected in the bucket wheel excavator equipment status monitoring area at the current time t; The control system introduces a PID control algorithm to control the rotation angle and pitch angle of the bucket wheel excavator. The PID control formula is: ； in, is the PID control parameter, is the deviation between the current angle and the target angle, is the actual angle of the bucket wheel machine at the current moment t, It is the desired angle preset before the bucket wheel machine operates.

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

4. The unmanned control method for bucket wheel excavator based on PLC data regional storage according to claim 1 is characterized in that: The remote monitoring platform has data visualization, alarm notification and remote control functions. On-duty personnel can use the platform to adjust the operating parameters of the bucket wheel excavator or remotely debug system failures.

5. The unmanned control method for bucket wheel excavator based on PLC data regional storage according to claim 1 is 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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