mechmine - Platform-as-a-Service (PaaS)
mechmine's Platform-as-a-Service (PaaS) Cloud solution is based on data-mining methods and algorithms, and uses the flexibility and scalability of the Cloud in order to efficiently perform complex and time consuming mathematical analysis to gain a swift response about the condition of a machine.
With the increase in data volume one can also exploit concepts known in the fields of big-data. The variety of the data leads to a new form of universal „machine intelligence" through merging and cross-breeding the acquired system knowledge. The traditional analysis, for example for processing vibration data, utilises methods such as the Fast Fourier Transform (FFT). mechmine's approach is, to merge and complement these kown methods with other algorithms, and offer its clients a more cost effective service due to automatisation.
Regular revisions or preventive repairs can ensure high machine availability. But at what cost, and is this work always necessary at the same time intervals?
This is where High-Res vibration monitoring, with high-resolution data, can assure reliability and support machine operating personnel in assessing the true condition of the machine. Why is high-resolution vibration monitoring necessary?
- To be able to resolve closely spaced frequencies, especially in slow-running machines with speeds below 600 RPM.
- To maximize the information content and thus the significance of the data for early detection of damage.
- Because a typical average value like mm/s only provides information about plant safety and not about the condition of components such as roller bearings, gears, etc.
The use of piezo-sensors for vibration monitoring yields up to 40 dB higher signal dynamics compared to MEMS-based vibration sensors and a measurement bandwidth of typically 17-25 kHz instead of 1-5 kHz only. High signal dynamics and measurement bandwidth clearly aid early detection and simplify the process of identifying the root-cause of an anomaly.
See also our publications under Downloads.
