Real-time detection method of equipment exceptions on the basis of synchronous data flow compression

Abstract

The invention discloses a real-time detection method of equipment exceptions on the basis of synchronous data flow compression. The characteristics of each piece of equipment are collected and are grouped, a group dataset which represents the normal operation state of the group of equipment and an own dataset which represents the normal operation state of the equipment are constructed, so that the records of the two datasets are compared to comprehensively obtain an exception detection result, and detection accuracy is improved. Meanwhile, since the operation states of the equipment are different under different environments, a concept drifting detection method based on principal component analysis is adopted to detect operation state data, whether the operation state data is evolved or not is judged, the two datasets are initialized again if the operation state data is evolved, and therefore, detection accuracy is further improved. In addition, the synchronous data flow compression is adopted to reduce a calculated amount of a comparison process so as to realize the real-time detection of the equipment exceptions.

Description

technical field [0001] The invention belongs to the technical field of equipment abnormality detection, and more specifically relates to a real-time detection method for equipment abnormality based on synchronous data stream compression. Background technique [0002] With the development of science and technology, various types of equipment used in the production and use of various fields of the national economy have become increasingly complex and refined. How to detect the status of these devices in real time, judge whether there is an abnormality, and prevent equipment failures will play an inestimable role in reducing equipment failure losses and reducing safety hazards. [0003] Traditional equipment anomaly detection methods, such as anomaly detection based on signal processing, not only require a large amount of expert knowledge, but also cannot detect equipment anomalies in real time. However, equipment operating status records are dynamically generated in real time,...

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Problems solved by technology

[0003] Traditional equipment anomaly detection methods, such as anomaly detection based on signal processing, not only require a large amount of expert knowledge, but also cannot detect equipment anomalies in real time. However, equipment operating status records are dynamically generated in real ti

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