Smart manufacturing systems are considered the next generation of manufacturing applications. One important goal of the smart manufacturing system is to rapidly detect and anticipate failures to reduce maintenance cost and minimize machine downtime. This often boils down to detecting anomalies within the sensor data acquired from the system which has different characteristics with respect to the operating point of the environment or machines, such as, the RPM of the motor. In this paper, we analyze four datasets from sensors deployed in manufacturing testbeds. We detect the level of defect for each sensor data leveraging deep learning techniques. We also evaluate the performance of several traditional and ML-based forecasting models for predicting the time series of sensor data. We show that careful selection of training data by aggregating multiple predictive RPM values is beneficial. Then, considering the sparse data from one kind of sensor, we perform transfer learning from a high data rate sensor to perform defect type classification. We release our manufacturing database corpus (4 datasets) and codes for anomaly detection and defect type classification for the community to build on it. Taken together, we show that predictive failure classification can be achieved, paving the way for predictive maintenance.
This content will become publicly available on July 9, 2024
Exploration Of Lossy Posture Classification Model Using In-Bed Flexible Pressure Sensors
Advances in flexible and printable sensor technologies have made it possible to use posture classification for providing timely services in digital healthcare, especially for bedsores or decubitus ulcers. However, managing a large amount of sensor data and ensuring accurate predictions can be challenging. While lossy compressors can reduce data volume, it is still unclear whether this would lead to losing important information and affect downstream application performance. In this paper, we propose LCDNN (Lossy Compression using Deep Neural Network) to reduce the size of sensor data and evaluate the performance of posture classification models. Our sensors, placed under hospital beds, have a thickness of just 0.4mm and collect pressure data from 28 sensors (7 by 4) at an 8 Hz cycle, categorizing postures into 4 types from 5 patients. Our evaluation, which includes reduced datasets by LCDNN, demonstrates that the results are promising.
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- Award ID(s):
- 1751143
- NSF-PAR ID:
- 10456004
- Date Published:
- Journal Name:
- IEEE International Conference on Flexible and Printable Sensors and Systems (FLEPS)
- Page Range / eLocation ID:
- 1 to 4
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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