Anomaly Detection in Mechanical Components via Multi-Modal Sensor Fusion
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Solution Overview
Problem
Current defect diagnosis techniques in mechanical components are reactive, often detecting issues after they have caused damage, leading to costly repairs and safety threats, especially in complex systems where environmental noise interferes with sensor data, resulting in unreliable evaluations.
Innovation Solution
A noise, vibration, and temperature analysis system that includes real-time data reception, preprocessing to remove noise and thermal shifts, feature extraction using ML models like DNNs and CNNs, and rendering of anomalies for proactive maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If conventional sensor-based detection techniques are used, then simple implementation is achieved, but detection accuracy deteriorates in noisy environments
Solution Approach 1:
The patent introduces an intermediary signal processing layer between the sensor and the anomaly detection algorithm. This intermediary layer includes noise filtering, feature extraction, and signal transformation modules that process raw sensor data into cleaned, feature-rich representations. The intermediary processing enables the system to maintain high detection accuracy in noisy environments while keeping the overall implementation relatively simple by using standard signal processing techniques rather than complex hardware modifications.
2Device complexity
If reactive maintenance approaches are used, then simple monitoring is achieved, but system reliability deteriorates due to late defect detection
Solution Approach 1:
The patent implements preliminary action through continuous monitoring of multiple parameters (vibration, temperature, noise) and real-time anomaly detection algorithms that identify defects in their early stages before they cause system failure. The system performs preliminary diagnostics by analyzing subtle changes in signal patterns, enabling proactive maintenance interventions that improve system reliability while maintaining manageable monitoring complexity through automated detection processes.
3Ease of manufacture
If manual inspection techniques are used, then low cost operation is achieved, but productivity deteriorates due to time-consuming inspections
Solution Approach 1:
The patent implements self-service by enabling the system to automatically monitor, detect, and diagnose anomalies without requiring manual inspection. The automated anomaly detection system continuously collects sensor data, processes signals through feature extraction algorithms, and identifies defects independently. This self-service capability maintains low operational costs by eliminating manual labor while dramatically improving productivity through continuous, uninterrupted monitoring that doesn't require human intervention.
4Stability of the object's composition
If environmental noise is present, then normal operation is maintained, but measurement precision deteriorates due to sensor interference
Solution Approach 1:
The patent applies the extraction principle by separating the useful signal from the harmful noise through multiple processing stages. The system extracts relevant features from the sensor data using feature extraction algorithms, filters out environmental noise through noise cancellation techniques, and isolates the anomaly signals from the background interference. This extraction process enables the system to maintain operational stability while achieving high measurement precision by removing the confounding effect of environmental noise from the sensor readings.
Data Source
AI summary
A method to detect anomaly in mechanical components based on sound, vibration, and temperature analysis is disclosed. The method includes receiving sound, vibration, and temperature data from the mechanical components in real-time. Further, the method includes generating synthesized sound data from the received sound data based on varying noise environments by performing pitch changing, temporal stretching, and noise injection. Furthermore, the method includes preprocessing the received data and the synthesized sound data to remove background noise and thermal shifting. Moreover, the method includes extracting one or more features from the preprocessed data. Additionally, the method includes identifying anomaly in the mechanical components based on the extracted one or more features by employing one or more Machine Learning (ML) models. The method also includes rendering at least the identified anomaly in the mechanical components to a user.