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

SBIR 第二阶段：使用声谱图和人工智能的制造监控系统

• 批准号: 2335395
• 负责人: Michael Meyer
• 金额: $ 100万
• 依托单位: MAIJKER CORP.
• 依托单位国家: 美国
• 项目类别: Cooperative Agreement
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-03-15 至 2026-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2335395&HistoricalAwards=false
• 关键词: SBIR; Phase; II; Manufacturing; Monitoring

The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase II project lies in its sound-based AI sensing technology. This innovation is poised to significantly elevate the quality and productivity of our nation's manufacturing sector. Central to this mission is its ability to "listen" to the subtle languages of machinery, translating every hum and rattle into actionable insights. This ensures early detection of potential issues, minimizes downtime, and drives optimal performance. Collaborative tests with large manufacturing enterprises are set to showcase its potential in not only averting costly disruptions but also championing a culture of accurate diagnostics and continuous improvement. The societal implications stretch beyond factories. This technology could be embedded in vehicles, bestowing even age-old models with a modern-day protective insight, or in homes, warding off unexpected appliance mishaps. By unlocking the 'speech' of machines, this SBIR project propels us towards a future where our relationship with machinery becomes more intuitive and proactive. Ultimately, this project is a stride towards a seamless conversation between machines and humans, ensuring enhanced safety, efficiency, and an enriched quality of life for all Americans.This Small Business Innovation Research (SBIR) Phase II project aims to push the boundaries of machine condition monitoring by harnessing the underexplored potential of internal machine sounds. Merging Industrial IoT (IIoT) with advanced AI, the project crafts a system primed to provide real-time health and status updates for industrial machinery. The innovation's intellectual merit is in its pioneering method of capturing and analyzing internal machine sounds, paralleling techniques used in AI-based speech recognition systems. The project’s mission is to develop a "machine speech" recognition system that decodes the subtle intricacies of machine sounds for humans. This initiative will enhance our understanding of machine operations and introduce cutting-edge predictive maintenance systems, drawing from a largely untapped data source: internal sounds of machines. Mirroring the precision of a medical doctor using a stethoscope to assess human health and fueled by robust neural network models behind human speech recognition, this project represents the fusion of AI and manufacturing. Empowering workers to "tune in" to machine sounds assures a profound understanding of equipment processes and performance nuances. Diverging from traditional solutions that lean on vibration or current monitoring, this sound-centric approach promises a comprehensive view of machine health, marking a pivotal evolution in manufacturing.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)第二阶段项目的更广泛/商业影响在于其基于声音的AI传感技术。这项创新将显著提升我国制造业的质量和生产率。这项任务的核心是它有能力“倾听”机器的微妙语言，将每一声嗡嗡作响转化为可操作的洞察力。这可确保及早发现潜在问题，最大限度地减少停机时间，并实现最佳性能。与大型制造企业的协作测试将展示其潜力，不仅可以避免代价高昂的中断，还可以倡导准确诊断和持续改进的文化。其社会影响超出了工厂的范畴。这项技术可以嵌入到车辆中，使即使是古老的车型也具有现代的保护性洞察力，或者嵌入到家庭中，防止意外的家电事故。通过解锁机器的‘语音’，这个SBIR项目将我们推向一个我们与机器的关系变得更加直观和主动的未来。归根结底，这个项目是迈向机器与人类之间无缝对话的一大步，确保提高安全性、效率和所有美国人的丰富生活质量。这个小型企业创新研究(SBIR)第二阶段项目旨在通过利用未被开发的内部机器声音的潜力来推动机器状态监测的边界。该项目将工业物联网(IIoT)与先进的人工智能相结合，打造了一个为工业机械提供实时健康和状态更新的系统。这项创新的智力优势在于其捕捉和分析内部机器声音的开创性方法，这是基于人工智能的语音识别系统中使用的并行技术。该项目的任务是开发一种“机器语音”识别系统，为人类解码机器声音的微妙复杂之处。这一倡议将加强我们对机器操作的了解，并引入尖端的预测性维护系统，利用一个基本上未被开发的数据来源：机器的内部声音。这个项目反映了医生使用听诊器评估人类健康的精确度，并以人类语音识别背后的稳健神经网络模型为动力，代表了人工智能和制造业的融合。使工人能够“调谐”到机器的声音，确保了对设备工艺和性能细微差别的深刻理解。与依靠振动或电流监测的传统解决方案不同，这种以声音为中心的方法承诺全面了解机器健康状况，标志着制造业的关键演变。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。