Coal equipment fault early warning system and method based on machine learning

Abstract

The invention discloses a coal equipment fault early warning system and method based on machine learning. A data signal acquisition unit acquires real-time data and acquires static data; a database acquires data from the data signal acquisition unit, data is organized according to a hierarchical structure of mine equipment to form an equipment hierarchical data model, and the equipment hierarchical data model is stored; a machine learning platform is connected with the data signal acquisition unit and/or the database for communication, and a machine learning basic algorithm and an operation environment can be provided; a machine learning algorithm module analyzes data and establishes an equipment fault early warning analysis model through the machine learning basic algorithm on the machinelearning platform and the operation environment, and the equipment fault early warning analysis model is used for equipment fault early warning and judgment; a fault early warning and judgment resultof the machine learning algorithm module is displayed through a result display unit. Early-stage abnormal conditions of the equipment can be found in time, and an early-warning message is released according to a detection result.

Description

technical field [0001] The invention belongs to the technical field of early warning of coal equipment failures, and in particular relates to a coal equipment failure warning system and method based on machine learning. Background technique [0002] Coal equipment is the core tool of coal production. Once a failure occurs, it will not only seriously damage the equipment itself, but also affect the entire production and transportation system, and even endanger the lives of personnel, bringing huge safety hazards and economic losses to coal mine production. Therefore, the research on coal equipment fault warning system is particularly important. [0003] At present, the online fault warning system for coal equipment mainly has the problems of untimely alarm and inaccurate alarm, among which the alarm threshold setting and the single early warning judgment rule are two key problems. In general, the alarm threshold is mostly obtained from the experience of the manufacturer or t...

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Problems solved by technology

[0003] At present, the online fault warning system for coal equipment mainly has the problems of untimely and inaccurate alarms, among which the alarm threshold setting and single warning judgment rules are two key problems

In general, the alarm threshold is mostly obtained from the experience of the manufacturer or through the historical data of the equipment. The above two acquisition methods have factors that do not consider the on-site environmental problems and that the online operati

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