14.2 Data Fusion Techniques for Multi-Sensor Systems

Data fusion in multi-sensor SHM systems combines information from various sensors to improve damage detection and structural health assessment. By integrating data from accelerometers, strain gauges, and acoustic emission sensors, these systems overcome individual sensor limitations and provide more comprehensive insights.

Different fusion architectures offer trade-offs between centralized processing and distributed decision-making. Statistical methods, machine learning algorithms, and feature extraction techniques are used to fuse sensor data effectively. Performance evaluation metrics help assess the accuracy, robustness, and efficiency of fusion techniques in real-world SHM applications.

Data Fusion in Multi-Sensor Structural Health Monitoring (SHM) Systems

Definition of data fusion

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• Combines information from multiple sources to improve overall understanding and decision-making capabilities of a system
  • Integrates data from various sensors in SHM to enhance accuracy and reliability of damage detection, localization, and quantification
• Overcomes limitations of individual sensors, reduces false alarms and missed detections, provides comprehensive assessment of structural health
• Leverages complementary nature of different sensor types
  • Accelerometers for vibration monitoring
  • Strain gauges for local deformation measurement
  • Acoustic emission sensors for capturing damage-related events (cracking, delamination)
• Enables more robust and reliable SHM system by combining information from multiple sensors (temperature, humidity, load)

Comparison of fusion architectures

• Centralized data fusion architecture
  • Transmits all sensor data to central processing unit for fusion
  • Optimal decision-making, access to all available information
  • High communication bandwidth, single point of failure, scalability issues
• Decentralized data fusion architecture
  • Sensor nodes process data locally and share fused information with other nodes
  • Reduces communication overhead, increases robustness and scalability
  • Suboptimal decision-making due to limited information sharing
• Hierarchical data fusion architecture
  • Organizes sensor nodes in hierarchical structure with local fusion at lower levels and global fusion at higher levels
  • Balances centralized and decentralized architectures, improves scalability and fault tolerance
  • Increases complexity in system design and management (node coordination, data synchronization)

Methods for multi-sensor fusion

• Statistical methods
  • Bayesian inference combines prior knowledge with sensor observations to update probability of damage states
  • Kalman filtering recursively estimates system state based on noisy sensor measurements
  • Dempster-Shafer theory handles uncertainty and conflicting evidence from multiple sensors
• Machine learning algorithms
  • Artificial Neural Networks (ANNs) learn complex relationships between sensor data and structural health states
  • Support Vector Machines (SVMs) classify damage states based on fused sensor features
  • Deep learning extracts hierarchical features from raw sensor data for improved fusion and damage assessment (convolutional neural networks, autoencoders)
• Sensor feature extraction and selection
  • Time-domain features (statistical moments, peak values, root mean square (RMS))
  • Frequency-domain features (Fourier coefficients, power spectral density (PSD))
  • Time-frequency domain features (wavelet coefficients, Hilbert-Huang transform (HHT))
• Feature-level fusion concatenates features extracted from multiple sensors before applying machine learning algorithms
• Decision-level fusion combines outputs of multiple classifiers or estimators to reach final decision (majority voting, weighted averaging)

Performance evaluation of fusion techniques

• Accuracy metrics
  • Probability of detection (POD) measures likelihood of correctly detecting damage when present
  • Probability of false alarm (PFA) measures likelihood of incorrectly indicating damage when absent
  • Classification accuracy calculates percentage of correctly classified damage states
• Robustness metrics
  • Sensitivity to sensor noise and failures assesses impact of sensor imperfections on fusion performance
  • Generalization ability evaluates performance on unseen data or new damage scenarios (transfer learning, domain adaptation)
• Computational efficiency metrics
  • Processing time measures time required to perform data fusion and decision-making
  • Memory usage assesses memory requirements of data fusion algorithms
  • Scalability evaluates ability to handle increasing numbers of sensors and data volumes (distributed computing, cloud-based solutions)
• Cross-validation techniques
  1. K-fold cross-validation divides data into K subsets, uses K-1 subsets for training and one subset for testing, repeated K times
  2. Leave-one-out cross-validation uses single data point for testing and remaining data for training, repeated for each data point
• Performance comparison benchmarks data fusion techniques against individual sensor approaches and other fusion methods, analyzes trade-offs between accuracy, robustness, and computational efficiency to select most suitable technique for given SHM application (bridges, wind turbines, aircraft)