Health monitoring system of speed reducer

Abstract

The invention discloses a health monitoring system of a speed reducer, and belongs to the field of mechanical equipment state monitoring. Comprising a sensor module, a data acquisition module, a data processing module, an early warning module and a display module, the sensor module comprises a displacement sensor, a pressure sensor, a rotating speed sensor, a vibration sensor and a temperature sensor, and the displacement sensor and the pressure sensor are arranged at the position of a brake piston of the speed reducer and used for monitoring piston displacement and brake pressure in real time; the rotating speed sensor is arranged at the output end of the reducer and used for monitoring the output rotating speed of the reducer. Real-time monitoring of key parameters of the speed reducer is achieved through multi-sensor fusion, potential faults can be found in time, and fundamental transformation from passive maintenance to active early warning and from planned maintenance to predicted maintenance is achieved. The brake performance and the abrasion degree of a friction plate are accurately evaluated through combined monitoring of displacement and pressure of a brake piston; and early fault diagnosis is realized through spectrum analysis of the vibration signals.

Description

Technical Field

[0001] This invention relates to the condition monitoring of mechanical equipment, and more particularly to a health monitoring system for a speed reducer. Background Technology

[0002] As the core hub and key link in the power transmission system of heavy engineering equipment, the wheel-side reducer's operating status directly determines the performance and safety of the entire machine. As a core actuator of the drive terminal, a failure of the wheel-side reducer will directly cause the vehicle or equipment to lose some or all of its driving force and effective braking force, resulting in serious equipment damage and safety accidents. Especially in critical equipment such as mining dump trucks and large cranes, sudden reducer failures can lead to unplanned downtime, causing the entire production line to be interrupted, resulting in high downtime losses, emergency repair costs, and production capacity losses, seriously affecting the overall project progress and operational efficiency.

[0003] In existing technologies, the monitoring of speed reducers mainly relies on manual inspections and periodic maintenance. This approach suffers from problems such as untimely monitoring, inability to detect problems in real time, and insufficient predictive maintenance capabilities. Although some equipment is equipped with simple sensors, they mostly monitor single parameters and lack the ability to comprehensively analyze multiple parameters such as braking performance, friction pad wear, and vibration characteristics, making it difficult to achieve accurate condition assessment and fault early warning.

[0004] Therefore, developing a health monitoring system capable of real-time status monitoring and intelligent early warning for wheel-side reducers has become an indispensable technical guarantee for ensuring the safety of heavy equipment, preventing major accidents, achieving predictive maintenance, and ensuring economic operation. Summary of the Invention

[0005] Purpose of the invention: The purpose of this invention is to provide a health monitoring system for speed reducers, which achieves comprehensive monitoring of the speed reducer's operating status and fault early warning through multi-sensor fusion and intelligent algorithm analysis.

[0006] Technical Solution: A health monitoring system for a speed reducer includes a sensor module, a data acquisition module, a data processing module, an early warning module, and a display module. The sensor module includes a displacement sensor, a pressure sensor, a speed sensor, a vibration sensor, and a temperature sensor. The displacement and pressure sensors are located at the brake piston position of the speed reducer to monitor piston displacement and braking pressure in real time. The speed sensor is located at the output end of the speed reducer to monitor the output speed. The vibration sensor is located in the speed reducer housing to collect vibration signals. The temperature sensor is located inside the speed reducer to monitor the operating temperature. The data acquisition module is connected to the sensor module to collect real-time data from each sensor. The data processing module is connected to the data acquisition module and has a built-in data processing algorithm to analyze and calculate the collected data and compare it with preset thresholds or theoretical curves. The early warning module is connected to the data processing module and generates an early warning signal based on the comparison results. The display module displays the speed reducer's operating status information and early warning prompts.

[0007] Furthermore, the data processing module includes a braking performance analysis unit. This unit compares and analyzes the piston displacement data monitored by the displacement sensor and the braking pressure data monitored by the pressure sensor, combined with the theoretical curve of braking pressure versus piston displacement. The theoretical curve formula is: P = K × n × (11 + Δl) / A, where P is the braking pressure, K is the spring stiffness, n is the number of springs, Δl is the piston displacement, and A is the piston area. When the positive displacement Δl reaches 2.0 mm, the system determines that the braking pressure should be ≥ 9 MPa. If the system pressure is < 9 MPa, the spring stiffness is determined to be too small. When the system pressure is ≥ 10.5 MPa and Δl < 4.0 mm, the spring stiffness is determined to be too large.

[0008] Furthermore, the data processing module includes a friction pad wear monitoring unit. This unit determines the degree of wear based on negative displacement data and braking pressure data, combined with the relationship between friction pad wear and braking pressure. When the negative displacement δ≤4mm and the system pressure≥4.8MPa, the system is deemed safe. When the negative displacement δ≤4mm and the system pressure≤4.8MPa, it indicates insufficient braking torque or insufficient spring force. When the negative displacement δ≥4mm, it indicates excessive friction pad wear and the need for replacement.

[0009] Furthermore, the data processing module includes a speed calculation unit, which calculates the real-time vehicle speed based on the output rotational speed n monitored by the speed sensor using the formula V=0.3014×n, where V is in km / h. The warning module sets different overspeed thresholds based on the vehicle's load status. When the vehicle is fully loaded, if V>10km / h, an overspeed warning is issued; when the vehicle is unloaded, if V>18km / h, an overspeed warning is issued.

[0010] Furthermore, the data processing module includes a temperature monitoring unit, which makes multi-level judgments based on the real-time temperature t monitored by the temperature sensor; when t≤-20℃ or t≥80℃, the warning module issues a buzzer reminder; when t≤-40℃ or t≥130℃, the warning module issues a risk warning; the temperature abnormality prompts include abnormal lubrication status, overload and abnormal friction alarms, and sealing system protection.

[0011] Furthermore, the data processing module includes a vibration analysis unit. In the early stage of system operation, the vibration analysis unit collects vibration signals and spectrum diagrams to establish a benchmark database of vibration signal amplitude ranges. During operation, it collects vibration signals in real time and compares them with the benchmark database. When the vibration signal amplitude exceeds the preset range, the early warning module issues a buzzer reminder. Combined with the speed sensor, it analyzes the gear status through the speed ratio conversion of the reducer to achieve early fault diagnosis and status assessment.

[0012] Furthermore, the data processing module includes a data storage unit, which stores real-time data collected by sensors, analysis and comparison results, and historical operating data, gradually forming a reducer operating status database to provide a data foundation for subsequent artificial intelligence analysis; the data processing module also includes a mileage calculation unit, which calculates and records the total mileage of the vehicle based on real-time speed and driving time.

[0013] Furthermore, the early warning module includes an audio prompt unit and a visual prompt unit; when the system detects an anomaly or risk, the audio prompt unit emits a buzzer alarm, and the visual prompt unit flashes a prompt through the display module; the early warning level is divided into a reminder level and a warning level according to the degree of anomaly. The reminder level corresponds to a temperature exceeding the range of -20℃ to 80℃ or an abnormal vibration signal amplitude, while the warning level corresponds to a temperature exceeding the range of -40℃ to 130℃ or insufficient braking torque or excessive wear of the friction pads.

[0014] Beneficial Effects: Real-time monitoring of key parameters of the reducer through multi-sensor fusion enables timely detection of potential faults, achieving a fundamental shift from "passive maintenance" to "proactive early warning," and from "planned maintenance" to "predictive maintenance." Joint monitoring of brake piston displacement and pressure accurately assesses braking performance and friction pad wear; spectral analysis of vibration signals enables early fault diagnosis; and temperature monitoring promptly detects lubrication anomalies and overload risks. The system gradually forms an operational status database, facilitating the introduction of artificial intelligence analysis to further improve the accuracy and timeliness of fault warnings. Ultimately, this ensures equipment safety, improves operational efficiency, reduces maintenance costs, and promotes the intelligent and high-end development of related industries. Attached Figure Description

[0015] Figure 1This is a block diagram of the overall structure of the speed reducer health monitoring system of the present invention;

[0016] Figure 2 This is a schematic diagram of the theoretical curve of braking pressure versus piston displacement.

[0017] Figure 3 This is a schematic diagram showing the relationship between friction pad wear and braking pressure.

[0018] Figure 4 This is a schematic diagram showing the relationship between braking pressure and braking torque. Detailed Implementation

[0019] To make the technical solution of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0020] Example 1: Overall Structure of the Gearbox Health Monitoring System

[0021] like Figure 1 As shown, the speed reducer health monitoring system provided by this invention includes a sensor module, a data acquisition module, a data processing module, an early warning module, and a display module. The sensor module consists of five types of sensors: a displacement sensor, a pressure sensor, a speed sensor, a vibration sensor, and a temperature sensor. The displacement sensor and pressure sensor are installed at the brake piston position of the speed reducer. The displacement sensor is a high-precision linear displacement sensor with a range of ±4mm. During installation, the initial position is taken as 0. Movement towards the motor end is positive displacement, with a maximum stroke of 4mm; movement towards the gearbox end is negative displacement, with a maximum stroke of -4mm. The pressure sensor is a high-precision pressure sensor with a range of 0-20MPa.

[0022] The speed sensor, a Hall effect type, is installed at the output end of the reducer. Its output signal is a pulse signal, and the data processing module calculates the speed value *n*, expressed in rpm, by measuring the pulse frequency. The vibration sensor, a piezoelectric accelerometer, is installed in a key part of the reducer housing. Its frequency response range is 0-10kHz, and it can acquire the time-domain waveform and frequency-domain characteristics of vibration signals. The temperature sensor, a PT100 resistance temperature detector (RTD) sensor, is installed inside the reducer's oil sump, with a measurement range of -50℃ to 150℃.

[0023] The data acquisition module employs a multi-channel data acquisition card to simultaneously acquire output signals from each sensor, with a sampling frequency of no less than 1kHz, ensuring the capture of rapidly changing pressure and vibration signals. The data acquisition module converts analog signals into digital signals and transmits them to the data processing module.

[0024] The data processing module is the core unit, employing an industrial-grade embedded computer with built-in data processing algorithms. It analyzes and calculates the collected data and compares it with preset thresholds or theoretical curves. The data processing module includes a braking performance analysis unit, a friction pad wear monitoring unit, a speed calculation unit, a temperature monitoring unit, a vibration analysis unit, a data storage unit, and a mileage calculation unit.

[0025] The early warning module includes an audible alert unit and a visual alert unit. The audible alert unit uses a buzzer, while the visual alert unit uses a display module to provide flashing alerts. The early warning levels are divided into a reminder level and a warning level based on the severity of the anomaly. The reminder level corresponds to temperatures exceeding the -20℃ to 80℃ range or abnormal vibration signal amplitude, while the warning level corresponds to temperatures exceeding the -40℃ to 130℃ range, insufficient braking torque, or excessive wear of the friction plates.

[0026] The display module uses an industrial-grade LCD screen, installed in the cab, to display the reducer's operating parameters, status information, and warning prompts in real time.

[0027] Example 2: Braking Performance Analysis and Judgment

[0028] The braking performance analysis unit compares and analyzes the piston displacement data monitored by the displacement sensor and the braking pressure data monitored by the pressure sensor, combined with the theoretical curve of braking pressure versus piston displacement. For example... Figure 2 As shown, the theoretical curve formula is: P = K × n × (11 + Δl) / A, where P is the braking pressure (MPa), K is the spring stiffness (N / mm), taken as 612 N / mm, n is the number of springs, taken as 26, Δl is the piston displacement (mm), and A is the piston area (mm²). 2 ), value 22990mm 2 .

[0029] The braking performance judgment logic is as follows: when the positive displacement Δl reaches 2.0mm, the system determines that the braking pressure should be ≥9MPa. If the system pressure is <9MPa, the spring stiffness is determined to be too small, and the spring needs to be checked or replaced. When the system pressure is ≥10.5MPa and Δl <4.0mm, the spring stiffness is determined to be too large, and the system pressure needs to be increased. The maximum system pressure shall not exceed 12MPa.

[0030] Example 3: Friction Plate Wear Monitoring and Judgment

[0031] The friction pad wear monitoring unit determines the degree of wear based on negative displacement data and braking pressure data, combined with the relationship between friction pad wear and braking pressure. For example... Figure 3 As shown, the wear of the friction pads is negatively correlated with the braking pressure. The table below shows the relationship between total friction pad wear and braking pressure:

[0032] Total wear of friction plates (mm) Braking pressure (MPa) 0 7.61 0.2 7.47 0.4 7.34 0.6 7.20 0.8 7.06 1.0 6.92 1.2 6.78 1.4 6.64 1.6 6.51 1.8 6.37 2.0 6.23 2.2 6.09 2.4 5.95 2.6 5.81 2.8 5.68 3.0 5.54 3.2 5.40 3.4 5.26 3.6 5.12 3.8 4.98 4.0 4.84

[0033] The friction pad wear judgment logic is as follows: when the negative displacement δ≤4mm and the system pressure≥4.8MPa, the system is considered safe; when the negative displacement δ≤4mm and the system pressure≤4.8MPa, it indicates insufficient braking torque or insufficient spring force, requiring inspection of the braking system; when the negative displacement δ≥4mm, it indicates excessive friction pad wear, requiring replacement of the friction pads. The braking torque calculation formula is: T=K×n×μ×m×R0×(11-δ) / 1000=691122×(11-δ) N·m, where μ is the friction coefficient of the friction pad, with a value of 0.09, m is the number of friction surfaces, with a value of 20, and R0 is the braking radius, with a value of 241.3mm.

[0034] Example 4: Speed ​​Monitoring and Overspeed Detection

[0035] The speed calculation unit calculates the vehicle's real-time speed based on the output rotational speed n monitored by the speed sensor, using the formula V = 0.3014 × n, where V is in km / h. This formula is based on a wheel diameter of 1.6m. The warning module sets different overspeed thresholds according to the vehicle's load status. When the vehicle is fully loaded, if V > 10 km / h, an overspeed warning is issued; when the vehicle is unloaded, if V > 18 km / h, an overspeed warning is issued.

[0036] In addition, the data processing module also includes a mileage calculation unit, which calculates and records the total mileage S of the vehicle based on the real-time speed V and the driving time t. The calculation formula is S=∫Vdt, and the total mileage of the vehicle is calculated cumulatively.

[0037] Example 5: Temperature Monitoring and Multi-level Early Warning

[0038] The temperature monitoring unit makes multi-level judgments based on the real-time temperature t monitored by the temperature sensor. The temperature sensor monitors the temperature of the oil sump inside the reducer in real time, reflecting the overall thermal balance state of the reducer. Temperature monitoring is used for: ① lubrication status monitoring; ② overload and abnormal friction alarms; ③ sealing system protection.

[0039] The temperature anomaly detection logic is as follows: when t ≤ -20℃ or t ≥ 80℃, the warning module issues a buzzer alert; when t ≤ -40℃ or t ≥ 130℃, the warning module issues a risk warning. When the temperature is below -20℃, the lubricating oil viscosity increases, potentially leading to poor lubrication; when the temperature is above 80℃, it may cause lubricating oil failure and accelerated aging of seals; when the temperature exceeds 130℃, there is a serious risk of overheating, which may damage the equipment.

[0040] Example 6: Vibration Monitoring and Early Fault Diagnosis

[0041] The vibration analysis unit collects vibration signals and spectrum diagrams during the early stages of system operation, establishing a benchmark database of vibration signal amplitude ranges. Vibration is the most sensitive and direct indicator of gearbox faults. Vibration can be used to monitor: ① early fault diagnosis; ② condition assessment. When the system is operating normally, regular periodic vibration signals appear; when an anomaly occurs, abnormal vibration point signals appear. Combined with a speed sensor and speed ratio conversion, the gear condition can be detected.

[0042] During operation, vibration signals are collected in real time and compared with a benchmark database. When the vibration signal amplitude exceeds the preset range, the early warning module issues a buzzer alert. The vibration analysis unit uses frequency domain analysis methods to extract the spectral characteristics of the vibration signal through Fast Fourier Transform (FFT), identifying gear meshing frequencies and their harmonics, bearing fault characteristic frequencies, etc., to achieve early fault diagnosis and condition assessment.

[0043] Example 7: Data Storage and Intelligent Analysis

[0044] The data processing module includes a data storage unit, which stores real-time data collected by sensors, analysis and comparison results, and historical operating data, gradually forming a database of the reducer's operating status. The data storage unit uses a high-capacity storage medium, storing at least six months of operating data.

[0045] Based on the gradually developed database of gearbox operating status, the system can incorporate artificial intelligence analysis and establish a gearbox health status assessment model through machine learning algorithms, improving the accuracy and timeliness of fault early warning. Artificial intelligence analysis can achieve advanced functions such as fault mode identification, remaining life prediction, and maintenance plan optimization, further promoting the development of gearbox health management towards intelligence and precision.

[0046] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A health monitoring system for a speed reducer, characterized in that, The system includes a sensor module, a data acquisition module, a data processing module, an early warning module, and a display module. The sensor module includes a displacement sensor, a pressure sensor, a speed sensor, a vibration sensor, and a temperature sensor. The displacement and pressure sensors are located at the brake piston position of the reducer to monitor piston displacement and braking pressure in real time. The speed sensor is located at the output end of the reducer to monitor the output speed. The vibration sensor is located in the reducer housing to collect vibration signals. The temperature sensor is located inside the reducer to monitor the operating temperature. The data acquisition module is connected to the sensor module to collect real-time data from each sensor. The data processing module is connected to the data acquisition module and has a built-in data processing algorithm to analyze and calculate the collected data and compare it with preset thresholds or theoretical curves. The early warning module is connected to the data processing module and generates early warning signals based on the comparison results. The display module displays the reducer's operating status information and early warning prompts.

2. The health monitoring system for the speed reducer according to claim 1, characterized in that, The data processing module includes a braking performance analysis unit. This unit compares and analyzes the piston displacement data monitored by the displacement sensor and the braking pressure data monitored by the pressure sensor, along with the theoretical curve of braking pressure versus piston displacement. The theoretical curve formula is: P = K × n × (11 + Δl) / A, where P is the braking pressure, K is the spring stiffness, n is the number of springs, Δl is the piston displacement, and A is the piston area. When the positive displacement Δl reaches 2.0 mm, the system determines that the braking pressure should be ≥ 9 MPa. If the system pressure is < 9 MPa, the spring stiffness is determined to be too small. When the system pressure is ≥ 10.5 MPa and Δl < 4.0 mm, the spring stiffness is determined to be too large.

3. The health monitoring system for the speed reducer according to claim 1, characterized in that, The data processing module includes a friction pad wear monitoring unit. This unit determines the degree of wear based on negative displacement data and braking pressure data, combined with the relationship between friction pad wear and braking pressure. When the negative displacement δ ≤ 4 mm and the system pressure ≥ 4.8 MPa, the system is deemed safe. When the negative displacement δ ≤ 4 mm and the system pressure ≤ 4.8 MPa, it indicates insufficient braking torque or insufficient spring force. When the negative displacement δ ≥ 4 mm, it indicates excessive friction pad wear and the need for replacement.

4. The health monitoring system for the speed reducer according to claim 1, characterized in that, The data processing module includes a speed calculation unit, which calculates the vehicle's real-time speed using the formula V=0.3014×n based on the output rotational speed n monitored by the speed sensor, where V is in km / h. The warning module sets different overspeed thresholds based on the vehicle's load status. When the vehicle is fully loaded, if V>10km / h, an overspeed warning is issued; when the vehicle is unloaded, if V>18km / h, an overspeed warning is issued.

5. The health monitoring system for the speed reducer according to claim 1, characterized in that, The data processing module includes a temperature monitoring unit, which makes multi-level judgments based on the real-time temperature t monitored by the temperature sensor; when t≤-20℃ or t≥80℃, the early warning module issues a buzzer reminder; when t≤-40℃ or t≥130℃, the early warning module issues a risk warning; the temperature abnormality prompts include abnormal lubrication status, overload and abnormal friction alarms, and sealing system protection.

6. The health monitoring system for the speed reducer according to claim 1, characterized in that, The data processing module includes a vibration analysis unit. In the early stage of system operation, the vibration analysis unit collects vibration signals and spectrum diagrams to establish a benchmark database of vibration signal amplitude range. During operation, it collects vibration signals in real time and compares them with the benchmark database. When the vibration signal amplitude exceeds the preset range, the early warning module issues a buzzer reminder. Combined with the speed sensor, it analyzes the gear status through the speed ratio conversion of the reducer to achieve early fault diagnosis and condition assessment.

7. The health monitoring system for the speed reducer according to claim 1, characterized in that, The data processing module includes a data storage unit, which stores real-time data collected by sensors, analysis and comparison results, and historical operating data, gradually forming a database of the reducer's operating status, providing a data foundation for subsequent artificial intelligence analysis. The data processing module also includes a mileage calculation unit, which calculates and records the vehicle's total mileage based on real-time speed and driving time.

8. The health monitoring system for the speed reducer according to claim 1, characterized in that, The early warning module includes an audio prompt unit and a visual prompt unit; when the system detects an anomaly or risk, the audio prompt unit emits a buzzer alarm, and the visual prompt unit flashes a prompt through the display module. The warning levels are divided into alert level and warning level according to the degree of abnormality. The alert level corresponds to the temperature exceeding the range of -20℃ to 80℃ or the vibration signal amplitude being abnormal. The warning level corresponds to the temperature exceeding the range of -40℃ to 130℃ or the braking torque being insufficient or the friction pad wear being excessive.

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