SAVAGE, Minnesota, December 2, 2015 – To answer a wide range of requirements in the implementation of Industry 4.0 and IoT concepts, particularly for machine diagnostics, Beckhoff Automation has announced TwinCAT Analytics. This feature-filled software solution includes online and offline condition analysis, predictive maintenance, pattern recognition, machine optimization and long-term data archival.
Seamless and cycle-synchronous data acquisition is a prerequisite for effective analysis and correction of processing errors in machines. To address these needs and more, Beckhoff has developed TwinCAT Analytics in the TwinCAT 3 software portfolio, serving the growing requirements of Industry 4.0 applications. Processing errors in machines frequently create excessive costs and lost production time. The situation becomes all the more serious if there is a lack of machine data and production parameters in order to analyze processing errors and avoid such errors in the future. The new TwinCAT Analytics tool can be used to rectify this information deficit by storing all process-relevant data in a cycle-synchronous manner. Data is stored in a standardized process data format with data compression, either locally in the controller, in a cloud-based solution on a server in the company network or in a public cloud, as required.
Seamless data logging opens up potential for optimizations
TwinCAT Analytics provides a complete temporal image of the manufacturing process and the production data. This offers an ideal information baseline, not only to assist in the event of an error, but also to enable comprehensive condition analysis of the machine, among other advanced functions. The recorded process and production data can be analyzed online or offline, and machine cycles can be examined for minimum, maximum and average values of the cycle times. Total runtimes and time differences of production processes can be gleaned from cycle counters or offline trace analyses, e.g. via “post-scope configuration” in TwinCAT Scope View Professional.
Further benefits arise for predictive maintenance. Logging of data from operating hour counters, frequency analysis or RMS calculations, for example, enables implementation of high-performance condition monitoring. Moreover, the system facilitates limit value monitoring for different process data. Pattern recognition for detecting irregularities and repetitions in the recorded data further improves the reliability of the process sequence.
In addition to optimum support for error analysis, TwinCAT Analytics offers numerous opportunities to further machine optimization. The status analysis provides all the information required for optimizing the machine or system in terms of energy consumption or process sequence. In addition, detailed knowledge of all processes simplifies configuration of drives, for example, and it may be possible to reduce the connected load of machines based on thorough measurement readings. This future-oriented software tool serves an increasing demand for highly flexible production down to lot size 1. In this case, TwinCAT Analytics can provide comprehensive production documentation for each individual workpiece.
Component-based analysis SoftwareTwinCAT Analytics consists of four main software components:
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