期刊
IEEE INTERNET OF THINGS JOURNAL
卷 10, 期 4, 页码 3037-3046出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JIOT.2022.3141382
关键词
Feature extraction; Laser excitation; Fault detection; Servers; Robot sensing systems; Industrial Internet of Things; Image edge detection; Condition monitoring; edge intelligence; Index Terms; Industrial Internet of Things (IIoT); pumping unit; signal reconstruction
This article discusses the importance of accurately monitoring the operating state and detecting faults of beam pumping units in Industrial Internet of Things systems. It proposes methods for period estimation, denoising, and fault detection to improve accuracy and efficiency. Experimental results confirm the effectiveness of the proposed approach.
Accurately estimating the state of equipment plays an important role in ensuring the efficient operation of Industrial 4.0 systems. This article focuses on monitoring the operating state and detecting the faults of beam pumping units under the condition of heavy noise within the Industrial Internet of Things. On the one hand, the equipment operating state monitoring system designed in this article uses an acceleration sensor, the signal of which contains considerable noise that greatly reduces the motion state estimation accuracy. On the other hand, the complexity of the indicator diagrams of beam pumping units makes it difficult to extract features, which limits the ability to improve the fault detection accuracy. To overcome these issues, first, a period estimation method based on self-checking that employs acceleration data is proposed to effectively overcome the influence of complex noise on the estimated data period; second, a denoising method based on a physical model is proposed to effectively reduce the influence of complex noise on the acceleration-based displacement estimation; and third, a method for detecting the faults of beam pumping units based on edge intelligence is proposed to effectively improve the fault detection accuracy while maintaining a low computational demand. Extensive experiments on real data verify the effectiveness of the proposed method. To the best of our knowledge, this is the first work to discuss the impact of the quality of data on the performance of fault detection of beam pump units.
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