SBIR Phase I: Software with Breakthrough Composite Distance Method for Zero Defects in Advanced Manufacturing

SBIR第一阶段项目旨在开发基于机器学习的新型软件，用于汽车和医疗设备行业的早期预警和消除潜在的现场故障。该软件核心的复合距离技术对于早期识别故障装置至关重要，因为这些行业的现场故障会带来伤害或死亡的风险。在汽车行业，涉及受伤或死亡的现场事故数量激增。在医疗器械行业，在过去四年中，五分之四的I类召回——即那些导致严重伤害或死亡的召回——是由于复杂电子设备的故障造成的。随着进入汽车和医疗设备的电子产品在产品中所占的份额越来越大，同时制造过程变得更加复杂，这使得在单元/设备发货之前检测缺陷变得困难。在这个项目中开发的软件将检测设备中许多细微缺陷的综合影响所导致的缺陷。这些缺陷不能用目前在制造中所做的标准测试来检测。这个项目符合美国国家科学基金？支持先进制造业的创新和转型技术，为社会带来实质性利益。该项目完成后，该技术将使制造商能够检测和消除在现场故障概率很高的设备，从而保护驾驶员和患者的生命。除了这些对社会的好处，这项技术的商业化将有助于税收，并为数十名工程师和管理人员创造就业机会。由于对设备的许多影响/变量的综合影响而导致的现场故障极难检测-这些设备在制造期间通过了所有规范（否则它们不会发货）。在汽车和医疗设备市场，现场故障可能是灾难性的，并可能导致生命和肢体的损失。在这个项目中，我们开发了突破性的技术来检测和标记预计在通过所有当前规范和控制限制的情况下下游会发生故障的单元。这种独特算法的有效性在现场客户评估的主要分析参与者的竞争中脱颖而出。在这个评价中，复合距离产生了最高的预测精度和最低的产量损失成本。这个方法有两个主要步骤。变量约简和复合距离计算，同时我们使用专有方法在这两个步骤之间进行迭代，以获得用于计算复合距离（CD）的重要关键变量。该参数CD在制造过程中为每个单元计算，反映了所有重要变量的相互作用，提供了异常行为的度量，并允许识别具有高现场故障可能性的特立独行的异型部件。知识产权，也就是新颖性，在于识别重要变量的方式，而那些只会增加噪音的不重要变量则会被迭代地移除。该项目的目标是开发一款具有低延迟大数据能力的实时异常检测示范软件。

This SBIR Phase I project aims to develop novel machine learning-based software for early warning and elimination of potential field failures in automotive and medical device industries. The Composite Distance technique at the heart of the software is crucial for early identification of failing units for these industries where field failures carry the risk of injury or death. In the automotive industry, there has been a surge in the number of field incidents involving injury or death. In the medical device industry, in the last four years, four out of five class I recalls -i.e. those leading to severe injury or death, are due to the failures from complex electronics. As the electronics going into cars and medical devices take up an increasing share of the product while simultaneously manufacturing processes become more complex, this is making it difficult to detect defects before units/devices are shipped. The software being developed in this project will detect defects resulting from the combined effects of many subtle flaws in the device. These defects are not detected using standard testing currently done in manufacturing. This project is in line with the National Science Foundation?s direction to support innovative and transformational technology for advanced manufacturing that has substantial benefits to society. On completion of this project, the technology will enable manufacturers to detect and eliminate devices that have a high probability of failing in the field, thereby protecting the lives of drivers and patients. Aside from these benefits to the society, commercialization of this technology will contribute to tax revenue and create jobs for dozens of engineers and managers. Field failures resulting from combined effects of many influences / variables on the unit are extremely difficult to detect - these units pass all specifications during manufacturing (or else they would not have been shipped). In the automotive and medical device markets field failure can be catastrophic and can result in loss of life and limb. In this project we develop breakthrough technology to detect and flag units that are predicted to fail downstream while passing all current specifications and control limits. The effectiveness of this unique algorithm stood out in a competition involving major analytics players in an onsite client evaluation. In this evaluation the Composite Distance produced the highest predictive accuracy and lowest cost due to yield loss. The method has two major steps ? variable reduction and Composite Distance computation, while we use proprietary methods to iterate between these two steps to arrive at the key variables of importance that are used to calculate the Composite Distance (CD). This parameter CD, computed for each unit during manufacturing reflects the interaction of all variables of importance and provides a measure of anomalous behavior and allows to identify maverick out-of-pattern parts with high likelihood of field failure. The intellectual property, and therefore the novelty, lies in the way important variables are identified and unimportant variables, that only serve to add noise, are removed iteratively. The goal of this project is to produce a demonstration software for real-time anomaly detection, with low latency big data capability.