TSML (Time Series Machine Learnng)
This addresses data processing bottlenecks in industrial automation for monitoring and maintenance, but appears incremental as it builds on existing pipeline and filtering concepts.
The paper tackles the challenge of extracting information from large volumes of industrial time series data for tasks like anomaly detection and fault prediction, by developing TSML, a system that uses lightweight filters in a pipeline to process data in parallel.
Over the past years, the industrial sector has seen many innovations brought about by automation. Inherent in this automation is the installation of sensor networks for status monitoring and data collection. One of the major challenges in these data-rich environments is how to extract and exploit information from these large volume of data to detect anomalies, discover patterns to reduce downtimes and manufacturing errors, reduce energy usage, predict faults/failures, effective maintenance schedules, etc. To address these issues, we developed TSML. Its technology is based on using the pipeline of lightweight filters as building blocks to process huge amount of industrial time series data in parallel.