Two key areas were developed: multiple parameter sensing, in a small footprint, and data fusion of condition indicators for health monitoring.
- A pack of state-of-the-art sensors was built, with a small footprint, covering a wide range of sensed inputs; for sensitivity and robustness, an array of condition indicators (CIs) was collected and computed. Concerning the sensors, a triaxial accelerometer, acoustic emission sensor, and temperature sensor were combined on one footprint for each measurement point, in a specially prepared sensor stack housing. The small footprint is necessary for the installation on an aircraft gearbox, because gearbox housing are designed to be light and strong, with curved surfaces and webs, with minimal flat areas. To avoid damage, the sensor stack housing is fitted to the surface with an adhesive. A high bandwidth microphone, shaft phase and oil quality sensor were also added. For the CIs, a wide range of features was computed from the sensor data. Sensors sampled at a high bandwidth offered a range of CIs, either individually or used together, in the time and frequency domain, using bulk statistical indicators as well as those combined with engineering knowledge.
- Data fusion: there are several advantages in using condition indicators (CIs) together, or in modified form. Some CIs offer very early detection of signs of degradation events such as sub-surface cracking, while some are better for later confirmation, such as geometrical change. A combination may show progression of the condition or sequence of related conditions. Some CIs rise early in response to transient events, but then may die away. Their cumulative record, by integration, summation, or simple binary latching, ensures that early indication is not lost. Many CIs vary in response to transients. A statistical observation can be matched to the conditions. This is important for helicopters, where climb, hover, and level flight are separated by transient states. The measurement strategy rejects the transients. Overall, the data fusion approach adds confidence to diagnosis, especially in machines with variable speed and load conditions.
The potential socio-economic impact is in future exploitation in machine health and reliability assurance for the RACER Fast Rotorcraft. A supply chain partnership will be needed to develop through to TRL9. An architecture review will lead to systems integration. The wider societal implications of the exploitation include jobs for those involved in hardware and software, future support roles in the remote support for systems tuning and supervision, and the sustainable capability and capacity of rotorcraft systems in the service of public and commercial applications.
