Motor hidden danger diagnosis edge calculation method and device based on multi-dimensional data

CN119740062APending Publication Date: 2025-04-01SHENZHEN DACHENG INTELLIGENT ELECTRICAL TECH CO LTD
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Patent Information

Application Number
CN202410312583.5
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-03-19
Publication Date
2025-04-01

AI Technical Summary

Technical Problem

The existing motor monitoring methods are difficult to achieve real-time perception and accurate early warning, resulting in the occurrence of production safety accidents. At the same time, cloud platforms have delay and scalability problems when processing motor data, which cannot meet the real-time and scalability requirements.

Method used

Adopting edge computing methods based on multi-dimensional data, artificial intelligence algorithms are pre-mounted to the edge terminal, and sensor data is received in real time for preliminary cleaning and processing, reducing data transmission and processing delays. Data processing is performed on the terminal device using the DNN model, including data acquisition, preprocessing and feature extraction, periodically stored and uploaded to the cloud for further analysis.

Benefits of technology

It realizes real-time and high-precision for motor operation potential diagnosis, reduces delay and bandwidth problems, improves the operation monitoring accuracy and data diversity of motor equipment, ensures the safe operation of the motor, and provides network communication, remote monitoring and early warning functions.

✦ Generated by Eureka AI based on patent content.

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Abstract

The invention provides a motor hidden danger diagnosis edge calculation method and device based on multi-dimensional data, a software calculation mode is executed on terminal equipment by adopting a DNN model, the software calculation mode comprises a data acquisition module and a data processing module, the DNN model comprises model design, model compression, hardware CPUs, GPUs and an integrated circuit TPU of a deep learning customized application program, and the DNN model comprises a data acquisition module and a data processing module. The acquisition function module comprises a voiceprint monitoring module, a temperature and humidity monitoring module and a vibration monitoring module, adopts an advanced edge data processing technology, has the characteristics of high precision, low time delay, multiple dimensions, preprocessing and the like, can be widely applied to the hidden danger diagnosis and maintenance field of various motor devices, and has a wide application prospect. The operation monitoring accuracy of the motor equipment and the diversity of data are improved, the safe operation of the motor is ensured, and the economic benefit is improved.
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