Instrumentation & Measurement Magazine 23-9 - 21

Structural Health Monitoring
and Prognostic of Industrial
Plants and Civil Structures:
A Sensor to Cloud Architecture
Federica Zonzini, Cristiano Aguzzi, Lorenzo Gigli, Luca Sciullo, Nicola Testoni, Luca De
Marchi, Marco Di Felice, Tullio Salmon Cinotti, Canio Mennuti, and Alessandro Marzani

T

he deployment of Structural Health Monitoring
(SHM) systems is a natively interdisciplinary task
that involves joint research contributions from sensing technologies, data science and civil engineering. The
capability to assess, also from remote stations, the working
conditions of industrial plants or the structural integrity of
civil buildings is widely requested in many application fields.
The technological development aims to continuously provide
innovative tools and approaches to satisfy these demands. As
a first instance, reliable monitoring strategies are needed to
detect structural damages while filtering out environmental
noise. Ongoing solutions to tackle these topics are based on the
exploitation of highly customized sensing technologies, such
as shaped transducers for Acoustic Emission (AE) testing or
Micro-Electro-Mechanical System (MEMS) accelerometers for
Operational Modal Analysis (OMA) [1]. On the other hand,
effective data acquisition and storage techniques must be employed to cope with the heterogeneity of the sensing devices
and with the amount of data produced by collecting raw measured signals. Finally, damage detection and prediction tasks
should be computed via data-driven algorithms that can complement the model-based alternatives traditionally used in
civil engineering. Layered SHM architectures [2] represent
straightforward approaches to address the system complexity originated by this interdisciplinary design; however, few
real-world implementations have been presented so far in
the literature. In this paper, we overcome these limitations by
presenting an Internet of Things (IoT)-based SHM architecture for the predictive maintenance of industrial sites and civil
engineering structures and infrastructures. The proposed cyber-physical system includes a monitoring layer, that consists
of accelerometer-based sensor networks, a data acquisition
layer, built on the recent W3C Web of Things standard [3], and
a data storage and analytics layer, which leverages distributed
database and Machine Learning tools. We extensively discuss
the hardware/software components of the proposed SHM

architecture, by stressing its advantages in terms of device versatility, data scalability and interoperability support. Finally,
the effectiveness of the system is validated on a real-world usecase, i.e., the monitoring of a metallic frame structure located
at the SHM research labs of the University of Bologna, Italy,
within the MAC4PRO project [4].

State of the Art and Related Works
A close analogy can be established between SHM tasks and
those performed by healthcare systems in the sense that they
require perfect coordination among the sensing, the communication and the cognitive/decision subsystems to achieve
a timely and reliable diagnosis. To be effective, an SHM
architecture must chase the optimal combination of the required hardware (HW) resources for signal recording and the
associated software (SW) infrastructure in charge of data management, data analytics and structural assessment. Coherently
with this joint HW-SW optimization, SHM platforms can be
considered as cyber-physical systems, in which the intrinsic
capability of smart devices to measure, pre-elaborate and forward physical data to virtual aggregating units is exploited
[5]. From a HW standpoint, the selection of the specific sensors
to be deployed and their relative positioning strictly depends
on the characteristics of the structure to be inspected, the complexity of which may demand the combination of different
sensing technologies, as well as several diagnostic approaches
(AE, OMA, others).
At higher abstraction levels, considerable research efforts have been made to: enhance the reliability in retrieving
and sharing structural information collected at multiple
locations; increase the quality of the extracted structural parameters while reducing the computational latency; and
bridge the gap between human and computer-aided prognostics about the remaining structural life cycle prediction,
possibly combining them with dedicated interfaces [6]. In this
paper, we focus on the vast and highly critical field of vibration

This work has been funded by INAIL within the BRIC/2018, ID=11 framework, project MAC4PRO: " Smart maintenance of
industrial plants and civil structures via innovative monitoring technologies and prognostic approaches. "
December 2020	

IEEE Instrumentation & Measurement Magazine	21
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Instrumentation & Measurement Magazine 23-9

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