| 摘要(英) |
In the factory production mode, the abnormal machines are easier damage the pieces during
production, leading to increase the bad pieces’ number. What is more serious is the abnormal machines
could stop that production line. All factories will arrange the regular maintenance, hoping to reduce the
situation happened. In Smart Manufacturing 4.0, proposed a method to add sensors or recorders on the
machines to assist in production. Even if the sensors or recorders makes a sound to reminder during
production. It still needs to stop the production line to repair the machine, which delays the production
line. After the concept of sensors, developing that using the detailed data on the sensors to predict the
machines remaining useful life (RUL). If the machines can be repaired before abnormal conditions, it can
reduce the bad pieces’ number and save the unnecessary stop production line time. The production cost
can be reduced. However, not every factory is willing to spend extra money to install sensors or not
every machine can embed with sensors for predictive maintenance.
In response to the above problems, this research proposes a method. Using the production data
provided by the factory every week to build a time series model of the recommended machine list. It
also uses the concept of Online Learning to update weekly production data over time and predict the
machines need to maintain in next period. Using the actual production maintenance record as the
comparison result, all machines in recommended machine list will be abnormally repaired in the future
period, the result can be used to arrange the machine schedule to prevent the abnormal machine will
influence the production line. Considering the actual production situation of the factory, using the next
six days (one week) maintenance as the comparison result, the average recall of ten days’ prediction of
recommended machine list is 57.1%, and then considering the factory’s limited daily maintain energy,
we decide a rule to rank the recommended machine list and only give the top 5 machine for
recommended machine list. The ten days’ prediction of top 5 recommended machine list average
precision is 58%, it means that there are three machines will be repaired in the recommended five
machines. |
| 參考文獻 |
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