Oerlikon Textile Gmbh & Co Kg

Jörg Huthmacher

Marc-André Nehrkorn-Ludwig

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.

Method and device for monitoring a yarn tension of a running yarn

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 way of a machine learning program for identifying a thread knot.

Method and device for texturing a synthetic thread

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.

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Marl, Germany

Jörg Huthmacher