Modeling of a three-dimensional dynamic thermal field under grid-based sensor networks in grain storage

Wang, Di; Liu, Kaibo; Zhang, Xi

doi:10.1080/24725854.2018.1504356

Cited by 8 publications

(2 citation statements)

References 29 publications

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“…Grain temperature can be divided into two parts [38]: global temperature trends caused by external factors (e.g., environmental temperature) and local temperature variations caused by internal factors (e.g., mildew and pests). In comparison with global temperature trends, local temperature variations have been given considerable attention by practitioners because such changes usually trigger systematic changes or even system failure.…”

Section: Real Case Studymentioning

confidence: 99%

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Dynamic Field Monitoring Based on Multitask Learning in Sensor Networks

Wang

Zhang

2019

Sensors

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Field monitoring serves as an important supervision tool in a variety of engineering domains. An efficient monitoring would trigger an alarm timely once it detects an out-of-control event by learning the state change from the collected sensor data. However, in practice, multiple sensor data may not be gathered appropriately into a database for some unexpected reasons, such as sensor aging, wireless communication failures, and data reading errors, which leads to a large number of missing data as well as inaccurate or delayed detection, and poses a great challenge for field monitoring in sensor networks. This fact motivates us to develop a multitask-learning based field monitoring method in order to achieve an efficient detection when considerable missing data exist during data acquisition. Specifically, we adopt a log likelihood ratio (LR)-based multivariate cumulative sum (MCUSUM) control chart given spatial correlation among neighboring regions within the monitored field. To deal with the missing data problem, we integrate a multitask learning model into the LR-based MCUSUM control chart in the sensor network. Both simulation and real case studies are conducted to validate our proposed approach and the results show that our approach can achieve an accurate and timely detection for an out-of-control state when a large number of missing data exist in the sensor database. Our model provides an effective field monitoring strategy for engineering applications to accurately and timely detect the products with abnormal quality during production and reduce product losses.

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Section: Real Case Studymentioning

confidence: 99%

“…Therefore, we remove the global temperature trends from the sensor observations of grain temperature and obtain the observed values of local temperature variations. The detailed information about the modeling of global temperature trends can be found in Reference [38].…”

Section: Real Case Studymentioning

confidence: 99%