Predictive Maintenance of Equipment
The equipment predictive maintenance of Metal
Data team makes
full use of its accuracy advantages in model prediction,
including not only the functions of existing enterprises in
digital system, but also the functions of intelligent system
based on high-precision model. In order to achieve high
accuracy of the model based on machine learning, the first
step is to build the model online. In terms of engineering
model, we have at least 3 years of working experience in the
fields of process, product, equipment and automation; In terms
of machine learning, it is based on the advantages of team
members in model development, big data and machine learning
after obtaining a doctorate in the United States.
The
first-line software engineering team has been in Germany for
more than 200 years, and has completed the first-line software
manufacturing project in the United States.
Predictive maintenance at the digital level
The service time and life limit of each vulnerable equipment
part are recorded in the database. When the equipment room is
about to reach the practical limit, let the system alarm and
prompt the operator to replace this part in time.
At the
same time, it also analyzes the use correlation between
devices according to big data, and determines that the
equipment is affected by other components in the use process;
Thus, the life prediction of equipment parts is obtained.
In addition, make use of a series of advantages of our team in
the engineering field, such as equipment use, product
production and incoming material quality.
Predictive maintenance at the intelligent level
Here, the high-precision model of our team and the technology
of machine learning based on online data are used. For
decades, our team has always won with the high precision of
our model in the competition with European and American
counterparts.
Based on the on-line data collected on site and the idea of
historical prediction of the future, an engineering model is
established for machine learning, so as to judge the life
cycle of equipment parts and the whole equipment. For example,
at the digital level, the service life of a piece of equipment
in normal use may be 2-4 years; However, the equipment parts
may be subjected to specific high-intensity continuous use.
Based on the judgment of online data, the service life of
equipment parts is different in different service
environments, which may be 1-3 years; A specific piece has
experienced several different service environments. Based on
the correctly established model, it may be judged that its
service life is 1.5 years! (the service life of the other part
may be 4.5 years in a relatively easy service environment!)
There may be remote logins on site, but many enterprises may
close remote logins. Therefore, the main data source may be
the data collected by the enterprise from MES or industrial
Internet, or other data based on SCADA data acquisition
system.
A large number of projects completed by our team are equipment
related projects. High accuracy of models based on engineering
modeling, machine learning and intelligent system architecture
development. For example, in the contract with a customer, it
is required to achieve a hit rate of 85%; The actual
acceptance reached 98%, and even some acceptance reached 100%!
The defect early warning system of our team is one of the key
projects promoted by Shenzhen quality month. Therefore, the
main designer was also invited to give a special lecture on
the opening day of Shenzhen quality month.
Information
about this product can be found in a special introduction
article.
Consultation and development of soft sensing system in defect
early warning system
Some parameters are difficult to measure directly and can only
be obtained by soft measurement. For example, one of the key
parameters defining tool quality is tool notch. However, the
value of knife notch can only be measured under a high-power
(commonly used 1000 times) microscope; However, in the
production process, the cutting tool is wrapped in the sliced
pole piece, so it is difficult to see the knife notch, and it
is more difficult to use a 100x microscope; At the same time,
the cutting speed in the production process is about 100
meters per minute, which corresponds to the speed of 100000
meters per minute under a 1000 times microscope, which also
makes it almost impossible to directly measure the knife notch
at each time in the production process. Based on these two
factors, soft sensing technology must be used to determine the
value of knife notch at any time.
Advance prediction of equipment failure
Based on the idea of historical prediction of the future,
before the equipment fails, it can predict when the equipment
will fail under the special use environment of the equipment,
so it can give an alarm to the equipment maintenance personnel
in advance; The prediction accuracy of the model can be taken
into account by setting the accuracy coefficient, and so on.
Planning &
Consulting
Biz discuss, Planning, Maturity,
Consulting Area,
Predict Maint.
Defect
Early Warning, Models, Manufacturing, Li-Battery, Steel
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