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Bearing equipment condition monitoring method based on clustering and multi-layer autoencoder network

A self-encoding network, equipment status technology, applied in the testing of mechanical components, testing of machine/structural components, instruments, etc., can solve problems such as reducing work efficiency and monitoring the status of equipment that cannot be used for bearing operation.

Active Publication Date: 2021-06-04
SHANGHAI MARITIME UNIVERSITY
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Problems solved by technology

At present, the status monitoring of bearings in industrial sites is only based on manual experience, and cannot effectively monitor the status of bearing operating equipment. Failures cannot be found in the early stage of failures, and irreparable economic losses are often caused when failures are found, and professionals are required to carry out failures. Troubleshooting and maintenance, greatly reducing work efficiency

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  • Bearing equipment condition monitoring method based on clustering and multi-layer autoencoder network
  • Bearing equipment condition monitoring method based on clustering and multi-layer autoencoder network
  • Bearing equipment condition monitoring method based on clustering and multi-layer autoencoder network

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Embodiment Construction

[0041] The equipment status monitoring method based on clustering and multi-layer self-encoding network, the overall structure diagram is as follows figure 1 shown.

[0042] The equipment status monitoring method based on clustering and multi-layer self-encoding network is mainly divided into data information acquisition and transmission method, cloud platform data information processing and analysis method, cloud platform monitoring and data visualization display method, and information release method. Among them, the data information acquisition and transmission method belongs to the equipment-side data acquisition method. The vibration acceleration sensor is deployed at the corresponding position of the rolling bearing, the vibration information of each point is collected, and the collected vibration state data information of the bearing is stored on the cloud platform. The cloud platform data information processing and Analysis methods, monitoring and data display and info...

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Abstract

The invention proposes a bearing equipment state monitoring method based on clustering and multi-layer self-encoding network. This method realizes the collection of vibration sensor data of rolling bearings, uses Internet of Things technology to upload vibration data to the cloud, and uses cloud computing technology to process and analyze vibration data, analyze the operating status of bearing equipment, and realize remote monitoring of bearings , not only can effectively identify the abnormal state, and take corresponding measures to avoid greater economic losses for the bearing equipment, but also can diagnose the fault state, which is easy to find the fault point, perform rapid maintenance, and save time and cost.

Description

Technical field: [0001] The invention belongs to the technical field of bearing equipment monitoring, and specifically relates to the remote monitoring of bearing equipment status by using cloud computing technology and adopting clustering and multi-layer self-encoding network modeling methods. Background technique: [0002] Rolling bearings are one of the core components of rotating machinery and are widely used in industrial fields. Working in high-speed, high-load, and noisy environments, monitoring their health status is particularly important. At present, the status monitoring of bearings in industrial sites is only based on manual experience, and cannot effectively monitor the status of bearing operating equipment. Failures cannot be found in the early stage of failures, and irreparable economic losses are often caused when failures are found, and professionals are required to carry out failures. Inspection and maintenance greatly reduce work efficiency. Through the c...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01M13/045
CPCG01M13/045
Inventor 刘娟黄细霞孟杭韩志亮
Owner SHANGHAI MARITIME UNIVERSITY