SBIR Phase I: A Manufacturing Monitoring System Using Sound Spectrograms and Artificial Intelligence

SBIR 第一阶段：使用声谱图和人工智能的制造监控系统

• 批准号: 2126939
• 负责人: Chandra Nath
• 金额: $ 25.58万
• 依托单位: MAIJKER CORP.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-15 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2126939&HistoricalAwards=false
• 关键词: SBIR; Phase; Manufacturing; Monitoring; System

The broader impact of this Small Business Innovation Research (SBIR) Phase I project is to improve manufacturing performance at decreased costs. The project will develop a smart internal sound sensor- and artificial intelligence (AI)-based machine and process monitoring system as a simple plug-and-play edge device. It aims to be a low-cost, versatile, and customizable solution for a wide range of equipment and devices on production floors. This technology can significantly reduce the product defect rate, improve product quality, prolong machine life, increase the overall equipment effectiveness, and enable better human decisions. While this monitoring system can be deployed by any new/modern manufacturers, small and mid-sized enterprise (SME) companies can retrofit legacy equipment/ devices for understanding their machines, process behaviors, timely maintenance, and remaining life, etc. The proposed project is a stethoscope-like sensor that captures internal sounds across many frequencies without contamination from external factory sounds. To interpret the captured sounds into useful information for dashboard publishing in terms of machine/process events (anomaly, situation, downtime, etc.), this project is developing AI-based sound classification or ‘machine speech’ recognition techniques. The sound data can be wirelessly acquired and processed, enabling a device for non-invasive application on the outside of target equipment.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.

这个小企业创新研究（SBIR）第一阶段项目的广泛影响是在降低成本的情况下提高制造性能。该项目将开发一种基于智能内部声音传感器和人工智能（AI）的机器和过程监控系统，作为一种简单的即插即用边缘设备。它旨在为生产车间的各种设备和设备提供低成本，多功能和可定制的解决方案。该技术可以显著降低产品不良率，提高产品质量，延长机器寿命，提高整体设备效率，并实现更好的人为决策。虽然该监控系统可以由任何新的/现代制造商部署，但中小型企业（SME）公司可以对遗留设备/设备进行改造，以了解其机器、过程行为、及时维护和剩余寿命等。这个提议的项目是一个类似听诊器的传感器，可以在不受外部工厂声音污染的情况下捕获多个频率的内部声音。为了将捕获的声音解释为有用的信息，以便在仪表板上发布机器/过程事件（异常、情况、停机等），该项目正在开发基于人工智能的声音分类或“机器语音”识别技术。声音数据可以无线采集和处理，使设备能够在目标设备外部进行非侵入性应用。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。