  Predictive maintenance, i.e. predicting failure to be few steps ahead of the
fault, is one of the pillars of Industry 4.0. An effective method for that is
to track early signs of degradation before a failure happens. This paper
presents an innovative failure predictive scheme for machines. The proposed
scheme combines the use of full spectrum of the vibration data caused by the
machines and data visualization technologies. This scheme is featured by no
training data required and by quick start after installation. First, we propose
to use full spectrum (as high-dimensional data vector) with no cropping and no
complex feature extraction and to visualize data behavior by mapping the high
dimensional vectors into a 2D map. We then can ensure the simplicity of process
and less possibility of overlooking of important information as well as
providing a human-friendly and human-understandable output. Second, we propose
Real-Time Data Tracker (RTDT) which predicts the failure at an appropriate time
with sufficient time for maintenance by plotting real-time frequency spectrum
data of the target machine on the 2D map composed from normal data. Third, we
show the test results of our proposal using vibration data of bearings from
real-world test-to-failure measurements provided by the public dataset, the IMS
dataset.
