Abstract
The embedded, computational and cloud elements of industrial cyber physical systems (CPS) generate large volumes of data at high velocity to support the operations and functions of corresponding time-critical and mission-critical physical entities. Given the non-deterministic nature of these entities, the generated data streams are susceptible to dynamic and abrupt changes. Such changes, which are formally defined as concept drifts, leads to a decline in the accuracy and robustness of predicted CPS behaviors. Most existing work in concept drift detection are classifier dependent and require labeled data. However, CPS data streams are unlabeled, unstructured and change over time. In this paper, we propose an unsupervised machine learning algorithm for continuous concept drift detection in industrial CPS. This algorithm demonstrates three types of unsupervised learning, online, incremental and decremental. Furthermore, it distinguishes between abrupt and reoccurring drifts. We conducted experiments on SEA, a widely cited synthetic dataset of concept drift detection, and two industrial applications of CPS, task tracking in factory settings and smart energy consumption. The results of these experiments successfully validate the key features of the proposed algorithm and its utility of detecting change in non-deterministic CPS environments.
Highlights
Recent advances in cyber-physical systems (CPS) have necessitated machine learning algorithms in embedded applications to operate in nonstationary, time variant environments [1]
It is pertinent to note that a primary recommendation for future work in concept drift is the detection and validation of change detection and adaptation in the absence, delay and on-demand labeling of CPS data streams. Drawing on this context of technological and operational characteristics required of industrial CPS, as well as the limited application of machine learning in the development of such features leads up towards the contribution of this paper, where we propose a novel machine learning algorithm for continuous detection and adaption to concept drifts from CPS data streams and the integration of this capability into an established closed loop framework for concept drift detection
We present experiments conducted on two industrial applications of CPS data streams, activity monitoring and energy consumption
Summary
Recent advances in cyber-physical systems (CPS) have necessitated machine learning algorithms in embedded applications to operate in nonstationary, time variant environments [1]. The underlying models generated by learning algorithms are influenced by changes in feature information (x) and target variables (y) due to such evolving concepts [2]. Concept drift occurs when this feature information (x) and target variables (y) change over time. In a smart factory setting, a large number of Industrial Internet of Things (IIoT) devices and sensors will be collecting data on machine status and factory operations [3]. These data are transmitted to CPS which will use a variety
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