Dynamic Sampling Policy for In Situ and Online Measurements Data Fusion in a Policy Network
Hongtao Yu, Zhongsheng Hua · IEEE Transactions on Automation Science and Engineering · 2021
The rapid development of sensing technologies has enabled sensors embedded into systems and generated continuous online data for anomaly monitoring and protection of the system. However, due to transmission errors and environmental noises, the online measurements collected by sensors are usually inaccurate and cannot be applied directly. A small amount of precisein situmeasurements, which are accurate but costly to acquire, are usually fused with online measurements for accuracy improvement. Over the past years, there are many studies concerning such data fusion approaches for obtaining reliable fused data. However, most existing works assume the availability ofin situmeasurements, without providing a strategy for sampling the expensivein situmeasurements. As a result, they may undersample or oversample thein situmeasurements that result in large fusion errors or unnecessary costs. To address this problem, this article proposes a dynamic policy for sampling thein situmeasurements adaptively using the information that the online measurements provide. Specifically, the proposed policy models the dependence betweenin situsampling decisions and influencing factors using a policy network, predicts the probability of recommendingin situsamplings with the policy network, and guides the sampling ofin situmeasurements dynamically. Theoretical and experimental results both verify the effectiveness of the proposed approach.Note to Practitioners—In modern industrial systems, a large number of online measurements of equipment parameters usually can be acquired through sensors at a small cost. However, such measurements are normally corrupted with noise and not appropriate for high-accuracy monitoring of equipment. In practice, a small number ofin situmeasurements, which are accurate but costly to acquire, are usually collected and fused with online measurements for accuracy improvement. Many studies have been conducted on such data fusion approaches, but most of them focus on the fusion models and overlook the role of the sampling policy ofin situmeasurement on the fusion results. This article addresses the problem of sampling a few expensivein situmeasurements to be fused with online measurements for obtaining high-accuracy and low-cost fused data. To support this objective, a dynamic sampling policy is proposed to guide the sampling ofin situmeasurements and balance the benefit and cost brought byin situsamplings. Experimental results on the real-world data set show the benefits of the proposed policy compared to relevant methods.