IntelliSense AI
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IntelliSense AI is a system for real-time structural health monitoring. Miniaturized, distributed AI modules can be installed in a structure -- large critical infrastructure such as bridges, tunnels, as well as onboard vehicles such as trains, aircraft, and trucks. AI analyzes performance and vibrations, and the results are stored and transmitted for 24/7 central monitoring.
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Applications are to flag preventative maintenance work, saving costs on early scheduled maintenance. To protect critical infrastructure, other interference/failures can be trained for detection and early warning. Solar-powered distributed AI allows remote installation.
Jointly developed by Janzen Research and TransPod.
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Applications:
- Railway
- Aerospace
- Marine
- Automotive
- Electrical grids
- Infrastructure
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Preventative Maintenance
Structural Health Monitoring
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IntelliSense AI is a distributed, embedded, intelligent system for critical infrastructure monitoring & health diagnostics. Applications are for early detection of atypical vibrations, tampering, sabotage, as well as natural aging of equipment and structures, to warn for predictive maintenance and save maintenance costs. IntelliSense AI provides advanced structural health assessment through continuous, real-time monitoring of acoustic signals. Unlike simple strain gauge sensors, which merely output a simple measurement, this system unlocks a rich dataset, by AI analysis and high-frequency signal analysis of complex wave propagation.
Novel features include: (a) miniaturized low-power AI engine, to analyze in-situ with solar power, unlike conventional AI dependent on large server clusters; (b) CFDR technology for high dynamic range sensing (able to detect extremely weak signals even during interference from large vibrations); (c) real-time high frequency AI analysis, rather than simply taking a gauge measurement.
Early detection enables proactive, predictive maintenance to ensure operational continuity, and anti-tampering for supply chain integrity. It is scalable up to large infrastructure and vehicles, from bridges to trucks to aircraft.
In defense and security, IntelliSense AI is for continuous monitoring of essential defense assets, early warning, and predictive maintenance, contributing to the readiness and effectiveness of defense capabilities.
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AI to Detect Faults, with
In-Situ AI Sensors
24/7 Remote Monitoring
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Applications:
- Infrastructure Monitoring: Real-time health monitoring of bridges, tunnels, roads, and buildings to detect wear, cracks, and structural fatigue.
- Transportation: Installed in railcars, aircraft, ships, and trucks to monitor vibration, axle stress, and component degradation for safety and cost-effective maintenance.
- Power and Utilities: Used to detect faults in power transmission lines, substations, and renewable infrastructure (e.g., wind turbines, solar arrays).
- Industrial Sites: Deployed on heavy equipment and machinery to prevent mechanical failures and reduce unplanned downtime.
- Shipping & Logistics: Embedded in cargo or packaging to track shock, handling quality, and transport conditions across global supply chains.
IntelliSense AI is a compact, rugged, and low-power device that delivers real-time structural health monitoring through embedded machine learning. The system connects with a variety of sensors -- such as vibration (geophones, accelerometers), acoustic (microphones, hydrophones), and flow-field (velocimeters) -- to detect and interpret dynamic behavior in physical infrastructure and vehicles.
At its core is a hybrid analog/digital circuit that performs real-time analysis and feature extraction directly on the device. Embedded machine learning algorithms convert raw sensor signals into diagnostic information, identifying early signs of stress, wear, or failure. This allows operators to take preventative action before faults escalate, reducing unplanned downtime and enhancing safety.
It can function autonomously in the field, powered by available sources (battery, power bus, solar), and is capable of storing fault histories, transmitting data wirelessly or offline, and interfacing with maintenance software for planning and visualization. It's available in multiple form factors, from miniature embedded modules to weatherproof enclosures, making it deployable across critical infrastructure, vehicles, and energy systems.
This edge-based, intelligent sensing architecture enables resilient monitoring and predictive maintenance in environments where traditional centralized computing or human inspections are impractical -- making it ideal for defence, emergency, and logistics operations.
Joint venture between Janzen Research and TransPod Inc.
Artificial Intelligence system for aircraft, marine, sound/audio, and fluid-dynamics, to extract patterns from turbulence and sound waves.
Technologies and Features
Extreme signals can be analyzed,
such as high-voltages, sound, and vibrations at extreme dynamic ranges.
D2R technology unlocks the power of tiny signals buried inside extremely strong signals.
This is perfect for underwater sensors, geology/mining vibration sensors,
infrastructure vibration sensors,
satellite signals, and other remote monitoring sensors.
These sensors can be connected to this dynamic-dynamic-range system (D2R) for real-time analysis.
World's first papers on HDR Audio, and the new concept of D2R:
Ryan Janzen and Steve Mann (2017), "Extreme-Dynamic-Range Sensing:
Real-Time Adaptation to Extreme Signals", IEEE MultiMedia, 24(2), pp.30-42.
[link]
- based on award-winning research from IEEE ISM 2016.
Ryan Janzen and Steve Mann (2016), "Feedback Control System for
Exposure Optimization in High-Dynamic-Range Multimedia Sensing",
Proc. IEEE ISM 2016, pp.119-125.
Ryan Janzen and Steve Mann (2012), "High Dynamic Range Simultaneous Signal Compositing, Applied to Audio", Proc. IEEE CCECE 2012, Montreal, 2012 April 29 to May 2, 6 pages.
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Veillance flux technology is used to map out sensory fields.
We can track and measure the capacity to sense, as it moves through space.
The first technology to use veillance flux was created by Janzen and team.
Papers on the new physics of veillance flux:
Ryan Janzen and Steve Mann (May 2014),
"Veillance Flux, Vixels, Veillons: An Information-Bearing Extramissive Formulation of Sensing, to Measure Surveillance and Sousveillance" Proc. IEEE CCECE 2014, pp.1-10.
DOI: 10.1109/CCECE.2014.6901060
R. Janzen, S.N. Yasrebi, A.J. Bose, A. Subramanian, S. Mann (2014),
"Walking through Sight: Seeing the Ability to See, in
a 3-D Augmediated Reality Environment"
Proc. IEEE GEM 2014, pp.323-4.
DOI: 10.1109/GEM.2014.7048124
Ryan Janzen and Steve Mann (2016),
"The Physical-Fourier-Amplitude Domain, and Application to Sensing Sensors",
Proc. IEEE ISM 2016, pp.317-320.
See papers.
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For ocean sensing, and industrial monitoring of pipeline flow, the following paper
shows different ways of how similar machine learning can be used:
Ryan Janzen and Steve Mann (2007),
"Arrays of water jets as user interfaces: Detection and estimation of flow by listening to turbulence signatures using hydrophone
s",
Proceedings of the 2007 Association of Computing Machinery (ACM) Multimedia
(ACM MM 2007) Conference,
Augsburg, Bavaria, Germany, Sept. 24-29, 2007. pp. 505-8.
[PDF]
[PDF] -->
[citation]
Paper on turbulence machine-learning.
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About Janzen Research
Janzen Research is the scientific and industrial arm to develop the research and inventions of Ryan Janzen and associates.
Collaborations with universities in USA, Canada, Europe, Asia, as well as
industries in manufacturing, automotive, aerospace, construction, and tech sectors,
have led to several products.
Starting in 1988 with electronics products, in 1996 with home automation, 1998 with AI, now the research
has impacted several sectors and applications.
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Publications - Introducing new scientific and industrial applications.
Projects - Read about large-scale R&D projects
People - Read about the team
Fields of Science/Technology - projects organized by field
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Applications:
- Medical diagnostics
- Aircraft cockpit avionics
- Marine
- Automotive
- Electrical grids, smart grids and intelligent power control
- Manufacturing
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