Abstract
As the computer hardware rapidly develops at a rate consistent with Moore's
Law, the cost of computer data storage, data transmission, and distributed computing
has been greatly Reduced. In the modern factories, a large number of sensors are
often arranged, and as the cost of storage decreases, the read device information
becomes more abundant, and thus a large amount of industrial data will be generated.
For this change, many countries around the world have put forward
corresponding measures in succession. Initially, Germany proposed the concept of
"Industry 4.0". After the United States introduced the "Industrial Internet," China
has also successively introduced the concept of "Made in China 2025." Its core
points to intelligent manufacturing. Industrial big data technology is a core part of
these contents.
During the operation of industrial equipment, natural wear, equipment overload,
improper operation, and other reasons can cause the performance of the equipment
to drop, and even result in failure or abnormality. By adding sensors to the equipment
for monitoring, obtaining real-time information from the equipment and combing
calculations, the real-time running status of various parts of the equipment can be
obtained, thereby realizing the monitoring of the equipment. If there is a device
failure, you can perform data mining and cleaning on the historical data to form a
fault model and import the latest operating data of the device for fault diagnosis.
The equipment fault diagnosis methods can be divided into three types
according to the diagnosis basis: based on the mechanism model method, based on
the data-driven method, based on the knowledge engineering method. This article
will use a classification-based approach based on a Data-Driven approach to build a
fault model. At the same time, in order to compare the performance of different
classification methods, this paper uses two classification methods to compare.
The main research work and achievements of this article are as follows:
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