UUV Sedimentation Diagnosis for Drainage Pipeline Waterlogging Warning

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Solution Overview

Problem

Existing methods for diagnosing sedimentation in drainage pipelines are inaccurate, inefficient, and unable to provide proactive maintenance due to low visibility and navigational challenges, leading to inadequate management of underground drainage networks and increased waterlogging risks.

Innovation Solution

A UUV-based system equipped with Doppler ultrasound, high-definition infrared cameras, and deep learning algorithms for real-time sedimentation diagnosis and waterlogging warning, integrating multi-objective optimization and fuzzy set methods for optimal maintenance decision-making.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If traditional pipeline robots are used for patrol diagnostic operation, then they can navigate through the pipeline network, but they are unable to navigate through sediment and scale under full-flow or clogged conditions

Engineering Contradiction:
Improvenavigational capabilityVSAvoiddiagnostic reliability
Core Design Contradiction:
Ease of operationVSReliability

Solution Approach 1:

The patent replaces traditional mechanical pipeline robots with an acoustic-based diagnostic system. The system uses acoustic sensors and signal processing to detect pipeline defects, sedimentation, and blockages without requiring physical navigation through the pipeline. This substitution of mechanical navigation with acoustic field-based detection resolves the contradiction by enabling reliable diagnosis without the navigational limitations of physical robots.

Inventive Principle:
Principle #28Mechanics substitution (Replace mechanical system)

2Productivity

If traditional flow measurement devices are used, then they can measure flow in drainage networks, but they have low accuracy with error rates fluctuating between 8% and 11%

Engineering Contradiction:
Improvemeasurement capabilityVSAvoidflow measurement accuracy
Core Design Contradiction:
ProductivityVSMeasurement precision

Solution Approach 1:

The patent replaces traditional mechanical flow measurement devices with an acoustic-based flow measurement system. By using acoustic sensors to detect flow characteristics and applying signal processing algorithms, the system achieves high-precision flow measurement without the errors associated with traditional mechanical devices. This substitution eliminates moving parts and contact with corrosive fluids, thereby improving measurement accuracy.

Inventive Principle:
Principle #28Mechanics substitution (Replace mechanical system)

3Ease of operation

If traditional pipeline robots diagnose pipelines segment by segment, then they can inspect the pipeline network, but they lead to delayed responses and inefficiencies

Engineering Contradiction:
Improveinspection capabilityVSAvoiddiagnostic efficiency
Core Design Contradiction:
Ease of operationVSProductivity

Solution Approach 1:

The patent creates a multi-functional acoustic diagnostic system that can simultaneously detect multiple types of pipeline defects, measure flow parameters, and assess pipeline conditions across the entire network. This universal system replaces the sequential, segment-by-segment inspection approach with parallel multi-parameter detection, thereby dramatically improving diagnostic efficiency and enabling real-time monitoring without the delays of traditional methods.

Inventive Principle:
Principle #6Universality (Multi-functionality)

4Measurement precision

If neural networks are used for extracting fine details of complex pipeline defect characteristics, then diagnosis accuracy can be improved, but the complexity of the system increases

Engineering Contradiction:
Improvedefect detection accuracyVSAvoidsystem complexity
Core Design Contradiction:
Measurement precisionVSDevice complexity

Solution Approach 1:

The patent replaces complex mechanical inspection systems with an acoustic-based diagnostic system enhanced by neural network algorithms. The acoustic sensors capture pipeline defect characteristics, and neural networks process these signals to extract fine details and classify defects. This substitution achieves high detection accuracy while reducing physical system complexity by using field-based sensing and computational analysis instead of complex mechanical inspection equipment.

Inventive Principle:
Principle #28Mechanics substitution (Replace mechanical system)

Applied Scientific Principles

This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.

Function Achieved in This Case

Enables accurate, efficient, and automated sedimentation diagnosis and waterlogging prediction, facilitating intelligent maintenance decisions with high precision and reduced response times.

Implementation Method 1

The Doppler ultrasound module, three-axis gyroscope, high-definition infrared camera, navigation interface, and deep learning development board are each connected to the main controller

Methodology Applied
Scientific EffectDoppler effect: Doppler Effect

Implementation Method 2

The Doppler ultrasound module, three-axis gyroscope, high-definition infrared camera, navigation interface, and deep learning development board are each connected to the main controller

Methodology Applied
Scientific EffectGyroscope effect: Gyroscope

Data Source

PatentUS12583566B2Unmanned vessel-based maintenance decision-making method for diagnosing sedimentation in drainage pipelines and providing early warning for waterlogging
Publication Date: 2026.03.24 ZHENGZHOU UNIV
  • US12583566B2 patent drawing
  • US12583566B2 patent drawing
  • US12583566B2 patent drawing

AI summary

This invention relates to the diagnosis of sedimentation in drainage networks, specifically providing an unmanned underwater vehicle (UUV) based maintenance decision-making method for drainage pipeline sedimentation diagnosis and waterlogging warning. The UUV of this invention consists of an integrated hull inside the pipeline and a manhole box. The integrated hull inside the pipeline includes a Doppler ultrasound module, a three-axis gyroscope, a high-definition infrared camera, a main controller, a deep learning development board, baffles, a cabin cover, a cabin, fins, towing rings, and a navigation interface. The Doppler ultrasound module, three-axis gyroscope, high-definition infrared camera, navigation interface, and deep learning development board are each connected to the main controller. The fins, towing rings, cabin cover, and baffles are connected to the cabin.