Multi-Modal Sensor Fusion for Equipment Health Monitoring 2026
Multi-Modal Sensor Fusion for Equipment Health Monitoring
Integrating vibration, thermal, acoustic, electrical, and spectral sensor streams into unified diagnostic frameworks enables fault detection and RUL prediction beyond single-sensor capability. This dataset snapshot covers 60+ records across aerospace, energy, manufacturing, and automotive domains from 2006 to 2026.
From Heterogeneous Sensor Streams to Unified Diagnostic Intelligence
Multi-modal sensor fusion for equipment health monitoring integrates heterogeneous sensor streams—vibration, thermal, acoustic, electrical current, pressure, and spectral data—into unified diagnostic frameworks capable of detecting, classifying, and predicting equipment faults with greater accuracy than single-sensor approaches. The technology sits at the intersection of industrial IoT, deep learning, and predictive maintenance.
The dominant technical mechanisms in this dataset include frequency-domain transformation of time-series sensor data, deep neural network classifiers (CNNs, LSTMs, transformers), probabilistic evidence accumulation via Dempster-Shafer theory, and cross-attention mechanisms for inter-modal correlation learning. Three core processes anchor every architecture: data ingestion and temporal synchronization, feature extraction and fusion, and health state estimation.
The foundational three-module pipeline architecture—data alignment, analysis, and high-level diagnostic fusion—was codified in RTX Corporation’s 2006 gas turbine health monitoring patent and remains structurally canonical across subsequent filings. More recent records replace rule-based analysis modules with deep learning models and transformer attention mechanisms, reflecting a clear shift from model-driven to data-driven design philosophies.
In this dataset, filing activity is concentrated in three eras: a foundational period from 2006–2013 anchored by RTX and early literature; a development era from 2014–2020 adding frequency-domain deep learning; and an acceleration era from 2021–2026 featuring Chinese and Indian institution entries alongside established aerospace and energy incumbents. In retrieved records, RTX Corporation and its predecessor entities represent the single largest patent family across at least 5 jurisdictions.
Filing Trends and Technology Cluster Distribution
Analysis of approximately 45 patent filings in this dataset reveals three distinct eras of innovation activity and four principal technology clusters, with the most concentrated filing activity appearing in the 2024–2026 window driven by Chinese and Indian entrants alongside established incumbents.
Patent Filings by Technology Cluster — Dataset Snapshot
In this dataset, Hierarchical Pipeline Fusion is the most historically represented cluster with filings from 2006 onward, while Attention-Based and Transformer architectures represent the newest and fastest-growing cluster concentrated in 2025–2026.
↗ Click bars to explorePatent Filing Activity by Era — Dataset Snapshot
In this dataset, the 2021–2026 acceleration era accounts for the highest filing volume, driven largely by Chinese and Indian applicants alongside established US and European incumbents entering new application domains.
↗ Click bars to exploreKey Deployment Domains for Multi-Modal Sensor Fusion Health Monitoring
In this dataset, multi-modal sensor fusion for equipment health monitoring is deployed across four principal domains: aerospace and defense, renewable energy and power generation, industrial manufacturing, and automotive and autonomous systems. Each domain features named assignees with distinct fusion architectures and monitoring objectives traceable to specific patent records.
Aerospace & Defense Gas Turbines
RTX Corporation / Raytheon Technologies holds the largest cluster of filings in this dataset spanning US, CA, EP, IN, and WO jurisdictions from 2006 onward, all targeting gas turbine health monitoring. Boeing applies multi-sensor machine learning to aircraft component remaining useful life estimation in its 2022 and 2025 US patents. The U.S. Air Force’s 2024 TDAML patent introduces topological data analysis for adversarial-resilient multi-modal fusion in contested environments.
Aerospace & DefenseRenewable Energy Wind Turbines
Utopus Insights, Inc. holds a patent family spanning US (2021, 2022), WO (2021), and AU (2023, 2024) jurisdictions, using SCADA-integrated machine learning to score gearbox and generator health in wind turbines. A 2024 US/WO filing additionally covers systems for displaying renewable energy asset health risk information. Mohammed Fazal Ur Rahman’s 2025 IN patent fuses vibration, thermal imaging, and lubrication-quality sensors via IoT-enabled gateways for real-time cross-domain turbine correlation.
Renewable EnergyIndustrial Manufacturing Plant Machinery
Robert Bosch GmbH holds three active US patents filed in 2024 for systems and methods for monitoring machine health in multi-sensor manufacturing plant environments. Chinese assignees including Nanning Huban Technology Co., Ltd. and Henan Huixuli Technology Co., Ltd. filed in 2025–2026 on enterprise equipment health monitoring using federated learning global models for multimodal feature vector extraction across distributed assets. The 2026 CN filing from Nanning Huban Technology specifically addresses modal misalignment and noise sensitivity challenges.
Industrial ManufacturingAutomotive & Autonomous Sensor Systems
GM Global Technology Operations LLC filed cross-sensor fusion patents for automotive object detection systems in US (2014, 2015) and CN (2014, 2016), using vision-radar matching scores to monitor the health of the object sensing fusion system itself. Zenseact AB filed an EP patent in 2025 for a monitoring platform addressing perception reliability monitoring for vehicle-mounted multi-sensor configurations. Cisco Technology, Inc. filed a 2025 US patent for adjusting computing devices based on data fusion from operational health performance metrics and situational sensor data.
Automotive & AutonomousLeading Assignees in Multi-Modal Sensor Fusion Health Monitoring — Dataset Snapshot
In this dataset, RTX Corporation and its predecessor entities (Raytheon Technologies, United Technologies Corporation) collectively represent the single largest patent family with filings across at least 5 jurisdictions on gas turbine fusion and PHM deep learning architectures. Utopus Insights, Inc. is the second most visible assignee in retrieved records with 5 records across US, WO, and AU for wind turbine health scoring systems.
Top Assignees by Filing Count in Retrieved Records (Dataset Snapshot)
↗ Click bars to exploreRTX Corporation / Raytheon Technologies
RTX Corporation and its predecessor entities hold the largest patent family in this dataset, with filings across US, CA, EP, IN, and WO jurisdictions spanning 2006–2022. Key patents include the foundational three-module gas turbine health monitoring data fusion system (2006, US) and the deep neural network frequency-domain prognostics and health monitoring fusion system (2018, 2022, US). Multiple filings are granted and active across international jurisdictions.
United StatesUtopus Insights, Inc.
Utopus Insights, Inc. holds 5 records in this dataset across US (2021, 2022), WO (2021), and AU (2023, 2024) jurisdictions, focused on scalable systems for assessing healthy condition scores in renewable asset management for wind turbine gearboxes and generators. A 2024 US and WO filing extends coverage to displaying renewable energy asset health risk information. AU grants were received in 2023 and 2024, indicating active international protection of the wind turbine health scoring portfolio.
United StatesFive Innovation Signals Shaping Multi-Modal Sensor Fusion (2024–2026)
Among records dated 2024–2026 in this dataset, five directional signals stand out: transformer and cross-attention architectures for inter-modal feature learning, federated learning for enterprise-scale monitoring, knowledge graph-augmented diagnostic reporting, causal inference for root-cause separation, and topological data analysis for adversarial-resilient fusion.
Cross-Attention and Transformer Architectures for Embedded Deployment
Indian Institute of Technology Hyderabad filed a cross-attention based multimodal health monitoring system in January and August 2025 (IN), deploying learned dynamic inter-sensor weighting. Vellore Institute of Technology followed in March 2026 (IN) with a hardware-efficient transformer-based sensor fusion system explicitly targeting resource-constrained embedded platforms through model compression and quality-aware reliability gating. These filings signal that edge-deployable transformer fusion is moving from concept to active IP protection.
Federated Learning for Privacy-Preserving Fleet-Scale Monitoring
Henan Huixuli Technology Co., Ltd. filed an enterprise equipment health condition monitoring method in 2025 (CN) introducing a federated learning global model for extracting multimodal feature vectors across distributed enterprise assets. This approach addresses both privacy preservation and fleet-scale scalability challenges that centralized cloud fusion architectures cannot solve. The filing represents a clear move toward decentralized, multi-site industrial monitoring.
Hierarchical Pipeline Fusion vs. Attention-Based Transformer Fusion
Click any row to explore further.
| Dimension | Hierarchical Pipeline Fusion | Attention-Based Transformer Fusion |
|---|---|---|
| First Patent in Dataset | RTX Corporation, 2006 US (gas turbine health monitoring data fusion) | IIT Hyderabad, January 2025 IN (cross-attention multimodal health monitoring) |
| Core Mechanism | Sequential modules: data alignment → feature extraction → decision fusion | Learnable cross-attention mechanisms dynamically weighting inter-modal feature relationships |
| Fusion Level | Data-level, feature-level, and decision-level hierarchy (fixed sequence) | Dynamic inter-modal weighting replacing fixed fusion hierarchies |
| Key Assignees in Dataset | RTX Corporation, Raytheon Technologies, United Technologies Corporation | IIT Hyderabad, Vellore Institute of Technology, Robert Bosch GmbH |
| Deployment Target | Gas turbines, aerospace propulsion systems, legacy industrial plant | Embedded resource-constrained platforms, rotating machinery, remote assets |
| Edge Deployment | Not explicitly addressed in retrieved filings | Vellore 2026 filing adds model compression and quality-aware reliability gating for embedded deployment |
| Explainability | Rule-based modules provide interpretable intermediate outputs | Attention weights provide partial interpretability; knowledge graph augmentation emerging (2025 CN filings) |
| Jurisdiction Spread | US, CA, EP, IN, WO (5+ jurisdictions, 2006–2022) | IN (2025–2026); US Air Force TDAML (2024 US) |
Frequently Asked Questions: Multi-Modal Sensor Fusion for Equipment Health Monitoring
The earliest foundational patent in this dataset is RTX Corporation’s 2006 US patent for a system for gas turbine health monitoring data fusion. It codified a three-module pipeline architecture: a data alignment component synchronizes multi-sensor streams, an analysis module extracts health features, and a high-level fusion module outputs a health assessment. This architecture remains structurally canonical in subsequent filings across the dataset.
In this dataset, RTX Corporation and its predecessor entities (Raytheon Technologies Corporation and United Technologies Corporation) collectively represent the single largest patent family, with filings across at least 5 jurisdictions—US, CA, EP, IN, and WO—covering gas turbine fusion architecture and PHM deep learning fusion systems from 2006 through 2022.
Based on retrieved records, four principal clusters are identified: (1) Hierarchical Pipeline Fusion using sequential data alignment, feature extraction, and decision fusion; (2) Deep Learning and Frequency-Domain Sensor Fusion converting time-series data into frequency-domain representations for neural network processing; (3) Attention-Based and Transformer-Driven Multi-Modal Fusion using learnable cross-attention mechanisms; and (4) Probabilistic and Knowledge-Driven Health Scoring using Dempster-Shafer evidence theory, Bayesian inference, and knowledge graphs.
All Chinese patent records in this dataset are filed between 2025 and 2026, indicating a concentrated, recent entry wave rather than a historical presence. Chinese assignees include Nanning Huban Technology Co., Ltd., Henan Huixuli Technology Co., Ltd., Nanjing Yixintong Control Equipment Technology Co., Ltd., and Guangdong Junhua Energy Technology Co., Ltd. Their filings cover federated learning models, knowledge graph-augmented diagnostics, causal inference, and multi-source information fusion.
The 2026 IN patent from Vellore Institute of Technology, titled ‘Hardware-efficient transformer-based sensor fusion system for embedded multi-modal perception,’ explicitly targets resource-constrained embedded platforms through model compression and quality-aware reliability gating. This reflects the emerging edge-AI deployment imperative for rotating machinery and remote asset monitoring environments.
Based on records in this dataset, integrated sensor modalities include vibration, thermal imaging, acoustic emission, electrical current, pressure, spectral data, lubrication-quality sensors, gas fingerprint sensors, and control command data. The 2026 CN filing from Nanjing Yixintong Control Equipment Technology Co., Ltd. is the most comprehensive in this dataset, explicitly listing vibration, temperature, current, spectral, gas fingerprint, acoustic emission, and control command data in a single multi-source fusion framework.
Data and insights on this page are based on a limited patent and literature dataset and are for reference only. Figures may not represent the complete technology landscape.