Techniques are directed to a method and a device for monitoring a yarn tension of a running yarn in a yarn treatment process. To this end, the yarn tension of the yarn is continuously measured and the measurement signals for the yarn tension are compared with a threshold value of an admissible yarn tension. In the event of an inadmissible tolerance deviation of the measurement signals, a short-term signal path of the yarn tension is detected as a fault graph. In order to enable a fault diagnosis, the fault graph of the yarn tension is analyzed using a machine learning program. The fault graph is then allocated to one of the existing fault categories or to a new fault category. A device for this purpose may include a diagnosis unit, which cooperates accordingly with the yarn tension evaluation unit.

Techniques are directed to a method and a device for monitoring a yarn tension of a running yarn in a yarn treatment process. To this end, the yarn tension of the yarn is continuously measured and the measurement signals for the yarn tension are compared with a threshold value of an admissible yarn tension. In the event of an inadmissible tolerance deviation of the measurement signals, a short-term signal path of the yarn tension is detected as a fault graph. In order to enable a fault diagnosis, the fault graph of the yarn tension is analyzed using a machine learning program. The fault graph is then allocated to one of the existing fault categories or to a new fault category. A device for this purpose may include a diagnosis unit, which cooperates accordingly with the yarn tension evaluation unit.

Techniques involve texturing a synthetic thread. The thread is pulled off a feed bobbin, which is connected via the thread end thereof to a beginning of a thread of a reserve bobbin by way of a thread knot. In order to monitor the texturing, a thread tension of the thread is measured and analyzed in a measuring point. Additionally, measuring signals of the thread tension are analyzed at the measuring point using a machine learning program, in order to identify the thread knot. To this end, a device has a diagnostic unit, which interacts with the thread tension measuring device in such a way that the measuring signals of the thread tension can be analyzed by means of a machine learning program for identifying a thread knot.

Techniques involve texturing a synthetic thread. The thread is pulled off a feed bobbin, which is connected via the thread end thereof to a beginning of a thread of a reserve bobbin by way of a thread knot. In order to monitor the texturing, a thread tension of the thread is measured and analyzed in a measuring point. Additionally, measuring signals of the thread tension are analyzed at the measuring point using a machine learning program, in order to identify the thread knot. To this end, a device has a diagnostic unit, which interacts with the thread tension measuring device in such a way that the measuring signals of the thread tension can be analyzed by means of a machine learning program for identifying a thread knot.

Techniques are directed to a method and a device for monitoring a yarn tension of a running yarn in a yarn treatment process. To this end, the yarn tension of the yarn is continuously measured and the measurement signals for the yarn tension are compared with a threshold value of an admissible yarn tension. In the event of an inadmissible tolerance deviation of the measurement signals, a short-term signal path of the yarn tension is detected as a fault graph. In order to enable a fault diagnosis, the fault graph of the yarn tension is analyzed using a machine learning program. The fault graph is then allocated to one of the existing fault categories or to a new fault category. A device for this purpose may include a diagnosis unit, which cooperates accordingly with the yarn tension evaluation unit.

Techniques are directed to a method and a device for monitoring a yarn tension of a running yarn in a yarn treatment process. To this end, the yarn tension of the yarn is continuously measured and the measurement signals for the yarn tension are compared with a threshold value of an admissible yarn tension. In the event of an inadmissible tolerance deviation of the measurement signals, a short-term signal path of the yarn tension is detected as a fault graph. In order to enable a fault diagnosis, the fault graph of the yarn tension is analyzed using a machine learning program. The fault graph is then allocated to one of the existing fault categories or to a new fault category. A device for this purpose may include a diagnosis unit, which cooperates accordingly with the yarn tension evaluation unit.

An improved system for handling delicate linear media and in particular to a method and apparatus for winding delicate linear media such as superconducting wire or tape or optical fibers onto a spool. A combination of direct closed loop control and media routing design facilitates the handling of the delicate media without causing damage. The axial tension in the linear media may be closely controlled during winding by means of feedback control loop using tension measurements to control the rotation speeds of the wind-from and wind-to spools. Further, during winding, the delicate linear media is only exposed to large radius bends with no reverse bending. Finally, output devices and features, commercial or otherwise, made possible by delicate linear media handling are revealed. This includes advanced SC devices and features.

An improved system for handling delicate linear media and in particular to a method and apparatus for winding delicate linear media such as superconducting wire or tape or optical fibers onto a spool. A combination of direct closed loop control and media routing design facilitates the handling of the delicate media without causing damage. The axial tension in the linear media may be closely controlled during winding by means of feedback control loop using tension measurements to control the rotation speeds of the wind-from and wind-to spools. Further, during winding, the delicate linear media is only exposed to large radius bends with no reverse bending. Finally, output devices and features, commercial or otherwise, made possible by delicate linear media handling are revealed. This includes advanced SC devices and features.

A delivery device with actuators for delivering yarns in a textile machine, controllers for controlling the actuators, which comprise produce measurement signals as a measure of the drive torque of the actuators, and monitors for monitoring the tensions of the yarns using the measurement signals. Additionally, at least one embodiment concerns a method for monitoring the tension of yarns which are delivered in a textile machine by means of actuators, comprising producing measurement signals as a measure of the drive torque of the actuators, which is a measure of yarn tension, and monitoring the tension of the yarns using the measurement signals.

A delivery device with actuators for delivering yarns in a textile machine, controllers for controlling the actuators, which comprise produce measurement signals as a measure of the drive torque of the actuators, and monitors for monitoring the tensions of the yarns using the measurement signals. Additionally, at least one embodiment concerns a method for monitoring the tension of yarns which are delivered in a textile machine by means of actuators, comprising producing measurement signals as a measure of the drive torque of the actuators, which is a measure of yarn tension, and monitoring the tension of the yarns using the measurement signals.