Machine tool top vibration and displacement detection device based on IMU and IOT

Abstract

The invention discloses a machine tool top vibration and displacement detection device based on IMU and IOT, and relates to the technical field of machine tool detection, the device comprises a machine tool three-color lamp, a sensor module, a positioning module, a communication module, a power supply module and a main control module; the sensor module is integrated on the machine tool three-color lamp, vibration detection misjudgment is reduced by means of the characteristic that the three-color lamp is less touched, the antenna is integrated by means of the vertical structure of the three-color lamp, GPS signal collection and 4G data transmission are optimized, real-time and continuous monitoring of the state of the ultra-low-power-consumption machine tool is achieved, and real-time monitoring of the state of the ultra-low-power-consumption machine tool is achieved. Through a filtering and feature extraction algorithm of the processing unit and SVM mode recognition and electronic fence judgment logic of the analysis unit, accurate analysis of vibration and displacement is realized, and black box recording of abnormal events such as carrying and tool collision of the numerical control machine tool is realized by combining a block chain to perform chaining and evidence storage on feature data; checking compliance that the high-precision machine tool is arbitrarily moved or resold; the device has the functions of machine tool after-sale responsibility definition, vibration detection precision improvement, position monitoring real-time performance enhancement, power supply continuity guarantee, data security evidence storage and the like, and the device has remarkable effects on machine tool after-sale responsibility definition, vibration detection precision improvement, position monitoring real-time performance enhancement, power supply continuity guarantee, data security evidence storage and the like.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of machine tool detection, and particularly relates to a machine tool top vibration and displacement detection device based on IMU and IOT. BACKGROUND

[0002] A machine tool refers to a machine for manufacturing machines, is also called a working mother machine or a tool machine, is mainly used for machining equipment for manufacturing parts, and realizes precise machining of parts through processes such as cutting, grinding, drilling and the like of metal or other materials, and is basic equipment for modern industrial production. In the use process of the machine tool, vibration and displacement of the machine tool will have an important influence on machining precision and equipment safety. On one hand, vibration generated during transportation, installation or operation of the machine tool can cause changes in relative positions of parts of the machine tool, thereby affecting machining precision. On the other hand, unauthorized displacement (such as unauthorized movement of the machine tool by an end customer who has not paid the final payment) not only affects normal use of the machine tool, but also brings risks of recovery of goods and payment to the machine tool factory. Therefore, vibration and displacement detection of the machine tool has important practical significance.

[0003] At present, there are still many deficiencies in detection of vibration and displacement of the machine tool. Traditional detection methods are mostly based on machine tool panels or electrical cabinets. These parts are easily disturbed by external factors, resulting in a high misjudgment rate. Meanwhile, during monitoring of the position of the machine tool, it is difficult to collect GPS signals due to the metal cavity structure of the machine tool, data transmission is not convenient, and real-time and effective monitoring of the state of the machine tool is difficult to realize. In addition, for the machine tool factory, how to effectively control the movement of the machine tool of a customer who has not paid the final payment and protect the safety of goods and payment is also a problem to be solved. Therefore, it is of great significance to develop a machine tool top vibration and displacement detection device based on IMU and IOT. SUMMARY

[0004] The application aims to make up for the deficiencies in the prior art and provides a machine tool top vibration and displacement detection device based on IMU and IOT. The device can realize accurate detection and real-time monitoring of vibration and displacement of the machine tool by integrating a sensor module, a positioning module, a communication module, a power supply module and a main control module into a three-color lamp on the top of the machine tool. The device solves the problem of power supply continuity by adopting a multi-channel power supply mode, realizes non-tamperable storage of data by introducing a blockchain technology, and enhances the credibility and security of data.

[0005] To solve the above technical problems, the application provides the following technical scheme: a machine tool top vibration and displacement detection device based on IMU and IOT, which comprises a machine tool three-color lamp, a sensor module, a positioning module, a communication module, a power supply module and a main control module.

[0006] The sensor module includes a gyroscope and an accelerometer integrated in the machine tool three-color light for collecting machine tool vibration parameters.

[0007] The positioning module includes a GPS device integrated in the machine tool three-color light for obtaining machine tool position information.

[0008] The communication module includes a SIM card device integrated in the machine tool three-color light for transmitting data to the server.

[0009] The power supply module adopts a multi-channel power supply mode, including 24V machine tool normal power supply, built-in lithium battery (cooperating with BMS charging management unit) and machine tool battery box (the machine tool battery box supplies power to all machine tool devices that need to maintain operation after power failure, and the machine tool system monitors the battery status in real time, sends alarm information to the user when an abnormality occurs, and provides maintenance guidance), for providing multi-channel power supply.

[0010] The main control module includes a processing unit, an analysis unit, a storage unit, and an IO communication unit. The processing unit is used for real-time filtering and feature value extraction of sensor data. The analysis unit performs vibration pattern recognition and position offset determination on the processed data based on a preset algorithm. The storage unit is used for cyclic storage of raw data and analysis logs. The IO communication unit is used to transmit abnormal signals to the machine tool PLC system through the IO communication line. The PLC determines the event type and further acts. The input signals include but are not limited to: power level, vibration detection trigger preset threshold, displacement amplitude trigger preset threshold, etc. The PLC resets these input signals through the response of the output signal. The PLC can perform human-computer interaction on these signals through actions such as locking the machine and alarming.

[0011] The machine tool three-color light is fixed to the original mounting hole position on the top of the machine tool by bolts, and each module is integrated in the cylindrical body of the machine tool three-color light.

[0012] Further, the power supply module further includes a BMS charging management chip, which has an intelligent charging algorithm built-in, and can collect voltage, current, temperature and state of charge data of the lithium battery in real time, use an adaptive filtering algorithm to eliminate data noise, divide the pre-charging, constant current charging and constant voltage charging stages according to the battery state, use PID control technology to accurately adjust the charging current and voltage, and when overcharge, overdischarge, overcurrent, high temperature abnormality is detected, immediately start the protection mechanism to cut off the circuit, and upload the fault information to the machine tool system.

[0013] Further, the processing unit adopts a filtering algorithm to process the vibration data, the sampling rate is not less than 10 kHz, and the root mean square value and the peak value are extracted as characteristic parameters, the processing unit is also integrated with a band-pass filter circuit for eliminating environmental noise, and an abnormal data elimination module is arranged, the sliding window algorithm is used to continuously detect the sampling data, when the difference between adjacent sampling points exceeds the preset threshold, the abnormal data is determined and eliminated, and the window size of the sliding window can be configured through the main control module, and the formula for real-time filtering of the sensor data by the processing unit is: Wherein, y(n) is the output value after filtering, x(n) is the current sampling value, a is a dynamic weight coefficient and 0 The adaptive filtering of the sudden signal is realized.

[0014] Further, the storage unit is an 8GB flash memory, which stores the original vibration data at an interval of 100 ms, stores the analysis log at an interval of 10 minutes, supports breakpoint storage, the communication module adopts the MQTT protocol to transmit data, supports QoS2 level alarm data transmission, the storage unit also includes a data compression module and a data encryption module, which compresses the original vibration data, encrypts the stored original data and analysis log, and prevents data leakage.

[0015] Further, the positioning module is arranged at the top of the vertical structure of the three-color lamp of the machine tool, the antenna penetrating the lampshade adopts an IP67 waterproof design, the single-point positioning accuracy of the GPS device is less than or equal to 10 meters, and the accuracy in the differential positioning mode is less than or equal to 1 meter, the positioning module also includes a GPS signal enhancement circuit, which uses a low-noise amplifier to improve the received signal strength, and uses a multipath suppression algorithm to reduce signal reflection interference, and integrates a Beidou positioning module to form a dual-mode positioning with the GPS, and automatically switches to the Beidou positioning mode in an environment with weak GPS signal to ensure the continuity of positioning.

[0016] Further, the device includes a blockchain node, which packs the vibration feature vector and the position hash value generated every minute into a block, the block includes a timestamp, a previous block hash, a Merkle root and a device operation log, the blockchain adopts a consortium chain architecture and supports an automatic alarm mechanism triggered by a smart contract, the smart contract also includes a data verification rule, when the position hash values of five consecutive blocks deviate from a preset range, the machine tool control authority is automatically frozen, and a data storage and traceability function is arranged, the vibration and position data in any time period can be queried through a blockchain browser, and the data is tamper-proof once it is uploaded to the chain.

[0017] Further, the analysis unit first high-pass filters and Kalman fusion filters the angular velocity and acceleration of the IMU sensor to estimate the attitude, and then uses the SVM algorithm to identify the machine tool vibration state according to the attitude data, and the analysis unit further includes a vibration mode database, which stores four vibration mode templates of normal operation (long time slight vibration), tool wear (long time medium and low intensity vibration), sudden tool collision (instantaneous high intensity vibration), and machine tool carrying (long time high intensity vibration), and the mode matching is performed by calculating the Euclidean distance between the to-be-identified data and the template, and the formula for identifying the machine tool vibration state based on the SVM algorithm of the analysis unit is: Wherein, D is the vibration mode matching distance, x i is the i th characteristic value of the to-be-identified data, m i and σ i are the mean and standard deviation of the template characteristics respectively, ω i is the characteristic weight coefficient, and n is the characteristic dimension, and the formula introduces the decay factor to enhance the robustness to outliers.

[0018] Further, when the analysis unit performs displacement processing, the GPS satellite coordinate data is preferentially acquired to determine the position information, in the indoor environment with weak and no GPS signal, the IMU attitude data is used, the integral operation of the acceleration sensor collected data is combined, the inertial navigation model is constructed to estimate the displacement, the displacement information of the machine tool is acquired, and the electronic fence management module is integrated, the circular electronic fence is set through the server, the fence radius is adjusted according to the actual requirement, and the formula for determining the position deviation of the analysis unit is: Wherein, P is the position deviation risk index, d is the distance between the current position and the boundary of the electronic fence, r is the radius of the electronic fence, t is the deviation duration, T is the preset time threshold, λ is the spatial deviation weight coefficient and 0≤ λ ≤ 1, and the formula realizes the nonlinear evaluation of the deviation distance through the exponential term .

[0019] Compared with the prior art, the machine tool top vibration and displacement detection device based on IMU and IOT has the following beneficial effects:

[0020] The application reduces vibration detection misjudgment by integrating the sensor module in the machine tool three-color lamp and using the less-activated characteristics of the three-color lamp, integrates the antenna with the vertical structure of the three-color lamp, optimizes GPS signal collection and 4G data transmission, realizes real-time and continuous monitoring of ultra-low-power machine tool state, realizes accurate analysis of vibration and displacement through filtering and feature extraction algorithms of the processing unit, SVM pattern recognition of the analysis unit and electronic fence determination logic, realizes black box recording of abnormal events such as carrying and tool collision of the numerical control machine tool, compliance inspection of unauthorized movement or resale of high-precision machine tools, geographical location tracking and payment control of machine tools with unpaid final payment, and other functions, and the device has a significant effect on machine tool after-sales responsibility definition, vibration detection accuracy improvement, position monitoring real-time enhancement, power supply continuity guarantee and data security evidence storage.

[0021] Other advantages, objects, and features of the application will be set forth in part in the following specification taken in conjunction with the accompanying drawings, and in part will become apparent to those skilled in the art from a consideration of the following specification and drawings, or can be learned from the practice of the application. BRIEF DESCRIPTION OF DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiment or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and those skilled in the art can also obtain other drawings from these drawings without creative labor.

[0023] Figure 1 It is a structure diagram of a machine tool top vibration and displacement detection device based on IMU and IOT.

[0024] Figure 2 It is a flowchart of a machine tool top vibration and displacement detection device based on IMU and IOT. DETAILED DESCRIPTION

[0025] In order to further illustrate the technical means and effects adopted by the present application to achieve the predetermined application purpose, the following will describe the specific embodiments, structures, features and effects of the present application in detail with reference to the drawings and preferred embodiments.

[0026] Referring to Figure 1 The machine tool top vibration and displacement detection device based on IMU and IOT provided by the present application has the following specific implementation contents:

[0027] Overall structure: the device includes a machine tool three-color lamp, the three-color lamp is fixed on the top of the machine tool through bolts, and each functional module is integrated in the columnar body inside the machine tool three-color lamp.

[0028] The sensor module is integrated in the machine tool three-color light, composed of a gyroscope and an accelerometer, for collecting machine tool vibration parameters. The positioning module is also integrated in the machine tool three-color light, including a GPS device, deployed at the top of the vertical structure of the three-color light, and further provided with a GPS signal enhancement circuit, a multipath suppression algorithm, and a Beidou positioning module, which can obtain machine tool position information and optimize signal collection.

[0029] The communication module is integrated in the machine tool three-color light, with a SIM card device, using the MQTT protocol to transmit data, which can transmit data to the server. The power supply module uses a multi-channel power supply mode, including 24V machine tool normal power supply, built-in lithium battery and machine tool battery box. The built-in lithium battery includes a BMS charging management chip with an intelligent charging algorithm.

[0030] The main control module includes a processing unit, an analysis unit, a storage unit, and an IO communication unit. The processing unit performs real-time filtering and feature value extraction on sensor data, integrating a band-pass filter circuit and an abnormal data rejection module. The analysis unit performs vibration pattern recognition and position offset determination on the processed data based on a preset algorithm, using a dual threshold determination mechanism, including a trend prediction module and an electronic fence management module. The storage unit is an 8GB flash memory that stores raw vibration data and analysis logs at regular intervals, supports breakpoint storage, and includes a data compression module and a data encryption module. The device includes a blockchain node that packages the vibration feature vector and position hash value generated every minute into a block using a consortium chain architecture, supports smart contract automatic alarm triggering mechanism and data storage traceability function. The IO communication unit transmits abnormal signals to the machine tool PLC system through the IO communication line, and the PLC determines the event type and further actions. The input signals include but are not limited to: power level, vibration detection trigger preset threshold, displacement amplitude trigger preset threshold, etc. The PLC resets these input signals by responding to the output signals. The PLC can perform human-machine interaction by locking the machine and alarming.

[0031] Embodiment one

[0032] This embodiment focuses on the real-time monitoring scenario of multi-machine tool cluster in the intelligent factory environment. For the vibration state and position safety requirements of multiple numerical control machine tools in the discrete manufacturing workshop, a large-scale deployment detection scheme is provided. In the intelligent factory, the cluster management of machine tools requires real-time acquisition of multi-device operation data, and the cross-region movement monitoring of devices without final payment is an important part of supply chain financial risk control. Through modular design and distributed storage of the blockchain, the device realizes collaborative analysis of multi-machine tool vibration characteristics, cross-device abnormal warning, and regional electronic fence management and control, and is suitable for intelligent factory scenarios such as automobile parts processing and aerospace manufacturing that have strict requirements on device state monitoring.

[0033] ReferenceFigure 2 The specific implementation process of the device is as follows: in the intelligent factory workshop, the three-color light detection device is fixed by bolts according to the original mounting hole position on the top of each machine tool. In view of the problem of shielding of GPS signals by high and large metal structures in the workshop, the positioning module adopts Beidou+GPS dual-mode positioning. A differential positioning reference station is deployed on the top of the workshop, so that the positioning accuracy of each device in the differential mode is improved to sub-meter level. The gyroscope and accelerometer of the sensor module are installed in three-dimensional orthogonal directions to ensure the collection of vibration parameters of each axis of the machine tool in all directions.

[0034] The sensor module of each device synchronously collects vibration data at a sampling rate of not less than 10 kHz. The processing unit adopts a filtering algorithm for real-time noise reduction. The filtering formula is: Among them, the dynamic weight coefficient a is adaptively adjusted according to the vibration noise intensity of the workshop environment. The change rate adjustment factor d is used to optimize the signal mutation processing at the start and stop stages of the machine tool. The band-pass filter circuit sets different passband ranges according to different types of machine tools (such as lathes and milling machines) to eliminate interference noise from irrelevant devices such as cutting fluid pumps and cooling systems.

[0035] An edge computing node is deployed in the workshop. The storage unit of each device transmits the compressed original vibration data to the edge node at an interval of 100 ms. The edge node synchronously analyzes the log every 10 minutes. The edge node adopts a distributed storage architecture to store the ciphertext output by the data encryption module in fragments, ensuring that single-node failure does not affect data integrity. The storage unit supports a breakpoint resume mechanism. When the network is interrupted, the data is automatically cached. After recovery, it is transmitted to the edge node in timestamp order.

[0036] The workshop is divided into multiple positioning sub-areas. The positioning module of each device interacts with the regional gateway: when a device detects a weak GPS signal, it automatically switches to Beidou positioning mode and requests position calibration data from adjacent devices. The current position is calculated through a multipath suppression algorithm. The GPS signal enhancement circuit of the positioning module cooperates with the signal repeater on the top of the workshop. Through the gain control of the low-noise amplifier and the repeater, the signal reception strength is improved to the optimal interval.

[0037] The communication module transmits alarm data using the QoS2 level of the MQTT protocol. Through the 5G private network of the workshop, multiple device data are transmitted concurrently. The edge node aggregates the vibration characteristic values and position data uploaded by each device. The data are packaged into batches according to the block generation period of the blockchain (every minute) and are synchronously transmitted to the cloud server and the blockchain node through the 5G network, thereby reducing network bandwidth occupation.

[0038] The analysis unit constructs a workshop-level vibration pattern database. In addition to storing five basic vibration templates for a single machine tool, it also adds equipment cluster coordination vibration characteristic templates (such as the coupled vibration pattern when adjacent machine tools are started simultaneously). The pattern recognition formula based on the SVM algorithm is: wherein the feature weight coefficient ω i According to the dynamic adjustment of the workshop equipment layout, higher weights are given to cross-equipment correlation characteristics (such as spindle speed synchronization). When the vibration pattern matching distance D of a machine tool exceeds the threshold, the system automatically correlates and analyzes the data of adjacent machine tools to identify whether there is a cluster abnormality.

[0039] The workshop is divided into multiple levels of electronic fences according to production areas. The analysis unit calculates the position deviation risk index through the formula: wherein the spatial deviation weight coefficient λ is set according to the importance of the area (such as λ = 0.3 for the waste area and λ = 0.7 for the finished product area). For machines that have not paid the final installment, a special level of pre-warning is set for cross-area movement: when the position hash values of the last five blocks deviate from the area fence, the blockchain smart contract triggers three mechanisms simultaneously - freezing the machine control authority, sending a risk prompt to the supply chain finance system, and starting the workshop access control system to intercept the device movement path.

[0040] The device integrates the alliance chain node, and realizes data interconnection with the intelligent factory ERP system and the supply chain finance platform. The vibration feature vector and the position hash value generated every minute additionally contain business identifiers such as device number and production order number, which are packaged into blocks and stored on the chain. The smart contract sets multiple verification rules:

[0041] Device state verification: when more than three devices in the same workshop trigger a level one warning simultaneously, it is automatically marked as a regional production anomaly;

[0042] Mobile compliance verification: cross-area movement of machines that have not paid the final installment requires approval by the supply chain finance platform smart contract. Movement without approval will trigger the machine lock and payment recovery process;

[0043] Data traceability: through the blockchain browser, the vibration curve, position trajectory, and work order execution record of any device during the production cycle can be queried, providing an unalterable evidence chain for quality traceability and payment settlement.

[0044] In summary, the embodiment expands the single-device detection scheme to a multi-dimensional collaborative monitoring system through cluster deployment in the intelligent factory scenario. The large-scale installation of the three-color light carrier realizes the non-reconstruction integration of the workshop equipment. The distributed filtering and edge computing improve the data processing efficiency. The cross-device SVM pattern recognition enhances the cluster anomaly warning capability. The linkage of the regional electronic fence and the blockchain smart contract realizes the whole-process mobile management of the equipment with unpaid final payment. The embodiment not only meets the real-time monitoring needs of the intelligent factory for the equipment state, but also builds a closed-loop ecology of "equipment operation-production management-fund risk control" through the collaboration of supply chain financial data, providing a complete solution with advanced technology and commercial feasibility for the intelligent upgrading of discrete manufacturing industry, and significantly improving the efficiency of factory equipment management and the safety of supply chain finance.

[0045] Embodiment Two

[0046] The embodiment provides a high-reliability vibration and displacement detection scheme for the large machine tool monitoring needs of heavy machinery processing workshops. In the heavy processing scene, the violent vibration of the machine tool during operation may cause tool breakage and workpiece scrap, and unauthorized movement may pose a threat to the safety of the workshop layout. The device, through the modular design integrated in the machine tool three-color light, realizes real-time vibration feature analysis and regional movement control of large equipment such as gantry milling machines and floor boring machines, and is suitable for heavy machinery manufacturing scenes such as nuclear power equipment processing and shipbuilding that require strict equipment stability.

[0047] Referring to Figure 2 The specific implementation process of the device is as follows. For the high-strength vibration environment on the top of the gantry milling machine, the three-color light detection device is fixed on the damping base of the machine tool top beam through high-strength bolts. The base is filled with a silica gel buffer layer to attenuate the high-frequency vibration interference generated by the machine tool spindle rotation. The gyroscope and accelerometer of the sensor module are packaged with a metal shielded shell and installed orthogonally along the X / Y / Z three-axis direction of the machine tool to ensure signal stability when collecting three-dimensional vibration parameters. The GPS device of the positioning module is deployed at the top of the three-color light, and the antenna penetrating the lampshade is designed with IP68 level waterproof sealing to adapt to the cutting fluid spraying environment of heavy workshops.

[0048] The sensor module collects vibration data of key parts such as the machine tool spindle and feed shaft at a sampling rate of more than 10 kHz. The processing unit uses a filtering algorithm for real-time noise reduction, with the formula being: wherein the dynamic weight coefficient a is dynamically adjusted according to the vibration amplitude during heavy machine tool cutting (a = 0.7 during rough machining and a = 0.3 during finishing), the change rate adjustment factor d optimizes the signal mutation processing under large cutting conditions, and the band-pass filter circuit sets the passband range to 0.1 to 5 kHz to eliminate low-frequency interference such as hydraulic systems and cooling pumps and machine tool electrical noise.

[0049] The processing unit extracts characteristic parameters such as peak value, kurtosis value, etc. from the filtered data. In view of the low-frequency and large-amplitude characteristics of heavy machine tools, the vibration intensity (velocity effective value) is additionally calculated as a key indicator. The storage unit uses an 8GB industrial-grade flash memory to store the original vibration data at an interval of 100ms. An analysis log containing cutting parameters (such as feed rate and spindle speed) is generated every 10 minutes. The data compression module uses the LZ4 algorithm to compress the vibration waveform data in real time, with a compression ratio of 3:1. The storage unit supports breakpoint storage in an environment of -40°C to 85°C, ensuring data integrity in extreme working conditions.

[0050] The positioning module uses Beidou + GPS dual-mode positioning. A differential positioning reference station is installed on the top of the workshop. The GPS signal strength is improved by more than 15dB through a low-noise amplifier. A multi-path suppression algorithm is combined with the metal structure characteristics of the machine tool to establish a reflection signal feature library, which can filter out multi-path interference caused by components such as columns and beams in real time. When the heavy machine tool mobile workbench causes shielding, the system automatically switches to the Beidou short message mode to ensure uninterrupted positioning data, with a single-point positioning accuracy of sub-meter level.

[0051] The communication module transmits data through a 5G industrial private network. It uses the QoS2 level of the MQTT protocol to transmit alarm information. In view of the metal shielding environment of the heavy workshop, a 5G repeater is deployed on the workshop column to ensure the anti-interference ability of the communication link. Before data transmission, the main control module encapsulates the vibration characteristic values and positioning data into industrial protocol data packets (OPCUA format) and uploads them to the workshop MES system through the edge gateway, while synchronizing them to the blockchain node.

[0052] The analysis unit constructs a heavy machine tool vibration mode database. In addition to the basic fault template, it adds exclusive mode templates such as heavy cutting (such as nuclear pressure vessel machining) and heavy load moving of the workbench. The pattern recognition formula based on the SVM algorithm is: Among them, for the low-frequency characteristics of heavy machine tools, a higher weight coefficient ω i is given to the vibration characteristics below 100Hz to enhance the recognition ability of abnormal signals such as cutting chatter. When the tool wear vibration pattern is detected, the system automatically associates with the cutting parameter database to generate a tool replacement suggestion.

[0053] The analysis unit sets up a polygon electronic fence according to the operating area of the heavy machine tool. It uses a double-threshold early warning mechanism: when the cumulative impact value exceeds the preset threshold and the continuous vibration time exceeds half of the processing period, a first-level warning is triggered; if the vibration causes the machine tool to move horizontally beyond the safety threshold (calculated by comparing the positioning data with the machine tool anchor bolt position), a second-level warning is triggered. The position deviation risk index calculation formula is: wherein the spatial offset weight coefficient λ is set according to the safety level of the workshop channel (λ = 0.8 for the main channel and λ = 0.5 for the equipment maintenance area), and the exponential term The offset evaluation for approaching the dangerous area is strengthened.

[0054] The device integration alliance chain node is connected with the equipment management system of the heavy machinery manufacturer, and the vibration feature vector, the position hash value and the processing work order number are packaged and chained every minute, and the smart contract sets the heavy equipment exclusive verification rule:

[0055] Processing compliance verification: if the heavy machine tool that has not completed the payment of the final payment starts the moving workbench, the system automatically checks the payment record stored in the blockchain, and the unapproved movement will trigger the machine tool hydraulic system locking;

[0056] Vibration anomaly tracing: the vibration curve and cutting parameters of any processing batch can be queried through the blockchain browser, which provides tamper-proof evidence for the quality traceability of key components such as nuclear power equipment;

[0057] Device health record: the vibration mode recognition result is associated with the maintenance record and chained, and a heavy machine tool full life cycle health record is constructed to support predictive maintenance.

[0058] In summary, the embodiment realizes accurate monitoring of large machine tool vibration and displacement through special design for heavy machinery processing scene, effectively reduces environmental vibration interference through the installation scheme of three-color lamp carrier combined with shock-absorbing base, improves the recognition accuracy of large load vibration mode based on the filter and SVM algorithm optimized for the characteristics of heavy machine tools, and realizes the movement control of unpaid equipment and the whole-process tracing of key component processing quality through the linkage of regional electronic fence and blockchain smart contract. The embodiment provides a complete solution integrating "state monitoring-fault warning-safety control-quality evidence" for heavy machinery manufacturing, significantly improves the operation reliability and workshop production safety of large equipment, and is especially suitable for high-end heavy manufacturing scenes with strict requirements on processing precision and equipment control.

[0059] The above is only a preferred embodiment of the present application, and does not limit the present application in any form. Although the present application has been disclosed as above, it is not intended to limit the present application. Any person skilled in the art can make some changes or modifications to the above disclosed technical content without departing from the scope of the technical solution of the present application, and any equivalent embodiments with equivalent changes are equivalent. Any modification, change, equivalent change and modification of the above embodiments made according to the technical essence of the present application are still within the scope of the technical solution of the present application.

Claims

1. A machine tool top vibration and displacement detection device based on IMU and IOT, characterized in that: The device includes: a machine tool three-color lamp, a sensor module, a positioning module, a communication module, a power supply module and a main control module; The sensor module includes a gyroscope and an accelerometer integrated into the three-color lamp of the machine tool, which is used to collect vibration parameters of the machine tool; The positioning module includes a GPS device integrated into the three-color light of the machine tool, which is used to obtain the position information of the machine tool; The communication module includes a SIM card device integrated into the machine tool's three-color light for transmitting data to the server; The power supply module adopts a multi-channel power supply method, including using a 24V machine tool normal power supply, a built-in lithium battery and a machine tool battery box to provide multi-channel power supply; The main control module includes a processing unit, an analysis unit, a storage unit, and an IO communication unit. The processing unit is used to perform real-time filtering and feature value extraction on sensor data. The analysis unit performs vibration pattern recognition and position offset determination on the processed data based on a preset algorithm. The storage unit is used to cyclically store raw data and analysis logs. The IO communication unit is used to transmit abnormal signals to the machine tool PLC system via the IO communication line, and the PLC determines the event type and takes further action. The three-color lamp of the machine tool is fixed to the original installation hole position on the top of the machine tool by bolts, and each module is integrated into the columnar body of the three-color lamp of the machine tool in sections.

2. The device for detecting vibration and displacement of a machine tool top based on IMU and IOT according to claim 1, characterized in that: The power supply module also includes a BMS charging management chip with a built-in intelligent charging algorithm. It collects the voltage, current, temperature and charge status data of the lithium battery in real time, divides the charging into pre-charging, constant current charging and constant voltage charging stages according to the battery status, and uses PID control technology to accurately adjust the charging current and voltage. When overcharging, over-discharging, overcurrent and high temperature abnormalities are detected, the protection mechanism is immediately activated to cut off the circuit and upload the fault information to the machine tool system.

3. The device for detecting vibration and displacement of a machine tool top based on IMU and IOT according to claim 1, characterized in that: The processing unit uses a filtering algorithm to process the vibration data, with a sampling rate of not less than 10kHz and extracting the root mean square value and peak value as characteristic parameters. The processing unit also integrates a bandpass filter circuit to eliminate environmental noise. At the same time, an abnormal data elimination module is set to perform continuity detection on the sampled data through a sliding window algorithm. When the difference between adjacent sampling points exceeds a preset threshold, it is determined to be abnormal data and eliminated. The formula for the real-time filtering of the sensor data by the processing unit is: Where y(n) is the filtered output value, x(n) is the current sampling value, α is the dynamic weight coefficient and 0<α<1, and δ is the rate of change adjustment factor and 0≤δ<1.

4. The device for detecting vibration and displacement of a machine tool top based on IMU and IOT according to claim 1, characterized in that: The storage unit is an 8GB flash memory, which stores raw vibration data at intervals of 100ms, stores analysis logs at intervals of 10 minutes, and supports breakpoint continuation. The communication module uses the MQTT protocol to transmit data. The storage unit also includes a data compression module and a data encryption module to compress the raw vibration data and encrypt the stored raw data and analysis logs.

5. The device for detecting vibration and displacement of a machine tool top based on IMU and IOT according to claim 1, characterized in that: The positioning module is deployed at the top of the vertical structure of the machine tool's three-color light. The positioning module also includes a GPS signal enhancement circuit, which enhances the received signal strength through a low-noise amplifier and uses a multipath suppression algorithm to reduce signal reflection interference. At the same time, it integrates a Beidou positioning module to form dual-mode positioning with GPS, and automatically switches to Beidou positioning mode when the GPS signal is weak.

6. The device for detecting vibration and displacement of a machine tool top based on IMU and IOT according to claim 1, characterized in that: The device includes a blockchain node, which packages the vibration feature vectors and position hash values ​​generated every minute into blocks. The blockchain adopts a consortium chain architecture and supports smart contracts to automatically trigger an alarm mechanism. The smart contract also includes data verification rules. When the position hash values ​​of five consecutive blocks deviate from the preset range, the machine tool control authority is automatically frozen, and a data evidence traceability function is set at the same time.

7. The device for detecting vibration and displacement of a machine tool top based on IMU and IOT according to claim 1, characterized in that: The analysis unit first performs high-pass filtering and Kalman fusion filtering on the angular velocity and acceleration of the IMU sensor to estimate the posture, and then uses the SVM algorithm to perform pattern recognition on the machine tool vibration state. The analysis unit also includes a vibration pattern database that stores four vibration pattern templates for normal operation, tool wear, sudden tool collision, and machine tool handling. Pattern matching is performed by calculating the Euclidean distance between the data to be identified and the template. The formula for pattern recognition of the machine tool vibration state based on the SVM algorithm is: Where D is the vibration mode matching distance, x i is the i-th eigenvalue of the data to be identified, m i and σ i are the mean and standard deviation of the template features, ω i is the feature weight coefficient, and n is the feature dimension.

8. The device for detecting vibration and displacement of a machine tool top based on IMU and IOT according to claim 1, characterized in that: When the analysis unit performs displacement processing, it first obtains GPS satellite coordinate data to determine the position information. In an indoor environment with weak or no GPS signal, it uses IMU attitude data, combined with the integral operation of the data collected by the acceleration sensor, to construct an inertial navigation model for displacement estimation and obtain the displacement information of the machine tool. At the same time, it integrates the electronic fence management module and sets a circular electronic fence through the server. The formula for determining the position offset of the analysis unit is: Where P is the position offset risk index, d is the distance between the current position and the boundary of the electronic fence, r is the radius of the electronic fence, t is the offset duration, T is the preset time threshold, and λ is the spatial offset weight coefficient and 0≤λ≤1.

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