Localization and recognition algorithm for fuzzy anomaly data in big data networks

Zhang, Huajie; Zhang, Sen; Hanafiah, Marlia Mohd

doi:10.1515/phys-2018-0128

Cited by 2 publications

(2 citation statements)

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“…With the rapid development of big data analysis, arti cial intelligence, machine learning, and other technologies, abnormal event monitoring methods based on real-time data (such as pressure, ow, temperature, and other monitoring data) have gradually become used for pipeline leak event identi cation [5][6][7][8]. At present, there are several oil pipeline anomaly detection methods based on real-time monitoring data, such as (1) the volume and mass balance method, which diagnoses abnormal events by observing the degree of balance between volume and ow at both ends of the pipeline.…”

Section: Introductionmentioning

confidence: 99%

“…However, due to the diverse causes of pipeline leak accidents, it is easy to ignore some potential variable characteristics when general statistical modeling methods are used, and false positives are often generated in complex production environments [21]. (8) e anomaly detection method is based on machine learning. is method uses the machine learning method to model oil pipeline monitoring data and then uses the model to detect anomalies from real-time oil pipeline data.…”

Section: Introductionmentioning

confidence: 99%

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Research on an Oil Pipeline Anomaly Identification Method for Distinguishing True and False Anomalies

Chen

Wang

et al. 2022

Mobile Information Systems

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Anomaly identification is important to ensure the safe and stable operation of oil pipelines and prevent leaks. Leak identification is performed to divide abnormal samples from normal oil transfer samples in monitoring data, and it is a dichotomous problem. However, the traditional machine learning binary classification method is no longer suitable for identifying leak anomalies in complex production environments. The main problem is that leaks in production environments are very rare, and traditional methods cannot directly identify the leaking pattern with their generalizability. The recognized normal pattern lacks the ability to adapt to dynamic environmental changes and an artificial adjustment of the pump frequency, instrument calibration, and other monitoring data mutations. These are known as false anomalies, and they are difficult to distinguish from true anomalies. This results in a lower recall rate for leak anomaly identification and a higher rate of false positives. To solve this problem, this study proposes a leak anomaly recognition method based on the distinction between true and false anomalies. A one-class SVM is used to learn the normal working mode of oil pipelines, and the model is used to screen out suspected pipeline anomalies, namely, true and false anomalies. It increases the morphological difference between true and false anomaly curves by superimposing multisource data and uses similarity clustering to discover anomaly patterns that indicate leak events. The results show that the leakage anomaly recall rate is 100%, and the false anomaly exclusion rate is 83.49%. This method achieves real-time and efficient monitoring of pipeline leaking events in complex production environments, and it is practical for the application of machine learning methods in production environments.

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