Modern mechanical systems can be exceedingly complex. The complexities of modem mechanical systems have led to increasing needs for automated prognosis and fault detection systems. These prognosis and fault detection systems are designed to monitor the mechanical system in an effort to predict the future performance of the system and detect potential faults. These systems are designed to detect these potential faults such that the potential faults can be addressed before the potential faults lead to failure in the mechanical system.
One type of mechanical system where prognosis and fault detection is of particular importance is aircraft systems. In aircraft systems, prognosis and fault detection can detect potential faults such that they can be addressed before they result in serious system failure and possible in-flight shutdowns, take-off aborts, delays or cancellations.
Turbine engines are a particularly critical part of many aircraft. Turbine engines are commonly used for main propulsion aircraft. Furthermore, turbine engines are commonly used in auxiliary power units (APUs) that are used to generate auxiliary power and compressed air for use in the aircraft. Given the critical nature of turbine engines in aircraft, the need for fault detection in turbine engines is of extreme importance.
Some previous fault detection systems for turbine engines have relied upon feature extraction to examine sensor data for evidence of failure and explicit knowledge such as heuristics to the evidence of failure. However, this type of approach will suffer in applications where explicit knowledge cannot be easily applied to extracted features. For example, where different types of failures are expressed in evidences spread over different durations of times. In these cases the disparity in space and time can interfere with the ability of interpret the sensor data and limit the effectiveness of the fault detection.
Thus, what is needed is an improved system and method for fault detection in mechanical systems such as turbine engines.