A typical airframe fatigue test is divided in a number of fatigue load blocks. At the end of each flight block the test is stopped and the specimen is inspected for cracks. These manual inspections are time consuming and the time interval between these inspections is relatively large. Structural abnormalities may be detected too late, which could lead to retrofitting in-service aircraft in a worst-case scenario. Condition Based Inspections (CBI) of the specimen, instead of Risk Based Inspections (RBI), is a potential solution to reduce the total fatigue test duration and to quickly detect abnormalities. One of the implications is that more sensors are required to monitor the behaviour of the test specimen and to detect or predict structural failures.
“A full-scale airframe fatigue test can generate data at rates of up to 10 MB/s,
totalling to hundreds of terabytes at completion.
Data processing and analysis is a major bottleneck.”
Gantner Instruments has developed an innovative software platform, called GI.cloud, aimed at efficient processing of large volumes of measurement data and rapid analysis. GI.cloud combines a time series database management system with a powerful stream processing engine, offering a number of distinct advantages.
- Minimise your investment cost for IT and storage infrastructure in the test lab, whilst maintaining the necessary computing performance for test-critical data analysis tasks. Measurement data that you need to accessed right away (hot data) is available in the database. Data that you access less frequently, and is only needed for auditing or bookkeeping purposes (cold data), is kept in the stream processing platforms.
- Raw measurement data is safely stored in redundant, fault-tolerant clusters for automated backup. Flexible data aggregation ensures that your measurement data is continuous logged to the database at low sample rate. The database can replay the same data and store it at a higher sample rate in case detailed analysis around an unexpected event or specimen failure is required.
- Powerful querying capabilities enable you to analyse large amount of measurement data on-the-fly. Trend monitoring over the entire life of the fatigue test will quickly identify any significant change in strain between repetitive load conditions. Fatigue prediction and crack probability algorithms can identify possible loss of structural integrity during the test and immediately inform you when deviations occur.
Contact your local sales representative to learn more about GI.cloud.
“Lunch and Learn” at EDF
In a very good atmosphere 18 specialists of EDF Energy Generation in Gloucester/UK listen the presentations from engineers of Gantner Instruments at the Lunch and Learn Meeting and enjoy sandwiches, chips, cookies and soft drinks as well as coffee and tea.Read more...
Q.series X A101 SV: Universal Measurement Module with Sensor Supply
The Q.series X A101 2 x DB15HD SV module from Gantner Instruments is a precisely engineered multi-functional measurement module that seamlessly integrates into a variety of industrial applications. With 2 universal analog inputs and a programmable sensor supply voltage and current limit, this module is crafted to provide precise and reliable data across a spectrum of measurement needs.Read more...
High-quality I/O produces High-Quality Vibration Measurement Data: It is really that simple
Whether your challenge is condition monitoring, predictive maintenance, structural health monitoring (SHM), or efficient data storage and analytics in various test applications – a comfortable and easy-to-run vibration measurement setup is crucial.Read more...
8 Tips to Consider When Buying a Strain Gage Amplifier
The strain gage is one of the most important tools of the electrical measurement technique applied to the measurement of mechanical quantities. As their name indicates, they are used for the measurement of strain.Read more...