TITLE: Anomaly Detection and Inter-Sensor Transfer Learning on Smart Manufacturing Datasets 

ABSTRACT: 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.
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.
Anomaly Detection and Inter-Sensor Transfer Learning on Smart Manufacturing Datasets
http://arxiv.org/abs/2206.06355v1