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

arXiv: https://arxiv.org/abs/2206.06355 · HTML: https://mlsanity.com/paper/2206.06355/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 66 references that are in the map.
- First posted on arXiv 13 Jun 2022.

## Builds on (most cited first, 7 of 7)

- [DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks](https://mlsanity.com/paper/1704.04110/) (2017)
- [Net2Net: Accelerating Learning via Knowledge Transfer](https://mlsanity.com/paper/1511.05641/) (2015)
- [Think Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series Forecasting](https://mlsanity.com/paper/1905.03806/) (2019)
- [Deep Factors for Forecasting](https://mlsanity.com/paper/1905.12417/) (2019)
- [Description and Discussion on DCASE2020 Challenge Task2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring](https://mlsanity.com/paper/2006.05822/) (2020)
- [Anomaly Detection through Transfer Learning in Agriculture and Manufacturing IoT Systems](https://mlsanity.com/paper/2102.05814/) (2021)
- [Context-Aware Collaborative-Intelligence with Spatio-Temporal In-Sensor-Analytics in a Large-Area IoT Testbed](https://mlsanity.com/paper/2005.13003/) (2020)

## Built on by (most cited first, 2 of 2)

- [A Comprehensive Survey of Deep Transfer Learning for Anomaly Detection in Industrial Time Series: Methods, Applications, and Directions](https://mlsanity.com/paper/2307.05638/) (2023)
- [ADs: Active Data-sharing for Data Quality Assurance in Advanced Manufacturing Systems](https://mlsanity.com/paper/2404.00572/) (2024)

## Abstract (arXiv)

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.