Vehicular Diagnostics Using Multi-Sensor Anomaly Detection
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Solution Overview
Problem
Current data sensing technologies are limited in collecting and analyzing optimal volumes of data, particularly failing to dynamically filter and address anomalies related to unfocused data types such as smells and tastes, and struggle to implement effective ameliorative actions in real-time.
Innovation Solution
A computer system and method that monitors data from multiple observational angles using sensor devices, employs machine learning algorithms to detect anomalies, and implements ameliorative actions by integrating sensor data from chemical analysis and user input, mimicking human senses to provide comprehensive diagnostics and corrections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If current data sensing technologies are used to collect and analyze data, then data collection capability is maintained at basic level, but data coverage and analysis efficiency are insufficient particularly for unfocused data types
Solution Approach 1:
The system segments data collection by implementing multiple specialized sensor devices for different observational angles (visual, chemical, tactile), allowing comprehensive data coverage while maintaining efficient specialized analysis for each data type
Solution Approach 2:
The system creates a universal diagnostic platform that handles multiple data types (visual, chemical, tactile) through integrated sensor arrays and machine learning algorithms, enabling one system to perform diverse diagnostic functions across different observation modalities
2Reliability
If traditional anomaly detection methods are used, then simple anomalies can be detected, but dynamic filtering and real-time detection of complex anomalies are insufficient
Solution Approach 1:
The system performs preliminary actions by continuously collecting and pre-processing data from multiple sensor sources, maintaining ready-to-analyze data streams that enable immediate anomaly detection when conditions change
Solution Approach 2:
The system implements feedback loops where machine learning algorithms continuously learn from detected anomalies and adjust detection thresholds, improving detection accuracy over time while maintaining real-time operational monitoring
3Loss of information
If comprehensive sensor data collection is implemented, then data coverage is improved, but system complexity increases
Solution Approach 1:
The system merges multiple sensor types and data sources into a unified diagnostic platform, combining visual, chemical, and tactile observation capabilities into integrated sensor arrays that share common processing infrastructure
Solution Approach 2:
The system introduces machine learning algorithms as intermediary layers that translate and harmonize data from diverse sensor sources, converting complex multi-source data into unified diagnostic insights without requiring direct complex integration of all sensor components
4Measurement precision
If dynamic filtering and machine learning algorithms are implemented, then anomaly detection accuracy is improved, but computational requirements and processing time increase
Solution Approach 1:
The system applies partial action by using dynamic filtering and machine learning algorithms selectively for complex anomaly patterns, while relying on simpler threshold-based detection for routine monitoring, reducing overall computational burden
Solution Approach 2:
The system applies local quality by intensifying computational resources (machine learning algorithms) only at specific decision points where anomaly detection is needed, rather than applying continuous heavy processing to all data streams
Data Source
AI summary
Embodiments of the present invention provide a computer system a computer program product, and a method that comprises monitoring observational data based on a plurality of observational angles; dynamically detecting an anomaly within the monitored data based on dynamically filtering the monitored data for a plurality of predicted deteriorations; in response to dynamically detecting the anomaly, generating a plurality of ameliorative actions, wherein each ameliorative action in the plurality of ameliorative actions is based on a generated notification transmitted to a graphic user interface for user input; and dynamically implementing at least one ameliorative action of the plurality of ameliorative actions that corrects the detected anomaly within a computing device.