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

TL;DR

This paper evaluates sensor data and machine learning models for anomaly detection in smart manufacturing, demonstrating transfer learning for defect classification to enable predictive maintenance.

Contribution

It introduces transfer learning from high-data-rate sensors to sparse sensors for defect classification in manufacturing datasets.

Findings

ML models effectively predict sensor time series.

Transfer learning improves defect classification accuracy.

Predictive maintenance becomes feasible with proposed methods.

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.