Evaluation of filtering techniques for aircraft engine condition monitoring and diagnostics

Engine condition monitoring and diagnostics are critical for safe, efficient and profitable aircraft operation. Snapshots of sensor measurements are typically used for engine health monitoring. Deviation of those measurements from a reference condition is a key feature for engine fault detection. Measurement noise and sensor failure, however, often contaminate those signals and affect performance of the fault detection. Filtering or signal smoothing is therefore a helpful technique to improve fault isolation and accuracy and robustness of the detection. Filtering is a very active area of research in the signal processing field; in the past several decades, a large variety of techniques have been developed. Each technique has its advantages and disadvantages, depending on the type of signal. The goal of this paper to review and evaluate various filtering techniques for aircraft engine condition monitoring applications, and recommend the most suitable filtering algorithms based on the unique characteristics of aircraft engine signals.