Trends in Sensors Fault Diagnosis

Witczak, Piotr

doi:10.3390/s21062224

Cited by 1 publication

(2 citation statements)

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“…Redundant sensor systems double the cost of the sensor system without identifying which sensor is faulty. Further analytical techniques to determine the specific faulty sensor in redundant systems is computationally expensive [1][2]. Commercial solutions to detect a sensor failure cannot be performed in real-time while some even require cycling of the power; rendering them impossible to perform in situ [9][10][11].…”

Section: Discussionmentioning

confidence: 99%

“…Several types of analytical sensor fault diagnoses were presented in the book titled "Trends in Sensor Fault Diagnosis". These methods involved the addition of neural networks to sensor data to detect faults in real-time with the associated added hardware and computational costs [2]. We patented a sensor fingerprinting approach [3] that combines lower-cost hardware redundancy with lowcomputational cost analysis to detect a real-time sensor failure.…”

Section: Introductionmentioning

confidence: 99%

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Current sensor fingerprint for real-time failure detection and transceiver identification in autonomous systems controlled by artificial intelligence (AI)

Heynssens,

Garrard,

Burke

et al. 2024

Autonomous Systems: Sensors, Processing, and Security for Ground, Air, Sea, and Space Vehicles and Infrastructure 2024

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Sensors are relied upon to represent the physical state of autonomous systems that are controlled by AI. Accurate data from these sensors is required to avoid collisions and catastrophic events in actual situations. Redundant sensors are often used in mission critical sensors to mitigate both unexpected failures and cyberattacks. Redundant sensors double or triple the sensor system cost without giving the user information about which sensor is faulty in real-time. We developed a method that exploits small sensor-to-sensor variations; thereby fingerprinting them. For this purpose, we designed circuitry measuring a TMCS1101A4 magnetic current sensor pair. The small differences between the two sensors and the measurement of the calibrated sensor are amplified and recorded in a database as the current sensed is varied over a range from 0 to 320 mA. Future measurements of the sensor pair may be compared to the stored table in real-time to check sensor accuracy. Sensor failure can be modeled by placing the output of one sensor in an open-circuit condition. Measurements then specify the percentage accuracy that is maintained in real-time without the sensor reporting a fault. Additionally, arrays of sensors are used to generate unique system identifiers that verify the authenticity of the source of the data. These identifiers are not fixed but can be regenerated by a challenge from the server making it more difficult for actors to falsify the sensor data. Larger arrays of sensors will be investigated to generate encryption keys and seeds for Post Quantum Cryptography (PQC).

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%