STTR Phase I: Machine Learning-Based Smart Data Compression Solutions for Structural Health Monitoring Sensors

STTR 第一阶段：用于结构健康监测传感器的基于机器学习的智能数据压缩解决方案

• 批准号: 2321884
• 负责人: Yasemin Cetin
• 金额: $ 27.5万
• 依托单位: ZQAI LLC
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2321884&HistoricalAwards=false
• 关键词: STTR; Phase; Machine; Learning; Based

The broader impact/commercial impact of this Small Business Technology Transfer (STTR) Phase I project is to enable efficient monitoring of civil infrastructures and rapid decision-making on their structural safety. The conditions of aging structures are monitored using structural health monitoring (SHM) sensors. These sensors produce very large datasets. In this project, a data compression solution will be developed to reduce the size of such datasets by 90%, without losing important information. As an example, one sensor can fill up a 128 Gigabyte hard disk in about 6 hours, but with the data compression solutions, it will take at least 60 hours to fill the hard disk. Data compression is thus a critical factor for both storage (disk space) and efficient transmission of sensor data. A microchip with a built-in data compression algorithm will be developed. The sensors with microchips will need to be visited less often for data retrieval and dramatically less bandwidth and power will be required for data transmission over existing wireless networks. This will enable monitoring of structures in remote areas. The data compression will be applicable to various market segments, however the initial target market will be the SHM of aging structures within the oil and gas industry.This Small Business Technology Transfer (STTR) Phase I project aims to develop sensor data compression schemes and encoder/decoder devices utilizing deep learning methods. The proposed system will consist of a data encoder and decoder, which will autonomously learn the characteristics of the sensor data, extract relevant features, and transmit these using low bit rates. Even users without prior experience in machine learning will be able to train the deep neural network with transform domain layers for different sensor types. The software version of the system will allow for data processing and transmission over the Internet when the sensor is connected to a computer, making it possible to handle stored data on-site. The embedded hardware version will be designed for "edge" usage, meaning it will be implemented next to the sensor itself. This approach will ensure computational efficiency, particularly for the feature extraction part of the network, which needs to be executed at the edge. The project's focus will be on detecting pipeline leakage using high-frequency acoustic emission data on the developed microchip system. By reducing the data transmission bitrate of SHM devices, this system will enable continuous transmission of SHM data to the cloud or data centers.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这一小型企业技术转移（STTR）I阶段项目的更广泛的影响/商业影响是有效地监控民用基础设施，并快速决策对其结构安全。使用结构健康监测（SHM）传感器监测衰老结构的条件。这些传感器生成非常大的数据集。 在此项目中，将开发数据压缩解决方案，以将此类数据集的大小减少90％，而不会丢失重要信息。 例如，一个传感器可以在大约6小时内填充128千兆的硬盘，但是使用数据压缩解决方案，填充硬盘至少需要60小时。因此，数据压缩是存储（磁盘空间）和传感器数据有效传输的关键因素。 将开发具有内置数据压缩算法的微芯片。带有微芯片的传感器需要较少访问数据检索，并且在现有无线网络上传输数据传输所需的带宽和功率将大大降低。 这将使对偏远地区的结构进行监视。 数据压缩将适用于各个市场领域，但是最初的目标市场将是石油和天然气行业内的老化结构的SHM。此小型企业技术转移（STTR）I阶段项目旨在使用深入学习方法开发传感器数据压缩方案以及编码器/解码器设备。所提出的系统将由数据编码器和解码器组成，该数据将自主学习传感器数据的特征，提取相关特征，并使用低比特速率传输这些特征。即使没有机器学习经验的用户也能够使用转换域层来训练不同传感器类型的深度神经网络。当传感器连接到计算机时，系统的软件版本将允许通过Internet进行数据处理和传输，从而可以在现场处理存储的数据。嵌入式硬件版本将用于“边缘”使用，这意味着它将在传感器本身旁边实现。这种方法将确保计算效率，特别是对于网络的特征提取部分，需要在边缘执行。该项目的重点将是使用开发的微芯片系统上的高频声发射数据来检测管道泄漏。通过降低SHM设备的数据传输比特率，该系统将使SHM数据连续传输到云或数据中心。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响来通过评估来支持的。