  Condition monitoring is one of the routine tasks in all major process
industries. The mechanical parts such as a motor, gear, bearings are the major
components of a process industry and any fault in them may cause a total
shutdown of the whole process, which may result in serious losses. Therefore,
it is very crucial to predict any approaching defects before its occurrence.
Several methods exist for this purpose and many research are being carried out
for better and efficient models. However, most of them are based on the
processing of raw sensor signals, which is tedious and expensive. Recently,
there has been an increase in the feature based condition monitoring, where
only the useful features are extracted from the raw signals and interpreted for
the prediction of the fault. Most of these are handcrafted features, where
these are manually obtained based on the nature of the raw data. This of course
requires the prior knowledge of the nature of data and related processes. This
limits the feature extraction process. However, recent development in the
autoencoder based feature extraction method provides an alternative to the
traditional handcrafted approaches; however, they have mostly been confined in
the area of image and audio processing. In this work, we have developed an
automated feature extraction method for on-line condition monitoring based on
the stack of the traditional autoencoder and an on-line sequential extreme
learning machine(OSELM) network. The performance of this method is comparable
to that of the traditional feature extraction approaches. The method can
achieve 100% detection accuracy for determining the bearing health states of
NASA bearing dataset. The simple design of this method is promising for the
easy hardware implementation of Internet of Things(IoT) based prognostics
solutions.
