An online monitoring method for high-speed rotating transmission devices based on multi-sensor collaboration

Abstract

The present invention belongs to the technical field of high-speed rotating transmission devices. Specifically, it is an online monitoring method for high-speed rotating transmission devices based on multi-sensor collaboration. By using sensors to collect industrial data on rotating components, including speed parameters, vibration parameters, torque parameters, angle parameters, and temperature parameters, as the power source equipment rotates, the vibration intensity value of the rotating components is calculated and fused to generate a multidimensional data matrix. At the same time, it is compared with the safety value to set a threshold mechanism, triggering a three-level response according to the severity. Based on the historical data training model, intelligent diagnosis is achieved, thereby generating a 3D dynamic model, mapping the real-time status of the transmission device, and comparing historical data to analyze trends. Finally, a monitoring report is automatically generated, and maintenance recommendations are uploaded to the data management terminal. Comprehensive data coverage is achieved, avoiding the one-sidedness of single sensor data, and reducing equipment life cycle costs through predictive maintenance.

Description

Technical Field

[0001] The present invention belongs to the technical field of high-speed rotating transmission devices, and in particular is an online monitoring method for high-speed rotating transmission devices based on multi-sensor collaboration. Background Art

[0002] Traditional monitoring methods for high-speed rotating transmissions often rely on contact sensors to collect basic data from power source equipment. These sensors primarily utilize slip rings or brushes to transmit signals, which are subject to wear and tear due to physical contact and require regular replacement. Furthermore, these sensors only monitor a single parameter, such as vibration or speed, and are unable to correlate multidimensional data.

[0003] Traditional threshold mechanisms for data monitoring suffer from the following major shortcomings: 1. Current monitoring systems still generally rely on historical maintenance experience or default values ​​from the system's initial configuration to determine thresholds for key transmission parameters, such as vibration amplitude, temperature gradient, and torque fluctuation. This approach significantly lacks objective data support. This experience-based threshold system lacks an effective traceability and verification mechanism. For example, if a bearing of a certain model experiences a temperature anomaly after 3,000 hours of operation, the traditional system cannot leverage historical data to trace the intrinsic connection between the threshold setting and the fault characteristics. Consequently, threshold optimization for similar equipment relies on an inefficient cycle of trial and error. 2. The operating parameters of high-speed rotating equipment exhibit significant temporal periodicity. For example, the load of a wind turbine gearbox fluctuates with diurnal wind speed, the temperature rise of high-speed rail bearings varies with the train's acceleration and deceleration cycles, and the vibration amplitude of an industrial steam turbine exhibits regular fluctuations with the alternation of production shifts. However, traditional static thresholds, with their one-size-fits-all approach, fail to capture the periodic patterns found in these time series.

[0004] Existing monitoring systems often rely on a single sensor data source, such as vibration or temperature monitoring, without effectively integrating multiple sensor information streams. This reliance on preliminary data often results in insufficient ability to diagnose complex faults, making it difficult to comprehensively identify equipment failures and, consequently, unable to analyze equipment aging trends. Summary of the Invention

[0005] In view of this, the present invention aims to propose an online monitoring method for high-speed rotating transmission devices based on multi-sensor collaboration. By utilizing sensors to collect industrial data including speed parameters, vibration parameters, torque parameters, angle parameters, and temperature parameters on rotating parts in real time when the power source equipment rotates, the vibration intensity value of the rotating parts is calculated, and then fused and processed to generate a multi-dimensional data matrix. At the same time, it is compared with the safety value to set a threshold mechanism, triggering a three-level response according to the severity, and realizing intelligent diagnosis based on the historical data training model, thereby generating a 3D dynamic model, mapping the real-time status of the transmission device, and comparing historical data to analyze trends. Finally, a monitoring report is automatically generated, and maintenance suggestions are uploaded to the data management terminal, effectively solving the problems mentioned in the background technology.

[0006] The object of the present invention can be achieved by the following technical solution: an online monitoring method for a high-speed rotating transmission device based on multi-sensor collaboration, characterized in that it includes a data acquisition terminal, a parameter calculation terminal, an intelligent diagnosis terminal, an automatic generation terminal, a control center, and a data management terminal, wherein the data acquisition terminal is arranged on the rotating component of the power source equipment, and specifically includes the following steps:

[0007] S1. Use sensors to collect industrial data of rotating parts in real time when the power source equipment rotates, including speed parameters, vibration parameters, torque parameters, angle parameters, and temperature parameters;

[0008] S2. Calculating the collected speed parameters and vibration parameters to obtain a vibration intensity value of the rotating component;

[0009] S3. Fusion processing is performed on the vibration intensity value, torque parameter, angle parameter, and temperature parameter of the rotating component to generate a multidimensional data matrix, and the matrix is ​​compared with the safety value to set a threshold mechanism;

[0010] S4, linkage threshold mechanism triggers three-level response according to severity, and realizes intelligent diagnosis based on historical data training model;

[0011] S5. Generate a 3D dynamic model based on the results of intelligent diagnosis, map the real-time status of the transmission device, and compare historical data to analyze trends;

[0012] S6. Automatically generate monitoring reports based on real-time status and trend analysis, and upload maintenance suggestions to the data management terminal.

[0013] Combining all the above technical solutions, the positive effects of the present invention are as follows: 1. The present invention realizes comprehensive data coverage through multi-sensor collaborative monitoring, avoids the one-sidedness of single sensor data through the collaborative collection of speed, vibration, torque, angle and auxiliary parameters, improves the accuracy of fault diagnosis, and the hierarchical upload mechanism is combined with the anti-interference device design to ensure high-reliability transmission of key data and reduce the risk of false alarms.

[0014] 2. The present invention adopts a dynamic threshold mechanism, which can adapt to different working conditions, reduce the misjudgment rate, achieve real-time early warning, prevent the expansion of sudden faults, and train models through historical data to predict gradual faults such as bearing wear and shaft bending, thereby reducing unplanned downtime.

[0015] 3. The present invention uses 3D dynamic model mapping to intuitively display the real-time status of the transmission device, assisting engineers in quickly locating the source of the fault. It also reduces the frequency of emergency repairs through predictive maintenance, thereby lowering the equipment life cycle cost. At the same time, the three-level response mechanism can avoid mechanical fracture accidents caused by overload or imbalance of high-speed rotating equipment, significantly improving the reliability, safety and operation and maintenance efficiency of industrial equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The present invention is further described with reference to the accompanying drawings. However, the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative effort.

[0017] Figure 1 The present invention is a flowchart of the steps for implementing the method.

[0018] Figure 2 This is a schematic diagram of the three-level response process triggered by the present invention.

[0019] Figure 3 A schematic diagram of the process of automatically generating a monitoring report according to the present invention. DETAILED DESCRIPTION

[0020] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0021] See also Figure 1 As shown, the present invention proposes an online monitoring method for a high-speed rotating transmission device based on multi-sensor collaboration, which is characterized by including a data acquisition terminal, a parameter calculation terminal, an intelligent diagnosis terminal, an automatic generation terminal, a control center, and a data management terminal, wherein the data acquisition terminal is arranged on the rotating component of the power source equipment.

[0022] In a more specific application of the present invention, the specific method for collecting industrial data of rotating components is:

[0023] 1. Set points A and B on the gear or flywheel of a rotating component and install sensors at both points. Measure the rotating component's rotational speed per second over a one-minute period and calculate the average rotational speed per second. Point A can be set at the tooth root, and Point B can be set at the tooth flank. For high-speed sensor rotation, a Hall effect sensor can be used. This generates a pulse signal by contactlessly detecting changes in the magnetic field, producing a fixed number of pulses per revolution. The formula: Speed ​​(RPM) = Number of pulses per second × 60 gives the speed / number of pulses per revolution (N).

[0024] 2. Set points a and b on the shaft of the rotating component and install sensors at both points. Measure the vibration amplitude of the rotating component every second for one minute and calculate the average amplitude per second. Point a can be located at a bearing, and point b can be located at a shaft shoulder, keyway edge, or a sudden change in cross section. Accelerometers can be used as sensors, using piezoelectric effect or MEMS technology to measure vibration acceleration and convert it into displacement amplitude. Accelerometers are suitable for high-frequency vibration and can be directly installed on the surface of points a and b, enabling real-time monitoring.

[0025] 3. Set points a and b on the shaft of the rotating component and install force sensors at both points. Measure the force F and the lever arm L per second for one minute. Calculate the torque M = F × L and the average torque per second. Point a can be located at a bearing, and point b can be located at a shaft shoulder, keyway edge, or a sudden change in cross section. A strain gauge force sensor can be used. Force F is calculated by measuring the deformation (strain gauge resistance change) generated by the force at points a and b. Combined with the fixed lever arm L, the torque is calculated.

[0026] The single point torque expression is:

[0027] Ma=Fa×La

[0028] Mb=Fb×Lb

[0029] The expression of the resultant torque is:

[0030] Mtotal=(Ma+Mb) / 2

[0031] This sensor measures directly with high accuracy and is suitable for dynamic rotation scenes.

[0032] 4. Set points a and b on the shaft of the rotating component and install sensors at both points. Measure the angle of the rotating component per second within one minute and calculate the average angle per second. Point a can be set at the bearing, and point b can be set at the shaft shoulder, keyway edge, or cross-section mutation. The sensor can be a photoelectric encoder, which detects changes in rotation angle through a grating disk and a photoelectric sensor, outputs a fixed number of pulses per revolution, and calculates the instantaneous angle using the formula:

[0033]

[0034] Where Z represents the angle, e represents the number of pulses per second, and f represents the number of pulses per revolution.

[0035] 5. Set points A and B on the shaft of the rotating component and install temperature sensors at both points. Measure the temperature of the rotating component every second for one minute and calculate the average temperature per second. Point A can be located at a bearing, and Point B can be located at a shaft shoulder, keyway edge, or a sudden change in cross section. An infrared temperature sensor can be used to calculate the temperature by detecting infrared radiation energy from the surface of the rotating component, eliminating the need for physical contact and making it suitable for high-speed rotation scenarios.

[0036] The vibration intensity can be calculated as follows: given the speed parameter and vibration parameter, the expression:

[0037] Get the angular velocity V, where n represents the rotational speed, and then use:

[0038] The vibration intensity K is obtained, where x represents the amplitude and g represents the acceleration due to gravity.

[0039] The multidimensional data matrix is ​​generated by fusing the vibration intensity value, torque parameter, angle parameter, and temperature parameter of the rotating component to generate a time-parameter matrix. Each row represents a time point, and each column corresponds to a different parameter. The matrix is ​​generated according to the following structure:

[0040]

[0041] Where t represents time, k represents the vibration intensity value, m represents the average torque, a represents the average angle, and c represents the average temperature.

[0042] The security value is set as follows:

[0043] Vibration intensity is determined based on the ISO 10816 mechanical vibration standard, with safety limits defined by equipment type. ISO 10816 classifications assign vibration limits based on equipment type, such as motors and gearboxes. For example, for small motors with power ≤ 15kW, the normal range for effective vibration velocity is ≤ 1.8 mm / s. A peak value exceeding the baseline by 10% is considered mild, a peak value exceeding the baseline by 20% is considered moderate, and a peak value exceeding the baseline by 30% is considered severe.

[0044] The torque parameter is calculated based on the yield strength of the shaft material to calculate the upper limit of the static torque, which can be expressed as:

[0045]

[0046] Where d represents the shaft diameter and os represents the yield strength of the material.

[0047] Torque fluctuations exceeding 5% of the baseline value were considered mildly abnormal, those exceeding 10% of the baseline value were considered moderately abnormal, and those exceeding 15% of the baseline value were considered severely abnormal.

[0048] For angle parameters, refer to the Fanuc angle limit parameters. Fanuc angle limits are commonly used in industrial robot joint axes. Under normal working conditions, the repeatability accuracy reaches ±0.02°. If the real-time angle deviates from the theoretical value by more than 0.5°, it is considered a mild abnormality; if it deviates by more than 1°, it is considered a moderate abnormality; and if it deviates by more than 2°, it is considered a severe abnormality.

[0049] Temperature parameters have thresholds set based on the bearing or gearbox material. For example, for bearing steel (GCr15): short-term tolerance ≤ 120°C, continuous operation ≤ 80°C. A temperature rise rate exceeding 3°C / min is considered a mild abnormality, exceeding 5°C / min is considered a moderate abnormality, and exceeding 8°C / min is considered a severe abnormality.

[0050] The steps of the threshold mechanism are:

[0051] 1. Based on the safety value of each parameter, determine the parameter threshold corresponding to each failure mode. The failure mode can be obtained through intelligent diagnosis;

[0052] 2. As the high-speed rotating transmission device's operating time increases and its real-time operating status changes, machine learning or data mining algorithms are used to automatically learn the parameter change patterns of the device under different operating conditions based on historical data and make dynamic adjustments;

[0053] 3. Use a multi-parameter joint triggering method to trigger a three-level response according to the severity.

[0054] like Figure 2 As shown, the three-level response refers to the first-level response. When a single parameter exceeds the limit or multiple parameters are slightly abnormal, an alert is issued. The response action is to pop up a yellow alert window on the local HMI interface, record the abnormal parameters and timestamp, and notify the equipment administrator via SMS or email without interrupting production. The processing process requires the operator to review the sensor status and equipment load on site within 30 minutes. If the alert is not resolved within 24 hours, it will automatically escalate to the second-level response.

[0055] Level 2 response: When a key parameter exceeds its limit or multiple parameters are moderately abnormal, an alarm is issued. The response action involves slowing the equipment down to 50%, activating the backup cooling system, triggering an audible and visual alarm, and sending a maintenance work order to the MES system. Maintenance personnel arrive on-site within two hours and retest using portable equipment such as a thermal imager or vibration analyzer. If the fault persists for 30 minutes and persists, a Level 3 response is triggered.

[0056] Level 3 response: When a safety interlock is triggered or multiple parameters are seriously abnormal, an emergency stop is initiated. The response involves immediately shutting off the power supply, activating the mechanical brake, locking device control permissions, and uploading black box data, which contains the 10 minutes of operation preceding the failure. Safety engineers will lead the fault analysis and issue a preliminary report within four hours. All parameter calibration and protective device testing must be completed before the equipment can be restarted.

[0057] In comprehensive bearing wear diagnosis, vibration characteristics include a sudden increase of more than 20% in time-domain vibration values, the appearance of characteristic bearing frequencies in the frequency domain, such as the cage transit frequency and rolling element rotation frequency, the presence of modulation sidebands in the acceleration envelope spectrum, a significant increase in the energy of the high-frequency resonance band, an abnormal temperature gradient (exceeding 5°C / cm on the outer ring, which should normally be less than 2°C / cm), and an instantaneous temperature rise rate greater than 3°C / min. Immediate machine shutdown and inspection are required. Auxiliary parameters such as a lubricant metal particle concentration greater than 150ppm or an accompanying metallic scraping sound in the acoustic signal, as specified in the ISO 4406 standard, can also be used for diagnosis.

[0058] In the judgment of shaft misalignment fault, the torque characteristics are torque fluctuation amplitude reaching the mean ±15%, the displacement characteristics are coupling radial offset > 0.05mm, shaft length or angle deviation > 0.5°, and periodic offset.

[0059] Abnormalities caused by continuous temperature rise and changes in the amplitude of the fundamental vibration frequency can be diagnosed as lubrication failure.

[0060] The equipment health status trend analysis system can be divided into the following categories:

[0061] Select the first 100 hours of normal equipment operation data and the statistical values ​​of each parameter as the baseline. Update the baseline range quarterly based on maintenance records. When all parameter fluctuations are less than 10% of the baseline value, the equipment is considered healthy. If the parameters show periodic small fluctuations, spectrum analysis is required to confirm that there are no abnormal frequency components.

[0062] At least two key parameters, such as vibration and temperature, exceed the baseline by 15% for 72 consecutive hours and show a monotonically increasing trend under non-instantaneous interference. It is necessary to correlate operating condition data, such as when the load rate is >90%, allow for temporary exceeding of the limit, and check whether the sensor is faulty before determining it as early degradation.

[0063] When the high-frequency vibration energy suddenly increases by 200% and the temperature gradient is greater than 5°C / min, automatic load reduction or shutdown protection is triggered to avoid cascading damage and it is determined to be accelerated failure.

[0064] Automatically generate monitoring reports based on real-time status and trend analysis, such as Figure 3 As shown, it may include: basic information, monitoring objectives, monitoring methods and equipment, monitoring data and analysis, equipment operating status assessment and maintenance recommendations.

[0065] The basic information section serves as the beginning of the report, recording the basic files of the equipment in detail, including key information such as the equipment name, model, manufacturer, installation location, and activation date. It also indicates information such as the monitoring period, report generation time, and the technician responsible for monitoring, building a basic framework for the equipment identity and monitoring background.

[0066] The monitoring objectives section clearly states the core objectives of this monitoring. Depending on the equipment type and application scenario, these objectives include ensuring safe and stable equipment operation, improving production efficiency, preventing unexpected failures, and optimizing equipment performance.

[0067] The Monitoring Methods and Equipment section systematically introduces the technical means and hardware used to achieve monitoring objectives. It details the types and operating principles of torque and angle monitoring terminals, such as the microstrain measurement principle of strain-gauge torque sensors and the binary coding detection method of absolute encoders. It also explains the functions and technical parameters of the data acquisition system, signal processing modules, and supporting transmission network equipment, clearly presenting the complete process and technical support from monitoring data acquisition to processing.

[0068] Monitoring and analyzing data is the core of the report. By deeply processing real-time torque values, angular positions, and operating times, statistical analysis, trend prediction models, and threshold comparisons are used to uncover the underlying information. Data trends are presented in visual formats such as line charts and bar graphs. For example, a torque fluctuation curve over time is plotted to visually demonstrate the load changes of the equipment under different operating conditions. By comparing historical data with current data, anomalies are analyzed to determine whether equipment operation deviates from normal conditions and to identify signs of performance degradation or failure.

[0069] The equipment operating status assessment phase comprehensively assesses the equipment's health based on monitoring data analysis results, combined with equipment design parameters and industry standards. The assessment utilizes quantitative scoring and grading to categorize equipment operating status into three levels: good, warning, and fault. For example, if torque fluctuations exceed the normal range and show a trend of continued deterioration, accompanied by a gradual increase in angular position error, the equipment is considered to be at a potential fault risk and warrants close attention. On the other hand, if all parameters are within standard ranges and stable, the equipment is considered to be in good condition.

[0070] The maintenance recommendations section provides targeted repair and maintenance strategies based on the equipment's operational status assessment results. For equipment in a warning state, preventive maintenance measures such as local inspection, cleaning, and parameter adjustment are recommended. If a device has already failed, the maintenance recommendations detail the replacement of the faulty component, calibration and debugging methods, and provide repair procedures and precautions. Furthermore, based on the equipment's operational trends, forward-looking plans are provided, such as regular maintenance interval adjustments and spare parts inventory recommendations, helping companies rationally allocate maintenance resources and reduce equipment lifecycle costs.

Claims

1. A method for online monitoring of a high-speed rotating transmission device based on multi-sensor collaboration, characterized in that: It includes a data acquisition terminal, a parameter calculation terminal, an intelligent diagnosis terminal, an automatic generation terminal, a control center, and a data management terminal, wherein the data acquisition terminal is set on the rotating component of the power source equipment, and specifically includes the following steps: S1. Use sensors to collect industrial data of rotating parts in real time when the power source equipment rotates, including speed parameters, vibration parameters, torque parameters, angle parameters, and temperature parameters; S2. Calculating the collected speed parameters and vibration parameters to obtain a vibration intensity value of the rotating component; S3. Fusion processing is performed on the vibration intensity value, torque parameter, angle parameter, and temperature parameter of the rotating component to generate a multidimensional data matrix, and the matrix is ​​compared with the safety value to set a threshold mechanism; The vibration intensity can be calculated as follows: given the speed parameter and vibration parameter, the expression: Get the angular velocity V, where n represents the rotation speed, and then pass Get the vibration intensity K, where x represents the amplitude and g represents the acceleration due to gravity; S4, linkage threshold mechanism triggers three-level response according to severity, and realizes intelligent diagnosis based on historical data training model; The threshold mechanism refers to: Based on the safety value of each parameter, determine the parameter threshold corresponding to each failure mode; As the high-speed rotating transmission device's operating time increases and its real-time operating status changes, machine learning or data mining algorithms are used to automatically learn the parameter change patterns of the device under different operating conditions based on historical data and make dynamic adjustments; Use a multi-parameter combined triggering method to trigger three-level responses according to severity; S5. Generate a 3D dynamic model based on the results of intelligent diagnosis, map the real-time status of the transmission device, and compare historical data to analyze trends; The trend analysis involves selecting the first 100 hours of normal equipment operation data and using the statistical values ​​of each parameter as a baseline. The baseline range is updated quarterly based on maintenance records. When all parameter fluctuations are less than 10% of the baseline value, the equipment is considered healthy. If at least two parameters exceed the baseline by 15% for three consecutive days, the equipment is considered to be in the early stages of degradation. A sudden increase of 200% in high-frequency vibration energy and a temperature gradient greater than 5°C / min indicate accelerated equipment failure. S6. Automatically generate monitoring reports based on real-time status and trend analysis, and upload maintenance suggestions to the data management terminal.

2. The method for online monitoring of a high-speed rotating transmission device based on multi-sensor collaboration according to claim 1, characterized in that: The specific method for collecting industrial data of the rotating component is as follows: setting points a and b on the gear or flywheel of the rotating component and installing sensors at the two points, measuring the rotation speed of the rotating component per second within one minute, and calculating the average rotation speed per second; Set points a and b on the axis of the rotating part and install sensors at the two points to measure the amplitude of the rotating part per second within one minute and calculate the average amplitude per second; Set points a and b on the axis of the rotating part and install force sensors at the two points. Measure the force F and the lever arm L of the rotating part per second within one minute. Obtain the torque M=F×L and calculate the average torque per second. Set points a and b on the axis of the rotating part and install sensors at the two points, measure the angle of the rotating part every second within one minute, and calculate the average angle per second; Set points a and b on the axis of the rotating part and install temperature sensors at the two points. Measure the temperature of the rotating part every second for one minute and calculate the average temperature per second.

3. The method for online monitoring of a high-speed rotating transmission device based on multi-sensor collaboration according to claim 1, characterized in that: The multidimensional data matrix is ​​generated by fusing the vibration intensity value, torque parameter, angle parameter, and temperature parameter of the rotating component to generate a time-parameter matrix. Each row represents a time point, and each column corresponds to a different parameter. The matrix is ​​generated according to the following structure: ; Where t represents time, k represents vibration intensity value, m represents torque parameter, a represents angle parameter, and c represents temperature parameter.

4. The method for online monitoring of a high-speed rotating transmission device based on multi-sensor collaboration according to claim 1, characterized in that: The safety value is: Vibration intensity refers to the ISO10816 mechanical vibration standard and is divided into safety ranges according to equipment type. A peak value exceeding the baseline value by 10% is considered a mild abnormality, a peak value exceeding the baseline value by 20% is considered a moderate abnormality, and a peak value exceeding the baseline value by 30% is considered a severe abnormality. The static torque upper limit is calculated based on the yield strength of the shaft material. A torque fluctuation exceeding 5% of the baseline value is considered mild abnormality, exceeding 10% of the baseline value is considered moderate abnormality, and exceeding 15% of the baseline value is considered severe abnormality. The angle parameters refer to the Fanuc angle limit parameters. If the real-time angle deviates from the theoretical value by more than 0.5°, it is considered a mild abnormality; if it deviates by more than 1°, it is considered a moderate abnormality; and if it deviates by more than 2°, it is considered a severe abnormality. The temperature parameter sets a threshold value based on the bearing or gearbox material. A temperature rise rate exceeding 3°C / min is considered a mild abnormality, exceeding 5°C / min is considered a moderate abnormality, and exceeding 8°C / min is considered a severe abnormality.

5. The method for online monitoring of a high-speed rotating transmission device based on multi-sensor collaboration according to claim 1, characterized in that: The three-level response refers to: level one response, when a single parameter exceeds the limit or multiple parameters are slightly abnormal, an early warning is issued; level two response, when a key parameter exceeds the limit or multiple parameters are moderately abnormal, an alarm is issued; level three response, when the safety interlock is triggered or multiple parameters are seriously abnormal, an emergency stop is directly performed.

6. The method for online monitoring of a high-speed rotating transmission device based on multi-sensor collaboration according to claim 1, characterized in that: The contents of the intelligent diagnosis are: When the vibration intensity increases and the temperature gradient is abnormal, it is diagnosed as bearing wear; When torque fluctuations rise and the angle shifts periodically, shaft misalignment is diagnosed; When the temperature continues to rise and the amplitude of the fundamental vibration frequency changes, lubrication failure is diagnosed.

7. The method for online monitoring of a high-speed rotating transmission device based on multi-sensor collaboration according to claim 1, characterized in that: The monitoring report includes: basic information, monitoring objectives, monitoring methods and equipment, monitoring data and analysis, equipment operating status assessment and maintenance recommendations.

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