Being one of the world's leading textile machinery manufacturers and specialised in machines, installations and accessories for spinning preparation, the nonwovens, man-made fiber and the card clothing industry, Trützschler, via its data management system T-DATA, collects the data from the data storage in the readily accessible T-Records database and enables the data transfer to higher-level systems so as to be evaluated and recorded, while reducing the costs as well as the labour.
T-DATA, the real-time monitoring system from Trützschler, provides management with solid data for the daily routine in spinning. Since the system is web-based, the data are available anytime and anywhere.
Individualisation and integration
T-DATA stands out with its features consisting of; ease of installation and the ability for individualisation, the user interface can easily be adjusted to the specifications of the customer. While T-DATA can be intuitively individualised and precisely tailored to the requirements of the user, the system also allows easy data transfer to higher-ranking customer systems. Trützschler has developed a number of sensors to determine the right and relevant data which is crucial for obtaining meaningful information. The optical sensor WASTECONTROL, which is developed for this purpose, is used to monitor the waste quality of the cleaners in the blow room and thus, prevents unnecessary fiber loss.
Taking place on the cards, the NEPCONTROL counts the neps, trash particles and seed-coat fragments in the card web. The data obtained allows targeted clothing maintenance.
Moreover, the signals of the DISC MONITOR sensors on cards, draw frames and combers enable the early detection of emerging faults via the spectrogram analysis. Power consumption is monitored by special energy meters in the machines. T-DATA shows the deviating values of individual machines. This allows a very targeted maintenance.
These special sensors, all of which are developed and manufactured by Trützschler, offer a new dimension in the management of data quality as well as providing an analysis that expands the limits of productivity, making a higher quality level accessible and thus, enhancing the efficiency of the production process of spinning mills.
