An air jet spinning machine, air jet spinning machine for producing a thread from a fed sliver, and method for detecting a thread strength, which falls below a target value, of a thread produced at a spinning unit of an airjet spinning machine are provided. A thread manipulation unit is arranged in the region between the airjet spinning machine and the spooling device, which thread manipulation unit acts mechanically on the thread and exerts an adjustable thread tension influencing force, in particular an adjustable braking force, compression force and/or clamping force, onto the thread in order to variably increase thread tension. In the method, an adjustable thread tension influencing force is exerted onto the thread, and a thread breakage and/or thread breakage rate are/is detected and compared with a specified target rate, and thread breakage and/or deviations of thread breakage rate beyond a predetermined limit value are displayed and/or evaluated.

A method for monitoring a texturing process for producing crimped threads is presented, in which a thread tension is measured continuously on the textured thread and in which the thread tension measuring signals are captured and analyzed continuously, at least in one time interval, characterized in that a sequence of the thread tension measuring signals occurring in the time interval is analyzed with a machine learning program for the early diagnosis of one of a plurality of fault sources.

The invention relates to a bobbin carrier 7 for receiving a bobbin 2 which is set up for unwinding a strand material I, wherein the bobbin carrier 7 is provided for use in a braiding, winding or spiralling machine and is set up to rotate relative to the machine during operation of the latter. The bobbin carrier 7 has a tensile-force measuring device 3 for measuring the tensile force of the strand material I unwound from the bobbin 2 and has a first data transfer device 4 for transferring data. According to the invention, the first data transfer device 4 is set up to transfer measured tensile force values to a second data transfer device 5 arranged outside the bobbin carrier. As a result, too low or too high tensile forces in the strand material I can be detected early at the individual bobbin carriers 7. The tensile force can be kept largely constant by the transfer of setpoint tensile force values from the second data transfer device 5 to the first data transfer device 4 and by a suitable control or regulation device 8 at the bobbin carrier 7.

The invention relates to a bobbin carrier 7 for receiving a bobbin 2 which is set up for unwinding a strand material I, wherein the bobbin carrier 7 is provided for use in a braiding, winding or spiralling machine and is set up to rotate relative to the machine during operation of the latter. The bobbin carrier 7 has a tensile-force measuring device 3 for measuring the tensile force of the strand material I unwound from the bobbin 2 and has a first data transfer device 4 for transferring data. According to the invention, the first data transfer device 4 is set up to transfer measured tensile force values to a second data transfer device 5 arranged outside the bobbin carrier. As a result, too low or too high tensile forces in the strand material I can be detected early at the individual bobbin carriers 7. The tensile force can be kept largely constant by the transfer of setpoint tensile force values from the second data transfer device 5 to the first data transfer device 4 and by a suitable control or regulation device 8 at the bobbin carrier 7.

A yarn monitoring system for textile machine uses sensors to indicate yarn over tensioning and breakage. The sensors are contained within, or adjacent to, eyelets and monitor, sending signals to the controller, the speed of the yarn. The eyelets, each with a sensor, are within a body that also contains a circuit board which is in constant communication with the eyelets and software contained within a controller. The controller, in turn, is in constant communication with the textile machine. The software is preprogrammed by a user with an acceptable signal range for passage of the yarn past the sensors and a predetermined time period for the signal to remain outside the acceptable signal range. To prevent unnecessary shut down of the textile machine, the software averages the signal and, when the averaged signal remains out of the acceptable signal range for the preprogrammed time initiates communication to the textile machine.

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 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 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.