Learn about new advanced WHT Tech With Delphisonic Real-Time Turbine Condition Monitoring Algorithms that improve machinery fault detection including torsional vibration, envelope detection, and bearing fault detection. Also, discover how some of the new implementations in FPGA, such as tachometer processing and fixed-point math, are revolutionizing the machine condition monitoring industry.
Health monitoring and predictive maintenance technology is essential for lowering predictive maintenance operating costs. However, reliable prognostic algorithms are challenging due to the dynamic conditions faced by engine turbines. Learn about health assessment analysis techniques for aviation including a multi-regime approach to segmenting data.
We have gained solid expertise in detecting and diagnosing potential failure modes at an early stage of development for all types of machines and applications, including aircraft.
As our monitoring strategy is primarily focused on the drive train portion of the aircraft engine turbine, most of the potential failure modes are associated with the rolling element bearings, gears and the mechanical equipment.
Although the fault detection techniques for these components are not new, the means to detect these faults present challenges due to the engine design and operation concept. The widely variable operating conditions coupled with non-rigid foundations, compact construction, complex gearbox and high rotating speed require an entirely different monitoring approach for engine turbine.
Important monitoring functionality that enables a turbine monitoring system to detect and isolate developing machine faults includes:
Intelligently reduces the flood of alarms that often occur when monitoring engine turbines, due to variations in operating conditions.
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