Utility Infrastructure Fault Prediction from Vibration Pattern Comparison
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Solution Overview
Problem
Utility infrastructure is often damaged by extreme weather and human accidents, leading to additional damage and deterioration, which existing technologies struggle to accurately detect and predict, resulting in potential harm to property and the environment.
Innovation Solution
A system that uses sensors to collect and analyze vibration data from utility infrastructure, comparing it to training data from similar structures to identify and predict faults, including deterioration and environmental damage, and transmits alerts to supervising users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional monitoring methods are used for utility infrastructure, then device complexity is low, but measurement precision and fault detection accuracy are insufficient
Solution Approach 1:
The system performs preliminary actions by collecting sensor data continuously and comparing it against training data from previous time periods and similar infrastructures before actual faults occur. This proactive comparison enables early fault detection and prediction, improving measurement precision by identifying deterioration trends before they lead to failures.
Solution Approach 2:
The system uses training sensor data from similar utility infrastructures as virtual copies to compare against current sensor readings. This copying approach allows the system to leverage data from multiple sources to improve fault detection accuracy without requiring physical duplicate monitoring systems, thus managing device complexity.
2Reliability
If comprehensive sensor monitoring is implemented, then reliability of fault detection improves, but loss of time for data processing and analysis increases
Solution Approach 1:
The system performs preliminary comparison of sensor data against training data stored from previous time periods and similar infrastructures. By having training data pre-collected and organized, the system can quickly compare current readings without extensive real-time processing, reducing time loss while maintaining reliable fault detection through comprehensive data comparison.
Solution Approach 2:
The system extracts only the most relevant features and parameters from comprehensive sensor data for comparison against training data. This extraction process filters out redundant information, allowing the system to maintain high reliability through comprehensive monitoring while reducing data processing time by focusing on critical fault indicators.
Data Source
AI summary
A method may include obtaining, at a server or analysis device, sensor data comprising at least one of vibration data and impulse data from one or more sensor devices coupled to a first utility infrastructure; obtaining training sensor data associated with at least one of the first utility infrastructure from a previous time period and one or more second utility infrastructures; comparing the sensor data with the training sensor data associated with the at least one of the first utility infrastructure from the previous time period and the one or more second utility infrastructures; and identifying or predicting a fault occurrence associated with the first utility infrastructure based on the comparing the sensor data associated with the first utility infrastructure to the training sensor data associated with the at least one of the first utility infrastructure from the previous time period and the one or more second utility infrastructures.