CNN Pipeline Leak Detection via Pressure Image Classification
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Solution Overview
Problem
Current pipeline leak detection methods are inefficient in accurately identifying leaks in real-time due to contamination by background noise and operational anomalies, leading to false alarms and high computational costs, and are not easily transferable between different pipeline systems.
Innovation Solution
Integration of deep learning technologies, specifically convolutional neural networks (CNNs) and adaptive neuro-fuzzy inference systems (ANFIS), with sensor data analysis to classify pressure surges and differentiate between actual leaks and operational anomalies, using continuous wavelet transform (CWT) for image-based pattern recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If simple threshold-driven logic is used for leak detection, then the system is easy to implement, but it produces many false alarms due to contamination by background noise and operational anomalies
Solution Approach 1:
The patent replaces simple threshold-driven logic with deep learning neural networks that process pressure wave signals. The neural network model learns complex patterns from training data and can distinguish between actual leaks and normal operational variations, significantly reducing false alarms while maintaining ease of implementation through automated decision-making.
Solution Approach 2:
The patent introduces an intermediary processing layer using neural networks between the sensor data collection and final leak detection decisions. This intermediary layer processes the raw pressure wave signals, extracts meaningful features, and makes intelligent decisions about whether a pressure change indicates a actual leak or normal operation, filtering out false alarms.
2Measurement precision
If intensive simulation is used for multi-sensor inferred leak detection, then leak differentiation accuracy improves, but computational cost increases and real-time decision making becomes unsuitable
Solution Approach 1:
The patent performs preliminary action by training neural network models offline using extensive simulation and historical data. The models learn the complex patterns of different leak types and operational anomalies during the training phase. Once trained, the models can make real-time predictions quickly without requiring intensive simulation during actual leak detection operations.
Solution Approach 2:
The patent substitutes intensive real-time simulation with pre-trained neural network inference. The neural networks have internalized the complex relationships between sensor readings and leak characteristics through training, allowing them to make accurate leak differentiation decisions rapidly without requiring computationally expensive real-time simulations.
3Measurement precision
If physical inspection techniques are used for leak detection, then detection accuracy can be achieved, but the process is costly and time consuming
Solution Approach 1:
The patent replaces physical inspection techniques with automated neural network-based detection systems that process pressure wave signals from sensors installed along the pipeline. The system continuously monitors pipeline conditions and can detect leaks in real-time without requiring human inspectors to physically traverse the pipeline, significantly reducing inspection time and costs while maintaining high detection accuracy.
Solution Approach 2:
The patent creates a virtual copy of the physical inspection process by using sensor networks and neural networks to simulate and detect leak conditions remotely. Instead of physically inspecting the pipeline, the system uses digital representations of pipeline behavior captured by sensors and processed by neural networks to identify leaks, eliminating the need for time-consuming physical inspections.
Data Source
AI summary
Provided herein are systems and methods to detect pipeline leaks. The systems and method can identify a pipeline pressure surge by applying a trained convolutional neural network (CNN) model for classifying pipeline pressure measurement images on each sensor site of a plurality of sensor sites, transfer pressure surge information obtained from at least a portion of the plurality of sensor sites to a cloud site, and determine whether the identified pressure surge is a pipeline leak at the cloud site using the pressure surge information. The plurality of sensor sites collect pipeline pressure measurement data. The pressure surge information corresponds to the identified pipeline pressure surge.