Instrumentation & Measurement Magazine 23-9 - 45

characteristics or patterns present on a signal. The IEEE 1451001 is a simple but effective representation of a sensory signal
that minimizes redundancy while maintaining the structure
of the information [4]. The main objective of this standard is
to represent a sensor signal as a sequence of known segments
instead of samples, and from this structure, infer information
and knowledge. A summary of the Mark, Class, Time (MCT)
sampling process is represented in Fig. 1. Keeping the intrinsic
structure of the information is what makes it possible to carry
out subsequent processes using this simplified representation.
There is no single signal processing algorithm to extract
knowledge, and it is mainly due to two reasons: first, to the
subjective nature of knowledge; and second to the innumerable objective functions. Instead, redundancy is common to all
forms of communication.

Earthquake Signal Monitoring as an Example of
Application
The sensor nodes in the structure will receive vibration signals
from the building that include both vibrations caused by man
and signals from earthquakes. The challenge in this application is to distinguish the different sources of signals and learn
which ones come from earthquakes [5].
A simplified classification of signals from earthquakes results from dividing the signals into type P and type S. P waves
carry information about the incoming earthquake while Swaves carry energy. Since the signals travel at different speeds,
analysis of the P-waves allows us to infer the magnitude of
the event that is approaching. Seismic data is obtained from
a ground acceleration in a limited frequency band, and any
estimate of the expected shaking should be a function of the
observed acceleration data and not the displacement data [6].
Seismographs are installed far from noise sources. In the
case of building monitoring, noise caused by human activity
is inevitable. Therefore, detection algorithms must be robust
and have adaptive signal trigger levels. The success of advance
warning depends on accurate detection of P-waves as they arrive and rejection of ground vibrations caused by local activity.

Fig. 2. Signal processing relationship embedded into the sensor node.
December 2020	

The algorithms should be able to distinguish man-made
from natural vibrations and the natural frequency of structure
oscillation must also be considered. In addition, on the upper
floors of tall buildings, wind causes vibration signals that can
interfere with seismic signals [7].
The approach to Early Earthquake Detection (EED) is
based on iterative filtering using the signal representation presented in the IEEE 1451-001. The signal obtained on each floor
of a building depends on its position, height, its relation to the
structure and the type of construction. Therefore, a scheme
with self-learning of the conditions normally faced by the sensor is proposed. The event trigger threshold will be a function
of this learning stage.
Filtering is the key process to detect seismic events [8]-[12].
The clue for detecting the arrival of P-waves from a large earthquake is a strong acceleration signal with high energy content
at low frequency. The objective of the iterative filter is to detect
this event and to discriminate all of the noise signals captured
by the sensor.
To take advantage of the installed sensor network, it is proposed to integrate the vibration energy of a building since it
has been constructed at each measurement point of interest.
This information will be useful to determine which part of the
structure has had the greatest impact and how the vibration
signals are distributed throughout the structure, even considering that it may not ever suffered an earthquake.
Once the MCT process has been applied, it is possible to
reconstitute the signal using interpolation to obtain an approximation of it among the essential samples. Since the
reconstruction uses simplified trajectories, for example linear,
the output signal is a low pass filtered version of the original.
Since MCT produces no time shift, the sampling and reconstitution process can be iterated to achieve a desired filtering.
The bandwidth of the filter is controlled by the number of iterations and the interpolation error. Fig. 3 shows the effect of
iterations on the bandwidth. A Sinc signal of 15 Hz sampled at
100 Hz was iterated, in steps of 10 to 1000, with an interpolation error close to zero (1.0E-08) [12].
Using the ability of MCT
to filter the signal without
producing phase shifts or
transients, it is possible to
observe that the quotient
of the energy obtained at
different parameters of the
MCT filter is a very good
estimator of the earthquake
magnitude. The main idea
is to compare the signal energy at low frequency to
the medium frequency energy using the first three
seconds of the P-wave arrival. Therefore, it has been
defined as ╬▒MCT with the following relationship:

IEEE Instrumentation & Measurement Magazine	45



Instrumentation & Measurement Magazine 23-9

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