Drift Detection Analytics for IoT Sensors. Munirathinam, S. Procedia Computer Science, 180:903–912, January, 2021. ![Drift Detection Analytics for IoT Sensors [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex The Industrial Internet of Things (IoT) has an unique opportunity to have a greater impact on the manufacturing sector. Monitoring the health of the expensive equipments in the factory is critical for the business and the opportunities where IoT can be truly transformational. Most often the industries operates on a primitive way of monitoring these equipments on a Statistical Process Control (SPC) Limits. The major flaw in this monitoring system is unable to detect drifts within the static limit and upon triggering of the limits, it is usually too late for the team in the manufacturing floor to take preventive actions before the system goes down. In this paper, we developed a generic model for detecting drifts and identifying potential outliers. The model uses a double linear regression method to identify both aggressive and progressive drift, as well as adjusted boxplot method to detect outliers in both symmetric and skewed distributions. Unlike conventional drift detection approaches, this model has low computational complexity and can be applied to both batch and stream data. This paper will also introduce the infrastructure and architecture on enabling near real-time analytics using the IoT platform and streaming cluster, which reduces the data latency available for analysis to 10 minutes. Enabling real-time monitoring allows the end users to react to the alarms in a timely manner. This system has proven that it is able to provide early detection before the impact was observed as compared to the existing system. Manufacturing operation team could establish a new business process to respond to the early drift alarms by using quality shift left approach.
@article{munirathinam_drift_2021,
series = {Proceedings of the 2nd {International} {Conference} on {Industry} 4.0 and {Smart} {Manufacturing} ({ISM} 2020)},
title = {Drift {Detection} {Analytics} for {IoT} {Sensors}},
volume = {180},
issn = {1877-0509},
url = {https://www.sciencedirect.com/science/article/pii/S1877050921003951},
doi = {10.1016/j.procs.2021.01.341},
abstract = {The Industrial Internet of Things (IoT) has an unique opportunity to have a greater impact on the manufacturing sector. Monitoring the health of the expensive equipments in the factory is critical for the business and the opportunities where IoT can be truly transformational. Most often the industries operates on a primitive way of monitoring these equipments on a Statistical Process Control (SPC) Limits. The major flaw in this monitoring system is unable to detect drifts within the static limit and upon triggering of the limits, it is usually too late for the team in the manufacturing floor to take preventive actions before the system goes down. In this paper, we developed a generic model for detecting drifts and identifying potential outliers. The model uses a double linear regression method to identify both aggressive and progressive drift, as well as adjusted boxplot method to detect outliers in both symmetric and skewed distributions. Unlike conventional drift detection approaches, this model has low computational complexity and can be applied to both batch and stream data. This paper will also introduce the infrastructure and architecture on enabling near real-time analytics using the IoT platform and streaming cluster, which reduces the data latency available for analysis to 10 minutes. Enabling real-time monitoring allows the end users to react to the alarms in a timely manner. This system has proven that it is able to provide early detection before the impact was observed as compared to the existing system. Manufacturing operation team could establish a new business process to respond to the early drift alarms by using quality shift left approach.},
language = {en},
urldate = {2021-02-22},
journal = {Procedia Computer Science},
author = {Munirathinam, Sathyan},
month = jan,
year = {2021},
keywords = {Analytics, Big Data, Concept Drift, Drift, IoT, Machine Learning, Sensor, Statistical Control Limits},
pages = {903--912},
}
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