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TIS: Trusted Sensor Integration

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N68335-20-C-0794
Agency Tracking Number: N20A-T011-0259
Amount: $140,000.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: N20A-T011
Solicitation Number: 20.A
Solicitation Year: 2020
Award Year: 2020
Award Start Date (Proposal Award Date): 2020-07-17
Award End Date (Contract End Date): 2021-01-13
Small Business Information
1855 1st Ave #103
San Diego, CA 92101-1111
United States
DUNS: 828934914
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Ulrich Lang
 (650) 515-3391
Business Contact
 Ulrich Lang
Phone: (650) 515-3391
Research Institution
 Mississippi State University
 Drew Hamilton
75 B. S. Hood Road
Mississippi State, MS 39762-9614
United States

 (334) 663-6860
 Nonprofit College or University

Condition-based maintenance plus (CBM+), and cyber-physical systems (CPS) in general, depend on correct sensor data for analysis, decision making and control loops. If the sensor data that arrives at the point of processing is not correct, or more accurate, is outside its accepted error range, then any further processing will be incorrect as well. This could result in, in the case of CBM+, not detecting indicators of failing, e.g. of vibrations or noise. It could also be an incorrect input to a control loop, leading to destruction of a system. The challenge includes correctness of measurement, but also the integrity of communication between sensor and e.g. a Programmable Logic Controller (PLC) or another processing system. In our previous work in cyber-physical systems, e.g. in Supervisory Control and Data Acquisition (SCADA) or Industrial Control Systems (ICS), we have already identified the correctness of sensor data, in security terms their integrity, as major issue, and started work to detect spoof and faked sensor data at the processing device. We propose to build both analytical/statistical and Machine Learning (ML) based models of the static and dynamic behavior of individual sensors and systems. We propose for example to model the dynamic response of a sensor, because the input signal is transformed to the output signal with a specific noise, delay and hysteresis. In other words, we use the imperfections of the sensor in translating the physical input to a numeric output to derive a fingerprint. Unfortunately, we have seen in our previous work that this is not enough. A single sensor always has to be considered in the context of the system as a whole, under non-stable environmental influence. First of all, looking only at the sensor characteristics is not sufficient to decide whether the measurement itself is sound from a physical point of view. In addition, there is a difference between a laboratory environment for sensor fingerprinting and an operational environment, e.g. in temperature and vibrations spectrum. Therefore, we need to model the system as a whole. For modeling both individual sensors and sensors in a system, we propose to use analytical approaches like Matlab/Simulink or OpenModelica, and, complementary, Machine Learning (ML), incl. Recurrent Neural Networks (RNN). We expect that ML will be especially useful for sensor fingerprinting and system models detecting deviations in sensor signals, while analytical models will better allow to analyze the connection of a sensor and an engine. For practical model building in Phase I, we propose to stimulate a simple cyber-physical system, e.g. an engine, to measure the sensor output, and to feed both stimulation signals and sensor outputs into an RNN. The realistic training here depends on the realistic stimulation. We especially expect that it will not be possible to build a complete repository of failures, instead, we will define a failure as a deviation from expected behavior.

* Information listed above is at the time of submission. *

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