Instrumentation & Measurement Magazine 23-6 - 15

IEEE TIM. We categorize the examples based on the DL technique used.

transportation [30]. As magnetic flux leakage (MFL) is a
common testing method for those purposes, the defects are
identified from MFL images based on CNNs.

CNN
CNN has been used extensively in VBM systems. For example,
a three-stage automatic defect inspection system for highspeed railways has been proposed in PVANET++ [17]. As split
pins have a key role in fixing joint components on catenary
support devices (CSDs), PVANET++ localizes and inspects
split pins by using CNN to detect defects. Another system uses
a two-stage deep CNN to detect insulator surface defects in
railway catenary [18] by firstly localizing the catenary components to obtain images of the insulators, and then using a deep
material classifier and a deep denoising autoencoder to detect
defects. Since the defect detection of the fasteners on a CSD is
essential for both safety and cost reductions in the operation
of high-speed railways, a VBM method based on a deep CNN
[19] is used to tackle the design of such detection systems.
A VBM system for the purpose of measuring pain intensity
through the analysis of facial expressions is presented in [20]
which uses the aforementioned AlexNet architecture to extract
critical features from patients' images and to draw conclusions
on the level of pain they are experiencing. The work in [21] proposes a vision-based evaluation (VBE) framework for VBM
systems that studies the capability of DL strategies to deal with
uncertainty contributions, a very important issue in I&M. This
framework is also centered around an AlexNet-based CNN and
handles uncertainty contributions during calibration processes.
Another VBM system is presented in [22] which uses both CNN
and DBM to design a food recognition system that analyzes pictures of meals, taken by a mobile device, to identify different food
items and estimate their calories and nutrition. Yet another VBM
system [23] tackles thermal image processing. A dataset of highresolution thermal facial images is built and used to train the deep
alignment network (DAN) algorithm (which is based on CNN)
for face analysis. CNN is also used in robotic VBM systems,
for example [24] uses a Faster Region CNN to aid robots in the
recognition of hand gesture, while [25] introduces a projection algorithm to generate RGB, depth, and intensity (RGB-DI) images
to measure the outdoor environments with a variable resolution.
A full CNN is then used to segment those RGB-DI images and use
them to realize the 3-D scene understanding for mobile robots.
The application of DL principles in light detection and
ranging (LiDAR)-based perception tasks is also studied, for
example, in [26]. This study, due to the limited availability of
LiDAR point cloud datasets, uses simulators to automatically
generate 3-D annotated LiDAR point clouds, and then uses
that data to train a deep model that incorporates both CNN
and RNN principles. Since network binarization is associated
with reductions in computational and memory costs in 2-D
computer vision tasks, [27] proposes a technique to train binary volumetric CNNs for 3-D object recognition.
CNN has also been used for extracting features from vibration signals to aid in the diagnosis of faults of a magnet
synchronous motor [28], fault diagnosis of spindle bearings
[29], and safety of the pipelines used for liquid petroleum
September 2020	

RNN
RNNs have been used in the detection of fundamental phasors and the identification of control and protection signals in
power systems [31], as well as estimating lithium-ion battery
remaining useful life to achieve an intelligent battery management system [32], and supporting predictive models for voltage
correction in a dc voltage reference source [33]. In nonlinear dynamical systems, RNNs are used to tackle the identification
problem: finding a time-dependent model for the behavior of
the process generating the data [34], as well as in the modelling
of dynamic systems in the absence of measurable state variables
[35]. Another use of RNNs is in modeling of processes that occur in various industries like steam-raising plants, gas turbines,
and automotive suspensions [36], since the output of such processes has unsymmetrical behavior. Finally, RNNs have been
used for fault detection: in wireless sensor networks for sensor
node fault detection [37], health condition monitoring of a machine [38], or early fault detection in industrial applications [39],
where a deep neural network is used for feature extraction and
the LSTM network for distribution estimator.

DBN
A discriminative DBN and ant colony optimization model has
been built in [40] for the purpose of health condition monitoring of machines. DBN is also used to distinguish acceptable
and unacceptable segments in an electrocardiogram (ECG)
signal [41], to reduce false alarms during atrial fibrillation
detection. Finally, DBN has been used in a soft sensor for estimating the deflection of a polymeric mechanical actuator [42].
The latter is based on ionic polymer-metal composites, which
are used in important fields like robotics and surgery.

Autoencoders
Autoencoders are used in rotating machinery measurements,
for example in fault identification for rotating machines [43] using a stacked sparse autoencoders-based deep neural network
coupled with the concept of compressed sensing, or in studying
the conditions of rotating machinery [44], where sensors generate fault signals, and features are extracted from these signals
and fused by sparse autoencoder (SAE) neural networks. The
fused features are then classified by a DBN. Another work that
uses autoencoders for fault diagnosis is based on the analysis of
vibration signals in wind turbine gearboxes [45], where stacked
multilevel-denoising autoencoders are used to assist in learning discriminative representations of fault features.
Localization is another application of autoencoders. For
example, the human step length in Pedestrian Dead Reckoning systems can be estimated with stacked autoencoders [46].
Finally, deep autoencoders are used for feature extraction
to mitigate the corruption of Photoplethysmographic (PPG)
measurements due to motion artifacts in personal healthcare
systems [47].

IEEE Instrumentation & Measurement Magazine	15



Instrumentation & Measurement Magazine 23-6

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