Equipment fault auxiliary diagnosis method and system for manufacturing industry
A technology for equipment failure and auxiliary diagnosis, applied in the field of machine learning, can solve the problems of high labor cost, large personnel dependence, long fault diagnosis time, etc., and achieve the effect of low labor cost, short fault diagnosis time, and reduced labor cost.
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Embodiment 1
[0028] Such as figure 1 , 2 As shown, in order to solve at least one problem existing in the prior art, this embodiment can provide a manufacturing-oriented auxiliary equipment fault diagnosis method, which can realize real-time monitoring of equipment operation status and efficient auxiliary diagnosis of equipment faults. The method includes, but is not limited to, the following procedures.
[0029] First, collect historical real-time operating data of the equipment to be diagnosed at a set frequency. The set frequency can be set according to actual needs, such as 0.1 Hz, etc. In this embodiment, real-time data from different sensors of the device can be obtained every 10 s according to the difference of the device. For example, various sensors can be used to collect corresponding real-time operation data of the machine, such real-time operation data includes but not limited to pressure, temperature, rotational speed, etc. During implementation, pressure data can be collect...
Embodiment 2
[0051] Such as figure 2 As shown, based on the same inventive concept as that of Embodiment 1, this embodiment provides a manufacturing-oriented equipment fault auxiliary diagnosis system. The framework of the entire equipment fault auxiliary diagnosis system can include three parts: data acquisition part, multi-level feature prediction model part and abnormal detection part based on active learning. The multi-level feature prediction model part can include two-level prediction. The idea comes from integrated learning stacking (stacked) framework, divided into a base learning part and a meta learning part. Specifically, the auxiliary fault diagnosis system may include, but not limited to, a state prediction module, a state monitoring module, a fault judgment module, and a troubleshooting reference module.
[0052] The state prediction module is used to generate the predicted value of the operation state of the equipment to be diagnosed. The state prediction module includes ...
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