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.

More articles
UCY Student’s industrial internship experience at Gantner Instruments
An internship at Gantner Instruments is a great way to connect classroom knowledge to real-world experience. Learning is one thing but taking those skills into the workforce and applying them is a great way to explore different career paths and specializations that suit individual interests. This internship can provide someone with experience in the career field they want to pursue, give individuals an edge over other candidates when applying for jobs, prepare them for what to expect in their field, and increase their confidence in their work.
Read more...Portable and Mobile Data Acquisition Systems
There are many reasons for a flexible and robust measurement system that must be easy to transport to collect measurement data at different locations. These can be, e.g., short-term measurements on machines or plant components during commissioning after maintenance or recurring measurements on bridges or other engineering structures.
Read more...The Smarter E Europe
Join us at The Smarter E Europe in hall B5, stand no. 320. Our experts, Jörg Scholz and Jürgen Sutterlüti will be present and ready to address your questions.
Read more...GI.cloud – High-Performance Edge Computing Data Platform
The future brings more distributed and adaptive monitoring and control applications, this requires better and faster utilization of data streams. Reliable and distributed data acquisition are mandatory, this is why Gantner Instruments developed GI.cloud.
Read more...