ADVIS, INC. — Department of Defense STTR Phase I: N22A-T026

Description

The development of low-cost smart vibration sensors (less than $100 per node) will enable wider deployment of machine health monitoring to military platforms, especially for less costly assets such as light tactical and utility vehicles where the high cost of existing health monitoring systems (typically in excess of $1000 per node) would be difficult to justify. There are hundreds of thousands of light utility vehicles in service throughout the military and the Navy and Marine Corps has more than 50,000 such vehicles in service.  Predictive maintenance and reliable estimates of the remaining useful service lifetime of individual vehicles will improve overall fleet reliability and reduce total maintenance costs. The goal of the proposed program is to create low-cost (less than $100 per node), machine health monitoring smart vibration sensors. The smart sensor will include the following hardware elements: a vibration sensor element with bandwidth and resolution required to monitor drive-train component (transmissions, gearboxes, differentials), low power sensor interface electronics and signal preconditioning, and an ultra-low power computational engine for data acquisition, data logging, analytics, and communications. The smart sensor will support the implementation of trained neural networks for various applications such as gearbox and transmission condition monitoring. The complete system also will include an application running on an external device to communicate with smart sensors to allow users to configure sensors, download trained models, and upload and aggregate data for off-line model training. The goal of reducing the hardware cost to below $100 per node requires a reassessment of existing approaches to smart vibration sensor design. We are exploring the tradeoffs in power, performance, and cost of solutions that employ commercial off the shelf hardware versus custom designs, including the sensing element itself, to data processing and analysis on custom application specific integrated circuits (ASICs). The outcome of the Phase 1 effort will be a complete conceptual system design, from the sensor element to the user interface. The platform will support the implementation of a novelty detection autoencoding neural network. This method does not require labeled fault data for training, which makes it a promising and practical approach for the detection of machine faults. The platform will be open and will support user-defined machine learning models that are consistent with data and program memory constraints of the platform, the target is 1 Mbyte. The availability of an inexpensive, flexible, and easily re-programmable platform will enable and encourage much wider adoption of machine health monitoring for both DoD and commercial applications.