Patent Description:
A loose tube fiber optic cable is a type of fiber optic cable where glass fibers for carrying the optical signals are loosely encapsulated by a semi-rigid protective sleeve or tube (a so-called loose tube). The quality of loose tube fiber optic cable manufactured in a so-called secondary coating line is commonly monitored by periodically performing offline measurements of different key properties of the recently manufactured fiber optic cable. For example, said key properties to be measured may comprise excess fiber length, tube shrinkage and fiber light attenuation. Obviously, such offline measurements of already manufactured optical fiber cable require additional resources and they can be quite time-consuming. Moreover, any drop in the quality of the manufactured loose tube fiber optic cable may be detected only after a considerable delay. At least some of said key properties may be measured also during the manufacturing though such online measurements are often inaccurate compared to the offline measurements. Thus, there is a need for a solution which would enable monitoring the quality of fiber optic cables in a more automated manner during production without sacrificing accuracy.

According to an aspect, there is provided the subject matter of the independent claims.

A loose tube fiber optic cable is a type of fiber optic cable especially suited for harsh outdoor environments. In loose tube fiber optic cables, glass fibers for carrying the optical signals are loosely encapsulated by a semi-rigid protective sleeve or tube (a so-called loose tube or loose buffer tube). The space inside said loose tube not filled by the glass fibers is typically filled with a tube filling compound such as a water-resistant gel or jelly or a water-resistant yarn.

In many ways, the most critical phase in the manufacturing of loose tube fiber optic cables is the secondary coating phase performed in a secondary coating line. In the secondary coating phase, the glass fibers (and possibly a tube filling compound) are inserted into the loose tube (made typically of plastic). Initially, when the loose tube is extruded in the secondary coating phase, it has a very high temperature. As the loose tube cools down, it contracts or shrinks causing an offset in the lengths of glass fibers within the loose tube and the loose tube itself (that is, an increase in so-called excess fiber length). To overcome this effect, a so-called compression caterpillar is employed to influence the line speed of the loose tube (relative to the line speed of the fibers) so as to compensate for the shrinking of the loose tube. With the help of the compression caterpillar, very high line speeds may be achieved (e.g., up to <NUM> meters per minute) while maintaining near-zero excess fiber length.

The quality of the fiber optic cable being manufactured in a secondary coating line is commonly monitored by periodically performing measurements of different key properties of the recently manufactured fiber optic cable. For example, said key properties to be measured may comprise excess fiber length, tube shrinkage and fiber light attenuation. Obviously, such offline measurements of already manufactured optical fiber cable require additional resources and can be quite time-consuming. For these reasons, said measurements may be carried out only sporadically and thus any drop in the quality of the manufactured fiber optic cable may be detected only after a considerable delay.

The embodiments to be discussed below seek to solve or at least alleviate at least some of said problems associated with monitoring quality of manufactured loose tube fiber optic cable.

In the following, different exemplifying embodiments will be described in detail. Said exemplifying embodiments are based on employing a machine-learning algorithm for predicting quality of fiber optic cable during manufacturing in a secondary coating line. To facilitate the detailed discussion on embodiments, the machine-learning algorithms which may be employed in connection with embodiments are discussed, first, in detail.

The machine-learning algorithm according to embodiments may be based on one or more neural networks. Neural networks (or specifically artificial neural networks) are computing systems comprised of highly interconnected "neurons" capable of information processing due to their dynamic state response to external inputs. In other words, an artificial neural network is an interconnected group of nodes (or "neurons"), where each connection between nodes is associated with a weight (i.e., a weighting factor), the value of which affects the strength of the signal at said connection and thus also the total output of the neural network. Usually, a bias term is also added to the total weighted sum of inputs at a node. Training of a neural network typically involves adjusting said weights and biases so as to match a known output given a certain known input.

The one or more neural networks employed in embodiments may comprise one or more feed-forward neural networks, one or more recurrent neural networks and/or one or more self-organizing maps (SOM). Moreover, the one or more feed forward neural networks may comprise one or more multi-level perceptron networks and/or one or more convolutional neural networks. The one or more recurrent neural networks may comprise one or more long-short term memories and/or one or more recurrent convolutional neural networks.

An example of a feedforward neural network which may be employed in embodiments is a multilayer perceptron model (though a network of simple perceptron may also be employed in some embodiments). A single layer perceptron can be used to learn linearly separable functions but cannot be used to perform complex tasks like learning a non-linear decision boundary in classification. On the other hand, a multilayer perceptron network, which uses two or more layers of perceptrons, may be used to learn complex functions and highly non-linear decision boundaries. A multilayer perceptron network is a basic form a feedforward neural network and typically consists of an input layer, one or more hidden layers and an output layer. The network uses forward passes and backpropagation to learn the weights and bias. Forward passes (from input to output) calculate the outputs, while backpropagation calculates the necessary updates for the weights and biases based on the error at the output layer.

Feedforward neural networks do not have the capability to store any information since there are no loops in feedforward neural networks. Recurrent neural networks (RNNs), on the other hand, have loops in them allowing information to be maintained. One example of a recurrent neural network which may be employed in embodiments is a long short term memory (LSTM) which is a special type of recurrent neural network specialized in learning long-term dependencies. A single LSTM cell consists of three gates (input, output and forget gate) and a memory cell. Gates act as regulators of information and help LSTM cells to remove old information or add new information. The extent to which the existing memory is forgotten is controlled by the forget gate.

The self-organizing map is a type of neural network that is trained using unsupervised learning to produce a low-dimensional discretized representation of the input space of the training samples (a so-called map). In other words, self-organizing may be employed to create a low-dimensional representation or view of high-dimensional data.

Additionally or alternatively, the machine-learning algorithm according to embodiments may be based, fully or partly, on a Bayesian classifier, that is, a classifier based on Bayesian probability. The Bayesian classifier used may be, for example, a naive Bayesian classifier.

A system to which embodiments may be applied is illustrated in <FIG> illustrates a simplified system only showing some elements and functional entities. It is apparent to a person skilled in the art that the systems also comprise other functions and structures. <FIG> is to be considered predominantly schematic in nature.

The system of <FIG> comprises two distinct parts: a secondary coating line <NUM> for manufacturing loose tube fiber optic cable and a control system <NUM> for managing, monitoring and/or controlling said secondary coating line <NUM>.

The secondary coating line <NUM> is used for extruding a loose tube, feeding optical fibers (being typically glass fibers) inside the loose tube in a controlled manner and cooling and spooling the resulting loose tube fiber optic cable <NUM>. The secondary coating line <NUM> comprises at least one or more actuators <NUM>, a fiber pay off <NUM>, an extruder <NUM>, a compression caterpillar <NUM>, a middle capstan <NUM>, an end capstan <NUM> and a spooling unit <NUM>.

The fiber pay off <NUM> is used for maintaining optical fiber (i.e., glass fiber) and for feeding one or more optical fibers to the extruder <NUM> and further along the secondary coating line <NUM> in a controlled manner, preferably at high speeds. The tension of the optical fiber may be controlled, for example, with pneumatic dancers. The fiber pay off <NUM> may comprise a plurality of reel positions for reels of optical fiber.

The extruder <NUM> (or an extruder unit) is used for extruding (plastic) material to form a loose tube structure around the one or more optical fibers originating from the fiber pay off <NUM>. The extruded plastic material may be, for example, one of polybutylene terephthalate (PBT), polypropylene (PP), polyethylene (PE) and polycarbonate (PC) or some other plastic polymer. The produced loose tube may have cylindrical shape. The extruder may be temperature controlled. In some embodiments, multiple extruders may be employed. In some embodiments, the element <NUM> may correspond to one or more extruders or a plurality of extruders.

In embodiments where the loose tube fiber optic cable to be manufactured is to be filled with water-resistant (or water-blocking) yarn, the secondary coating line <NUM> may further comprise one or more yarn pay-offs for feeding yarn into the loose tube (not shown in <FIG>). In embodiments where the loose tube fiber optic cable to be manufactured is to be filled with water-resistant (or water-blocking) gel or jelly, the secondary coating line <NUM> may further comprise a gel or jelly injection system for feeding gel or jelly into the loose tube (not shown in <FIG>). Said gel or jelly injection system may be integrated into of one or more of the extruder <NUM>. In some embodiments, the secondary coating line <NUM> may further comprise a dry (or gel-free) tube diameter control for controlling diameter of dry tube fiber optic cables.

Initially, when the loose tube is extruded by the extruder <NUM>, it has a very high temperature. To efficiently cool down the loose tube, the loose tube fiber optic cable <NUM> may be passed through a cooling through or a cooling bath (not shown in <FIG>). Said cooling through may extend from the extruder <NUM> all the way to the spooling unit <NUM>. The cooling may be adjusted by changing the temperature of the cooling water used in the cooling through.

The compression caterpillar <NUM> is used for pulling the loose tube extruded by the extruder <NUM> at a speed which is faster than the line speed set point (i.e., faster than the speed of middle capstan <NUM> to be discussed below) in order to compensate for the shrinking of the loose tube as it cools down. In other words, the compression caterpillar <NUM> is used for minimizing the excess fiber length (EFL) and/or the tube shrinkage of the manufactured loose tube fiber optic cable. Excess fiber length is a measure for how much longer the one or more optical fibers inside the loose tube are compared to the loose tube encapsulating said one or more optical fibers. The compression caterpillar may comprise two moving compression belts extending parallel to each other and arranged facing each other.

The compression belts of the compression caterpillar <NUM> may be made of rubber or other material providing sufficiently high friction between the compression belts and the extruded loose tube. The loose tube may be fed between said compression belts which compress the loose tube and though friction and high speed of the compression belts (compared to the line speed without compression) cause an increase in the line speed of the loose tube (i.e., a lengthening of the loose tube) without affecting the line speed of the one or more optical fibers therein. The compression caused by the compression belts should be such that only elastic deformation (and not plastic deformation) is caused. In some embodiments, compression caterpillar may comprise, instead of compression belts, two compression wheels (e.g., made of rubber) operating in an analogous manner with the compression belts (as discussed above).

The middle capstan <NUM> is the next point of physical contact for the loose tube fiber optic cable <NUM> after the compression caterpillar <NUM>. Consequently, the middle capstan <NUM> also has a significant effect on excess fiber length and tube shrinkage of the manufactured loose tube fiber optic cable and the parameters (e.g., motor torque) of the middle capstan <NUM> may be used for adjusting the excess fiber length and tube shrinkage.

In addition to the middle capstan <NUM>, the secondary coating line may further comprise the end capstan <NUM> for providing further control of the line speed of the loose tube fiber optic cable. The end capstan <NUM> may be arranged between the middle capstan <NUM> and the spooling unit <NUM>.

The spooling unit <NUM> is used for spooling the finished loose tube fiber optic cable <NUM>.

Finally, the secondary coating line <NUM> comprises one or more actuators <NUM> for moving and controlling one or more devices <NUM>, <NUM> to <NUM> of the secondary coating line <NUM>. Said one or more actuators may power at least the compression caterpillar <NUM> and middle capstan <NUM>. The one or more actuators <NUM> may comprise, for example, one or more of an electric motor, a hydraulic actuator and a pneumatic actuator. The one or more actuators <NUM> are powered and controlled by the control system <NUM> (or specifically by a programmable logic controller, PLC, automation system <NUM> of the control system <NUM>). The operation of each of the one or more actuators <NUM> may be controlled by adjusting one or more control parameters of the corresponding actuator. The one or more control parameters for an actuator may comprise, for example, voltage, current, power and/or frequency of the signal fed to the actuator. In other words, the operation of the secondary coating line <NUM> may be adjusted by tuning one or more control parameters (in most cases, a plurality of control parameters) of the secondary coating line, where each control parameter is associated with one of the one or more actuators <NUM> of the secondary coating line <NUM>. The control parameters may be equally called running parameters.

In addition to or alternative to controlling the secondary coating line <NUM> by adjusting the one or more control parameters of the one or more actuators <NUM>, at least one of the other elements <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM> of the secondary coating line <NUM> may be controlled according to one or more control parameters. For example, said one or more control parameters of said other elements in the secondary coating line may comprise one or more temperatures of one or more respective extruders <NUM>, a temperature of cooling water (in the cooling through), a line tension and a fiber pay-off tension (i.e., tension at the fiber pay-off <NUM>). The line tension may be defined as a metric (a value of which is given in newtons) quantifying how stretched the loose tube is during spooling. The fiber pay-off tension may be defined as a metric (value of which is given in newtons) quantifying how tense the fiber optic cable is at the beginning of the secondary coating line (i.e., near the fiber pay-off <NUM>).

The control system <NUM> comprises a programmable logic controller (PLC) automation system <NUM>, a process supervisory unit <NUM>, a first computing device <NUM>, a user device <NUM>, a machine-learning database <NUM>, a history database <NUM> and a second computing device <NUM>. The first computing device <NUM>, the machine-learning database <NUM>, the history database <NUM> and the second computing device <NUM> form a computing system <NUM> for performing machine-learning-based analysis according to embodiments. Said control system <NUM> may be located in the same premises as the secondary coating line <NUM> (i.e., it may be a local system). Alternatively, at least some of the elements (e.g., the second computing device <NUM> and databases <NUM>, <NUM>) of the control system <NUM> may be remote elements (e.g., remote servers or databases). In some embodiments, some of said remote elements may be cloud computing -based elements or other distributed elements. The connections between apparatuses <NUM> to <NUM> of the control system <NUM> illustrated in <FIG> may comprise one or more wired connections (or communications links) and/or one or more wireless connections (communications links).

The PLC automation system <NUM> is used for monitoring and controlling manufacturing in the secondary coating line <NUM>. Specifically, the PLC automation system <NUM> may be used for monitoring production process parameter(s) of the secondary coating line <NUM> and adjusting control parameter(s) of the secondary coating line. To enable said monitoring and controlling, the PLC automation system <NUM> may be connected, via wired and/or wireless communication links, to each individual element <NUM> to <NUM> of the secondary coating line <NUM> (including the cooling through not shown in <FIG>) or at least some of said individual elements <NUM> to <NUM>. Moreover, the PLC automation system <NUM> is connected to the process supervisory unit <NUM>. The PLC automation system <NUM> may be a ruggedized computing system so as to enable reliable operation in a harsh usage environment.

In regards to the monitoring of the manufacturing in the secondary coating line <NUM>, measurement data (i.e., production process data) produced by each individual element <NUM> to <NUM> of the secondary coating line <NUM> connected to the PLC automation system <NUM> during the running of the secondary coating line <NUM> may be transmitted, periodically or continuously, to the PLC automation system <NUM>. The production process data collected by the PLC automation system <NUM> may comprise measured values for each of one or more production process parameters of the secondary coating line <NUM> (e.g., tension, speed and/or torque measurements associated with the compression caterpillar and/or the middle capstan).

In regards to the controlling of the manufacturing in the secondary coating line <NUM>, the PLC automation system <NUM> may be configured to at least adjust the operation of the one or more actuators <NUM> of the secondary coating line <NUM>. In other words, the PLC automation system <NUM> may provide control signalling (or control inputs) for the one or more actuators <NUM> to adjust their control parameters. The one or more production process parameters may be adjustable by adjusting said one or more control parameters of the secondary coating line <NUM>. For example, increasing a frequency of an AC electric motor causes an increase in the torque of the AC electric motor and consequently also in the speed of the moving element which the AC electric motor is driving (e.g., a speed of the compression caterpillar).

The process supervisory unit (PSU) <NUM> of the control system <NUM> is used for handling recipe management and alarm systems as well as maintaining production process parameters received from the PLC automation system <NUM> in a database of the PSU <NUM> (not shown in <FIG>). A recipe may be defined, in this context, as a set of instructions or steps needed to operate the secondary coating line to achieve a desired end product. The PSU <NUM> may further maintain in said database of the PSU <NUM> information on one or more recipes and/or nominal set values associated with the secondary coating line. The PSU <NUM> may be configured to run Supervisory Control and Data Acquisition (SCADA) software associated with the secondary coating line <NUM>. Moreover, the PSU may be connected, via a wired or wireless communication link, to one or more monitors in a control room (not shown in <FIG>). Said one or more monitors may be used for displaying (real-time) production process information to a human operator. The PSU <NUM> may be configured to forward or relay (current) production process data (i.e., monitored production process parameters) received from the PLC automation system <NUM> to the computing system <NUM> (specifically to the first computing device <NUM> in the illustrated embodiment) and/or to the user device <NUM>. Furthermore, the PSU <NUM> may be configured to forward or relay control signalling (i.e., messages comprising one or more control parameters of the secondary coating line to be adjusted) from the computing system <NUM> (specifically from the first computing device <NUM> in the illustrated embodiment) and/or from the user device <NUM> to the PLC automation system <NUM>.

The first computing device <NUM> of the computing system <NUM> is configured to monitor and possibly control quality of the manufactured loose tube fiber optic cable according to embodiments. Specifically, the first computing device <NUM> may evaluate current quality of the loose tube fiber optic cable using the current production process data and a (pre-)trained machine-learning algorithm maintained in the machine-learning database <NUM> which is connected to first computing device <NUM>. The first computing device <NUM> may also be electrically connected to the history database <NUM>, the PSU <NUM> and the user device <NUM>. The first computing device may be configured to transmit information on predicted quality of the manufactured loose tube fiber optic cable and/or suggestions for possible adjustments of control parameters of the secondary coating line <NUM> for improving said predicted quality to the user device. The first computing device may also be configured to transmit control signals to the PSU <NUM> (which may be configured to forward said control signals to the PLC automation system <NUM>). The first computing device <NUM> may maintain in a database (e.g., in an internal database or in the machine-learning or history database <NUM>, <NUM>) information on quality predictions it has performed and/or production line data received from the PSU <NUM>. The first computing device may also maintain in a memory a buffer of control values and/or have have access to the database of the PSU <NUM>. The PLC automation system <NUM> and/or the PSU <NUM> may be controlled remotely by the computing system <NUM> (specifically by the first computing device <NUM> in the illustrated embodiment) and/or the user device <NUM>.

The user device <NUM> may refer to a portable or non-portable computing device (equipment, apparatus, terminal device). Computing devices which may be employed include wireless mobile communication devices operating with or without a subscriber identification module (SIM) in hardware or in software, including, but not limited to, the following types of devices: desktop computer, laptop, touch screen computer, mobile phone, smart phone, personal digital assistant (PDA), handset, e-reading device, tablet, game console, note-book, multimedia device, sensor, actuator, video camera, car, wearable computer, telemetry appliances, and telemonitoring appliances. The user device <NUM> may be connected to the computing system <NUM> via a first wireless or wired communications link and to the PSU <NUM> via a second wireless or wired communications link. The user device <NUM> is configured at least to receive information outputted by the first computing device <NUM> (or in general the computing system <NUM>) and display said information on a screen (or a display) of the user device <NUM>. As mentioned above, the user device <NUM> may also be used to control the PSU <NUM> and, via the PSU <NUM>, the PLC automation system <NUM> (i.e., to adjust the control parameters of the secondary coating line <NUM>). To enable a user of the user device <NUM> to issue control commands, the user device <NUM> may comprise at least one user input device (e.g., a touchscreen, one or more pushbuttons, a keyboard and/or a mouse). In practical scenarios, the user device <NUM> may be operated by a (production) supervisor of the secondary coating line <NUM> or some other person who is knowledgeable of the operation and control of the secondary coating line <NUM>.

Finally, the second computing device <NUM> of the computing system <NUM> is configured at least to generate and train the machine learning algorithm employed by the first computing device <NUM>. To achieve this functionality, the second computing device is connected to the history database <NUM> which maintains at least history data comprising quality data for fiber optic cable previously manufactured using the secondary coating line <NUM> or using another similar secondary coating line and corresponding production process data for the same secondary coating line acquired during manufacturing of said fiber optic cable. Said quality data may be based on offline measurements of quality metrics such as excess fiber length, tube shrinkage and fiber light attenuation. These offline quality measurements may have been performed, for example, at a quality control laboratory, that is, they are not performed using the elements illustrated in <FIG> (i.e., using the secondary coating line <NUM> or the control system <NUM>). The element <NUM> is used for indicating that the offline measurements -based quality data maintained in the history database <NUM> is transferred to the history database <NUM> from outside of the control system <NUM> via an external interface of the computing system <NUM>. If the quality data maintained in the history database <NUM> corresponds to fiber optic cable not manufactured by the secondary coating line <NUM> but by another corresponding secondary coating line (i.e., another secondary coating line of the same type and composition), the corresponding production process data also needs to be transferred to the history database <NUM> from the outside. The second computing device <NUM> may be configured to store the trained machine learning algorithm to the machine learning database <NUM> to which both the first and second computing devices <NUM>, <NUM> have access.

In some embodiments, the history database <NUM> may also maintain control parameter data (i.e., control parameters), that is, control parameters according to which the secondary coating line <NUM> (or another corresponding secondary coating line) was previously controlled. Said control parameter data may correspond to the production process data maintained in the database (i.e., control parameters defined in said control parameter data may have been used for controlling a secondary coating line when values of the production process parameters were measured).

While <FIG> illustrates an exemplary computing system <NUM> according to embodiments comprising two separate computing devices <NUM>, <NUM> and two databases <NUM>, <NUM>, in other embodiments the computing system <NUM> may comprise another number of distinct computing devices and/or databases. In some embodiments, the computing system <NUM> may, for example, comprise a single computing device (carrying out functions of both the first and second computing devices <NUM>, <NUM>) and two databases <NUM>, <NUM> or a single database (maintaining information of both databases <NUM>, <NUM>) connected or comprised in said single computing device.

<FIG> illustrates a process according to embodiments for predicting quality of fiber optic cable manufactured in a secondary coating line in real time. The fiber optic cable manufactured in the secondary coating line may specifically be loose tube fiber optic cable. The illustrated process may be carried out by a computing system <NUM> of <FIG> or by any alternative computing system discussed in relation to <FIG>. The process illustrated in <FIG> may be specifically carried out by a first computing device <NUM> of <FIG> or by any corresponding computing device (i.e., a computing device having access at least to current process data and to pre-trained machine learning algorithm and connected to a user device) in any alternative computing system discussed in relation to <FIG>. The computing system or device carrying out the process of <FIG> may be configured to monitor and possibly control (possibly via a PSU and a PLC automation system) a secondary coating line comprising at least some of the elements discussed in relation the secondary coating line <NUM> of <FIG>. In some embodiments, said secondary coating line is assumed to comprise at least a compression caterpillar and a middle capstan. In the following, the entity performing the process is called a computing system merely for simplicity.

Initially, the computing system maintains, in a first database (e.g., in a machine-learning database as discussed in relation to <FIG>) in block <NUM>, a trained machine-learning algorithm for calculating expected values of one or more quality metrics of the fiber optic cable manufactured in the secondary coating line based on values of the one or more production process parameters of the secondary coating line. In other words, the trained machine-learning algorithm takes as its input values of one or more production process parameters and provides as its output expected values for one or more quality metrics.

The one or more quality metrics (i.e., inputs of the trained machine-learning algorithm) are metrics indicative of at least some aspect of quality of the manufactured fiber optic cable. At least some of the one or more quality metrics may correspond to quality metrics which are conventionally assessed using offline measurements of the manufactured fiber optic cable. Said one or more quality metrics may comprise, for example, one or more of excess fiber length, tube shrinkage and light attenuation in the fiber optic cable, loose tube dimensions, stability of loose tube dimensions, a metric associated with a crush test and a metric associated with a kink test.

In some embodiments, said one or more quality metrics comprise at least one (preferably all) selected from a group of excess fiber length, tube shrinkage and light attenuation in the fiber optic cable. Said three metrics may be considered the most critical metrics for the quality of loose tube fiber optic cable and are thus discussed here in more detail.

Excess fiber length (EFL) may be defined as the length of optical fiber in a loose tube divided by the length of the loose tube (sometimes given in percents). A large EFL means that the optical fibre is bent in a coil-like manner inside the loose tube. Excessive bending of the optical fiber may lead to degradation of the performance of the optical fiber cable. Excess fiber length is a metric conventionally measured offline for each fiber as standard quality control procedure. Excess fiber length may be evaluated also online during production by measuring the speed of the fibers at the payoff and speed of the loose tube before spooling though these measurements are typically not very accurate.

Tube shrinkage is a phenomenon caused by relaxation of the plastic loose tube after production. The amount of tube shrinkage depends on the production process conditions like line speed, cooling temperatures and compression caterpillar parameters. Tube shrinkage is conventionally measured offline for each manufactured loose tube as standard quality control procedure.

Having low light attenuation is the most important feature for any fiber optic cable. The attenuation of optical fibers increases if they are subjected to mechanical stress during production or when spooled on a reel. The light attenuation is conventionally measured offline as a standard quality control procedure. The light attenuation may be expressed as decibels per meter.

In some embodiments, the one or more quality metrics used as inputs of the trained machine-learning algorithm comprise (or consist solely of) an overall quality metric. The overall quality metric is a metric indicating overall or total quality of the manufactured fiber optic cable. The overall quality metric (or its value) may be equally called a quality class. The overall quality metric may be defined so that it has a value selected from a discrete set of numeric values (e.g., <NUM>, <NUM> and <NUM>). Each numeric value in said discrete set may correspond to a particular literal assessment of overall quality (e.g., <NUM> being "Bad", <NUM> being "Mediocre" and <NUM> being "Good") which may specifically be displayed to a user of the user device via the screen of the user device.

The overall quality metric (or class) may be defined to be a function of two or more quality metrics values of which may be determinable using (offline) measurements of the manufactured loose tube fiber optic cable (e.g., excess fiber length, tube shrinkage and/or light attenuation in the fiber optic cable). For example, the overall quality metric may be defined as a sum, a weighted sum, an average or a weighted average of said two or more quality metrics. The results of said calculation may further be rounded or cut-off (e.g., to an integer value) so as to match a value in the pre-defined discrete set (i.e., to match one of pre-defined quality classes).

In some embodiments, the computing system may first calculate one or more expected values of one or more quality metrics (such as excess fiber length, tube shrinkage and/or light attenuation in the fiber optic cable) using the trained machine-learning algorithm and then calculate an expected value of an overall quality metric based on said one or more expected values of the one or more quality metrics.

Said one or more production process parameters are parameters or properties of different processes of the secondary coating line. Said one or more production process parameters may be associated with one or more of a fiber pay-off, an extruder, a compression caterpillar, a middle capstan, an end capstan and a spooling unit or with any other apparatuses of the secondary coating line. The parameters of the compression caterpillar and the middle capstan often have an especially pronounced effect on the end quality of the loose tube fiber optic cable. Therefore, said one or more production process parameters may comprise one or more production process parameters associated with the compression caterpillar and/or the middle capstan. Said production process parameters may comprise one or more of speed of the compression caterpillar, tension of the compression caterpillar, torque of a motor of the compression caterpillar and torque of a motor of a middle capstan, a standard deviation of the speed of the compression caterpillar over a pre-defined amount of time, a standard deviation of the tension of the compression caterpillar over a pre-defined amount of time, a standard deviation of the torque of the motor of the compression caterpillar over a pre-defined amount of time and a standard deviation of the torque of the motor of the middle capstan over a pre-defined amount of time.

The generation and training of the trained machine learning algorithm may be carried out by the computing system (or specifically by a second computing device as discussed in relation to <FIG>), for example, as is discussed below in relation to <FIG>. In other embodiments, the trained machine learning algorithm may be generated and/or trained by an entity (e.g., a network node or a computing device) other than the computing system performing processes of <FIG>. As discussed above, the machine learning algorithm according to embodiments may be based, for example, on one or more feed forward neural networks, a Bayesian classifier, a self-organizing map (SOM) or a combination thereof.

The computing system monitors, in block <NUM>, one or more values of the one or more (respective) production process parameters of the secondary coating line during running of the secondary coating line. Monitoring is required as changes in the production process parameters of the secondary coating line may occur during the running of the secondary coating line due to various factors such as changes in the environment in which the secondary coating line is run (e.g., increased temperature) or wear and tear of the elements of the secondary coating line (e.g., in a compression belt of the compression caterpillar). The one or more production process parameters which are monitored may comprise at least the one or more production process parameters used as an input of the trained machine-learning algorithm. The monitoring may be carried out via a PSU and a PLC automation system as was discussed in relation to <FIG>. In other words, production process data (comprising at least said one or more value of the one or more production process parameters) may be measured and collected, in real time during the running of the secondary coating line, by a PLC automation system and transmitted to a PSU which forwards said data to the computing system for analysis (in addition to storing said data to a database of the PSU). The values of the one or more production process parameters may be measured and provided for the computing system periodically (with one or more different periods associated with different parameters).

During the monitoring, the computing system calculates, in block <NUM>, in real time one or more expected values of the one or more quality metrics using the trained machine-learning algorithm, where the monitored values of the one or more production process parameters are used as an input of the trained machine-learning algorithm.

The computing system outputs, in block <NUM>, at least the one or more expected values of the one or more quality metrics to a user device. Additionally, the computing system may also output the monitored (i.e., current) values of the one or more production parameters corresponding the one or more expected values of the one or more quality metrics.

In some embodiments, the outputting in block <NUM> may comprise causing displaying at least one of the expected values of the one or more quality metrics on a screen of a user device in real-time for guiding a user of the user device in managing the secondary coating line. In some cases (e.g., in the case of overall quality metric), a visual or written indication of an expected value may be displayed (e.g., indicating that overall quality is "Good" or indicating value of a quality metric with a visual element such as bar diagram), instead of or in addition to the raw number value. Based on the displayed information, the user of the user device may determine whether at least one of the control parameters of the secondary coating line should be adjusted so as to induce a change in production process parameters of the secondary coating line which, in turn, would result in improved quality of the end product. Subsequently, the user device may transmit, upon corresponding user input, control commands to the secondary coating line for adjusting at least one of the control parameters of the secondary coating line.

The term "causing displaying" information on a screen of the user device may comprise, here and in the following embodiments, transmitting a command to the user device to display a corresponding information on said screen. In other embodiments, the user device may be configured to display any expected quality and/or production process information received from the computing system on the screen of the user device automatically (i.e., without explicit command to do so).

Actions pertaining to blocks <NUM> to <NUM> may be carried out by the computing system continuously as the secondary coating line is in operation, as indicated by the arrow connecting block <NUM> to block <NUM>.

<FIG> illustrates a process for generating and training the machine learning algorithm described in relation to <FIG>. The process may be carried out by the same computing system which subsequently carries out the predicting using the (pre-)trained machine learning algorithm according to embodiments. Considering the system of <FIG>, the processes of <FIG> may be carried out by the first computing device <NUM> while the processes to be discussed in relation to <FIG> may be carried out by a second computing device <NUM>. Alternatively, the generating and training of the machine-learning algorithm may be carried out by another entity not forming a part of the computing system.

Referring to <FIG>, the computing system maintains, in a database (e.g., in the history database) in block <NUM>, history data comprising quality data for fiber optic cable previously manufactured using the secondary coating line and production process data of the secondary coating line acquired during manufacturing of said fiber optic cable. The quality data comprises a plurality of measured values for each of one or more quality metrics and the production process data comprises a plurality of measured values for each of one or more production process parameters of the secondary coating line. The one or more quality metrics and the one or more production process parameters may be defined as described in relation to <FIG>. The quality data may have been gathered through conventional offline quality measurements while the production process data may have been gathered through online measurements (i.e., monitoring similar to as described in relation to block <NUM> of <FIG>) during the running of the secondary coating line.

The computing system initializes, in block <NUM>, a machine-learning algorithm for calculating expected values of one or more quality metrics based on values of the one or more production process parameters. The machine learning algorithm may be any machine learning algorithm as discussed above, e.g., a neural network -based algorithm employing one or more feedforward neural networks and/or one or more recurrent neural networks. The initialization may comprise defining the inputs (i.e., features) and outputs (i.e., labels) of the machine-learning algorithm and setting or selecting initial values for weights and/or parameters of the machine learning algorithm (e.g., weights of one or more neural networks). Here, features (i.e., input) of the machine learning algorithm may be defined to correspond quality metrics of the manufactured fiber optic cable and labels (i.e., output) of the machine learning algorithm may be defined to correspond to production process parameters of the secondary coating line. The initial values may be random values or they may correspond to a pre-defined set of values known to result in a well-performing algorithm. Any known initialization technique may be employed in the initialization in block <NUM>.

The computing system trains, in block <NUM>, the machine-learning algorithm using the history data. In other words, the history data (or part thereof) is used as training data of the machine-learning algorithm. Specifically, the production process data is used as input of the machine learning algorithm while the quality data defines corresponding desired outputs of the machine learning algorithm. The training may comprise feeding the production process data (i.e., values of one or more production process parameters) to the machine-learning algorithm, comparing the expected values of one or more quality metrics outputted by the machine-learning algorithm to the measured values of said one or more quality metrics in the history data and adjusting one or more weights and/or parameters of the machine-learning algorithm so as to minimize the difference between the expected and measured values. The comparing may be performed by evaluating a cost function which is a measure of how incorrect the machine-learning algorithm is in terms of its ability to estimate the relationship between its inputs and outputs. The cost function may be expressed as a difference or distance between the predicted value and the actual value (or predicted value vector and the actual value vector in the case of multiple output variables).

Finally, the computing system stores, in block <NUM>, the trained machine learning algorithm to the machine-learning database (or some other database). Subsequently, the computing system (or specifically the first computing device of the computing system) may employ said stored trained machine learning algorithm for performing the prediction according to embodiments (e.g., as described in relation to <FIG>).

In some embodiments, the actions pertaining to blocks <NUM> to <NUM> may be carried out offline (i.e., when the secondary coating line disabled or non-operational). In other embodiments, the actions pertaining to blocks <NUM> to <NUM> may be carried out online during normal operation of the secondary coating line.

<FIG> illustrates an alternative process according to embodiments for predicting quality of (loose tube) fiber optic cable manufactured in a secondary coating line in real time. The illustrated process may be performed by any entity described in relation to <FIG> as performing the process of <FIG> (e.g., by a computing system or a particular computing device therein). In general, the process of <FIG> corresponds to a large extent to the process of <FIG>. Any definitions given in relation to <FIG> apply or may be combined with the process of <FIG> (unless otherwise stated).

Referring to <FIG>, the initial blocks <NUM> to <NUM> may correspond fully to blocks <NUM> to <NUM> of <FIG>. Thus, the actions pertaining to said blocks are not discussed here for brevity. Instead, the discussion is concentrated on the additional/alternative feature introduced in <FIG> as blocks <NUM>, <NUM>.

After the computing system has calculated one or more expected values of the one or more quality metrics using the trained machine-learning algorithm in block <NUM>, the computing system further calculates, in block <NUM>, in real-time during the monitoring, one or more optimal values of one or more control parameters of the secondary coating line for improving quality of the fiber optic cable being manufactured. The quality of the fiber optic cable may mean, here specifically quality as defined by the one or more expected values of the one or more quality metrics (e.g., an expected value for an overall quality metric or one or more expected values of one or more quality metrics conventionally evaluated through offline measurements). The one or more control parameters for which said one or more optimal values are calculated may comprise some or all of the control parameters which may be used (by the PLC automation system) for controlling the secondary coating line.

The calculating of the one or more optimal values for the one or more control parameters in block <NUM> may comprise, first, comparing the one or more monitored values of the one or more production process parameters to one or more corresponding optimal values of the one or more production process parameters to determine which production process parameters deviate from the optimal values and thus require adjustment. Said optimal values of the one or more production process parameters may be determined based on history data (i.e., quality and production process data) maintained in the history database or based on the trained machine-learning algorithm and/or they may be maintained in the history or machine-learning database. Regarding the first option, said history data may comprise data based on offline measurements and/or data based on previous quality predictions by the computing system. In some embodiments, the optimal values of the one or more production process parameters may correspond simply to values which have previously resulted in fiber optic cable having high(est) quality. Pre-defined mappings between production process parameters and control parameters may be maintained in a database (e.g., in the history database, the machine-learning database or the database of the PSU). Based on said pre-defined mappings and the optimal values of the one or more production process parameters in need of adjustment, the one or more optimal values of the one or more control parameters may be calculated (in block <NUM>).

Alternatively, the calculation in block <NUM> may be carried out using a pre-defined algorithm taking as input at least the one or more monitored values of the production process parameters (and possibly the one or more expected values of the one or more quality metrics and/or monitored control parameters). In other words, the pre-defined algorithm used in block <NUM> may correspond to a process model that captures the internal mechanisms of the manufacturing process of the secondary coating line and models the relationships (or connections or interactions) between different parameters (that is, at least between different control parameters and production process parameter). The pre-defined algorithm may be maintained, e.g., in the machine-learning database or in another memory or database connected or comprised in the computing system. The pre-defined algorithm may have been generated or built using knowledge of the manufacturing process and by analyzing data produced by said manufacturing process (e.g., production process parameters and/or control parameters). Said pre-defined algorithm may have been generated, for example, based on one or more of history data maintained in the history database, monitored production process parameters, monitored control parameters and/or said pre-defined mappings (if they exist). In some embodiments, the generation of the pre-defined algorithm may, also or alternatively, employ the trained machine-learning algorithm. Said pre-defined algorithm may employ the trained machine-learning algorithm in the calculation of block <NUM>. In other embodiments, said pre-defined algorithm may correspond to or comprise the trained machine-learning algorithm. In other words, a single pre-defined algorithm may be provided connecting the control parameters not only to the production process parameters but also to quality metrics, in some embodiments. In these embodiments, the pre-defined algorithm used in block <NUM> may be the same as the machine-learning algorithm described above.

The computing system causes, in block <NUM>, adjusting, in real-time during the monitoring, the one or more control parameters of the secondary coating line to match the one or more optimal values of the one or more control parameters (or at least to reduce the difference between the one or more optimal values and the one or more current values of the one or more control parameters). The causing adjusting may comprise transmitting control data for adjusting the one or more control parameters to a PSU which consequently forwards said control data to a PLC automations system which adjusts the operation of the one or more actuators driving the secondary coating line (and possibly operation of other elements of the secondary coating line) accordingly. In this particular embodiment, the adjusting of the control parameters may be fully automatized and thus no separate user device is necessarily required for managing the secondary coating line.

The embodiments illustrated in <FIG> and <FIG> may also be combined so that the computing system outputs, between blocks <NUM>, <NUM> or directly following block <NUM>, at least the one or more expected values of the one or more quality metrics to a user device, as described in relation to block <NUM> of <FIG>. Any additional features described in relation block <NUM> of <FIG> may also be applied in this case.

In some embodiments, the actions pertaining to blocks <NUM>, <NUM> may be carried out only if it is determined, by the computing system, that at least one of the one or more expected values of the one or more quality metrics (calculated in block <NUM>) falls below at least one corresponding pre-defined threshold defined for corresponding at least one quality metric, that is, if the quality of the manufactured fiber optic cable is detected to be sufficiently low.

In the embodiment illustrated in <FIG>, the adjusting of the control parameters is fully automatic requiring no feedback from a user. In some cases, it may, however, be beneficial, for example, to improve safety and ensure high quality of production, to allow the user to make the final decision on any changes to the control parameters of the secondary coating line. <FIG> illustrates a signaling diagram according to embodiments for enabling said user-driven decision-making functionality. Specifically, <FIG> illustrates signaling between a computing system, a user device, a PSU and a PLC automation system which correspond to corresponding elements illustrated in <FIG>. The computing system of <FIG> may correspond to any entity described in relation to <FIG> as performing the process of <FIG> (e.g., a computing system or a particular computing device therein). In general, the process of <FIG> corresponds to a large extent to the processes of <FIG> and/or <FIG>. Any definitions given in relation to <FIG> and/or <NUM> apply or may be combined with the process of <FIG> (unless otherwise stated).

Referring to <FIG>, the initial blocks <NUM> to <NUM> may correspond fully to blocks <NUM> to <NUM> of <FIG>. Thus, the actions pertaining to said blocks are not discussed here for brevity. Instead, the discussion is concentrated on the additional/alternative feature introduced in <FIG> as elements <NUM> to <NUM>.

In <FIG>, after the computing system has calculated, in block <NUM>, one or more optimal values for the one or more control parameters so as to improve quality of the fiber optic cable being manufactured, the computing system causes displaying a prompt for changing one or more current values of the one or more control parameters to match the one or more optimal values for the one or more control parameters on a screen of a user device. Specifically, the causing displaying may comprise transmitting, in message <NUM>, to the user device control data for displaying said prompt. Upon receiving the control data in block <NUM>, the user device displays, in block <NUM>, said prompt. The prompt may be defined here as a question or statement that appears on the screen and which may indicate selectable options to the user (here, at least options for changing the control parameters and not changing the control parameters). This way any adjustments of the control parameters have to be approved by a human operator.

Upon receiving, in block <NUM>, a positive user input approving the adjustment(s) to the one or more control parameters suggested by the prompt via a user input device of the user device, the user device transmits, in message <NUM>, to the PSU control data for adjusting the one or more control parameters. Consequently, upon receiving the control data in block <NUM>, the PSU transmits (or forwards), in message <NUM>, said control data for adjusting the one or more control parameters to the PLC automation system. In some embodiments, the PSU may also store the control data to a database of the PSU.

In response to receiving the control data in block <NUM>, the PLC automation system adjusts, in block <NUM>, the one or more control parameters according to the control data.

In some alternative embodiments, the computing system may cause displaying, instead of the prompt, merely the one or more optimal values for the one or more control parameters on the screen of the user device and optionally also the current values for the one or more control parameters. In such embodiments, the user may not be provided an option for quickly changing the one or more control parameters to match the optimal values, but the user may, instead, have to manually set the control parameters with the user device (using a dedicated application for managing the secondary coating line).

In some embodiments, the computing system may maintain, in a database (e.g., in the history database, in the machine-learning database or in the database of the PSU), one or more pre-defined lists of actions (i.e., actions relating to tuning of the control parameters) which may be performed if a deviation in a certain production process parameter is detected. Based on said one or more pre-defined lists of actions, the computing system may cause displaying information on one or more options for adjusting one or more control parameters for improving the quality of the fiber optic cable on the screen of the user device. For example, if it is detected that a value of a standard deviation of motor torque is much higher than the optimal value, the computing system may cause displaying on the screen of the user device information on one or more options for what the operator can do to reduce the instability of the process indicated by the high standard deviation.

After the machine-learning algorithm has been trained (e.g., as discussed in relation to <FIG>) and the trained machine-learning algorithm has been in use for a certain amount of time for evaluating quality of the manufactured fiber optic cable, it may be beneficial to retrain the machine-learning algorithm using the most up-to-date quality data. <FIG> illustrate two different processes for carrying out the retraining according to embodiments. The illustrated processes (or only one of them) may be performed by any entity described in relation to <FIG> as performing the process of <FIG>, e.g., by a computing system for predicting quality of fiber optic cable manufactured in a secondary coating line or a particular computing device therein. The computing system or the particular computing device therein may be configured to perform one or both of the processes illustrated in <FIG>. For example, the process of <FIG> may be performed periodically with a first period and the process of <FIG> may be performed periodically using a second period (preferably much larger than the first period) or only upon receiving a request, e.g., from a user device.

Referring to <FIG>, the computing system receives, in block <NUM>, one or more sets of history data via an external interface of the computing system. Each set of history data may be similar to the history data described in relation to above embodiments. In other words, each set of history data comprises quality data for loose tube fiber optic cable previously manufactured using one of the secondary coating line (i.e., the secondary coating line manufacturing fiber optic cable whose quality is to be predicted by the computing system) and another corresponding secondary coating line (e.g., a dedicated reference secondary coating line) and production process data of the same secondary coating line acquired during manufacturing of said loose tube fiber optic cable. Here (similar to above embodiments), the quality data comprises a plurality of measured values for each of one or more quality metrics and the production process data comprises a plurality of measured values for each of one or more production process parameters of the secondary coating line (or equally of said another corresponding secondary coating line). If said one or more sets of history data consists of a plurality of sets of history data, said plurality of sets of history data may be received, in block <NUM>, as a single transmission or transfer or as a plurality of transmissions or transfers received periodically or sporadically. Regarding the latter option, any new history data may be transferred to the computing system as soon as it becomes available, but the reception of a single set does not necessarily trigger any retraining process. Instead, the retraining process of <FIG> may be triggered based on a timer (e.g., after a pre-defined time or period has passed after the initial training or the last update) and/or based on the amount of (new) history data collected by the computing system.

Upon receiving each of the one or more sets of history data in block <NUM>, the computing system stores, in block <NUM>, the received set to the history database of the computing system. Block <NUM> may be omitted in some embodiments.

The computing system generates, in block <NUM>, an updated trained machine-learning algorithm by retraining the trained machine-learning algorithm (maintained in the machine-learning database) using said one or more sets of history data. In other words, the current trained machine-learning algorithm is used as a starting point for the retraining in this embodiment. This way the current trained machine-learning algorithm may be further fine-tuned to improve accuracy of the predictions. The retraining in block <NUM> may comprise, for example, if the machine-learning algorithm is based on one or more neural networks, adjusting one or more values of one or more weighting factors of said one or more neural networks.

Once the retraining has completed and an updated trained machine-learning algorithm is generated in block <NUM>, the computing device stores, in block <NUM>, the updated trained machine-learning algorithm to machine-learning database. Subsequently, the computing system may employ said updated trained machine-learning algorithm for quality prediction (e.g., according to any of the above embodiments). In the illustrated embodiment, it is assumed that the updating of the trained machine-learning algorithm is repeated once new set(s) of history data is received (as described above in relation to block <NUM>) and thus the process proceeds from block <NUM> back to block <NUM>.

Referring to <FIG>, actions pertaining to blocks <NUM>, <NUM>, <NUM> may correspond fully to actions described above in relation to blocks <NUM>, <NUM>, <NUM> of <FIG> and are thus not repeated here for brevity. In other words, the difference between the two illustrated processes lies solely (or predominantly) in the training step illustrated with blocks <NUM>, <NUM>. While block <NUM> of <FIG> related to fine-tuning the existing trained machine-learning algorithm, the computing system generating, in block <NUM>, an updated trained machine-learning algorithm by initializing a new machine-learning algorithm for calculating expected values of the one or more quality metrics based on values of the one or more production process parameters and training this new machine-learning algorithm using at least the one or more sets of history data (possibly also using other history data maintained in the history database). In other words, the training is essentially restarted "from scratch". Thus, the generation of the updated machine-learning algorithm in block <NUM> may be carried out in a similar manner as described for the initial generation of the trained machine-learning algorithm, that is, as described in relation to block <NUM> of <FIG> (though obviously using a different set or sets of history data as input). This type of updating of the machine-learning algorithm may enable more drastic changes to the machine-learning algorithm compared to the process of block <NUM> of <FIG>. For example, the process of <FIG> may lead to an updated trained machine-learning algorithm having a different topology (e.g., a different topology for one or more neural networks) compared to the previous trained machine-learning algorithm, as opposed to, e.g., only causing changes to weighting factors of the one or more neural networks. Therefore, if it is noticed that the predictions produced by the trained machine-learning algorithm are no longer dependable even despite of periodic retraining efforts according to <FIG>, the process of <FIG> may be carried out so as to generate a restructured trained machine-learning algorithm which is more suitable for the prediction of the quality of the fiber optic cable currently produced by the secondary coating line. In the illustrated embodiment of <FIG> in contrast to the embodiment illustrated in <FIG>, it is assumed that the generating of a new trained machine-learning algorithm is not automatically repeated (i.e., the process terminates in block <NUM>).

The retraining performed by the computing system according to <FIG> and/or <FIG> may be manual, semi-automatic or fully automatic. Most typically, the computing system may perform the retraining semi-automatically, meaning the history data (comprising corresponding quality and production process data) from offline measurements is collected continuously, but the trained machine learning model is retrained according to <FIG> and/or 6B periodically or on a need basis. Triggering the retraining on a need bases may comprise, e.g., triggering the retraining upon receiving a request received a user device, where the user device may transmit said request upon receiving a pre-defined user input via a user input device of the user device. The computing system may, alternatively, be programmed to be fully automatic so that the trained machine-learning algorithm is updated, e.g., weekly using all the new history data that is available (that is, all the data which has been collected since the last update). The nature of the fiber optic cable production using a secondary coating line is such that there are reel changes and line stops occurring multiple times per day. These breaks or interruptions in the production may be used for loading the updated machine-learning model for use (and possibly also for generating the updated trained machine-learning algorithm).

Training and/or retraining of the computing system according to embodiments may be supervised or unsupervised. In supervised learning, a set of history data (comprising quality data and production process data) is used for training the machine-learning algorithm to produce a desired calculation result with certain production process data as input. In unsupervised training, the computing system itself tries to classify the data into a certain number of feature groups. The feature groups may or may not correspond to the aforementioned quality classes which may be communicated to the user. The quality data may be used to verify how well the unsupervised learning has managed to differentiate feature groups, but it is not actively used in the machine-learning training. For example, multilayer perceptron networks may be trained in a supervised manner, whereas training of self-organized maps may be unsupervised.

<FIG> illustrates an apparatus <NUM> configured to perform the functions described above in connection with a computing system such as the computing system <NUM> shown in <FIG> or with a computing device such as the first computing device <NUM> of the computing system <NUM> in <FIG>. The apparatus may be an electronic device comprising electronic circuitries. The apparatus may be a separate (network) entity or a plurality of separate entities. The apparatus may comprise a control circuitry <NUM>, such as at least one processor, and at least one memory <NUM> including a computer program code (software) <NUM> wherein the at least one memory and the computer program code (software) are configured, with the at least one processor, to cause the apparatus to perform any one of the embodiments of the computing system described above.

The memory <NUM> may comprise at least one database <NUM>. Said at least one database <NUM> may comprise at least the machine-learning database and/or the history database as described in relation to above embodiments. In other embodiments, one or both of the machine-learning database and the history database as described in relation to embodiments may be external databases or database servers accessible by the apparatus <NUM> via the communication interfaces <NUM>. The memory <NUM> may also comprise other databases (e.g., a database for maintaining at least monitored production process parameters) which may or may not be related to the described quality prediction functionalities according to embodiments.

Referring to <FIG>, the control circuitry <NUM> may comprise machine-learning circuitry <NUM> configured to provide the apparatus functionalities for predicting quality (or specifically one or more quality metrics) of the manufactured (loose tube) fiber optic cable and optionally providing results of the predicting to a user device according to any of presented embodiments. Optionally, the machine-learning circuitry <NUM> may also be configured to cause adjusting of one or more control parameters of the secondary coating line. For example, the machine-learning circuitry <NUM> may be configured to perform at least some of processes of <FIG>, <FIG> and/or some of processes performed by the computing system in <FIG>. In some other embodiments, the control circuitry <NUM> may be divided into two or more individual circuitry.

The apparatus <NUM> may further comprise (communication) interfaces <NUM> comprising hardware and/or software for realizing communication connectivity according to one or more communication protocols. The communication interface may provide the apparatus with communication capabilities to communicate, for example, with one or more user devices, a process supervisory unit for a secondary coating line and/or one or more external databases or database servers. The one or more communication interfaces <NUM> may provide the apparatus <NUM> with communication capabilities to communicate in a cellular communication system and enable communication with one or more network nodes and one or more terminal devices.

The communication interface <NUM> may comprise standard well-known components such as an amplifier, filter, frequency-converter, (de)modulator, and encoder/decoder circuitries and one or more antennas.

The memory <NUM> of the apparatus <NUM> described in relation to <FIG> may be implemented using any suitable data storage technology, such as semiconductor-based memory devices, flash memory, magnetic memory devices and systems, optical memory devices and systems, fixed memory and removable memory.

As used in this application, the term "circuitry" may refer to one or more or all of the following: (a) hardware-only circuit implementations (such as implementations in only analog and/or digital circuitry) and (b) combinations of hardware circuits and software, such as (as applicable): (i) a combination of analog and/or digital hardware circuit(s) with software/firmware and (ii) any portions of hardware processor(s) with software (including digital signal processor(s)), software, and memory(ies) that work together to cause an apparatus, such as a mobile phone or server, to perform various functions) and (c) hardware circuit(s) and or processor(s), such as a microprocessor(s) or a portion of a microprocessor(s), that requires software (e.g., firmware) for operation, but the software may not be present when it is not needed for operation.

In an embodiment, at least some of the processes described in connection with <FIG>, <FIG> may be carried out by an apparatus comprising corresponding means for performing at least some of the described processes Some example means for carrying out the processes may include at least one of the following: detector, processor (including dual-core and multiple-core processors), microprocessor, digital signal processor (DSP), controller, micro-controller, receiver, transmitter, encoder, decoder, memory, RAM, ROM, software, firmware, display, user interface, display circuitry, user interface circuitry, user interface software, display software, circuit, antenna, antenna circuitry, application-specific integrated circuit (ASIC), digital signal processing device (DSPD), programmable logic device (PLD) and field programmable gate array (FPGA). For firmware or software, the implementations according embodiments may be carried out through modules of at least one chipset (procedures, functions, and so on) that perform the functions described herein. The software codes may be stored in a memory unit and executed by processors. The memory unit may be implemented within the processor or externally to the processor. In the latter case, it can be communicatively coupled to the processor via various means, as is known in the art. In an embodiment, the at least one processor, the memory, and the computer program code form processing means or comprises one or more computer program code portions for carrying out one or more operations according to any one of the embodiments of <FIG>, <FIG> or operations thereof.

Embodiments as described may also be carried out in the form of a computer process defined by a computer program or portions thereof. Embodiments of the methods described in connection with <FIG>, <FIG> may be carried out by executing at least one portion of a computer program comprising corresponding instructions. The computer program may be in source code form, object code form, or in some intermediate form, and it may be stored in some sort of carrier, which may be any entity or device capable of carrying the program. For example, the computer program may be stored on a computer program distribution medium readable by a computer or a processor. The computer program medium may be, for example but not limited to, a record medium, computer memory, read-only memory, electrical carrier signal, telecommunications signal, and software distribution package, for example. The computer program medium may be a non-transitory medium. Coding of software for performing the embodiments as shown and described is well within the scope of a person of ordinary skill in the art.

Claim 1:
A method for monitoring quality of loose tube fiber optic cable (<NUM>) during manufacture in a secondary coating line (<NUM>), the method comprising:
maintaining, in a machine-learning database (<NUM>), a trained machine-learning algorithm for calculating expected values of one or more quality metrics of the loose tube fiber optic cable manufactured in the secondary coating line based on values of one or more production process parameters of the secondary coating line;
monitoring, by a computing system (<NUM>), one or more values of the one or more production process parameters of the secondary coating line during running of the secondary coating line;
calculating, by the computing system (<NUM>), in real-time during the monitoring, one or more expected values of the one or more quality metrics using the trained machine-learning algorithm, wherein monitored values of the one or more production process parameters are used as an input of the trained machine-learning algorithm; and
outputting, by the computing system, at least the one or more expected values of the one or more quality metrics to a user device (<NUM>); or
controlling the quality of loose tube fiber optic cable (<NUM>) during the manufacture by calculating, in real-time during the monitoring, one or more optimal values of one or more control parameters of the secondary coating line for improving quality of the loose tube fiber optic cable being manufactured as defined by the one or more expected values of the one or more quality metrics, and causing adjusting, in real-time during the monitoring, the one or more con-trol parameters of the secondary coating line to match the one or more optimal values of the one or more control parameters.