Title: Reservoir Computing for Detection of Steady State in Performance Tests of Compressors,
Abstract: Fabrication of devices in industrial plants often includes undergoing quality
assurance tests or tests that seek to determine some attributes or capacities
of the device. For instance, in testing refrigeration compressors, we want to
find the true refrigeration capacity of the compressor being tested. Such test
(also called an episode) may take up to four hours, being an actual hindrance
to applying it to the total number of compressors produced. This work seeks to
reduce the time spent on such industrial trials by employing Recurrent Neural
Networks (RNNs) as dynamical models for detecting when a test is entering the
so-called steady-state region. Specifically, we use Reservoir Computing (RC)
networks which simplify the learning of RNNs by speeding up training time and
showing convergence to a global optimum. Also, this work proposes a
self-organized subspace projection method for RC networks which uses
information from the beginning of the episode to define a cluster to which the
episode belongs to. This assigned cluster defines a particular binary input
that shifts the operating point of the reservoir to a subspace of trajectories
for the duration of the episode. This new method is shown to turn the RC model
robust in performance with respect to varying combination of reservoir
parameters, such as spectral radius and leak rate, when compared to a standard
RC network.