Vehicle Fault Detection Using Sensor Data Image Analysis
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Solution Overview
Problem
Conventional fault detection systems in vehicles, such as aircraft, rely on statistical analysis that reduces vast amounts of sensor data into single numbers, often ignoring subtle changes and complex patterns, leading to incomplete fault identification and requiring manual feature generation, which is time-consuming.
Innovation Solution
A vehicle fault detection system that converts time series sensor data into graphical representations, using deep learning models to detect anomalies and predict component failures, allowing for the analysis of entire vehicle excursions and eliminating the need for manual feature generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If statistical analysis reduces time series sensor data into single numbers, then processing time is reduced, but measurement precision and ability to capture subtle changes deteriorates
Solution Approach 1:
The patent transforms time series sensor data into image representations, adding spatial dimensions to the temporal data. This allows deep learning models to process the data in a different format that preserves subtle patterns while enabling efficient computation through image processing algorithms.
Solution Approach 2:
The patent introduces an intermediate image representation layer between the raw sensor data and the fault detection analysis. This intermediary transformation enables the system to maintain high measurement precision while facilitating faster processing through the use of image processing techniques.
2Measurement precision
If manual feature generation is used, then detection accuracy can be improved, but productivity and ease of operation deteriorates
Solution Approach 1:
The patent implements self-service by using deep learning models to automatically extract features from the image representations of sensor data. The system eliminates the need for manual feature engineering, as the neural networks automatically identify relevant patterns and characteristics for fault detection.
Solution Approach 2:
The patent replaces the mechanical process of manual feature generation with an automated computational system based on deep learning. This substitution transforms the labor-intensive manual feature extraction process into an efficient automated feature learning process through neural network algorithms.
3Device complexity
If conventional statistical analysis is used, then device complexity is reduced, but ability to detect complex patterns and relationships deteriorates
Solution Approach 1:
By converting time series data into image representations, the system enables deep learning models to detect complex spatial and temporal patterns that conventional statistical methods cannot identify. The image format allows visualization and analysis of multi-dimensional relationships in the sensor data.
Solution Approach 2:
The patent changes the fundamental parameters of data representation from numerical time series to visual image formats. This parameter transformation enables the system to capture complex patterns and relationships that are invisible in traditional statistical representations, while the deep learning models automatically adapt to the new data format.
Data Source
AI summary
A vehicle fault detection system including at least one sensor configured for coupling with a vehicle system, a vehicle control module coupled to the at least one sensor, and being configured to receive at least one time series of numerical sensor data from the at least one sensor, at least one of the at least one time series of numerical sensor data corresponds to a respective system parameter of the vehicle system being monitored, generate a graphical representation for the at least one time series of numerical sensor data to form an analysis image of at least one system parameter, and detect anomalous behavior of a component of the vehicle system based on the analysis image, and a user interface coupled to the vehicle control module, the user interface being configured to present to an operator an indication of the anomalous behavior for the component of the vehicle system.