Research on Cloud-Edge-Device Collaborative Intelligent Monitoring System of Grinding Wheel Wear State for High-Speed Cylindrical Grinding of Bearing Rings
Abstract
:1. Introduction
2. Cloud-Edge Device Collaborative Monitoring System Framework for Grinding Wheel Wear State
2.1. Overall System Framework
2.1.1. Demand Analysis
- (1)
- Monitoring model with high accuracy and reliability
- (2)
- Online monitoring system with fast response
- (3)
- Reasonable allocation of computing storage resources
2.1.2. Cloud-Edge Device Collaborative Monitoring Architecture
- (1)
- Machine Tool Device End
- (2)
- Edge End
- (3)
- Cloud End
- (4)
- Data
2.2. Key Technology of Grinding Wheel Wear State Monitoring with Multi-Sensor
- (1)
- Real-time sensing
- (2)
- Signal processing and analysis
- (3)
- Multi-sensor information fusion technique
- (4)
- Quantitative evaluation
- (5)
- Online identification
2.3. Cloud-Edge Device Collaboration Mechanism for Grinding Wheel Wear State Monitoring
- (1)
- The machine tool device transmits the grinding state data to the edge end for storage in real time. At the same time, the device status data are transmitted to the cloud end to manage the grinding performance and operation remotely.
- (2)
- After the data processing and analysis in the edge end, the grinding wheel wear state will be identified.
- (3)
- The real-time monitoring results will be transmitted to the cloud and the machine tool device end, and the operation instructions will be sent simultaneously.
- (4)
- The machine tool device end receives alarms and decision-making instructions from the cloud and edge ends.
3. Monitoring Method Based on Multi-Sensor Information Fusion
3.1. Data Acquisition Platform and Grinding Experiment
3.2. Grinding Wheel Wear State Identification Model Based on Multi-Sensor Information Fusion
3.3. Experimental Analysis and Model Verification
3.3.1. Relationship between the Sensing Signal and Grinding Wheel Wear
3.3.2. Verification of Condition Monitoring Method
4. Application Development of Cloud-Edge Device Collaborative Intelligent Monitoring System
4.1. Machin Tool Device End-High-Speed Cylindrical Grinding and Data Acquisition of the Bearing Ring
4.2. Edge End Online Monitoring System of Grinding Wheel Wear Based on Labview and Matlab
4.3. Cloud-End Bearing Ring High-Speed Cylindrical Grinding Wheel Wear Remote Monitoring System
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Method | Characteristics | Advantage | Inferiority |
---|---|---|---|
Laser measurement | Measuring the height change of abrasive wear. | High precision and reliability. | It is expensive and inconvenient to detect. |
Visual imaging | The surface morphology of the grinding wheel is obtained by optical imaging. | In-situ detection, high reliability, and cost-effective. | It has high requirements for installation space and accuracy. |
Re-engraving | The worn grinding wheel is ground into thin metal sheets, and its profile is reproduced. | The principle is simple. | The precision of operation is very high, and the error rate is high. |
|r| | [0, 0.3] | (0.3, 0.5] | (0.5, 0.8] | (0.8, 1] |
---|---|---|---|---|
correlative degree | No linear correlation | Low correlation | Significant correlation | High correlation |
Signal | Time Domain Feature | Correlation | Frequency Domain Feature | Correlation | Time-Frequency Domain Feature | Correlation |
---|---|---|---|---|---|---|
AE signal | Root mean square | 69.5% | Pulse factor | 84.8% | Kurtosis factor | 82.8% |
Kurtosis factor | 68.0% | Crest frequency | 76.9% | Crest frequency | 80.8% | |
Peak-peak value | 63.2% | Mean frequency | 66.8% | Frequency variance | 76.2% | |
Variance | 61.6% | Frequency skewness | 66.5% | Mean frequency | 74.9% | |
Pulse factor | 74.0% | |||||
Centroid frequency | 71.6% | |||||
Frequency skewness | 63.6% | |||||
Vibration signal | Kurtosis factor | 71.8% | Margin factor | 86.6% | Crest frequency | 73.7% |
Margin factor | 66.7% | Average frequency | 63.9% | waveform factor | 66.3% | |
Root mean square | 62.8% | Centroid frequency | −71.7% | Average frequency | 63.3% | |
Absolute mean | 60.9% | Frequency standard deviation | −74.1% | Kurtosis factor | 62.4% | |
Pulse factor | 61.0% | |||||
Root mean square frequency | −61.5% | |||||
Centroid frequency | −64.0% |
Sample | Amount | Range of Amrv (cm3) | Wear Stages of the Grinding Wheel | Amount |
---|---|---|---|---|
Training samples | 24 | 0~16.53 | Initial wear | 8 |
22.04~38.57 | Stable wear | 8 | ||
44.08~60.01 | Severely wear | 8 | ||
Testing samples | 6 | 0~16.53 | Initial wear | 2 |
22.04~38.57 | Stable wear | 2 | ||
44.08~60.01 | Severely wear | 2 |
Signal | Input Feature Number | Number of Hidden Neurons | Output Feature Number | Convergence Error | Rate of Learning | Transfer Function | Learning Algorithm |
---|---|---|---|---|---|---|---|
AE | 14 | 6 | 3 | 0.03 | 0.05 | Sigmoid | Trainlm |
Vibration | 17 | 8 | 3 | 0.03 | 0.05 | Sigmoid | Trainlm |
Sample | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Feature subset | The original feature subset | The preferred feature subset | The optimal feature subset | |||||||||||||||
Predicted Result | T1 | T1 | T2 | * | * | T3 | T1 | T1 | * | T2 | * | T3 | T1 | T1 | T2 | T2 | T3 | T3 |
Actual Result | T1 | T1 | T2 | T2 | T3 | T3 | T1 | T1 | T2 | T2 | T3 | T3 | T1 | T1 | T2 | T2 | T3 | T3 |
Reliability support (%) | 87 | 85 | 91 | 46 | 43 | 85 | 91 | 93 | 50 | 92 | 51 | 96 | 95 | 96 | 95 | 96 | 94 | 93 |
Accuracy (%) | 66.7 | 66.7 | 100 |
Relation | Example | The Principle of Building Tables |
---|---|---|
One for one | Machine tool model table | The foreign key is the primary key and unique |
One-to-many | Machine tools and tool types | A foreign key in the table points to the main table’s primary key. |
Many-to-many | Workpiece and material composition | The intermediate table has two foreign keys, which point to the primary key of the associated table. |
Field Name | Field Type | Field Length | Whether the Primary Key | Remark |
---|---|---|---|---|
PRODUCT_ID | char | 19, Not null | Yes | Primary key ID |
MACHINE_TOOL_ID | char | 19, Not null | No | Machine tool ID |
MACHINE_TOOL_NAME | varchar | 200, Not null | No | Equipment name |
MACHINE_TOOL_CODE | varchar | 36 | No | Equipment number |
MACHINE_TOOL_MODEL | varchar | 36 | No | Equipment type |
MACHINE_TOOL_TYPE | varchar | 36 | No | Equipment type |
MACHINE_TOOL_MAINER | varchar | 36 | No | Equipment Manager |
DELIVERY_TIME | datetime | Not null | No | Factory time |
Type/Content | Technology Selection | Type/Content | Technology Selection |
---|---|---|---|
Back-end development language | JAVA (JDK 1.7) | Persistence layer framework | Mybatis-Plus |
Back-end core framework | SpringBoot | Template engine | Thymeleaf |
Front-end development language | Html, CSS, JavaScript | Cache framework | Redis |
Front-end core framework | LAYUI | Security framework | Apache Shiro |
Database management system | MYSQL 5.7/SQL |
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Share and Cite
Zhuo, R.; Deng, Z.; Ge, J.; Liu, W.; Lv, L.; Yan, C. Research on Cloud-Edge-Device Collaborative Intelligent Monitoring System of Grinding Wheel Wear State for High-Speed Cylindrical Grinding of Bearing Rings. Actuators 2024, 13, 327. https://doi.org/10.3390/act13090327
Zhuo R, Deng Z, Ge J, Liu W, Lv L, Yan C. Research on Cloud-Edge-Device Collaborative Intelligent Monitoring System of Grinding Wheel Wear State for High-Speed Cylindrical Grinding of Bearing Rings. Actuators. 2024; 13(9):327. https://doi.org/10.3390/act13090327
Chicago/Turabian StyleZhuo, Rongjin, Zhaohui Deng, Jimin Ge, Wei Liu, Lishu Lv, and Can Yan. 2024. "Research on Cloud-Edge-Device Collaborative Intelligent Monitoring System of Grinding Wheel Wear State for High-Speed Cylindrical Grinding of Bearing Rings" Actuators 13, no. 9: 327. https://doi.org/10.3390/act13090327