In a paper manufacturing system, it can be substantially important to
detect machine failure before it occurs and take necessary maintenance
actions to prevent a detrimental breakdown of the system. Multiple
sensor data collected from a machine provides useful information on the
system's health condition. However, it is hard to predict the system
condition ahead of time due to the lack of clear ominous signs for
future failures, a rare occurrence of failure events, and a wide range
of sensor signals which might be correlated with each other. In this
paper, we present two versions of feature extraction techniques based on
the nearest neighbor combined with machine learning algorithms to detect
a failure of the paper manufacturing machinery earlier than its
occurrence from the multi-stream system monitoring data. First, for each
sensor stream, the time series data is transformed into the binary form
by extracting the class label of the nearest neighbor. We feed these
transformed features into the decision tree classifier for the failure
classification. Second, expanding the idea, the relative distance to the
local nearest neighbor has been measured, results in the real-valued
feature, and the support vector machine is used as a classifier. Our
proposed algorithms are applied to the dataset provided by IISE 2019
data competition, and the results show the better performance than the
given baseline.