Submerged chain conveyor fault early warning system based on Internet of Things

Abstract

The invention provides a submerged chain conveyor fault early warning system based on the Internet of Things, and the system comprises a data collection module which periodically collects various parameter data during the operation of a submerged chain conveyor; the data transmission module is used for preprocessing the collected data; the index analysis module is used for analyzing a chain wear degree index, a motor load imbalance index and a slag quantity overload index of the slag conveyor in the current period; the fault early warning analysis module is used for fusing the indexes and comprehensively calculating a fault early warning score; the early warning module is used for judging whether a fault early warning signal and corresponding early warning information are sent out or not according to the fault early warning score; according to the method, the motor current, the chain tension and the slag quantity parameters during operation of the submerged chain conveyor are comprehensively collected, the chain abrasion degree index, the motor load unbalance index and the slag quantity overload index of the submerged chain conveyor are analyzed, the fault early warning score is comprehensively obtained, and accurate early warning of the faults of the submerged chain conveyor is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial equipment monitoring, and in particular to a slag picker fault early warning system based on the Internet of Things. Background Art

[0002] In the industrial production process, especially in thermal power, steel, chemical and other industries, slag scoop machines are key equipment for processing solid waste such as slag; their operating stability directly affects the continuity and safety of the entire production process; with the continuous improvement of industrial automation level, the operation and management of slag scoop machines are also receiving increasing attention; the operation of early slag scoop machines mainly relied on manual inspections, and the staff regularly performed simple operations such as appearance inspections and sound identification on the equipment to determine whether the equipment was operating normally; this method is inefficient and it is difficult to detect potential problems in time before a failure occurs;

[0003] In recent years, the rise of Internet of Things technology has brought new opportunities for the intelligent management of industrial equipment. The Internet of Things deploys a large number of sensors on the equipment to realize the real-time collection and transmission of equipment operation data, and uses cloud computing, big data and other technologies to conduct in-depth analysis of the data, thereby realizing remote monitoring, fault diagnosis and predictive maintenance of the equipment. However, in the field of slag pickers, the application of Internet of Things technology is still in its infancy, and the existing slag picker monitoring system based on the Internet of Things still has deficiencies in the accuracy and comprehensiveness of the fault warning model. For example, some existing systems rely too much on empirical weight coefficients when calculating fault diagnosis indicators. These coefficients often lack scientific basis and may not be applicable under different equipment operating environments, resulting in a significant reduction in the accuracy of fault warning. At the same time, the existing system has limited ability to integrate and process multi-source data of slag pickers, cannot fully explore the inherent connection between data, and cannot provide comprehensive and effective support for fault warning.

[0004] Therefore, there is an urgent need in the art for a slag picker fault warning system based on the Internet of Things to solve the above problems. Summary of the invention

[0005] The present invention provides a slag picker fault early warning system based on the Internet of Things. The system comprehensively collects motor current, chain tension and slag volume parameters of the slag picker during operation, analyzes the chain wear index, motor load imbalance index and slag volume overload index of the slag picker, and comprehensively obtains a fault early warning score to achieve accurate early warning of slag picker faults. The system solves the problems of inaccurate and untimely fault early warning and insufficient multi-source data fusion processing capabilities in the prior art, thereby improving the reliability and safety of the slag picker operation, reducing equipment maintenance costs, and ensuring the stable operation of industrial production.

[0006] The present invention provides a slag scoop machine fault early warning system based on the Internet of Things, comprising:

[0007] The data acquisition module is used to periodically collect various parameter data of the slag scoop machine during operation, including motor current, chain tension, and slag volume data;

[0008] A data transmission module, connected to the data acquisition module, for preprocessing the collected data;

[0009] The index analysis module is connected to the data transmission module, and analyzes the chain wear index, motor load imbalance index and slag overload index of the slag extractor in the current cycle based on various pre-processed parameter data;

[0010] A fault warning analysis module, which is connected to the index analysis module, is used to integrate the chain wear index, motor load imbalance index and slag overload index of the slag picker in the current cycle to comprehensively calculate the fault warning score;

[0011] The early warning module is connected to the fault early warning analysis module and determines whether to issue a fault early warning signal and corresponding early warning information according to the fault early warning score.

[0012] According to a slag loader fault warning system based on the Internet of Things provided by the present invention, the data acquisition module includes:

[0013] The motor current sensor unit is installed on the power supply line of the motor and converts the current signal into an electrical signal through the principle of electromagnetic induction to obtain the three-phase current data of the motor;

[0014] The chain tension sensor unit is installed at the key part of the chain of the slag picker. Through the strain gauge element, the deformation caused by the chain tension is converted into an electrical signal for data collection to obtain the chain tension information;

[0015] The slag amount detection unit is installed at the slag inlet and outlet of the slag picker to obtain slag amount data by measuring the weight change of the slag, that is, the amount of slag processed by the slag picker per unit time;

[0016] A periodic control unit is connected to the motor current sensor unit, the chain tension sensor unit and the slag amount detection unit, and is used for periodically controlling the collection action of each sensor unit.

[0017] According to a slag machine fault warning system based on the Internet of Things provided by the present invention, the data transmission module includes:

[0018] A data receiving unit, which is connected to each sensor unit, is used to receive three-phase current data, chain tension information and slag volume data, and insert a corresponding identifier according to the period to which each data belongs;

[0019] A signal conditioning unit connected to the data receiving unit and used to condition the received data in the form of various electrical signals to remove noise effects;

[0020] The transmission interface unit is connected to the signal conditioning unit and provides an interface for transmission.

[0021] According to a slag loader fault early warning system based on the Internet of Things provided by the present invention, the index analysis module includes:

[0022] A wear degree analysis unit connected to the transmission interface unit, which performs calculation based on the chain tension information collected by the chain tension sensor unit and in combination with a preset chain wear degree model to determine the chain wear degree index of the slag extractor in the current cycle;

[0023] A load balance analysis unit connected to the transmission interface unit, which performs calculation based on the three-phase current data collected by the motor current sensor unit and in combination with a preset motor load balance model to determine the motor load imbalance index of the slag extractor in the current cycle;

[0024] A slag overload analysis unit is connected to the transmission interface unit, and is configured to determine the slag overload index of the slag remover in the current cycle by performing calculations based on the slag amount data and chain tension information collected by the slag amount detection unit and the chain tension sensor unit in combination with a preset slag overload model.

[0025] According to a slag loader fault early warning system based on the Internet of Things provided by the present invention, the chain wear degree model is:

[0026]

[0027] Among them, WCI is the chain wear index of the slag picker in the current cycle; n is the number of sampling times in this cycle, T max and T min are the maximum tension and minimum tension of the chain during normal operation, respectively, determined based on the design parameters of the slag picker; T i is the tension value measured at the i-th sampling;

[0028] The chain wear index WCI is used to reflect the physical wear of the chain. The higher the value, the closer the chain is to or exceeds its safe service life. Continuing to use the chain may lead to chain breakage.

[0029] According to a slag loader fault early warning system based on the Internet of Things provided by the present invention, the motor load balancing model is:

[0030]

[0031] Among them, MBI is the motor load imbalance index of the slag picker in the current cycle; j is the number of phases, I j is the j-th phase current among the three-phase currents of the motor; Represents the sum of the three-phase current of the motor, Represents the absolute value of the difference between the proportion of the j-th phase current in the total three-phase current and the proportion of each phase current in the ideal equilibrium state;

[0032] The motor load imbalance index MBI is used to reflect the balance state of the motor's three-phase load; when the motor has a winding short circuit, an open circuit, or an uneven distribution of mechanical load among the three phases, the three-phase current will be unbalanced, and the index MBI is used to capture this unbalanced state.

[0033] According to a slag dredger fault warning system based on the Internet of Things provided by the present invention, the slag overload model is:

[0034]

[0035] Among them, SLOI is the slag overload index of the slag remover in the current cycle, Q act The maximum slag handling capacity per unit time designed for the slag remover, Q des The actual amount of slag processed by the slag remover per unit time;

[0036] The slag overload index SLOI is used to reflect the relationship between the actual amount of slag processed by the slag recovery machine and the designed processing capacity. At the same time, when the slag is overloaded, the load borne by the chain will increase, and the change in chain tension can indirectly reflect whether the slag is overloaded. This indicator is used to reflect the jamming and blockage failures of the slag recovery machine.

[0037] According to a slag machine fault warning system based on the Internet of Things provided by the present invention, the fault warning analysis module includes:

[0038] A summarizing unit, connected to the wear degree analysis unit, the load balance analysis unit, and the slag overload analysis unit, and used to summarize the obtained indicators;

[0039] The comprehensive calculation unit is connected to the summary unit and is used to integrate various indicators and comprehensively calculate the fault warning score FWS of the slag picker in the current cycle; the calculation formula is:

[0040]

[0041] Among them, k represents the number of indicators, and since the number of indicators is three, the upper limit of k is three;

[0042] The fault warning score FWS is used to reflect the overall failure risk of the slag recovery machine. When the score is low, it means that the operating status of the slag recovery machine in all aspects is relatively good in the current cycle, and all key components and operating parameters are within the normal range, and the overall failure risk is low; when the score is high, it means that the slag recovery machine has a high failure risk, which is caused by one or more of the factors such as severe chain wear, high motor load imbalance or slag overload.

[0043] According to a slag machine fault early warning system based on the Internet of Things provided by the present invention, the early warning module includes:

[0044] A threshold setting unit, which is used to pre-set a threshold of a fault warning score;

[0045] An early warning judgment unit is connected to the threshold setting unit and the comprehensive calculation unit, and if the fault early warning score exceeds the set threshold, it is determined that the slag remover is in an abnormal state requiring an early warning;

[0046] The information generating unit is connected to the warning judging unit, and when it is determined that the slag extractor is in an abnormal state requiring a warning, a warning signal is generated, and various indicators are displayed to the management personnel in a visualized manner as warning information.

[0047] Compared with the prior art, the beneficial effects of this application are:

[0048] The present application accurately considers the relationship between the actual sampling value of the chain tension and the maximum and minimum tension values ​​of normal operation, which can more accurately reflect the degree of chain wear; analyzes the load imbalance from the complex proportional relationship of the three-phase current of the motor, avoiding the one-sidedness of simple threshold judgment; comprehensively considers the changes in slag volume and chain tension, and more accurately reflects the slag volume overload situation; the present application calculates the fault warning score based on these accurate indicators, which greatly improves the accuracy of fault warning compared with the inaccurate empirical calculation method in the prior art;

[0049] The precise fault warning of this application makes equipment maintenance more accurate and targeted; based on the indicator trend, problems can be discovered in time before a fault occurs, and maintenance can be arranged in advance to prevent small faults from turning into large faults and reduce serious damage to equipment; for example, if serious chain wear is discovered in time, the chain can be replaced at the right time to prevent chain breakage from causing more serious equipment damage and long-term downtime; compared with the existing technology that only performs maintenance after a fault occurs, this system can effectively reduce the complexity and cost of equipment maintenance, while shortening the downtime caused by faults, improving production efficiency, and ensuring the continuity and economy of industrial production;

[0050] As an important equipment in industrial production, the stable operation of the slag remover is crucial to the entire production process. The accurate fault warning and timely maintenance measures of this system can prevent safety accidents caused by sudden equipment failures, such as fires caused by motor overload and burning, and slag overload causing the slag remover to jam or even be damaged, affecting the operation of other equipment on the production line. By ensuring the safe and stable operation of the slag remover, the safety and stability of the entire industrial production process are improved, which meets the requirements of modern industrial safety production and efficient operation.

[0051] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0052] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0054] Figure 1 It is a structural schematic diagram of a slag machine fault warning system based on the Internet of Things provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0055] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0056] Embodiment 1:

[0057] The embodiment of the present invention provides a slag machine fault warning system based on the Internet of Things. Figure 1 ,include:

[0058] The data acquisition module is used to periodically collect various parameter data of the slag scoop machine during operation, including motor current, chain tension, and slag volume data;

[0059] A data transmission module, which is connected to the data acquisition module and is used to pre-process the collected data;

[0060] The index analysis module is connected to the data transmission module, and analyzes the chain wear index, motor load imbalance index and slag overload index of the slag extractor in the current cycle based on various pre-processed parameter data;

[0061] The fault warning analysis module is connected to the index analysis module and is used to integrate the chain wear index, motor load imbalance index and slag overload index of the slag picker in the current cycle to comprehensively calculate the fault warning score;

[0062] The early warning module is connected to the fault early warning analysis module and determines whether to issue a fault early warning signal and corresponding early warning information according to the fault early warning score.

[0063] The principles and beneficial effects of this embodiment are: being able to know in advance possible problems with the equipment, thereby arranging preventive maintenance and reducing the risk of sudden failures; continuous monitoring and intelligent analysis help keep the equipment in optimal working condition and extend its service life; by optimizing maintenance plans and reducing unplanned downtime, operating costs can be effectively reduced; timely warnings allow operators to take measures to avoid more serious accidents and protect personnel safety and property losses.

[0064] In order to further optimize the above embodiment, the data acquisition module includes:

[0065] The motor current sensor unit is installed on the power supply line of the motor and converts the current signal into an electrical signal through the principle of electromagnetic induction to obtain the three-phase current data of the motor;

[0066] The chain tension sensor unit is installed at the key part of the chain of the slag picker. Through the strain gauge element, the deformation caused by the chain tension is converted into an electrical signal for data collection to obtain the chain tension information;

[0067] The slag amount detection unit is installed at the slag inlet and outlet of the slag picker to obtain slag amount data by measuring the weight change of the slag, that is, the amount of slag processed by the slag picker per unit time;

[0068] The periodic control unit is connected with the motor current sensor unit, the chain tension sensor unit and the slag amount detection unit, and is used for periodically controlling the collection action of each sensor unit.

[0069] It should be noted that a high-precision, high-reliability current transformer or Hall current sensor should be selected; the current transformer is suitable for AC current measurement, and has the advantages of a wide measurement range and high accuracy; the Hall current sensor can measure both AC and DC current, with fast response speed and good linearity; select a sensor with a suitable range and accuracy level according to the current type, rated current size and actual working conditions of the slag scoop motor. For example, for a motor with a large rated current, select a current transformer with a suitable range to ensure that the three-phase current of the motor can be accurately measured, providing a reliable data basis for subsequent fault diagnosis;

[0070] Connect the primary winding of the sensor in series in the power supply line of the motor to ensure that the current can flow through the sensor for measurement; for current transformers, pay attention to the correct installation direction and ensure that the same-name terminals are connected correctly to obtain accurate current signals; during installation, ensure that the sensor is in good contact with the power supply line to reduce contact resistance and avoid signal errors or instability caused by poor contact; at the same time, take appropriate insulation measures to prevent sensor leakage from causing safety hazards to equipment and personnel, such as using insulating sleeves to insulate the connection between the sensor and the line to ensure that the sensor operates safely and reliably electrically;

[0071] The sensor converts the three-phase current signal of the motor into a corresponding electrical signal (such as a voltage signal or a small current signal) through the principle of electromagnetic induction; in terms of signal transmission, a shielded cable can be used to transmit the signal output by the sensor to the subsequent unit of the data acquisition module. The shielded cable can effectively reduce the impact of external electromagnetic interference on the signal and ensure the accuracy of signal transmission; in the process of cable wiring, try to stay away from strong electromagnetic interference sources, such as large motors, transformers and other equipment, and avoid laying cables in parallel with strong power lines such as power cables to prevent electromagnetic interference from coupling into the signal cable;

[0072] Select a suitable strain gauge chain tension sensor with high sensitivity, good linearity and stability; install the sensor at key parts of the chain, such as the joints or parts with greater stress, according to the structure and size of the slag loader chain; ensure that the sensor is in close contact with the chain during installation, and can accurately sense the changes in chain tension. At the same time, avoid obstacles to the normal operation of the chain or additional stress concentration caused by the sensor installation; for example, a special clamp can be used to fix the sensor on the chain to ensure that the sensor will not loosen or shift during the operation of the chain, and ensure that the collected tension data is true and reliable;

[0073] The strain gauge converts the deformation caused by the chain tension into a weak electrical signal (resistance change). It is necessary to design a matching signal conditioning circuit to amplify, filter and convert it into a voltage signal suitable for subsequent processing. A high-precision instrument amplifier can be used in the signal conditioning circuit to amplify the weak signal and increase the amplitude of the signal for easy collection and processing. At the same time, a low-pass filter is added to remove high-frequency noise interference in the signal and improve the signal-to-noise ratio. The conditioned signal is transmitted to the data acquisition module through the data transmission line. The transmission line must also take shielding measures to prevent the signal from being interfered during the transmission process.

[0074] In order to ensure the accuracy of the chain tension sensor measurement, the sensor needs to be calibrated. During the calibration process, a standard tension test device can be used to apply a known tension to the chain, and the signal value output by the sensor can be recorded at the same time. By comparing the actual tension with the sensor measurement value, a calibration curve or calibration coefficient is established to correct the measurement error of the sensor. In addition, since environmental factors such as temperature changes may affect the performance of the strain gauge and cause measurement errors, it is also necessary to design a temperature compensation circuit or use a temperature compensation algorithm to compensate for the error caused by temperature changes to ensure that the sensor can accurately measure the chain tension under different working environment temperatures.

[0075] Weighing sensors or flow sensors can be used to measure the slag volume. If a weighing sensor is used, it should be installed under the bearing structure of the slag inlet and outlet of the slag picker, and the slag volume can be calculated by measuring the change in the weight of the slag. Weighing sensors can be selected from strain gauge weighing sensors or capacitive weighing sensors with high accuracy and good stability, and sensors with appropriate ranges can be selected according to the weight range of the slag handled by the slag picker. If a flow sensor is used, it can be selected according to the flow characteristics of the slag, such as electromagnetic flow sensors (suitable for conductive slag), ultrasonic flow sensors (suitable for a variety of slag), etc., and installed on the slag conveying pipeline, and the slag volume can be calculated by measuring the flow rate of the slag and the cross-sectional area of ​​the pipeline.

[0076] For weighing sensors, during installation, it is necessary to ensure that the sensor is firmly connected to the bearing structure and that the force is evenly applied to avoid measurement errors caused by improper installation. The signal output by the weighing sensor is usually a weak electrical signal, which also needs to be amplified, filtered, and processed by the signal conditioning circuit, and then converted into a digital signal and transmitted to the data acquisition module. For flow sensors, they must be correctly installed on the pipeline according to their installation requirements to ensure that the slag can smoothly pass through the sensor measurement area. At the same time, the signal output by the sensor must be processed accordingly, such as amplifying and converting the induced electromotive force signal output by the electromagnetic flow sensor, so that it can accurately reflect the slag volume information.

[0077] Since the slag may contain impurities, it is easy to cause the slag amount detection unit to be blocked or damaged. For weighing sensors, appropriate protective devices should be installed at the slag inlet and outlet to prevent large pieces of slag from directly impacting the sensor. For flow sensors, filters can be installed in the pipeline or regular cleaning and maintenance can be performed to prevent the slag from blocking the sensor measurement channel. At the same time, a regular calibration and maintenance system should be established to regularly check the performance and measurement accuracy of the sensor, and promptly clean up the accumulated slag and other debris on the sensor surface to ensure that the slag amount detection unit can work stably and reliably for a long time, and provide accurate slag amount data for the fault warning system of the slag picker.

[0078] Select a stable and programmable microcontroller (such as a single-chip microcomputer) or a programmable logic controller (PLC) as the core control chip of the cycle control unit; select the appropriate chip model based on the system's requirements for the accuracy of the data acquisition cycle, processing power requirements, and cost factors; for example, for a system that requires high accuracy of the acquisition cycle and needs to process complex logic control, a high-performance single-chip microcomputer or PLC can be selected, which has an accurate timer function and powerful programming capabilities, and can meet the system's requirements for precise control of the acquisition actions of each sensor unit;

[0079] Utilize the timer resources inside the control chip to set different timing cycles to control the collection actions of the motor current sensor unit, chain tension sensor unit and slag detection unit; determine the collection cycle by programming the overflow time of the timer, for example, trigger the collection operation of the sensor unit every certain time (such as 1 second, 5 seconds, etc.); at the same time, ensure that the collection actions of each sensor unit can be performed synchronously or in a certain order to avoid confusion in data collection; send a trigger signal to each sensor unit through the output pin of the control chip, so that the sensor starts to collect data after receiving the trigger signal, and feed back the collection completion signal to the control chip after the collection is completed, so that the control chip can perform the next step of operation, such as data transmission, etc.;

[0080] Data interaction is required between the periodic control unit and each sensor unit to realize the control of the acquisition action and the monitoring of the acquisition status; suitable communication interfaces can be used, such as serial communication (RS232, RS485, etc.), I2C communication or SPI communication, etc.; in hardware, the communication lines between the control chip and the sensor unit should be connected well, and the correct level conversion and matching should be performed according to the electrical characteristics of the selected communication interface; in software, the corresponding communication protocols and drivers should be written to realize the functions of command sending, data receiving and status feedback between the control chip and the sensor unit, so as to ensure that the periodic control unit can accurately control the operation of each sensor unit, and timely obtain the acquisition data and status information of the sensor unit to ensure the orderly progress of the entire data acquisition process.

[0081] In order to further optimize the above embodiment, the data transmission module includes:

[0082] A data receiving unit, which is connected to each sensor unit, is used to receive three-phase current data, chain tension information and slag volume data, and insert a corresponding identifier according to the period to which each data belongs;

[0083] A signal conditioning unit, which is connected to the data receiving unit and is used to condition the data received in the form of various electrical signals to remove noise effects;

[0084] The transmission interface unit is connected to the signal conditioning unit and provides an interface for transmission.

[0085] It should be noted that the corresponding hardware interface is selected according to the communication method with each sensor unit; if the sensor adopts serial communication (such as RS232 or RS485), the data receiving unit is equipped with a corresponding serial communication chip (such as MAX232 for RS232 level conversion, MAX485 for RS485 communication), and its serial port pin is connected to the serial port output of the sensor; if I2C or SPI communication is adopted, the corresponding I2C or SPI interface is configured in the microcontroller of the receiving unit and connected to the corresponding communication pin of the sensor; when connecting, ensure that the electrical connection is correct, including the connection of the power line, ground line and signal line, to prevent data transmission errors or equipment damage caused by connection errors;

[0086] A data buffer is set inside the data receiving unit. When the sensor data is received, the data is temporarily stored in the buffer first. At the same time, a corresponding identifier is inserted for each batch of received data according to the collection cycle set by the system. For example, the motor current data is set to be collected once every 1 second. When the motor current data is received once, a specific identification code is added to the head or tail of the data (such as "01" indicates motor current data, and the data belongs to the current 1st second collection cycle), so that the subsequent data processing unit can distinguish data of different types and different collection cycles according to the identifier; the size of the buffer should be reasonably designed according to the data flow and processing speed of the system to avoid data loss caused by data overflow;

[0087] If the data formats output by different sensors are inconsistent (e.g., some sensors output binary data, and some output ASCII data), the data receiving unit needs to perform data format conversion operations to convert them into a format that can be recognized by subsequent processing of the system; for example, all data are converted into a unified binary format or hexadecimal format. During the conversion process, the accuracy and integrity of the data must be ensured, and no valid information is lost;

[0088] Since the electrical signal output by the sensor is often weak, it is necessary to design an amplifier circuit to amplify the signal. An amplifier circuit composed of an operational amplifier can be used, and the appropriate amplification factor can be selected according to the amplitude of the sensor output signal and the requirements of the subsequent processing circuit. For example, for a signal at the millivolt level, an amplifier circuit with an amplification factor of 100-1000 times can be designed to amplify the signal to the volt level for subsequent analog-to-digital conversion and processing. In the design of the amplifier circuit, attention should be paid to selecting a high-precision, low-noise operational amplifier, and reasonably designing circuit parameters, such as feedback resistance, input resistance, etc., to ensure the stability and linearity of the amplifier circuit.

[0089] In order to remove the noise interference in the signal, a filter circuit is used to filter the signal; according to the frequency characteristics of the noise, a low-pass filter, a high-pass filter or a band-pass filter can be selected; for example, for common high-frequency electromagnetic interference, a low-pass filter is designed, and the cut-off frequency can be determined according to the actual noise frequency distribution, generally selected between tens of kilohertz and hundreds of kilohertz, allowing only signals below the cut-off frequency to pass through and filtering out high-frequency noise; the filter circuit can use a passive filter (such as an RC filter) or an active filter (such as a filter composed of an operational amplifier and a capacitor and a resistor). The active filter has better filtering effect and load capacity, but the design is relatively complex;

[0090] In some occasions where high signal quality and system safety are required, a signal isolation circuit can be added to the signal conditioning unit to prevent electrical interference or overvoltage problems that may exist at the sensor end from affecting the subsequent data processing circuit; signal isolation can be achieved by means of optocoupler isolation or transformer isolation, which electrically isolates the input signal from the output signal while ensuring that the signal can be accurately transmitted; for example, an optocoupler isolator achieves electrical isolation through the transmission of optical signals, and has a high insulation resistance between its input and output ends, which can effectively prevent common-mode interference and ground loop current problems;

[0091] If wired transmission is adopted, common interfaces include Ethernet interface (RJ45), RS485 interface, USB interface, etc.; for Ethernet interface, it is necessary to integrate Ethernet controller chip (such as W5500, etc.) in the transmission interface unit, and configure corresponding network parameters, such as IP address, subnet mask, gateway, etc., so that it can access LAN or Internet and realize remote data transmission; RS485 interface is suitable for medium and long-distance multi-node communication. By connecting RS485 transceiver chip (such as MAX485), the conditioned data is sent to the bus to communicate with other devices; USB interface can be used to connect to local computer or other USB devices, which is convenient for fast data transmission and debugging. It is necessary to implement USB communication protocol stack in the interface unit and correctly connect and program with microcontroller;

[0092] If wireless transmission is adopted, Wi-Fi module, Bluetooth module, 4G / 5G communication module, etc. can be selected; Wi-Fi module (such as ESP8266, etc.) can enable the device to access the wireless network and realize data transmission with other devices in the local area network or cloud servers. By configuring parameters such as Wi-Fi hotspot name and password, a wireless connection is established; Bluetooth module is suitable for short-distance data transmission, such as data interaction with nearby handheld devices. Low-power Bluetooth module (such as BLE) can be selected to reduce power consumption while ensuring data transmission; 4G / 5G communication module (such as relevant modules of Quectel Communications) can realize long-range wide-area data transmission, which is suitable for transmitting the data of the slag scooper to the remote monitoring center. It is necessary to install the corresponding SIM card and configure it according to the network requirements of the operator to ensure that the data can be stably sent to the designated server or receive instructions from the server;

[0093] Whether it is a wired or wireless transmission interface, the protection measures of the interface must be considered to improve the reliability of the system; for wired interfaces, it is necessary to prevent electrostatic discharge (ESD), overvoltage, overcurrent and other problems from damaging the interface circuit, and ESD protection devices, overvoltage protection diodes, fuses and other components can be added to the interface; for wireless transmission modules, attention should be paid to the selection and installation position of the antenna to ensure good signal reception and transmission effects, and electromagnetic compatibility (EMC) issues should be considered at the same time to prevent the electromagnetic interference generated by the module itself from affecting other devices or being affected by external interference and affecting data transmission; in addition, the interface design should consider the interface's plug-in life, waterproof and dustproof performance, etc. to meet the needs of different industrial environments.

[0094] In order to further optimize the above embodiment, the indicator analysis module includes:

[0095] The wear degree analysis unit is connected to the transmission interface unit, and is used to calculate the chain wear degree index of the slag extractor in the current cycle based on the chain tension information collected by the chain tension sensor unit and in combination with a preset chain wear degree model;

[0096] A load balance analysis unit is connected to the transmission interface unit, and is used to calculate the motor load imbalance index of the slag extractor in the current cycle based on the three-phase current data collected by the motor current sensor unit and in combination with a preset motor load balance model;

[0097] The slag overload analysis unit is connected to the transmission interface unit, and performs calculations based on the slag volume data and chain tension information collected by the slag volume detection unit and the chain tension sensor unit, combined with a preset slag overload model, to determine the slag overload index of the slag remover in the current cycle.

[0098] It should be noted that the chain wear degree model is:

[0099]

[0100] Among them, WCI is the chain wear index of the slag picker in the current cycle; n is the number of sampling times in this cycle, T max and T min are the maximum tension and minimum tension of the chain during normal operation, respectively, determined based on the design parameters of the slag picker; T i is the tension value measured at the i-th sampling;

[0101] The chain wear index WCI is used to reflect the physical wear of the chain. The higher the value, the closer the chain is to or exceeds its safe service life. Continuing to use it will lead to chain breakage.

[0102] Specifically, it reflects the physical wear of the chain. During long-term operation, the chain of the slag picker will gradually wear out due to factors such as continuous friction with the slag and the mutual movement between the chain components.

[0103] This indicator reflects the degree of wear by analyzing the chain tension data. Chain wear will cause changes in the chain tension distribution. For example, as the wear increases, the chain may become loose and the tension value will deviate from the normal tension value.

[0104] It is an important basis for evaluating whether the chain of the slag machine needs to be replaced or repaired. If the chain wear index is too high, it means that the chain may be close to or exceed its safe service life. Continuing to use it may cause serious faults such as chain breakage, affecting the normal operation of the slag machine and even causing safety accidents.

[0105] The motor load balancing model is:

[0106]

[0107] Among them, MBI is the motor load imbalance index of the slag picker in the current cycle; j is the number of phases, I j is the j-th phase current among the three-phase currents of the motor; Represents the sum of the three-phase current of the motor, Represents the absolute value of the difference between the proportion of the j-th phase current in the total three-phase current and the proportion of each phase current in the ideal equilibrium state;

[0108] The motor load imbalance index MBI is used to reflect the balanced state of the motor's three-phase load; when the motor has a winding short circuit, open circuit, or an uneven distribution of mechanical load among the three phases, the three-phase current will be unbalanced, and the indicator MBI is used to capture this unbalanced state.

[0109] Specifically, this indicator mainly reflects the balance state of the three-phase load of the motor. The motor is the power source of the slag picker. Under normal circumstances, the three-phase current of the motor should be relatively balanced, which means that the load on the three-phase winding of the motor is uniform.

[0110] When a motor fails (such as a short circuit or open circuit in one phase winding, or the mechanical load is unevenly distributed among the three phases), the three-phase current will be unbalanced. This indicator can capture this unbalanced state in time.

[0111] Unbalanced motor load will cause problems such as extra heating, reduced efficiency, and increased vibration. Long-term unbalanced state will accelerate the damage of the motor. By monitoring this indicator, potential motor failure hazards can be discovered in advance, avoiding damage to the motor due to overload or overheating, and ensuring the stable operation of the slag extractor.

[0112] The slag overload model is:

[0113]

[0114] Among them, SLOI is the slag overload index of the slag remover in the current cycle, Q act The maximum slag handling capacity per unit time designed for the slag remover, Q des The actual amount of slag processed by the slag remover per unit time;

[0115] The slag overload index SLOI is used to reflect the relationship between the actual amount of slag processed by the slag recovery machine and its designed processing capacity. At the same time, when the slag is overloaded, the load on the chain will increase, and the change in chain tension can indirectly reflect whether the slag is overloaded. This indicator is used to reflect the jamming and blockage failures of the slag recovery machine.

[0116] Specifically, this indicator reflects the relationship between the actual slag volume handled by the slag picker and its designed processing capacity. The slag picker has its designed maximum slag processing capacity. When the actual slag volume exceeds this design value, slag overload will occur.

[0117] This indicator takes into account the slag volume data and chain tension data. When the slag volume is overloaded, the load on the chain will increase. The change in chain tension can indirectly reflect whether the slag volume is overloaded.

[0118] Slag overload will cause excessive pressure on various parts of the slag picker, including chains, motors, etc. It will not only accelerate the wear of the equipment, but may also cause the slag picker to become stuck, blocked, and other faults, affecting its normal slag cleaning function. By monitoring this indicator, damage to the slag picker caused by slag overload can be avoided.

[0119] In order to further optimize the above embodiment, the fault warning analysis module includes:

[0120] A summary unit, which is connected to the wear degree analysis unit, the load balance analysis unit, and the slag overload analysis unit, and is used to summarize the various indicators obtained;

[0121] The comprehensive calculation unit is connected to the summary unit and is used to integrate various indicators and comprehensively calculate the fault warning score FWS of the slag picker in the current cycle; the calculation formula is:

[0122]

[0123] Among them, k represents the number of indicators, and since the number of indicators is three, the upper limit of k is three;

[0124] The fault warning score FWS is used to reflect the overall failure risk of the slag machine. When the score is low, it means that the operating status of the slag machine in all aspects is relatively good in the current cycle, and all key components and operating parameters are within the normal range, and the overall failure risk is low; when the score is high, it means that the slag machine has a high failure risk, which is caused by one or more of the factors such as severe chain wear, high degree of motor load imbalance or slag overload.

[0125] It should be noted that the fault warning score is a quantitative representation of the overall failure risk of the slag picker. It comprehensively considers multiple key factors such as the chain wear index, motor load imbalance index and slag overload index, and combines these factors through specific calculations.

[0126] For example, when the score is low, it means that the slag recovery machine is in relatively good operating condition in all aspects in the current cycle, all key components and operating parameters are within the normal range, and the overall failure risk is low; when the score is high, it means that the slag recovery machine may have a higher failure risk, which may be caused by one or more of the following factors: severe chain wear, high degree of motor load imbalance, or slag overload.

[0127] The score is closely related to the possibility and urgency of the fault. It can help operators intuitively judge the health of the slag picker. If the score gradually increases, it means that the possibility of a fault is increasing, and the speed of increase can also reflect the urgency of the fault to a certain extent.

[0128] For example, if the fault warning score rises rapidly in a short period of time, it may mean that the slag machine has a serious sudden problem, such as a large amount of slag suddenly pouring in, causing serious slag overload, or a sudden motor failure causing a sharp load imbalance. In this case, timely measures need to be taken to prevent the fault from further deteriorating.

[0129] The fault warning score is a key reference for formulating equipment maintenance strategies. Different maintenance measures can be taken for situations where the score is in different ranges. When the score is low, only routine inspection and maintenance may be required; when the score reaches a certain threshold, more detailed inspections need to be arranged, such as key inspections of the chain and performance tests of the motor; and when the score is too high, approaching or exceeding the set severe fault threshold, it may be necessary to immediately shut down for maintenance to avoid severe damage to the equipment or safety accidents.

[0130] This score-based maintenance decision can make equipment maintenance more scientific and reasonable, avoid waste of resources caused by excessive maintenance, and promptly detect and resolve potential fault hazards, thereby improving the service life and operating efficiency of the slag extractor.

[0131] In order to further optimize the above embodiment, the early warning module includes:

[0132] A threshold setting unit, which is used to pre-set a threshold of a fault warning score;

[0133] An early warning judgment unit is connected to the threshold setting unit and the comprehensive calculation unit. If the fault early warning score exceeds the set threshold, it is determined that the slag remover is in an abnormal state requiring an early warning.

[0134] The information generating unit is connected to the early warning judging unit. When it is determined that the slag collecting machine is in an abnormal state requiring early warning, an early warning signal is generated, and various indicators are displayed to the management personnel in a visualized manner as early warning information.

[0135] It should be noted that the historical operation data of the slag picker is collected, including various parameter data during normal operation and parameter changes in the period before the previous failure; these data are deeply analyzed to understand the value range of various fault diagnosis indicators (such as chain wear degree index, motor load imbalance index, slag overload index, etc.) under different operating conditions and the corresponding fault warning score distribution; at the same time, refer to the equipment manual, industry standards and expert experience of the slag picker to determine a reasonable threshold range; for example, for the chain wear degree index, according to the normal wear stage of the chain and the wear degree data before the failure in the historical data, combined with the expert's judgment experience on the safe operation of the chain, set an appropriate threshold value. When the indicator exceeds the threshold, it indicates that the chain wear may have reached a dangerous level;

[0136] There are many ways to set the threshold. One common method is to calculate the mean, standard deviation and other statistical parameters of each indicator data under normal operating conditions based on statistical analysis, and then set the threshold according to a certain multiple relationship (such as the mean plus several times the standard deviation). This can take into account the fluctuation of the data to a certain extent and avoid frequent triggering of early warnings due to normal data fluctuations. Another method is to combine fault case analysis to determine the critical values ​​of each indicator before the fault occurs for faults that have occurred, and use these critical values ​​as an important reference for threshold setting. In addition, the threshold can be appropriately adjusted according to the actual use environment, workload and other factors of the slag picker. For example, when the workload is large, the threshold of certain indicators can be appropriately lowered to improve the sensitivity of the early warning. The threshold setting unit should provide a visual interface or configuration file to facilitate operators to adjust and optimize the threshold according to actual conditions.

[0137] After setting the initial threshold, it needs to be verified; through a period of actual operation monitoring, compare the warning results with the actual equipment status to evaluate the rationality of the threshold; if it is found that there are many false alarms or missed alarms, it is necessary to re-analyze the data and adjust the threshold; at the same time, as the use time of the slag machine increases, the equipment ages, and the operating environment changes, the threshold is updated regularly (such as monthly or quarterly) to ensure that the threshold can always accurately reflect the actual operating status of the equipment; an automatic update mechanism can be established to automatically calculate and adjust the threshold based on a large amount of newly collected data, or it can be combined with manual review to ensure the accuracy and reliability of the threshold update;

[0138] The early warning judgment unit receives the fault early warning score data from the comprehensive calculation unit of the fault early warning analysis module in real time; at the same time, obtains the preset fault early warning score threshold from the threshold setting unit; compares the received fault early warning score with the threshold in real time; the comparison process must ensure the accuracy and timeliness of the data to avoid misjudgment due to data transmission delays or errors; in order to improve the efficiency and accuracy of the comparison, efficient algorithms and data structures can be used, such as using a binary search algorithm to quickly find the corresponding threshold in an ordered threshold list for comparison, or using data structures such as hash tables to quickly locate threshold data;

[0139] When the fault warning score exceeds the set threshold, the slag scraper is judged to be in an abnormal state requiring a warning. In addition, the judgment logic of the abnormal state can be further refined. For example, according to the degree to which the fault warning score exceeds the threshold, the abnormal state is divided into different levels (such as mild abnormality, moderate abnormality, and severe abnormality). Different levels correspond to different processing methods and urgency. At the same time, a comprehensive judgment is made based on the specific situation of each fault diagnosis indicator. If the fault warning score does not exceed the threshold, but a key indicator (such as a sudden and substantial increase in the motor load imbalance indicator) shows an abnormal change, a warning signal can also be issued to remind the operator to pay attention to the equipment status. This multi-condition comprehensive judgment logic can more comprehensively and accurately discover potential problems of the equipment and improve the effectiveness of the warning.

[0140] After determining that the slag remover is in an abnormal state, the early warning judgment unit immediately sends a trigger signal to the information generation unit to notify it to generate early warning information; at the same time, the abnormal state information (such as abnormal level, abnormal occurrence time, etc.) is fed back to other relevant units of the system, such as the data recording unit (used to record the details of abnormal events), the remote monitoring unit (used to send abnormal notifications to the remote monitoring center), etc., so that the entire system can work together and take corresponding measures in time to deal with abnormal equipment conditions; the early warning judgment unit can also receive manual query instructions from the operator, and when the operator needs to understand the current early warning status of the equipment, the current judgment result and related data are returned in time;

[0141] After receiving the trigger signal from the early warning judgment unit, the information generation unit first generates an early warning signal; the early warning signal can be in various forms, such as sound and light alarm signals, SMS notifications, system pop-up window prompts, etc. For the sound and light alarm signal, by connecting to an external sound and light alarm, the control signal is output to make it emit flashing lights and loud sounds to attract the attention of the operator; SMS notifications require the integration of an SMS sending module (such as by connecting to a GSM module or using the SMS interface of a cloud communication platform) to send the early warning information to a pre-set mobile phone number of the manager; the system pop-up window prompt can pop up a striking prompt box on the monitoring software interface, displaying the brief content of the early warning information, such as "Slag machine fault warning: The current fault warning score has exceeded the threshold, please check the equipment in time!";

[0142] Visualize various fault diagnosis indicators (such as chain wear index, motor load imbalance index, slag overload index, etc.) and related equipment operating parameters (such as current motor current, chain tension, slag volume, etc.) in the form of intuitive charts or tables; use data visualization libraries (such as Matplotlib, Seaborn and other libraries in Python, or use visualization components such as Echarts in front-end development) to draw real-time change curves to show the change trend of each indicator over time, helping managers to quickly understand the change process of equipment operation status; at the same time, list the specific values, thresholds and comparisons with the normal range of each current indicator in a table, which is convenient for managers to conduct detailed analysis; in addition, abnormal indicators can be marked on the visualization interface and highlighted with different colors or special icons, so that managers can identify the problem at a glance;

[0143] In addition to local visual display, the information generation unit should also have the function of pushing information to push the warning information to the remote monitoring center or other related management platforms to achieve information sharing and collaborative processing; at the same time, the generated warning information will be recorded in the system log, including warning time, warning content, abnormal indicator value and other detailed information, so as to facilitate subsequent fault analysis and tracing; the recorded log data can be stored in the local database (such as SQLite, MySQL, etc.) or cloud storage service, which is convenient for query and statistical analysis at any time, and provides data support for equipment maintenance and management;

[0144] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A slag machine fault warning system based on the Internet of Things, characterized in that: include: The data acquisition module is used to periodically collect various parameter data of the slag scoop machine during operation, including motor current, chain tension, and slag volume data; A data transmission module, connected to the data acquisition module, for preprocessing the collected data; The index analysis module is connected to the data transmission module, and analyzes the chain wear index, motor load imbalance index and slag overload index of the slag extractor in the current cycle based on various pre-processed parameter data; A fault warning analysis module, which is connected to the index analysis module, is used to integrate the chain wear index, motor load imbalance index and slag overload index of the slag picker in the current cycle to comprehensively calculate the fault warning score; The early warning module is connected to the fault early warning analysis module and determines whether to issue a fault early warning signal and corresponding early warning information according to the fault early warning score.

2. The slag machine fault early warning system based on the Internet of Things according to claim 1 is characterized in that: The data acquisition module comprises: The motor current sensor unit is installed on the power supply line of the motor and converts the current signal into an electrical signal through the principle of electromagnetic induction to obtain the three-phase current data of the motor; The chain tension sensor unit is installed at the key part of the chain of the slag picker. Through the strain gauge element, the deformation caused by the chain tension is converted into an electrical signal for data collection to obtain the chain tension information; The slag amount detection unit is installed at the slag inlet and outlet of the slag picker to obtain slag amount data by measuring the weight change of the slag, that is, the amount of slag processed by the slag picker per unit time; A periodic control unit is connected to the motor current sensor unit, the chain tension sensor unit and the slag amount detection unit, and is used for periodically controlling the collection action of each sensor unit.

3. The slag machine fault warning system based on the Internet of Things according to claim 2 is characterized in that: The data transmission module comprises: A data receiving unit, which is connected to each sensor unit, is used to receive three-phase current data, chain tension information and slag volume data, and insert a corresponding identifier according to the period to which each data belongs; A signal conditioning unit connected to the data receiving unit and used to condition the received data in the form of various electrical signals to remove noise effects; The transmission interface unit is connected to the signal conditioning unit and provides an interface for transmission.

4. The slag machine fault early warning system based on the Internet of Things according to claim 3 is characterized in that: The indicator analysis module includes: A wear degree analysis unit connected to the transmission interface unit, which performs calculation based on the chain tension information collected by the chain tension sensor unit and in combination with a preset chain wear degree model to determine the chain wear degree index of the slag extractor in the current cycle; A load balance analysis unit connected to the transmission interface unit, which performs calculation based on the three-phase current data collected by the motor current sensor unit and in combination with a preset motor load balance model to determine the motor load imbalance index of the slag extractor in the current cycle; A slag overload analysis unit is connected to the transmission interface unit, and is configured to determine the slag overload index of the slag remover in the current cycle by performing calculations based on the slag amount data and chain tension information collected by the slag amount detection unit and the chain tension sensor unit in combination with a preset slag overload model.

5. The slag machine fault warning system based on the Internet of Things according to claim 4 is characterized in that: The chain wear degree model is: Among them, WCI is the chain wear index of the slag picker in the current cycle; n is the number of sampling times in this cycle, T max and T min are the maximum tension and minimum tension of the chain during normal operation, respectively, determined based on the design parameters of the slag picker; T i is the tension value measured at the i-th sampling; The chain wear index WCI is used to reflect the physical wear of the chain. The higher the value, the closer the chain is to or exceeds its safe service life. Continuing to use the chain may lead to chain breakage.

6. The slag machine fault warning system based on the Internet of Things according to claim 5 is characterized in that: The motor load balancing model is: Among them, MBI is the motor load imbalance index of the slag picker in the current cycle; j is the number of phases, I j is the jth phase current among the three-phase currents of the motor; Represents the sum of the three-phase current of the motor, Represents the absolute value of the difference between the proportion of the j-th phase current in the total three-phase current and the proportion of each phase current in the ideal equilibrium state; The motor load imbalance index MBI is used to reflect the balance state of the motor's three-phase load; when the motor has a winding short circuit, an open circuit, or an uneven distribution of mechanical load among the three phases, the three-phase current will be unbalanced, and the index MBI is used to capture this unbalanced state.

7. The slag scoop machine fault warning system based on the Internet of Things according to claim 6 is characterized in that: The slag overload model is: Among them, SLOI is the slag overload index of the slag remover in the current cycle, Q act The maximum slag handling capacity per unit time designed for the slag remover, Q des The actual amount of slag processed by the slag remover per unit time; The slag overload index SLOI is used to reflect the relationship between the actual amount of slag processed by the slag recovery machine and the designed processing capacity. At the same time, when the slag is overloaded, the load borne by the chain will increase, and the change in chain tension can indirectly reflect whether the slag is overloaded. This indicator is used to reflect the jamming and blockage failures of the slag recovery machine.

8. The slag machine fault warning system based on the Internet of Things according to claim 7 is characterized in that: The fault warning analysis module includes: A summarizing unit, connected to the wear degree analysis unit, the load balance analysis unit, and the slag overload analysis unit, and used to summarize the obtained indicators; The comprehensive calculation unit is connected to the summary unit and is used to integrate various indicators and comprehensively calculate the fault warning score FWS of the slag picker in the current cycle; the calculation formula is: Among them, k represents the number of indicators, and since the number of indicators is three, the upper limit of k is three; The fault warning score FWS is used to reflect the overall failure risk of the slag recovery machine. When the score is low, it means that the operating status of the slag recovery machine in all aspects is relatively good in the current cycle, and all key components and operating parameters are within the normal range, and the overall failure risk is low; when the score is high, it means that the slag recovery machine has a high failure risk, which is caused by one or more of the factors such as severe chain wear, high motor load imbalance or slag overload.

9. The slag machine fault warning system based on the Internet of Things according to claim 8 is characterized in that: The early warning module comprises: A threshold setting unit, which is used to pre-set a threshold of a fault warning score; An early warning judgment unit is connected to the threshold setting unit and the comprehensive calculation unit, and if the fault early warning score exceeds the set threshold, it is determined that the slag remover is in an abnormal state requiring an early warning; The information generating unit is connected to the warning judging unit, and when it is determined that the slag extractor is in an abnormal state requiring a warning, a warning signal is generated, and various indicators are displayed to the management personnel in a visualized manner as warning information.

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