Have you ever heard the words “preventive maintenance” or “predictive maintenance”? Regarding the maintenance of equipment in production lines, the Internet of Things (IoT) has recently attracted attention. Also, these two words seem to be similar and have different meanings. This section explains the overall definition of conservation activities such as preventive maintenance and predictive maintenance, the difference between preventive maintenance and predictive maintenance, the benefits of predictive maintenance, and the relationship between predictive maintenance and edge computing

1. Preventive and Predictive Maintenance and
Edge Computing
Have you ever heard the words “preventive maintenance” or “predictive maintenance”? Regarding
the maintenance of equipment in production lines, the Internet of Things (IoT) has recently attracted
attention. Also, these two words seem to be similar and have different meanings. This section
explains the overall definition of conservation activities such as preventive maintenance and
predictive maintenance, the difference between preventive maintenance and predictive maintenance,
the benefits of predictive maintenance, and the relationship between predictive maintenance and edge
computing.
What are Conservation activities?
What is a conservation activity? JIS (Japanese Industrial Standard) defines maintenance activities as
“a general term for activities that eliminate failures and keep equipment in normal and good
condition, including planning, inspection, adjustment, repair, replacement, etc.”.
In other words, it can be thought of as a human influence on the production line to maintain the
performance of the production line. As defined in the JIS, conservation activities are divided into
maintenance activities and improvement activities.
Maintenance activities are activities to maintain the quality of products and the performance of
production equipment, that is, activities to maintain the perfect condition of production facilities.
This includes preventive and reactive maintenance. On the other hand, improvement activities refer
to activities such as “improvement maintenance” that reviews machinery to prevent recurrence when
it breaks down, and “maintenance prevention” that replaces machinery and equipment to prevent
breakdowns and mistakes.
Preventive maintenance is the prevention of failure by daily inspections and replacement of
deteriorated parts before they occur. It includes predictive and periodic maintenance. Follow-up
maintenance refers to restoring the function of equipment when a failure is discovered in the
equipment due to a malfunction or the like. In other words, it is assumed that it will be “repaired”
when the equipment is broken.
Periodic maintenance is the act of determining the cycle based on fault records and equipment
characteristics, and replacing and inspecting parts for each cycle. It can also be rephrased as
maintenance performed based on elapsed time. Generally, “preventive maintenance” refers to
periodic maintenance.
On the other hand, predictive maintenance is to detect or predict deterioration from the state of
equipment measured continuously, and to take the best measures at the optimal time before a failure
occurs. It is based on the condition of the device.
The main difference between periodic maintenance and predictive maintenance is that preventive
maintenance is performed at a certain time cycle regardless of the condition of the equipment,

2. whereas predictive maintenance constantly monitors the condition of the equipment and responds
when signs of failure are detected.
Conservation Activities to Date
Conventional conservation activities mainly include periodic maintenance, predictive maintenance,
and post-mortem maintenance. Improvement activities (such as modifications and upgrades) may be
carried out to extend the life of the equipment, but the response is limited, such as in the case of
expensive equipment.
Periodic maintenance, as already mentioned, involves the replacement of parts on a regular basis,
regardless of the condition of the equipment. In addition, predictive maintenance was carried out by
on-site workers and engineers relying on the intuition cultivated through many years of experience
that “it is about time to replace that part.”
Periodic maintenance time intervals are tailored to the most important and shortest-lived parts, but
other parts are often replaced during this replacement. The reason is that the life of the parts varies
depending on the type, so if the equipment is stopped and replaced each time, the operation rate of
the equipment will deteriorate. For this reason, parts that have not yet reached the end of their useful
life will also be replaced, and there is a problem that there is a lot of waste in periodic maintenance.
Predictive maintenance
Predictive maintenance conducts is based on the signs between anticipation of a failure and the actual
failure. This means that you can think of a maintenance plan at the expected stage and act, such as
replacing parts before a failure occurs. This minimizes the condition that the machine is in an
emergency stop due to failure.
In addition, if predictive maintenance is automated using IoT, an alarm is issued when there is a sign
of failure. Since it is only possible to think about response when an alarm is issued, it does not take
time and effort, and it leads to a reduction in labor costs in this aspect.
Specific examples of Predictive maintenance
In manufacturing equipment, motors are frequently used. There is a component called “bearing” that
supports the “shaft” (drive shaft) that transmits power from this motor. If the bearing fails, the axis
may not turn or the load on the shaft cannot be distributed, which can lead to serious accidents.
Therefore, this bearing is very important.
Bearings themselves are inherently highly reliable, but when higher reliability is required, for
example, in the process of stretching heated iron at a steel mill (rolling process), bearing monitoring
is often performed.
The bearing monitoring system installs a vibration sensor on the bearing to detect the waveform,
frequency, and amplitude conditions of the vibration. Then, an alarm is issued when the state of
vibration indicates a sign of failure.

3. For example, if a bearing is damaged in one place, the load applied to the bearing changes. As the
load changes, the distance between the vibration sensor and the bearing changes, but the vibration
sensor detects a minute change in distance. Then, since the rotating part of the bearing rotates at a
constant period, the change in the distance will occur every rotation. This causes vibration, and it is
possible to detect this vibration and make it a sign of failure. Skilled engineers took this vibration
from changes in machine sound and used it as a sign of damage.
This example is simplified for clarity but predicting failures from sound requires experience because
there is no change in vibration even if multiple places are damaged or damaged. For this reason, it
was not possible to make such a judgment unless from a skilled engineer. Unfortunately, the number
of skilled technicians is decreasing. Artificial intelligence (AI) is considered as a solution. Artificial
intelligence, which has been attracting attention in recent years, enables “deep learning” to learn by
itself by a structure modeled on human actions. Of course, although it is necessary to have learning
first, artificial intelligence can learn and execute like humans who have captured signs of failure from
experience. With the number of skilled engineers decreasing, it can be said that it is one effective
means to respond to the shortage of human resources.
Predictive maintenance and edge computing
In this way, automation of predictive maintenance is an effective means of eliminating the shortage
of human resources and optimizing maintenance activities. However, there are some things to be
aware of when automating predictive maintenance.
The structure that imitates human actions of artificial intelligence described earlier is called “neural
network” and contributes greatly to the realization of deep learning. However, neural networks are a
very complex mechanism. Therefore, it is a practical issue to realize predictive maintenance while
maintaining the speed necessary for predictive maintenance.
There is also a method called “neurochip” that realizes neural networks with hardware, but it is not a
very common method. The problem here is speed, so cloud computing, which always sends and
receives from servers on the Internet, is impractical. So, let’s think about deploying edge
computing and putting artificial intelligence on edge servers. This is called edge AI. As a result, it
can be said that the best answer at this time is to automate predictive maintenance while maintaining
the speed as much as possible.
Possibility of predictive maintenance and artificial intelligence
This paper mainly describes the difference between predictive maintenance and preventive
maintenance, and the merits of predictive maintenance. With the development of artificial
intelligence, the possibilities for predictive maintenance are greatly expanding. And enabling
predictive maintenance using artificial intelligence with edge computing is the most ideal figure at
present.
Follow Stratus ztC Edge
Follow Stratus ztC Edge