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Analysis of Self-Diagnostic Technology for Hydraulic Systems in Truck-Mounted Pumps
Release time:
2026-06-05
Source:
Author:
Summary:
The modern vehicle-mounted pump hydraulic system’s self-diagnostic function, leveraging an intelligent sensor network and advanced control algorithms, enables real-time monitoring of equipment operating conditions and early fault warning. This technology significantly enhances maintenance efficiency and reduces the risk of unexpected failures.
I. Sensor Monitoring Network
Pressure Monitoring System
Key pressure monitoring point configuration:
1. Main pump outlet pressure sensor: monitors the pump’s operating condition.
2. System main pressure sensor: detects the system’s operating pressure.
3. Pilot pressure sensor: monitors the pressure in the control oil circuit.
4. Return oil backpressure sensor: monitors the system’s return oil condition.
Temperature Monitoring System
Temperature monitoring point layout:
Hydraulic oil temperature sensor: monitors the operating temperature of the hydraulic fluid.
Pump body temperature sensor: detects the pump’s operating temperature rise.
Motor temperature sensor: monitors the temperature of the actuator.
Ambient Temperature Sensor: Records the temperature of the operating environment.
II. Analysis of State Parameters
Operating Parameter Collection
Real-time data collection content:
Pump displacement setpoint and actual value
System Flow Demand and Supply
Action speed of each actuator
Engine speed and load matching
Performance Metric Calculation
Key Performance Parameter Analysis:
1. Calculation of Pump Volumetric Efficiency
2. System Leakage Assessment
3. Component Response Time Measurement
4. Energy Efficiency Analysis
III. Fault Diagnosis Algorithm
Threshold Judgment Method
Diagnosis based on preset thresholds:
Pressure anomaly detection: Alarm triggered when the value exceeds the set range.
Temperature Alert: Prompt when the safety threshold is exceeded
Flow anomaly: Alarm triggered when the deviation from the setpoint is excessive.
Response Timeout: Diagnosis of Excessive Action Execution Time
Trend Analysis Method
Data-Trend-Based Diagnosis:
1. Performance Degradation Trend Prediction
2. Analysis of Wear Development Patterns
3. Fault Precursor Feature Recognition
4. Remaining Life Assessment and Prediction
IV. Fault Code System
Fault Classification Code
Systematic Fault Code System:
Level 1 Code: System Category Identification
Secondary Code: Fault Type Classification
Level 3 Code: Specific Fault Localization
Level 4 Code: Fault Severity
Alarm Level Settings
Hierarchical alarm mechanism:
1. Alert Level: Performance Parameter Anomaly
2. Alert Level: Functionality is affected.
3. Emergency Level: May Cause Damage
4. Severity Level: Shut down immediately for resolution.
V. Self-Diagnosis Procedure
Real-time monitoring of circulation
Continuous Monitoring Process:
Data collection cycle: 100ms
Parameter update frequency: 1s
Trend Analysis Period: 1min
System self-test cycle: 10min
Fault Confirmation Mechanism
Multi-Step Confirmation Process:
1. Initial test abnormality
2. Continuous monitoring and verification
3. Associated Parameter Validation
4. Final diagnosis confirmed
VI. Diagnostic Results Show
The operation interface displays
Human-Computer Interaction Interface:
Fault codes are displayed clearly.
Detailed Fault Description
The handling recommendation is clearly indicated.
Historical records are available for inquiry.
Remote transmission function
Remote Data Transmission:
1. Through CAN Bus transmission
2. Leveraging the IoT cloud platform
3. Real-time reception on mobile devices
4. Synchronized diagnostics at the service center
VII. Maintenance and Support Functions
Maintenance Guidance System
Intelligent Maintenance Support:
Provide a troubleshooting procedure.
Display relevant technical parameters
Tools and equipment required for the prompt
Recommended spare part model and specifications
Historical Data Analysis
Fault Data Management:
1. Establish a fault database
2. Analyze the patterns of fault occurrence.
3. Optimize the preventive maintenance plan
4. Improve equipment design defects
Conclusion
The fault self‑diagnosis technology for vehicle‑mounted pump hydraulic systems, leveraging an intelligent monitoring network and advanced analytical algorithms, enables real-time equipment condition monitoring and early fault warning. Its implementation has significantly enhanced equipment reliability, reduced maintenance costs, and provided robust assurance for safe, efficient operation. As artificial intelligence and big data technologies continue to evolve, fault self‑diagnosis systems will become even more intelligent and precise, ushering in a new era of transformation in equipment management. It is recommended that equipment operators fully understand and make effective use of these smart features, establish a comprehensive equipment health management framework, and maximize equipment performance.
RELATED INFORMATION
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Changsha Base:Hunan Zenxon Heavy Industry Technology Co., Ltd.
Address: Jinrong Tongxin International Industrial Park, Changsha High-tech Zone
Loudi Base:Hunan Haoda Heavy Industry Technology Co., Ltd.
Address: Loudi Avenue, Loudi High-tech Industrial Development Zone, Lianyuan City, Loudi City, Hunan Province
Telephone:400-186-0868
E-mail:3804246750@qq.com
E-mail:19967177733@163.com
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