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Received: 9 December 2024
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Published: 7 January 2025
Citation: Idzik, M.; Rybicki, T. Real-
Time Prediction of the Yarn Break
Position Using Vibration
Measurement. Sensors 2025,25, 299.
https://doi.org/10.3390/s25020299
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Article
Real-Time Prediction of the Yarn Break Position Using Vibration
Measurement
Marcin Idzik 1, 2, * and Tomasz Rybicki 1
1
Institute of Automatic Control, Lodz University of Technology, 90-537 Lodz, Poland; tomasz.rybicki@p.lodz.pl
2Lukasiewicz Research Network—Lodz Institute of Technology, 90-570 Lodz, Poland
*Correspondence: marcin.idzik@lit.lukasiewicz.gov.pl
Abstract: Warping is a crucial process that connects two main stages of production: yarn
manufacturing and fabric creation. Two interrelated parameters affect the efficiency of
this technological process: warping speed and the ability to swiftly detect the yarn breaks
caused by various defects. The faster a break is detected and the warping machine stopped,
the higher the machine’s working speed can be. Since the beginning of such devices,
various types of yarn break detectors have been proposed, primarily based on different
mechanical solutions. To enhance the break detection process, a solution involving the use
of an accelerometer to measure yarn vibrations and thereby detect whether the moving
yarn has broken is proposed. Based on the detection of a threshold value of 22 m/s
2
, the
warping machine could be stopped within 2.752 to 2.808 ms, which is 50 times faster than
in the traditional mechanical detectors under investigation. Furthermore, through a precise
analysis of yarn vibration patterns, it became possible to determine the distance from the
sensor at which the break occurred. This analysis was conducted using the proprietary
MRSCEK coefficient, which aggregates data obtained from six standard coefficients: mean,
root mean square, standard deviation, crest factor, energy, and kurtosis. This information
could potentially lead to the development of automated systems for removing breaks
without human intervention in the future. Research efforts focused on analyzing the
vibration signals received from yarns made with different linear densities. The results
showed that such a system could effectively replace commonly used mechanical yarn break
detectors and operate much faster.
Keywords: warping machine; yarn; detection; MEMS
1. Introduction
The yarn break detection system described in this work is a highly significant compo-
nent of warping machines. It is responsible for sending timely information to the machine
about breaks and the immediate need to stop. If the system did not detect the break, the
end of the yarn would be wound onto the warp beam and covered by subsequent layers
of yarns that were being wound simultaneously, parallel to each other. In such a scenario,
identifying a defect would be extremely difficult. Due to the crucial role played by the
yarn break detection system in the production process, it must possess not only high speed
but also absolute reliability and relatively low installation costs. Consequently, dozens of
diverse solutions for detecting breaks have been developed to date. Despite the passage of
time, this issue remains relevant and continues to be a subject of interest [1–4].
Despite the continuous development of newer prototypes, the most widely used
system for yarn break detection, combining simplicity, reliability, cost-effectiveness, and
Sensors 2025,25, 299 https://doi.org/10.3390/s25020299
Sensors 2025,25, 299 2 of 17
speed, remains the “dropper”. Its construction relies on a dropper, which is suspended
from the yarn that passes through it. When a break occurs, the tension in the yarn vanishes,
causing the dropper to fall downward (sometimes assisted by a spring) and make contact
with two rods, thus closing an electrical circuit. This action sends a signal to the machine
about the yarn break and the immediate need to halt operations [5].
Given the necessity of examining hundreds of yarns simultaneously, many studies
have explored the potential use of cameras employing image analysis techniques to detect
emerging yarn breaks. These solutions are based on CCD (charge-coupled devices) or
CIS (contact-type image sensor) cameras. Their setup involves moving the yarn over
a special
table with a contrasting color to the yarn and illuminated by dedicated light
sources, with
a camera
capturing the yarn’s movement. When a break occurs, the yarn
disappears from the camera’s field of view, generating an appropriate signal [
6
,
7
]. Many
solutions focused on analyzing very narrow spatial segments [
8
,
9
]. An example includes
a device
that examines enlarged images of viscose yarn to detect broken fibers protruding
from the yarn [
10
]. Devices were also designed to analyze images of already woven fabric,
identifying broken yarns, which constitute defects in the final product [
11
]. The latest and
most advanced project involves real-time yarn break detection by analyzing the entire
frame of the warping machine [12].
For many years, a widely researched method for yarn break detection has involved
using light-sensitive elements. Initially, the focus was on employing sensors to detect
light from a series of diodes placed beneath the moving yarns, which would be blocked
by the yarn under normal conditions [
13
,
14
]. However, yarn vibrations and variations in
warp production from different types of yarn significantly compromised the universality of
such solutions. With the development of lasers, these were also examined as yarn break
detectors. Their operational logic closely resembled the previously described method.
The laser beam from an emitter was meant to be blocked by the yarn before reaching the
receiver [
15
,
16
]. In this case, substantial vibrations from the moving yarn and its differing
linear mass proved to be significant limitations, requiring the precise calibration of the
system each time. Another approach involved illuminating the yarn with laser light and
capturing the reflected beam with a detector [17,18].
Summing up the above examples, despite significant technological advancement,
a completely
new yarn break detection system based on modern electronic systems rather
than classical mechanical detection setups has not been developed. By leveraging the
phenomenon of vibrations, which has been problematic and minimized until now, it seems
possible to design a system capable not only of detecting the occurrence of a yarn break
but also of determining how far from the detector the break occurred. Implementing such
a solution
could streamline machine operation and yield information that might enable full
automation of the yarn break removal process, which is currently performed manually.
The introduction of measurements using MEMS (microelectromechanical system)
sensors into the textile industry was initiated within the scope of detecting yarn breakage
in Jacquard weaving. The system operated by recording the movement of the harness
and checking for any changes in the behavior of the analyzed element. This enabled the
detection of the moment when the yarn broke, leading to alterations in the operation
characteristics of the harness [19–21].
This article presents the concept of yarn break detection using an accelerometer. This
research involved designing the measurement system and analyzing the received signals.
The results exhibit better performance parameters than traditional yarn break detectors,
indicating promising prospects for the widespread adoption of this developed solution
in the future. Additionally, the proposed measurement system provides information that
was previously unavailable from mechanical yarn break detectors—the precise location of
Sensors 2025,25, 299 3 of 17
the break. It opens up wide possibilities for utilizing such information in future systems
aiming to fully automate the warping process.
This work is divided into three main sections. Sections 2and 3, “Materials” and
“Methods”, the components used and their arrangement in the project are presented, along
with a brief description of the idea behind the project. Section 4, titled “Results and
Discussion”, contains a detailed description of the conducted research and its discussion.
Lastly, Section 5, titled “Conclusions”, summarizes all the studies and work carried out
during the execution of this interdisciplinary project.
2. Materials
2.1. Warping Machine
The research was conducted using a direct warping machine of the DS. 14P type from
Karl Mayer Textilmaschinenfabrik GmbH, Obertshausen, Germany [
22
]. This machine
allows for the production of warps from both natural and synthetic yarns. The warp width
is adjustable within a range of 8 to 15 inches. The machine is equipped with a “magazine”
frame that accommodates dual yarn packages, enabling the creation of warps that are
twice as long. Being a direct warping machine, it is intended for producing long, uniform
warps, and equipping it with a yarn compensator significantly increases the warping speed.
The type of the studied object does not affect the final results; however, implementing the
system with a different machine will require its initial calibration.
2.2. Yarns
During this research, yarns made from various materials were used, encompassing
both natural fibers like cotton and synthetic ones such as polyester and viscose. These
examined yarns also varied in their linear mass, ranging from 20 to 50 tex.
2.3. Tension Measurement
An electronic strain gauge from Rothschild-Instruments, type R-1092, was used to
measure the yarn tension.
2.4. Hairiness Measurement
The Uster Tester 3, a universal device used to measure yarn parameters, was employed
to measure the hairiness coefficient. This device enables the measurement of various yarn
parameters, including the Uster evenness value CVm, imperfections, hairiness, hairiness
length classification diameter, density, shape, twist, dust and trash, and yarn fineness.
2.5. Software
During the construction of the research setup, three software tools were utilized. The
first was Arduino IDE ver. 2.3.0, which was used to write the algorithm for collecting data
from the accelerometer. Additionally, the use of PuTTY ver. 0.81 software was necessary to
gather data from the serial port and save it in a comma-separated values (.csv) file. Finally,
the acquired data underwent analysis using MATLAB R2023b software.
2.6. Hardware
The designed measurement system consisted of three devices. The primary component
was the Arduino Uno R3 microcontroller, which was responsible for retrieving specific data
from the sensor. The next device was the SparkFun DEV-14495 converter (https://www.
sparkfun.com/products/14495 (accessed on 3 January 2025)), facilitating the connection
of signal wires from the microcontroller and, on the other end, the QWIIC standard cable
from the accelerometer. The use of this adapter was necessary to minimize the influence
of the wires on the sensor’s movement as much as possible. The final and most crucial
Sensors 2025,25, 299 4 of 17
element of the system was the 3-axis accelerometer and gyroscope, LSM6DSO32 6DoF IMU,
from Adafruit (https://www.adafruit.com/product/4692 (accessed on 3 January 2025)). It
allows for measuring acceleration up to
±
32 g, with a maximum measurement frequency
of 6.66 kHz. For the purposes of this work, to increase accuracy, the measurement range
was reduced to ±8 g and the maximum reading speed was set to 6.66 kHz.
3. Methods
3.1. The Idea of Measuring Yarn Vibration
Despite significant technological advancements, the operation of looms remains rel-
atively unautomated. Major tasks causing machines to spend more time in preparation
than in actual operation involve linking old warps with new ones (to avoid threading the
yarn throughout the entire machine). Depending on the type of machine, these linking
tasks can range from several hundred to several thousand. Furthermore, despite thorough
machine preparation, yarn breaks occur during operation, leading to machine stoppage
and the need to locate and manually remove such defects [23–25].
To reduce the time needed to locate yarn breaks and expedite the machine’s response
to such breaks, a concept for a detector has been developed. This detector, utilizing yarn
vibration measurements, aims not only to identify the occurrence of a yarn break but also to
calculate the precise location along the several-meters-long frame where the break occurred.
3.2. Measuring System
In Figure 1, a schematic of the measurement system is depicted, comprising:
1—accelerometer
Adafruit LSM6DSO32 6DoF IMU, 2—supporting frame made from PCL
material with a 3D printer, 3—isolating springs to insulate the sensor from solid surfaces
and external vibrations, 4—signal converter SparkFun DEV-14495, and 5—microcontroller
Arduino Uno R3. The principle of operation of the detector involves its deviation from its
initial position due to tensioned yarn passing beneath it. When a yarn break occurs, the
tension in the yarn vanishes, causing the sensor to return to its initial position.
Sensors 2025, 25, x FOR PEER REVIEW 5 of 17
Figure 1. Measuring system (1—accelerometer, 2—supporting frame, 3—isolating springs, 4—
signal converter, 5—microcontroller).
3.3. Production of Yarn
One of the primary characteristics indicating the quality of yarn is the frequency and
size of the naturally occurring slubs and thickened areas within it. In commonly available
yarns, these defects are not only minimized (understandably) but also their exact location
within the yarn is typically unknown. Consequently, for the fundamental purpose of in-
ducing yarn breaks in very specific locations as part of the described research, it was nec-
essary to produce special yarn using available industrial-laboratory machines.
The yarns intended for this research were made from staple coon, viscose, and pol-
yester fibers. A medium-spindle spinning system of coon and coon-like fibers was uti-
lized. Spinning slivers were produced on a carding machine separately for each raw ma-
terial. During the carding process, impurities, short fibers, and neps (tangled fiber frag-
ments) were removed from the fibrous mass. The neps were collected for subsequent yarn
modeling. To even out the linear mass distribution and parallelize the fibers, the slivers
were joined (6 splices in total) and drafted in the drafting rollers of two consecutive pas-
sages in the stretching machines.
All the carding and stretching slivers had a uniform linear mass of 4 ktex.
From the slivers after the second stretching, rovings were produced using a ring spin-
ning frame. A stretch ratio of 10 was applied in the drafting units of the spinning frame,
allowing for the production of rovings with a linear mass of 400 tex. These rovings were
given a twist of 50 turns per meter, forming cylindrical–conical packages.
The process of forming yarn from the rovings was carried out on a classic ring spin-
ning frame. Various drafting ratios were employed in the machine, resulting in yarns with
different linear masses. Yarns with linear masses ranging from 20 tex to 50 tex were pro-
duced on the ring spinning frame. The thicker yarns used in the research were obtained
by joining and twisting finer yarns on a laboratory double-twisting machine [26].
The technological process aimed to produce yarns with the most even distribution of
linear mass. However, for research purposes, yarns with a specific model structure were
also needed, which were characterized by a distinct distribution of slubs and thickened
areas in the form of neps. This model structure was obtained directly on the ring spinning
frame by periodically removing some fibers locally from the feeding roving, resulting in
the formation of slubs in the created yarn. Additionally, neps were added to the feeding
Figure 1. Measuring system (1—accelerometer, 2—supporting frame, 3—isolating springs, 4—signal
converter, 5—microcontroller).
Sensors 2025,25, 299 5 of 17
3.3. Production of Yarn
One of the primary characteristics indicating the quality of yarn is the frequency and
size of the naturally occurring slubs and thickened areas within it. In commonly available
yarns, these defects are not only minimized (understandably) but also their exact location
within the yarn is typically unknown. Consequently, for the fundamental purpose of
inducing yarn breaks in very specific locations as part of the described research, it was
necessary to produce special yarn using available industrial-laboratory machines.
The yarns intended for this research were made from staple cotton, viscose, and
polyester fibers. A medium-spindle spinning system of cotton and cotton-like fibers was
utilized. Spinning slivers were produced on a carding machine separately for each raw
material. During the carding process, impurities, short fibers, and neps (tangled fiber
fragments) were removed from the fibrous mass. The neps were collected for subsequent
yarn modeling. To even out the linear mass distribution and parallelize the fibers, the
slivers were joined (6 splices in total) and drafted in the drafting rollers of two consecutive
passages in the stretching machines.
All the carding and stretching slivers had a uniform linear mass of 4 ktex.
From the slivers after the second stretching, rovings were produced using a ring
spinning frame. A stretch ratio of 10 was applied in the drafting units of the spinning frame,
allowing for the production of rovings with a linear mass of 400 tex. These rovings were
given a twist of 50 turns per meter, forming cylindrical–conical packages.
The process of forming yarn from the rovings was carried out on a classic ring spinning
frame. Various drafting ratios were employed in the machine, resulting in yarns with
different linear masses. Yarns with linear masses ranging from 20 tex to 50 tex were
produced on the ring spinning frame. The thicker yarns used in the research were obtained
by joining and twisting finer yarns on a laboratory double-twisting machine [26].
The technological process aimed to produce yarns with the most even distribution of
linear mass. However, for research purposes, yarns with a specific model structure were
also needed, which were characterized by a distinct distribution of slubs and thickened
areas in the form of neps. This model structure was obtained directly on the ring spinning
frame by periodically removing some fibers locally from the feeding roving, resulting in the
formation of slubs in the created yarn. Additionally, neps were added to the feeding roving
at specific intervals; these had previously been removed during the carding process. This
addition allowed them to twist into the yarn, creating characteristic thickened areas. During
the experiments on the tensile tester, these thickened areas led to momentary increases in
yarn tension and yarn breaks at the slub locations.
3.4. Theoretical Assumptions
The project assumes that it is possible to detect yarn breakage and determine its
location by measuring vibrations using a sensor placed at the end of the warping machine
frame. This assumption stems from the fact that, during machine operation, the sensor
experiences a tension force (S), which is the sum of two friction forces. The first force is
generated in the tensioner (T), while the second force is the friction force (F) acting on
the elements supporting the yarn along its entire path from the bobbin to the warp beam.
The combined action of these forces causes the extension of the spring where the sensor
is positioned.
S=T+F(1)
During a yarn break, the force system undergoes a sudden change. The friction force
related to the tensioner disappears because it is the first element in the yarn’s path, and
Sensors 2025,25, 299 6 of 17
the break always occurs before it. However, the friction force is reduced according to
Formula (2):
T=µ·m·g(2)
where µ—friction coefficient, g—gravitational acceleration, and m—yarn mass.
Since the coefficient of friction remains constant, the only change lies in the mass
acting on the frictional surfaces. This mass depends on a crucial parameter—the location of
the yarn break and, hence, the length and weight of the remaining yarn.
In summary, during a break, a force acts on the sensor that is equal to the difference
between the forces present before and after the event, according to Formula (3). Therefore,
it can be inferred that the acceleration measured by the sensor will be constant for
a specific
break point and will vary depending on the location of the break along the warping
machine frame:
X=−(T+µ·g(m2−m1)) (3)
where m1—mass of the yarn before the break, and m2—mass of the yarn after the break.
To estimate the possibility of detecting changes in sensor acceleration concerning
the location of yarn breaks, calculations were performed based on measurements of the
technological parameters during machine operation. This research was conducted for three
linear masses of yarn: 20, 50, and 100 tex, assuming that there are three fixed break points
located at distances of 1, 2, and 3 m from the sensor. The results of these measurements
are presented in Table 1. For each yarn type, two measurements were taken. The first was
conducted during normal machine operation, while the second omitted the tensioning
system. This allowed the subtraction of the two values to determine the force acting on the
sensor during a break. Additionally, for each measuring point, the weight of the remaining
yarn after the break was determined and added to the sensor’s own mass, which is 4.52 g.
This process enabled the calculation of the acceleration that would be measured by the
sensor in the actual setup.
Table 1. Acceleration of the yarn during breakage.
Linear Mass [tex] 20 50
Tension during work [cN] 31 31
Tension without tensioner [cN] 17 17
Difference in forces after a break [cN]
14 14
Break distance from sensor [m] 1 2 3 1 2 3
Mass of the sensor with yarn [g] 4.56 4.58 4.60 4.63 4.68 4.73
Acceleration [m/s2]30.70 30.56 30.43 30.23 29.91 29.60
Based on the obtained results, it can be observed that the measured accelerations
do not significantly differ depending on the break location. Additionally, the yarn is
not a uniform object—it has various surface defects that influence the generated tension
force [
27
–
29
]. Moreover, there is the effect of variable friction force and the formation
of balloons during the unwinding of the yarn from the bobbins [
3
,
30
]. Confirmation of
this hypothesis is presented in Figure 2, depicting the acceleration as measured by the
sensor along the vertical x-axis during the warping machine’s operation. These described
fluctuations cause significant variations in the sensor’s acceleration pattern, consequently
leading to an inability to extract the desired data.
Sensors 2025,25, 299 7 of 17
Sensors 2025, 25, x FOR PEER REVIEW 7 of 17
Tension without tensioner [cN] 17 17
Difference in forces after a break [cN] 14 14
Break distance from sensor [m] 1 2 3 1 2 3
Mass of the sensor with yarn [g] 4.56 4.58 4.60 4.63 4.68 4.73
Acceleration [m/s2] 30.70 30.56 30.43 30.23 29.91 29.60
Based on the obtained results, it can be observed that the measured accelerations do
not significantly differ depending on the break location. Additionally, the yarn is not a
uniform object—it has various surface defects that influence the generated tension force
[27–29]. Moreover, there is the effect of variable friction force and the formation of bal-
loons during the unwinding of the yarn from the bobbins [3,30]. Confirmation of this hy-
pothesis is presented in Figure 2, depicting the acceleration as measured by the sensor
along the vertical x-axis during the warping machine’s operation. These described fluctu-
ations cause significant variations in the sensor’s acceleration paern, consequently lead-
ing to an inability to extract the desired data.
The problem described above was addressed by adding a layer of nonwoven fabric
just beneath the moving yarn along the warping machine’s frame. Its purpose is to signif-
icantly increase the frictional force when the yarn breaks and falls onto it. This action leads
to a several-fold increase in the difference in frictional force between consecutive meas-
urement points. The research findings are presented in Table 2; Figure 3 shows the nonwo-
ven fabric under the yarns in the warping machine.
Figure 2. Plot showing an example of sensor acceleration along the vertical x-axis over time during
normal warping machine operation and during breakage of the yarn.
The problem described above was addressed by adding a layer of nonwoven fabric
just beneath the moving yarn along the warping machine’s frame, which is depicted in
Figure 3.
Figure 2. Plot showing an example of sensor acceleration along the vertical x-axis over time during
normal warping machine operation and during breakage of the yarn.
The problem described above was addressed by adding a layer of nonwoven fabric
just beneath the moving yarn along the warping machine’s frame. Its purpose is to sig-
nificantly increase the frictional force when the yarn breaks and falls onto it. This action
leads to
a several-fold
increase in the difference in frictional force between consecutive
measurement points. The research findings are presented in Table 2; Figure 3shows the
nonwoven fabric under the yarns in the warping machine.
Table 2. Acceleration of the yarn during breakage with the increase in friction force.
Linear Mass [tex] 20 50
Tension during work [cN] 31 31
Tension without tensioner [cN] 17 18 19 17.5 19 21
Difference in forces after a break [cN]
14 13 12 13.5 12 10
Break distance from sensor [m] 1 2 3 1 2 3
Mass of the sensor with yarn [g] 4.56 4.58 4.60 4.63 4.68 4.73
Acceleration [m/s2]30.48 28.38 26.09 29.16 25.64 21.14
Sensors 2025, 25, x FOR PEER REVIEW 7 of 17
Tension without tensioner [cN] 17 17
Difference in forces after a break [cN] 14 14
Break distance from sensor [m] 1 2 3 1 2 3
Mass of the sensor with yarn [g] 4.56 4.58 4.60 4.63 4.68 4.73
Acceleration [m/s2] 30.70 30.56 30.43 30.23 29.91 29.60
Based on the obtained results, it can be observed that the measured accelerations do
not significantly differ depending on the break location. Additionally, the yarn is not a
uniform object—it has various surface defects that influence the generated tension force
[27–29]. Moreover, there is the effect of variable friction force and the formation of bal-
loons during the unwinding of the yarn from the bobbins [3,30]. Confirmation of this hy-
pothesis is presented in Figure 2, depicting the acceleration as measured by the sensor
along the vertical x-axis during the warping machine’s operation. These described fluctu-
ations cause significant variations in the sensor’s acceleration paern, consequently lead-
ing to an inability to extract the desired data.
The problem described above was addressed by adding a layer of nonwoven fabric
just beneath the moving yarn along the warping machine’s frame. Its purpose is to signif-
icantly increase the frictional force when the yarn breaks and falls onto it. This action leads
to a several-fold increase in the difference in frictional force between consecutive meas-
urement points. The research findings are presented in Table 2; Figure 3 shows the nonwo-
ven fabric under the yarns in the warping machine.
Figure 2. Plot showing an example of sensor acceleration along the vertical x-axis over time during
normal warping machine operation and during breakage of the yarn.
The problem described above was addressed by adding a layer of nonwoven fabric
just beneath the moving yarn along the warping machine’s frame, which is depicted in
Figure 3.
Figure 3. Non-woven layer under the yarns.
The problem described above was addressed by adding a layer of nonwoven fabric
just beneath the moving yarn along the warping machine’s frame, which is depicted in
Figure 3.
Hydro-needled nonwoven fabric weighing 35 g/m
2
was used, which was immersed
in a solution of water and resin with the addition of soot. Then, the excess solution
was squeezed out between pressure rollers, and the prepared nonwoven material was
Sensors 2025,25, 299 8 of 17
subjected to a drying and stabilization process at a temperature of 150
◦
C. This resulted in
a relatively
stiff material in a black color—contrasting with the white yarns. Its purpose is to
significantly increase the frictional force when the yarn breaks and falls onto it. This action
leads to a several-fold increase in the difference in frictional force between consecutive
measurement points. The research findings are presented in Table 2and Figure 4shows
a conceptual diagram of the presented assumption.
Sensors 2025, 25, x FOR PEER REVIEW 8 of 17
Figure 3. Non-woven layer under the yarns.
Hydro-needled nonwoven fabric weighing 35 g/m2 was used, which was immersed
in a solution of water and resin with the addition of soot. Then, the excess solution was
squeezed out between pressure rollers, and the prepared nonwoven material was sub-
jected to a drying and stabilization process at a temperature of 150 °C. This resulted in a
relatively stiff material in a black color—contrasting with the white yarns. Its purpose is
to significantly increase the frictional force when the yarn breaks and falls onto it. This
action leads to a several-fold increase in the difference in frictional force between consec-
utive measurement points. The research findings are presented in Table 2 and Figure 4
shows a conceptual diagram of the presented assumption.
Figure 4. Conceptual diagram.
Thanks to the nonwoven fabric pad, the differences between individual measure-
ment points became significantly greater, allowing for a reduction in the impact of yarn
tension fluctuations during operation on the ability to detect breakage.
Table 2. Acceleration of the yarn during breakage with the increase in friction force.
Linear Mass [tex] 20 50
Tension during work [cN] 31 31
Tension without tensioner [cN] 17 18 19 17.5 19 21
Difference in forces after a break [cN] 14 13 12 13.5 12 10
Break distance from sensor [m] 1 2 3 1 2 3
Mass of the sensor with yarn [g] 4.56 4.58 4.60 4.63 4.68 4.73
Acceleration [m/s2] 30.48 28.38 26.09 29.16 25.64 21.14
Due to the described yarn defects, the measured signal at the moment of breakage
does not consistently exhibit the same initial amplitude. Therefore, a straightforward com-
parison of amplitudes and the calculation of the breakage location using Table 2 is not
possible. To achieve the set goal, it was decided that the system would halt the warping
machine when the acceleration exceeded the threshold value of 22 m/s². Subsequently, the
obtained signal was examined from the moment of stoppage to 0.3 s after it. This meas-
urement time is necessary because the high-speed warping machine must not allow the
broken yarn from the tensioners closest to the frame’s end to pass through the detector.
This broken yarn causes an acceleration spike, disrupting comparisons with other yarns.
Based on an analysis of the nature of the measured vibration, six coefficients were
selected, which were determined from the obtained vibration signal. They are described
below.
1. Mean
Figure 4. Conceptual diagram.
Thanks to the nonwoven fabric pad, the differences between individual measurement
points became significantly greater, allowing for a reduction in the impact of yarn tension
fluctuations during operation on the ability to detect breakage.
Due to the described yarn defects, the measured signal at the moment of breakage
does not consistently exhibit the same initial amplitude. Therefore, a straightforward
comparison of amplitudes and the calculation of the breakage location using Table 2is not
possible. To achieve the set goal, it was decided that the system would halt the warping
machine when the acceleration exceeded the threshold value of 22 m/s². Subsequently,
the obtained signal was examined from the moment of stoppage to 0.3 s after it. This
measurement time is necessary because the high-speed warping machine must not allow
the broken yarn from the tensioners closest to the frame’s end to pass through the detector.
This broken yarn causes an acceleration spike, disrupting comparisons with other yarns.
Based on an analysis of the nature of the measured vibration, six coefficients
were selected, which were determined from the obtained vibration signal. They are
described below.
1 Mean
The Mean (4) of the analyzed signal indicates its degree of symmetry. If it tends toward
zero, the measured vibrations are symmetrical [31]:
Mean =1
N∑N
n−1x(n)(4)
where x(n) is the value of a particular sample n, and Nis the number of all nsamples.
2 Root Mean Square (RMS)
The root mean square (5) is a measure of the power contained in the signal [32].
RMS(x(n)) =r1
N∑N
n−1x(n)2(5)
3 Standard Deviation (SD)
The standard deviation (6) is a coefficient indicating how much the measured signal
deviates from its mean [32].
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SD(x(n)) =r1
N∑N
n−1[x(n)−mean(x(n))]2T=µ·m·g(6)
4 Crest Factor
The crest factor (7) determines the ratio of the peak values of the signal to its root mean
square value. For a sinusoidal wave, this is equal to 1.414 [32].
Crest(x(n)) =max |x(n)|
RMS(x(n)) (7)
5 Energy
The energy (8) of the signal is measured by summing the square of all its values [31].
Energy(x(n)) =∑N
n−1(x(n))2(8)
6 Kurtosis Coefficient
Kurtosis (9) is a coefficient designed to detect minor defects in vibrations [33].
Kurtosis(x(n)) =
1
N∑N
n−1(x(n)−mean(x(n)))4
h1
N∑N
n−1(x(n)−mean(x(n)))2i2−3 (9)
4. Results and Discussion
4.1. Detection of Yarn Breaks with Different Tex Values
To verify the validity of the method, which was drawn up from the earlier theoretical
considerations, a series of tests was conducted. Specifically, prepared yarn was used as
the warp in the normal cycle of the warping machine’s operation. Introducing a pair of
defects into the yarn caused a significant increase in tension when a large thickened area
passed through the tensioners, leading to breakage at the intentionally created weak point.
Three representative breaking points were selected, each at distances of 1, 2, and 3 m from
the tensioner. At this stage of the research, yarns of 20 and 50 tex (grams per kilometer of
yarn) were used, and five measurements were taken at each point. Figures 5–10 depict the
accelerations in the vertical x-axis that were registered at each measurement point.
Sensors 2025, 25, x FOR PEER REVIEW 10 of 17
A summary of the obtained results is presented in Tables 3 and 4, from which the
coefficients described in the previous section were calculated for yarn breaks with linear
masses of 20 and 50 tex.
Figure 5. Plot of sensor acceleration over time: yarn, 20 tex; break, 1 m.
Figure 6. Plot of sensor acceleration over time: yarn, 20 tex; break, 2 m.
Figure 7. Plot of sensor acceleration over time: yarn, 20 tex; break, 3 m.
Figure 5. Plot of sensor acceleration over time: yarn, 20 tex; break, 1 m.
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A summary of the obtained results is presented in Tables 3 and 4, from which the
coefficients described in the previous section were calculated for yarn breaks with linear
masses of 20 and 50 tex.
Figure 5. Plot of sensor acceleration over time: yarn, 20 tex; break, 1 m.
Figure 6. Plot of sensor acceleration over time: yarn, 20 tex; break, 2 m.
Figure 7. Plot of sensor acceleration over time: yarn, 20 tex; break, 3 m.
Figure 6. Plot of sensor acceleration over time: yarn, 20 tex; break, 2 m.
Sensors 2025, 25, x FOR PEER REVIEW 10 of 17
A summary of the obtained results is presented in Tables 3 and 4, from which the
coefficients described in the previous section were calculated for yarn breaks with linear
masses of 20 and 50 tex.
Figure 5. Plot of sensor acceleration over time: yarn, 20 tex; break, 1 m.
Figure 6. Plot of sensor acceleration over time: yarn, 20 tex; break, 2 m.
Figure 7. Plot of sensor acceleration over time: yarn, 20 tex; break, 3 m.
Figure 7. Plot of sensor acceleration over time: yarn, 20 tex; break, 3 m.
Sensors 2025, 25, x FOR PEER REVIEW 11 of 17
Figure 8. Plot of sensor acceleration over time: yarn, 50 tex; break, 1 m.
Figure 9. Plot of sensor acceleration over time: yarn, 50 tex; break, 2 m.
Figure 10. Plot of sensor acceleration over time: yarn, 50 tex; break, 3 m.
Table 3. Values of coefficients for the performed measurement of 20 tex yarn.
Break [m] Probe Mean RMS SD Crest Energy Kurtosis
1 1 −0.1150 12.8432 12.3591 2.1958 18,509 2.5680
1 2 −0.1924 13.2199 12.6775 2.8192 19,399 2.7608
1 3 0.0418 10.6006 10.2035 2.5593 12,773 2.6911
1 4 −0.1147 15.9945 15.3479 2.5853 28,096 2.6142
Figure 8. Plot of sensor acceleration over time: yarn, 50 tex; break, 1 m.
Sensors 2025,25, 299 11 of 17
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Figure 8. Plot of sensor acceleration over time: yarn, 50 tex; break, 1 m.
Figure 9. Plot of sensor acceleration over time: yarn, 50 tex; break, 2 m.
Figure 10. Plot of sensor acceleration over time: yarn, 50 tex; break, 3 m.
Table 3. Values of coefficients for the performed measurement of 20 tex yarn.
Break [m] Probe Mean RMS SD Crest Energy Kurtosis
1 1 −0.1150 12.8432 12.3591 2.1958 18,509 2.5680
1 2 −0.1924 13.2199 12.6775 2.8192 19,399 2.7608
1 3 0.0418 10.6006 10.2035 2.5593 12,773 2.6911
1 4 −0.1147 15.9945 15.3479 2.5853 28,096 2.6142
Figure 9. Plot of sensor acceleration over time: yarn, 50 tex; break, 2 m.
Sensors 2025, 25, x FOR PEER REVIEW 11 of 17
Figure 8. Plot of sensor acceleration over time: yarn, 50 tex; break, 1 m.
Figure 9. Plot of sensor acceleration over time: yarn, 50 tex; break, 2 m.
Figure 10. Plot of sensor acceleration over time: yarn, 50 tex; break, 3 m.
Table 3. Values of coefficients for the performed measurement of 20 tex yarn.
Break [m] Probe Mean RMS SD Crest Energy Kurtosis
1 1 −0.1150 12.8432 12.3591 2.1958 18,509 2.5680
1 2 −0.1924 13.2199 12.6775 2.8192 19,399 2.7608
1 3 0.0418 10.6006 10.2035 2.5593 12,773 2.6911
1 4 −0.1147 15.9945 15.3479 2.5853 28,096 2.6142
Figure 10. Plot of sensor acceleration over time: yarn, 50 tex; break, 3 m.
A summary of the obtained results is presented in Tables 3and 4, from which the
coefficients described in the previous section were calculated for yarn breaks with linear
masses of 20 and 50 tex.
Table 3. Values of coefficients for the performed measurement of 20 tex yarn.
Break [m] Probe Mean RMS SD Crest Energy Kurtosis
1 1 −0.1150 12.8432 12.3591 2.1958 18,509 2.5680
1 2 −0.1924 13.2199 12.6775 2.8192 19,399 2.7608
1 3 0.0418 10.6006 10.2035 2.5593 12,773 2.6911
1 4 −0.1147 15.9945 15.3479 2.5853 28,096 2.6142
1 5 −0.0161 14.2478 13.9217 2.6137 22,233 2.7306
2 1 −0.3014 13.6565 13.1104 2.6288 20,701 2.8685
2 2 −0.1053 11.0380 10.5852 2.6427 13,524 3.0994
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Table 3. Cont.
Break [m] Probe Mean RMS SD Crest Energy Kurtosis
2 3 0.2063 10.1475 9.6638 2.6641 11,956 2.9912
2 4 −0.1040 9.2037 8.8075 2.9227 9603 3.1783
2 5 −0.0576 14.7197 13.9899 2.8750 22,101 3.2164
3 1 −0.1821 13.0912 12.6884 3.0356 20,605 3.3977
3 2 0.0180 10.7358 10.3783 2.9424 11,321 4.6655
3 3 −0.0163 12.7506 12.1491 2.8430 16,546 4.1061
3 4 0.0829 14.0465 13.4887 2.7354 22,729 3.5541
3 5 0.1892 10.3428 9.8647 2.9209 11,874 4.1275
Table 4. Values of coefficients for the performed measurement of 50 tex yarn.
Break [m] Probe Mean RMS SD Creast Energy Kurtosis
1 1 −0.1709 123857 11.8999 2.5182 16,028 2.4413
1 2 −0.0969 11.5041 11.0278 2.6721 13,690 2.6120
1 3 0.0982 10.1163 9.6865 2.7164 11,360 2.5425
1 4 0.2168 10.9402 10.5168 2.5767 12,285 2.6263
1 5 −0.1098 14.2205 13.6876 2.3747 19,447 2.5456
2 1 0.0853 9.4016 8.9916 2.8761 9611 2.8575
2 2 0.0279 9.0800 8.7188 2.6266 9152 2.7106
2 3 −0.0431 9.4056 9.0049 2.7739 9820 2.7399
2 4 0.0587 9.8296 9.2512 2.6321 9990 2.7038
2 5 −0.0944 8.3405 8.0238 2.6210 7722 2.8460
3 1 −0.0341 6.2277 5.9646 2.7731 4905 3.1923
3 2 −0.0629 8.1979 7.8247 2.9386 7460 3.3687
3 3 0.0245 8.5824 8.2675 2.9188 8176 3.4965
3 4 0.1461 6.1142 5.8526 3.0879 4750 3.5695
3 5 0.0432 8.8920 8.4823 3.1770 8277 3.7876
Comparing the results shown in Tables 3and 4, it can be seen that the only parameter
allowing us (although not in every case) to determine the location of the yarn break is the
value of the calculated vibration energy. Nevertheless, these values are not very precise,
and some results coincide with those obtained at a different break location. However, it
was noticed that with greater vibration energy, the value of the RMS and SD coefficients
also increased. To obtain more valuable data from the calculated coefficients and based on
the observed dependencies between the coefficients, a special Formula (10) was developed,
calculating the MRSCEK coefficient (Table 5). It sums up four coefficients: mean, root
mean square, standard deviation, and crest factor. Then, the calculated sum is squared
and divided by the products of energy and kurtosis. The results obtained in this way are
presented in Table 4.
MRSCEK =(Mean +R MS +SD +Crest)2
Energy·Kurtosis (10)
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Table 5. The results of the MRSCEK coefficient calculations.
Break [m] Probe 20 tex 50 tex
1 1 0.0157 0.0181
1 2 0.0152 0.0176
1 3 0.0159 0.0177
1 4 0.0156 0.0182
1 5 0.0156 0.0184
Mean 0.0156 0.0180
2 1 0.0143 0.0166
2 2 0.0139 0.0169
2 3 0.0144 0.0166
2 4 0.0142 0.0169
2 5 0.0140 0.0162
Mean 0.0142 0.0166
3 1 0.0117 0.0142
3 2 0.0110 0.0142
3 3 0.0113 0.0137
3 4 0.0114 0.0136
3 5 0.0111 0.0135
Mean 0.0113 0.0139
Summarizing the above, it can be seen that the average measurement results for
a break
occurring 2 m from the sensor compared to a break occurring 1 m from the sensor are
smaller by 0.00144 for 20 tex yarn and 0.00138 for 50 tex yarn. Between the breaks at 3 and
2 m, the difference is 0.00285 and 0.00278, respectively. Therefore, they are approximately
twice as large. Consequently, it can be stated that by determining the coefficients described
in the article and substituting them into the proposed formula, it is possible to determine
with a certain accuracy the location along the warping machine frame where the yarn break
occurred. Furthermore, the application of the proposed formula minimizes the impact of
linear mass on the final measurement result. Figure 11 portrays the results of MRSCEK
coefficient calculations for all trials and for the average value.
To make it easier to read the position of the yarn break, based on the average values
from the measurements, the course of the distance values relative to the MRSCEK coefficient
was approximated using a quadratic polynomial. The course of this polynomial is shown
as solid lines in Figure 11. For a yarn of 20 tex linear weight, the function has the form
f20(x) = −85920x2+ 1846.1x −6.8903
, while for 50 tex, f
50
(x) =
−
83882x
2
+ 2188x
−
11.207,
where x is the value of the MRSCEK factor. As the measurements taken were not iden-
tical, an expanded uncertainty value was determined for each measurement point with
a coverage factor of 2 and, therefore, for a confidence level of 95%. Conservatively, the
highest value of the expanded uncertainty of 0.000269 was assumed for all measurements,
which is presented in Figure 11 as continuous semi-transparent lines above and below
the main function. From this figure, the approximate yarn break distances can be easily
read off, based on the calculated MRSCEK coefficient. For example, for a 20 tex yarn and
an MRSCEK coefficient value of 0.01345, the break occurred at a distance of 2.3966 m
(+0.1189/−0.1313).
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Figure 11. Plot of MRSCEK values (the average is represented by solid lines and the expanded un-
certainty is represented by transparent solid lines; measuring points are marked with dots).
4.2. Detection of Yarn Breaks with Different Materials
Another fundamental variable during warping yarn is the material from which it was
made. For the study, yarns made from three types of fibers, namely, coon, polyester, and
viscose, were used. All of them were produced using the same method, as described in
the previous section for coon yarns. To verify if the material used for yarn-making affects
the obtained results, a series of tension measurements were conducted. The results proved
inconclusive because no noticeable change in tension was observed for the different ma-
terial types. Dynamic measurements exhibit fluctuations in tension, stemming not only
from the variable frictional forces generated while unwinding yarn from bobbins (cross-
winding) but also from fluctuations in the friction forces associated with the non-uni-
formity of the yarn along its length.
To precisely examine the impact of material on the obtained results, the hairiness
coefficient H [29,34] was measured using the Uster Tester 3 laboratory device. All yarns
had the same linear mass of 50 tex. The measurement results are presented in Table 6.
Table 6. Testing the hairiness of yarn.
Material Cotton Polyester Viscose
Sample 1 4.58 4.62 4.58
Sample 2 4.61 4.64 4.65
Sample 3 4.65 4.59 4.60
Hairiness is one of the key factors that can affect the coefficient of friction of yarns.
Based on the obtained results, it can be stated that regardless of the material, the hairiness
coefficient is very similar, and its fluctuations are so minor that they do not significantly
impact the acceleration measured by the sensor and, thus, the ability to determine the
Figure 11. Plot of MRSCEK values (the average is represented by solid lines and the expanded
uncertainty is represented by transparent solid lines; measuring points are marked with dots).
4.2. Detection of Yarn Breaks with Different Materials
Another fundamental variable during warping yarn is the material from which it
was made. For the study, yarns made from three types of fibers, namely, cotton, polyester,
and viscose, were used. All of them were produced using the same method, as described
in the previous section for cotton yarns. To verify if the material used for yarn-making
affects the obtained results, a series of tension measurements were conducted. The results
proved inconclusive because no noticeable change in tension was observed for the different
material types. Dynamic measurements exhibit fluctuations in tension, stemming not only
from the variable frictional forces generated while unwinding yarn from bobbins (cross-
winding) but also from fluctuations in the friction forces associated with the non-uniformity
of the yarn along its length.
To precisely examine the impact of material on the obtained results, the hairiness
coefficient H [
29
,
34
] was measured using the Uster Tester 3 laboratory device. All yarns
had the same linear mass of 50 tex. The measurement results are presented in Table 6.
Table 6. Testing the hairiness of yarn.
Material Cotton Polyester Viscose
Sample 1 4.58 4.62 4.58
Sample 2 4.61 4.64 4.65
Sample 3 4.65 4.59 4.60
Hairiness is one of the key factors that can affect the coefficient of friction of yarns.
Based on the obtained results, it can be stated that regardless of the material, the hairiness
coefficient is very similar, and its fluctuations are so minor that they do not significantly
impact the acceleration measured by the sensor and, thus, the ability to determine the
Sensors 2025,25, 299 15 of 17
location of yarn breakage. The differences between yarn samples made from the same
material are greater than the differences between values for different materials.
4.3. Algorithm Running Time
The stopping speed of the machine after detecting a broken yarn is a critical parameter
for any warping machine. This duration is influenced by two primary factors. The first is
the efficiency of the braking system, which is responsible for stopping the rotating warp
beam. The second is the activation speed of the yarn breakage detection system. Table 7
presents the measurements of the descent speed of mechanical detectors, conducted in four
different warping machines.
Table 7. Mechanical break detector activation times in seconds.
The Name of the Machine Sample 1 Sample 2 Sample 3
KARL MAYER DS.14P 0.123 0.123 0.124
HUYS & VANHEVEL NV type Sigma 0.116 0.115 0.115
LIBA type 23 0.121 0.121 0.121
ELITEX type 2206 0.119 0.119 0.118
In the designed measurement system, successive samples are collected every 1 ms.
Since only information about the occurrence of yarn breakage is required to stop the
warping machine, while the precise localization of the breakage can be determined later, it
is necessary to check if the measured acceleration value has exceeded a certain threshold.
Executing these actions and generating the yarn status signal takes between 2752 and
2808 ms, enabling the detection of yarn breakage about 50 times faster than with classical
mechanical solutions.
5. Conclusions
Based on our research, a new method for measuring yarn breakage using the accelera-
tion signals obtained from an accelerometer has been designed and tested. This method
allows us not only to determine the occurrence of yarn breakage but, more importantly, to
identify the location where the breakage occurred. Key to achieving these results was the
development of the proprietary MRSCEK coefficient, which proved to be more effective
than analyzing individual coefficients when examining the recorded signal. It takes into
account six coefficients: mean, root mean square, standard deviation, crest factor, energy,
and kurtosis. As demonstrated in the conducted studies, the MRSCEK coefficient is pri-
marily dependent on the distance of the yarn breakage from the accelerometer. The second
significant parameter is the linear mass of the yarn, in which an increase causes a parallel
shift of the MRSCEK values upward.
The parameters obtained from the proposed method are naturally dependent on the
warping machine (friction of the underlying fabric, braking distance, operating speed, etc.)
and the type and mass of the yarn. However, they can be easily determined and recorded
in tables referenced by the system for analyzing the location of the breakage.
An important aspect of the research was the development of a method for producing
yarn with the desired properties, with particular emphasis on defects being placed at
designated locations. This was precisely the opposite approach to most studies conducted
on the yarn-processing process, which focuses on obtaining products with the best, most
flawless parameters.
As the study of yarn hairiness showed, hairiness is very similar regardless of the type
of material from which the yarn was made. The H values of all samples ranged from 4.58 to
Sensors 2025,25, 299 16 of 17
4.65. However, in future studies, it is worth analyzing in detail the influence of the degree
of yarn hairiness on the friction coefficient and comparing it with the values obtained from
the MRSCEK coefficient calculations.
It is also worth noting that the developed measurement system allows for very fast
measurements. As the research showed, classical mechanical detectors achieve operating
times ranging from 0.116 to 0.124 s, translating into obtaining a reaction time over
50 times
faster by the proposed accelerometer-based solution. By achieving operating speeds rang-
ing from 2.752 to 2.808 ms, it can be concluded that the designed system operates in real
time and drastically shortens the braking distance of the warping machine.
The implementation of this project opens up wide possibilities for the development
of robotic systems allowing for the automatic tying of broken yarns. So far, there have
been no systems in common use that allow for determining the exact location of yarn
breakage; thus, there has been a lack of the necessary data for automating the process of
removing breakages.
Author Contributions: Conceptualization: M.I.; investigation, M.I.; software, M.I.; supervision, T.R.;
writing—original draft, M.I.; writing—review and editing, T.R. All authors have read and agreed to
the published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: The data used to support the findings of this study are available from
the author upon request.
Acknowledgments: This work was completed while the first author was a Doctoral Candidate in the
Interdisciplinary Doctoral School at the Lodz University of Technology, Poland.
Conflicts of Interest: The authors declare no conflicts of interest.
References
1.
Ghandi, K.L. Yarn preparation for weaving: Warping. In Woven Textiles: Principles, Technologies and Applications; Woodhead
Publishing Limited: Cambridge, UK, 2012.
2. Kitamura, Y. Snail Wire Arrangement for Yarn Breakage Detection in Ring Frames. Japan Patent US 4,393,647, 19 July 1983.
3.
Praˇcek, S.; Pušnik, N.; Simonˇciˇc, B.; Tavacer, P.F. Model for simulating yarn unwinding from packages. Fiber Terxtiles Estern Eur.
2015,23, 23–32.
4.
Zhao, B. The prediction of warp breakage rate of weaving by considering sized yarn quality using artificial neural network theory.
In Proceedings of the International Conference on Computer Design and Applications, Qinhuangdao, China, 25–27 June 2010.
5.
Abe, F.; Cauthen, J.V.; McCoy, D.W. Apparatus for Stopping Textile Machinery upon Detection of Yarn-Break and for Indicating
Location Therefor. U.S. Patent 3,456,310, 22 July 1969.
6.
Hou, X.B.; Zhang, M.; Chen, J.; Li, K.L.; Li, Z.; Yu, B.Q. Broken Yarn Self-Stop Device for Warping Machine. China Patent CN
202039177U, 16 November 2011.
7.
Pan, J.P.; Bao, W.T.; Li, J.T. Warping Machine Broken Yarn Detection Device Based on Machine Vision. China Patent CN
202576745U, 5 December 2012.
8.
Wang, W.; Zhou, P.; Lin, X. Development of yarn breakage detection software system based on machine vison. In Proceed-
ings of the 2nd International Conference on Materials Science, Resource and Environmental Engineering, Wuhan, China,
27–29 October 2017.
9. Yan, X. Design of the break yarn online detecting system for textile machine. Int. Core J. Eng. 2019,5, 113–117.
10.
De Jun, L. The Research of Broken Filaments Detection Device on Viscose Filament Yarn. In Proceedings of the International
Conference on Computational Intelligence and Security Workshops, Harbin, China, 15–19 December 2007.
11.
Chen, Y.; Yu, T.; Wan, K.; Yuan, J. Real-time detection of the broken thread of textile products. In Proceedings of the 2021 IEEE
International Conference on Advances in Electrical Engineering and Computer Applications (AEECA), Dalian, China, 27–28
August 2021; pp. 988–991. [CrossRef]
Sensors 2025,25, 299 17 of 17
12.
Idzik, M.; Rybicki, T. Real-Time Yarn Breakage Detection in the Warping Machine. IEEE Access 2023,11, 47501–47509. [CrossRef]
13. Mizuuchi, S.; Nishimura, S. Warp Yarn Breakage Detection System. Japan Patent US 5,313,692, 24 May 1994.
14. Wang, Y.S. Yarn Broken Sensor of Warping Machine. China Patent CN 203487311U, 19 March 2014.
15. Gao, S. Laser Broken Yarn Monitoring Device for Warp Knitting Machine. China Patent CN 103215745B, 18 February 2015.
16. Ye, C.; Chen, L.; Li, J.T. Novel Laser-Based Broken Yarn Detection Device. China Patent CN 202548067U, 21 November 2012.
17. Musa, E. Line laser-based break sensor that detects light spots on yarns. Opt. Lasers Eng. 2009,47, 741–746. [CrossRef]
18. Musa, E. Line-laser-based yarn shadow sensing break sensor. Opt. Lasers Eng. 2011,7, 313–317. [CrossRef]
19.
Slusser, T.J. The Application of MEMS Accelerometers for Accurately Finding Warp Yarn Breaks in Textile Machinery. Master’s
Thesis, North Carolina State University, Raleigh, NC, USA, 2003.
20.
Seyam, A.M.; Lee, J.H.; Hodge, G.; Oxenham, W.; Grant, E. Prevention of warp breaks in Jacquard weaving. Fibers Polym. 2007,
8, 79–83. [CrossRef]
21.
Lee, J.H.; Seyam, A.M.; Hodge, G.; Oxenham, W.; Grant, E. Warp Breaks Detection in Jacquard Weaving using MEMS: Effect of
Weave on Break Signals. J. Eng. Fibers Fabr. 2008,3, 25–31. [CrossRef]
22.
Karl Mayer Stoll Textilmaschinenfabrik GmbH. Technical and Operating Documentation for DS 14P Warping Machine; Karl Mayer
Stoll Textilmaschinenfabrik GmbH: Obertshausen, Germany, 1974.
23.
Thakkar, A.; Bhattacharya, S. Analysis of design aspects of textile warping: Part I: Review of literature. Int. J. Eng. Technol. Sci.
Res. 2017,9, 897–900.
24. Alemtwally, A.A.; Mourand, M.M.; Eldosky, M.A.E. A study of yarn breaks on warping machines. Life Sci. J. 2013,10, 108–114.
25.
Thakkar, A.; Bhattacharya, S. Mechanical Developments in Design Aspects of Textile Warping Machines. Int. J. Eng. Technol. Sci.
Res. 2018,5, 1623–1626.
26.
Stepanovic, J.; Radivojevic, D.; Petrovic, V.; Golubovic, S. Analysis of the breaking characteristics of twisted yarns. Fibres Text.
East. Eur. 2010,2, 40–44.
27.
Gun, A.D.; Oner, E. Investigation of the quality properties of open-end spun recycled yarns made from blends of recycled fabric
scrap wastes and virgin polyester fibre. J. Text. Inst. 2019,110, 1569–1579.
28.
Doran, E.C.; Sahin, C. The prediction of quality characteristics of cotton/elastane core yarn using artificial neural networks and
support vector machines. Text. Res. J. 2020,90, 1558–1580. [CrossRef]
29. Haleem, N.; Wang, X. Recent research and development on yarn hairiness. Text. Res. J. 2015,85, 211–224. [CrossRef]
30.
Celik, O.; Eren, R. Experimental Investigation of the Relationship Between the Yarn Tension and Bobbin Diameter in the Warping
Process. Fibres Text. East. Eur. 2019,1, 23–31. [CrossRef]
31.
Nadi, A.K.; Liu, C.; Wong, M.L.D. Intelligent vibration signal processing for condition monitoring. In Proceedings of the
International Conference Surveillance, Chartres, France, 29–30 October 2013.
32.
Emadi, M.; Payvandy, P.; Tavanaie, M.A.; Jalili, M.M. Measurement of vibration in polyester filament yarns to detect their
apparent properties. J. Text. Inst. 2022,113, 1270–1280. [CrossRef]
33.
Pradhan, M.K.; Gupta, P. Fault detection using vibration signal analysis of rolling element bearing in time domain using an
innovative time scalar in-dicator. Int. J. Manuf. Res. 2017,12, 305–317. [CrossRef]
34. Krupincová, G.; Meloun, M. Yarn hairiness versus quality of yarn. J. Text. Inst. 2013,104, 1312–1319. [CrossRef]
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