Hoist overload protection method, storage medium and system

Abstract

The invention relates to the technical field of hoist safety protection, in particular to a hoist overload protection method, a storage medium and a system. According to the method provided by the invention, parameters such as torque and vibration frequency are collected through a multi-mode sensor, and the parameters are transmitted to a central control unit after edge calculation processing; establishing a three-dimensional mathematical model to calculate a theoretical maximum safety torque; a two-stage early warning mechanism is set, first-stage early warning slows down and starts an intelligent lubricating system, and second-stage early warning cuts off a power supply and brakes after data analysis; and establishing a prediction model by using an LSTM neural network, and generating a maintenance work order. The system comprises a multi-mode sensing module, an edge calculation module, an energy feedback device and the like. And the program stored in the storage medium provided by the invention can realize the corresponding method. Single detection limitation is broken through, the threshold value is dynamically calculated, predictive maintenance and energy recovery are achieved, and the operation safety and reliability of the hoist are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of hoist safety protection, and particularly relates to a method, a storage medium and a system for overload protection of a hoist. Background Art

[0002] In water conservancy projects, a hoist is the core equipment for controlling the opening and closing of gates, and its operating safety directly affects the flood control, irrigation and ecological regulation functions of water conservancy projects. Traditional hoist overload protection methods mainly rely on current threshold detection. Patent CN115425616B judges the overload state by monitoring the motor current.

[0003] However, such methods have significant defects. Relying only on current signals, they cannot perceive latent faults such as wear and jamming of mechanical components in real time, resulting in delayed or misjudged overload warnings. The protection threshold is set based on fixed water level differences and historical data, and cannot adapt to dynamic working conditions, easily resulting in insufficient protection or mis-stopping. It can only respond passively after an overload occurs, and cannot predict potential overload risks through historical data. Equipment maintenance relies on manual inspections, with low efficiency and high costs.

[0004] In the prior art, some solutions have tried to introduce vibration detection or temperature monitoring, but have not formed an intelligent decision-making system for multi-parameter fusion, and have not solved the technical problems of energy recovery and mechanical and electrical collaborative protection. Therefore, there is an urgent need for an innovative solution that integrates multi-modal perception, dynamic modeling, hierarchical protection and predictive maintenance to improve the operating safety and reliability of hoists. Summary of the Invention

[0005] The present invention proposes a method, a storage medium and a system for overload protection of a hoist. The object of the present invention is to provide a visual brightness measurement method that can improve measurement accuracy and stability and is easy to operate.

[0006] The technical solution of the present invention is as follows: A method for overload protection of a hoist, comprising the following steps: S1. Synchronously collect the real-time torque T, vibration frequency f, motor temperature θ and gate inclination α during the operation of the hoist through a torque sensor, a vibration sensor, a temperature sensor and an inclinometer, and transmit the original signal to the central control unit after noise reduction and filtering processing by the edge computing module; S2. Based on historical operation data and ANSYS finite element simulation, establish a three-dimensional mathematical model of "water level difference - hoisting force - mechanical loss", and calculate the theoretical maximum safe torque T_safe = f(Δh, L, μ) of the hoist under the current working condition in real time, where Δh is the water level difference on both sides of the gate, L is the hoisting stroke of the gate, and μ is the friction coefficient of mechanical components; S3. When the real-time torque T≥0.8T_safe, the first-level warning is triggered, the central control unit controls the gate hoist speed to drop to 60% of the rated speed, and starts the intelligent lubrication system to add lubricant to the transmission parts; When T≥T_safe and the vibration frequency f exceeds the normal operating threshold value f>f0+3σ, the second-level warning is triggered, where f0 is the mean value of the vibration frequency obtained based on the historical normal operating data of the gate hoist, and σ is the standard deviation of the vibration frequency under normal operating conditions. The temperature θ and inclination angle α data are analyzed simultaneously. If θ exceeds the rated temperature by 110% or the fluctuation amplitude of α is >5°, it is judged as "abnormal overload", and the motor power supply is immediately cut off and the mechanical brake device is activated; S4. Use LSTM neural network to learn historical overload data, equipment operation time, water temperature or sediment content, and establish an overload fault prediction model. When the overload probability is predicted to be >70% within the next 2 hours, a maintenance work order is automatically generated and pushed to the operation and maintenance terminal.

[0007] It should be noted that the intelligent lubrication system includes a pressure sensor to monitor the oil pressure P of the lubrication pipeline in real time. When P < 80% of the rated pressure, it is judged as "lubrication failure" and the backup oil pump is triggered to start; The oil injection cycle is dynamically adjusted based on the gate opening and closing frequency. The oil injection cycle formula is T_oil=k×(1+Δh / H_max), where k is the basic cycle and H_max is the historical maximum water level difference. On-demand lubrication is achieved to reduce the risk of overload caused by friction loss.

[0008] A system for overload protection of a gate hoist, comprising: Multimodal sensing module, with integrated torque sensor accuracy of ±0.5%FS, vibration acceleration sensor range of ±50g, infrared temperature sensor resolution of 0.1℃ and MEMS inclinometer accuracy of ±0.1°, for real-time acquisition of gate hoist operating parameters; The edge computing unit uses FPGA chips to perform real-time FFT spectrum analysis on sensor signals, extract the main frequency, harmonic components and kurtosis value of the vibration signal, and identify early fault characteristics of components such as gearboxes and bearings; The central control unit is equipped with a deep learning inference engine, with the built-in dynamic load model and LSTM prediction model, which supports real-time simulation of the operating status of the gate hoist through digital twin technology and predicts the fatigue degree of mechanical components; The actuator, including the frequency converter, electromagnetic clutch and hydraulic brake device, will sequentially execute the three-level protection actions of "speed reduction - clutch separation - mechanical braking" when receiving the overload warning signal, and the response time is ≤200ms.

[0009] A non-transitory computer-readable storage medium stores a computer program thereon. When the program is executed by a processor, it implements the intelligent overload protection method for a hoist described above, including receiving multi-modal sensor data and performing spatio-temporal alignment processing, calling a pre-trained mechanical load prediction model and a fault classification model, generating hierarchical control instructions and outputting them to an actuator.

[0010] A hoist overload protection system further includes a permanent magnet generator coaxially connected to the hoist motor. When it is detected that the overload torque T > T_safe, the motor is switched to the power generation mode through a control circuit, and the inertial kinetic energy is converted into electrical energy and stored in a super capacitor. The super capacitor is electrically connected to a hydraulic buffer device. When the stored energy reaches a threshold value, it drives a hydraulic piston to push out a mechanical limit block to forcibly fix the position of the gate. At the same time, it balances the grid load through an energy management system to avoid instantaneous power-off shocks.

[0011] The beneficial effects of the present invention are as follows: The present invention breaks through the limitation of single current detection. Through the fusion analysis of parameters such as torque, vibration, temperature, and inclination angle, it can identify potential faults of mechanical components in advance and solve the problem of protection lag. Based on real-time working condition parameters, water level difference, stroke, and friction coefficient, it dynamically calculates the safety torque threshold to avoid protection failure or misoperation caused by a fixed threshold. By recovering the overload kinetic energy with a permanent magnet generator to drive a hydraulic braking device, it reduces mechanical shock and improves the system reliability. At the same time, it balances the grid load, combining economy and safety. Using an LSTM neural network to predict the overload risk, it realizes the transformation from passive protection to active prevention and reduces the unplanned shutdown rate. Description of the Drawings

[0012] The present invention will be further described in detail below in conjunction with the drawings and specific embodiments.

[0013] Figure 1 It is a flowchart of an embodiment of the present invention. Specific Embodiments

[0014] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of the present invention.

[0015] Embodiment 1 As Figure 1 shown, this embodiment proposes a method for hoist overload protection, including the following steps: S1. Synchronously collect the real-time torque T, vibration frequency f, motor temperature θ, and gate inclination angle α during the operation of the hoist through a torque sensor, vibration sensor, temperature sensor, and inclinometer. After denoising and filtering the original signals through the edge computing module, transmit them to the central control unit; S2. Based on historical operation data and ANSYS finite element simulation, establish a three-dimensional mathematical model of "water level difference - hoisting force - mechanical loss" to calculate the theoretical maximum safe torque T_safe = f(Δh, L, μ) of the hoist under the current working conditions in real time, where Δh is the water level difference on both sides of the gate, L is the hoisting stroke of the gate, and μ is the friction coefficient of mechanical components; S3. When the real-time torque T ≥ 0.8T_safe, trigger a first-level warning. The central control unit controls the hoist speed to drop to 60% of the rated speed and starts the intelligent lubrication system to add lubricant to the transmission components; When T ≥ T_safe and the vibration frequency f exceeds the normal working condition threshold f > f0 + 3σ, trigger a second-level warning, where f0 is the average vibration frequency statistically obtained based on the historical normal operation data of the hoist, and σ is the standard deviation of the vibration frequency under normal operation. Synchronously analyze the temperature θ and inclination angle α data. If θ exceeds 110% of the rated temperature or the fluctuation range of α > 5°, it is determined as "abnormal overload", and immediately cut off the motor power supply and start the mechanical braking device; S4. Use the LSTM neural network to learn historical overload data, equipment operation duration, water temperature, or sediment content, establish an overload fault prediction model. When the predicted overload probability within the next 2 hours > 70%, automatically generate a maintenance work order and push it to the operation and maintenance terminal.

[0016] It should be noted that the intelligent lubrication system includes a pressure sensor to monitor the oil pressure P in the lubrication pipeline in real time. When P < 80% of the rated pressure, it is judged as "lubrication failure" and triggers the start of the standby oil pump; Dynamically adjust the oil injection cycle based on the gate hoisting frequency. The oil injection cycle formula is T_oil = k × (1 + Δh / H_max), where k is the basic cycle and H_max is the historical maximum water level difference, to achieve lubrication on demand and reduce the overload risk caused by friction loss.

[0017] A system for overload protection of a hoist, including: A multi-modal perception module, integrating a torque sensor with an accuracy of ±0.5% FS, a vibration acceleration sensor with a range of ±50g, an infrared temperature sensor with a resolution of 0.1°C, and a MEMS inclinometer with an accuracy of ±0.1°, for real-time collection of hoist operation parameters; The edge computing unit uses an FPGA chip to perform real-time FFT spectrum analysis on sensor signals, extract the main frequency, harmonic components, and kurtosis value of the vibration signal, and identify the early fault characteristics of components such as gearboxes and bearings; The central control unit is equipped with a deep learning inference engine, an built-in dynamic load model and an LSTM prediction model, and supports real-time simulation of the operation state of the hoist through digital twin technology to predict the fatigue degree of mechanical components; The actuator includes a variable frequency speed regulator, an electromagnetic clutch, and a hydraulic braking device. When receiving an overload warning signal, it sequentially performs three-level protection actions of "speed reduction - clutch separation - mechanical braking", and the response time ≤ 200ms.

[0018] A non-transitory computer-readable storage medium stores a computer program thereon. When the program is executed by a processor, it implements the hoist intelligent overload protection method described above, including receiving multi-modal sensor data and performing spatio-temporal alignment processing, calling a pre-trained mechanical load prediction model and a fault classification model, generating hierarchical control instructions and outputting them to the actuator.

[0019] A hoist overload protection system further includes a permanent magnet generator coaxially connected to the hoist motor. When it detects that the overload torque T > T_safe, it switches the motor to the power generation mode through a control circuit, converts the inertial kinetic energy into electrical energy and stores it in the super capacitor; The super capacitor is electrically connected to the hydraulic buffer device. When the stored energy reaches the threshold, it drives the hydraulic piston to push out the mechanical limit block to forcibly fix the position of the gate. At the same time, it balances the grid load through an energy management system to avoid instantaneous power-off impact.

[0020] In this embodiment, S1 is real-time multi-dimensional parameter perception. The real-time torque T, vibration frequency f, motor temperature θ, and gate inclination α during the operation of the hoist are synchronously collected through a torque sensor, a vibration sensor, a temperature sensor, and an inclinometer. After the original signal is denoised and filtered by the edge computing module (such as wavelet transform denoising), it is transmitted to the central control unit through the industrial Ethernet to ensure the accuracy and real-time of the data.

[0021] S2 is the construction of a dynamic load model. Based on historical operation data (including torque data under different water level differences, hoisting strokes, and mechanical states) and ANSYS finite element simulation, a three-dimensional mathematical model of "water level difference - hoisting force - mechanical loss" is established to calculate the theoretical maximum safety torque in real time: Tsafe = f(Δh, L, μ); Among them, Δh is the water level difference on both sides of the gate, which is collected in real time by an ultrasonic water level gauge, L is the hoisting stroke of the gate, which is fed back in real time by an encoder; μ is the friction coefficient of mechanical components, which is dynamically corrected according to the lubrication state and the wear degree of components.

[0022] S3 is the overload risk classification assessment. First-level warning (warning adjustment): When the real-time torque T ≥ 0.8Tsafe, it is determined as a mild overload. The central control unit controls the frequency converter to reduce the hoist speed to 60% of the rated speed, and at the same time starts the intelligent lubrication system to inject lubricant into transmission components such as the gearbox and bearings to reduce frictional losses. Second-level warning (emergency protection): When T ≥ Tsafe and the vibration frequency f exceeds the normal operating condition threshold (f > f0 + 3σ, where f0 is the normal main frequency and σ is the standard deviation of historical vibration data), the temperature θ and the inclination angle α are analyzed synchronously: If θ exceeds 110% of the rated temperature or the fluctuation range of α > 5°, it is determined as "abnormal overload", and the motor power supply is immediately cut off, the power transmission is separated through the electromagnetic clutch, and the hydraulic braking device is started to force a stop, with a response time ≤ 200ms.

[0023] S4 is the predictive maintenance decision. The LSTM neural network is used to learn environmental parameters such as historical overload data, equipment operation duration, water temperature, and sediment content, and an overload fault prediction model is established. When the model predicts that the overload probability within the next 2 hours > 70%, a maintenance work order including the fault type and component location is automatically generated and pushed to the operation and maintenance terminal through the 4G / 5G network to guide preventive maintenance. Furthermore, the intelligent lubrication system includes a pressure sensor to real-time monitor the oil pressure P in the lubrication pipeline. When P < 80% of the rated pressure, the standby oil pump is triggered to start to ensure lubrication reliability; the oil injection cycle is dynamically adjusted according to the gate opening and closing frequency, and the formula is: Toil = k × (1 + HΔh) where k is the basic cycle (default 8 hours), and Hmax is the historical maximum water level difference, realizing lubrication on demand to reduce the risk of frictional overload.

[0024] An overload protection system for a hoist. The system includes a multi-modal perception module, integrating high-precision sensors: a torque sensor (accuracy ±0.5%FS), a vibration acceleration sensor (range ±50g), an infrared temperature sensor (resolution 0.1°C), and a MEMS inclination meter (accuracy ±0.1°), which real-time collects the operation parameters of the hoist, covering multi-dimensional states such as mechanical stress, vibration characteristics, temperature, and gate attitude.

[0025] Edge computing unit The FPGA chip is used to realize real-time signal processing. Through FFT spectrum analysis, the main frequency, harmonic components, and kurtosis value of the vibration signal are extracted to identify early fault characteristics such as gearbox cracks and bearing roller wear, reducing the calculation pressure of the central control unit.

[0026] Central control unit Equipped with a deep learning inference engine, with a dynamic load model and an LSTM prediction model built-in, supporting digital twin technology: through a virtual simulation model, it can real-time simulate the mechanical stress distribution of the hoist, predict the fatigue degree of components such as gears and wire ropes, and provide data support for hierarchical protection decision-making.

[0027] Actuator It includes a variable frequency speed regulator, an electromagnetic clutch, and a hydraulic braking device. After receiving the instructions from the central control unit, it sequentially performs three-level protection actions of "speed reduction - clutch separation - mechanical braking", forming a collaborative protection mechanism of "flexible adjustment + rigid braking".

[0028] Furthermore, the system also includes a permanent magnet generator coaxially connected to the hoist motor: when an overload torque (T > Tsafe) is detected, the control circuit switches the motor to the power generation mode, converts the inertial kinetic energy into electrical energy and stores it in the super capacitor; the super capacitor drives the hydraulic buffer device to push out the mechanical limit block to forcibly fix the position of the gate, and at the same time balances the grid load through the energy management system to avoid instantaneous power-off impact.

[0029] A non-transitory computer-readable storage medium stores a computer program, and when the program is executed by a processor, it realizes the following functions: Receives multi-modal sensor data and performs spatio-temporal alignment processing to ensure the time synchronization of different sensor data; Invokes a pre-trained mechanical load prediction model and a fault classification model to calculate Tsafe in real-time and evaluate the overload risk level; Generates hierarchical control instructions (such as speed reduction, braking, work order push) and outputs them to the actuator to achieve intelligent closed-loop control.

[0030] In this embodiment, the applicable scenarios are: Taking the 2×500kN hoist of a certain reservoir as an example, with a motor power of 110kW, a maximum designed water level difference of 15m for the gate, and configuring the system described in the present invention for overload protection.

[0031] Method implementation steps: S1 is data collection and preprocessing. The torque sensor real-time collects torque signals, the vibration sensor monitors the vibration frequency of the gearbox, the infrared temperature sensor scans the motor temperature, and the MEMS inclinometer detects the inclination of the gate. The edge computing unit performs a 50Hz low-pass filter on the original signal, removes the power frequency interference, and then transmits it to the central control unit.

[0032] S2 is the calculation of the dynamic load model. Based on 2000 groups of historical operation data, a friction coefficient correction formula is trained: μ = 0.08 + 0.005Δh + 0.001L. When the real-time water level difference Δh = 10m and the hoisting stroke L = 8m, Tsafe = 4500N⋅m is calculated.

[0033] S3 is for hierarchical warning and protection. When it is detected that T = 3600 N⋅m (0.8Tsafe) and the vibration frequency is normal, the central control unit controls the rotational speed to drop to 60%, that is, 300 rpm, and starts the lubrication system to inject oil. The oil injection cycle is automatically adjusted to 12 hours. When T = 4800 N⋅m (>Tsafe) and the vibration kurtosis value suddenly increases to 3.5, exceeding the normal threshold of 2.0, and at the same time the motor temperature rises to 85°C, which is 113% of the rated temperature of 75°C, the system determines abnormal overload, immediately cuts off the power supply, the electromagnetic clutch disengages, and the hydraulic braking device completes braking within 150 ms.

[0034] S4 is for predictive maintenance. The LSTM model, based on data in the past week, water temperature of 25°C, sediment content of 50 kg / m³, and equipment operation duration of 400 hours, predicts that the overload probability in the next 2 hours is 75%, automatically generates a work order to prompt "insufficient gearbox lubrication, it is recommended to repair", and the operation and maintenance personnel execute preventive maintenance after confirmation through the remote terminal.

[0035] System hardware coordination: The multi-modal perception module collects data in real time. The edge computing unit completes the spectral analysis of the vibration signal. If the abnormal gear meshing frequency is identified, the central control unit combines the digital twin model to judge the gear wear degree, triggers a secondary warning and starts the energy feedback device. The permanent magnet generator converts the motor kinetic energy into electrical energy, with an energy storage efficiency ≥ 90%. The super capacitor drives the hydraulic piston to push out the limit block to ensure that the gate stops moving within 10 cm. At the same time, the redundant electrical energy is fed back to the power grid through the inverter to avoid voltage fluctuations.

[0036] Implementation of the storage medium: The computer program is written in C++ language, integrated with the TensorFlow deep learning framework, and stored in an industrial-grade SSD hard disk. After the program starts, it reads the sensor data in real time, calls the pre-trained LSTM model, with the training data volume ≥ 100,000 groups, conducts overload probability prediction, and sends the control instructions to the actuator through the ModbusTCP protocol to achieve millisecond-level response.

Claims

1. A method for overload protection of a gate hoist, characterized in that: The following steps are involved: S1, synchronously collect the real-time torque T, vibration frequency f, motor temperature θ and gate inclination α of the gate hoist during operation through torque sensor, vibration sensor, temperature sensor and inclinometer, and transmit the original signal to the central control unit after noise reduction and filtering through edge computing module; S2. Based on historical operation data and ANSYS finite element simulation, a three-dimensional mathematical model of "water level difference-opening and closing force-mechanical loss" is established to calculate the theoretical maximum safe torque T_safe=f(Δh,L,μ) of the gate hoist under the current working conditions in real time, where Δh is the water level difference on both sides of the gate, L is the gate opening and closing stroke, and μ is the friction coefficient of the mechanical parts; S3. When the real-time torque T≥0.8T_safe, the first-level warning is triggered, the central control unit controls the gate hoist speed to drop to 60% of the rated speed, and starts the intelligent lubrication system to add lubricant to the transmission parts; When T≥T_safe and the vibration frequency f exceeds the normal operating threshold value f>f0+3σ, the second-level warning is triggered, where f0 is the mean value of the vibration frequency obtained based on the historical normal operating data of the gate hoist, and σ is the standard deviation of the vibration frequency under normal operating conditions. The temperature θ and inclination angle α data are analyzed simultaneously. If θ exceeds the rated temperature by 110% or the fluctuation amplitude of α is > 5°, it is judged as "abnormal overload", and the motor power supply is immediately cut off and the mechanical brake device is activated; S4. Use LSTM neural network to learn historical overload data, equipment operation time, water temperature or sediment content, and establish an overload fault prediction model. When the overload probability is predicted to be >70% within the next 2 hours, a maintenance work order is automatically generated and pushed to the operation and maintenance terminal.

2. A method for overload protection of a gate hoist according to claim 1, characterized in that: The intelligent lubrication system comprises: The pressure sensor monitors the oil pressure P of the lubrication pipeline in real time. When P < 80% of the rated pressure, it is judged as "lubrication failure" and the backup oil pump is triggered to start; The oil injection cycle is dynamically adjusted based on the gate opening and closing frequency. The oil injection cycle formula is T_oil=k×(1+Δh / H_max), where k is the basic cycle and H_max is the historical maximum water level difference. On-demand lubrication is achieved to reduce the risk of overload caused by friction loss.

3. A system for overload protection of a gate hoist, characterized in that: include: Multimodal sensing module, with integrated torque sensor accuracy of ±0.5%FS, vibration acceleration sensor range of ±50g, infrared temperature sensor resolution of 0.1℃ and MEMS inclinometer accuracy of ±0.1°, for real-time acquisition of gate hoist operating parameters; The edge computing unit uses FPGA chips to perform real-time FFT spectrum analysis on sensor signals, extract the main frequency, harmonic components and kurtosis value of the vibration signal, and identify early fault characteristics of components such as gearboxes and bearings; The central control unit is equipped with a deep learning inference engine, and has a built-in dynamic load model and LSTM prediction model as described in claim 1, which supports real-time simulation of the operating status of the gate hoist through digital twin technology and prediction of the fatigue degree of mechanical components; The actuator, including the frequency converter, electromagnetic clutch and hydraulic brake device, will sequentially execute the three-level protection actions of "speed reduction - clutch separation - mechanical braking" when receiving the overload warning signal, and the response time is ≤200ms.

4. A system for overload protection of a gate hoist according to claim 3, characterized in that: It also includes a permanent magnet generator coaxially connected to the gate hoist motor. When an overload torque T>T_safe is detected, the motor is switched to a power generation mode through a control circuit to convert inertial kinetic energy into electrical energy and store it in a supercapacitor. The supercapacitor is electrically connected to the hydraulic buffer device. When the stored energy reaches a threshold, it drives the hydraulic piston to push out the mechanical limit block to forcibly fix the gate position. At the same time, the energy management system is used to balance the grid load to avoid instantaneous power outage shock.

5. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by the processor, the intelligent overload protection method for the gate hoist according to any one of claims 1 to 2 is implemented, including: Receive multimodal sensor data and perform spatiotemporal alignment processing; Call pre-trained mechanical load prediction model and fault classification model; Generate hierarchical control instructions and output them to the actuators.

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