(187e) Next-Generation Process Monitoring for Iot-Enabled Smart Manufacturing
In this work, we first present a roadmap of statistical process monitoring, which divides the development of statistical process monitoring into three generations: 1st generation: statistical process control (SPC); 2nd generation: multivariate statistical process monitoring (MSPM); and 3rd generation: yet to be properly defined and named. For the first two generation of process monitoring methods, their development history was briefly reviewed, significant contribution to manufacturing discussed, and major limited identified. For the next-generation, i.e., 3rd generation, process monitoring methods, their desired capabilities are discussed, and recent developments in the area are summarized.
Next, some major challenges that process monitoring could face in addressing the 4 Vâs of Big Data, i.e., volume, variety, velocity and veracity, are discussed. In our opinion, to effectively address the 4V-challenges associated with Big Data, drastically different approaches are needed. Specifically, we expect that process feature based monitoring, instead of process variable based monitoring, may offer an effective way to address these challenges, and could play a significant role in the enhancement of process monitoring for smart manufacturing.
Finally, some of the future opportunities and application areas brought by IoT-enabled smart manufacturing are discussed, which include feature identification and extraction, self-adaptive modeling, preventive maintenance and sensor fault self-correction.
This paper has an Extended Abstract file available; you must purchase the conference proceedings to access it.
Do you already own this?
Log In for instructions on accessing this content.
|AIChE Graduate Student Members||Free|
|AIChE Undergraduate Student Members||Free|