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.
