Smart manufacturing systems are being deployed at a growing rate because of
their ability to interpret a wide variety of sensed information and act on the
knowledge gleaned from system observations. In many cases, the principal goal
of the smart manufacturing system is to rapidly detect (or anticipate) failures
to reduce operational cost and eliminate downtime. This often boils down to
detecting anomalies within the sensor date acquired from the system. The smart
manufacturing application domain poses certain salient technical challenges. In
particular, there are often multiple types of sensors with varying capabilities
and costs. The sensor data characteristics change with the operating point of
the environment or machines, such as, the RPM of the motor. The anomaly
detection process therefore has to be calibrated near an operating point. In
this paper, we analyze four datasets from sensors deployed from manufacturing
testbeds. We evaluate the performance of several traditional and ML-based
forecasting models for predicting the time series of sensor data. 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.
Taken together, we show that predictive failure classification can be achieved,
thus paving the way for predictive maintenance.

本研究分析了制造测试台的四个传感器数据集，评估了若干传统和基于机器学习的时间序列预测模型的性能，并通过从高数据速率传感器中执行转移学习来对缺乏数据的传感器进行缺陷类型分类，因此为实现预测维护铺平了道路。