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.

Graph autoencoder with mirror temporal convolutional networks for traffic anomaly detection

“…The intelligent transportation system discussed in this paper is an artificial intelligence-based technology that aims to detect intelligent traffic anomalies. By learning the relationships between sensors, we could detect anomalies from sensors data [3][4][5] . However, traffic anomalies usually exhibit complex forms due to two aspects: high dimensionality, sparsity, abnormal scarcity (i.e., the need to correlate time and space, including speed or flow), and difficulty in capturing the hidden relationship between nodes (i.e., spatial modeling in the face of different data sources with varying degrees of anomalies in density or distribution and scale) 6,7 .…”

“…3 School of Computer and Software Engineering, Xihua University, Chengdu 610039, China. 4 These authors contributed equally: Zhiyu Ren and Xiaojie Li. * email: scsz@scsz.com; xi.wul@cuit.edu.cn…”

MTGAE: Graph Autoencoder with Mirror TCN for Traffic Anomaly Detection

“…The intelligent transportation discussed in this paper is an artificial intelligence-based technology in the field of transportation to automate road vehicles and intelligent traffic anomaly 3 . And learning the relationship of inter-sensor allows us to know which sensor is abnormal and which aspects deviate from normal behavior 4,5 . However, traffic anomalies usually exhibit complex forms due to two aspects: high dimensionality, sparsity, abnormal scarcity (i.e., the need to correlate time and space, including speed or flow), and difficulty in capturing the hidden relationship between nodes (i.e., spatial modeling in the face of different data sources with varying degrees of anomalies in density or distribution and scale) 6 .…”