## 1 Introduction  
The spline structure of helicopter gearbox is prone to develop cracks and spalling due to excessive loads, insufficient lubricants, manufacturing defects, installation problems, or material fatigue. The common nondestructive evaluation (NDE) methods to monitor flaws in splines are debris monitoring, acoustic emission, and vibration. The methods can be applied individually [1; 2; 3; 4; 5; 6] or concurrently [7; 8]. While debris monitoring does not require any electronics, it is simple to interpret and has excellent sensitivity to wear-related failure; this method is insufficient to non-benign cracks as no debris is produced. Acoustic emission (AE) and vibration signals have more quantitative results to detect the earliest stage of damage in rotating machinery. AE and vibration methods are based on recording transient signals in two different frequency spectrums. While vibration method is based on features that are extracted from time and frequency domain signals recorded by low frequency accelerometers in order to assess the changes in vibrational properties as related to the damage [9; 10], AE method is based on detecting propagating elastic waves released from active flaws. While the AE source in metals has broad spectrum of up to 1 MHz [11], and typical frequency range is 60-300 kHz to overcome the challenge of signal attenuation in higher frequencies and increase the distance between source and sensor. The common sources that generate AE in gearbox include plastic deformation, microfracture, wear, bubbles, friction and impact [12]. Once transient signals are collected, signal processing methods, such as wavelet decomposition [13], empirical mode decomposition [6], and multivariate pattern recognition [14] are applied. Typical parameters extracted from the transient signals are root mean square value, frequency domain characteristics, energy, spectral kurtosis, and peak-to-peak vibration level. Due to the difference in the frequency bandwidth, AE method is more sensitive to micro cracks as compared to the vibration method. Typical AE data acquisition approach is based on threshold: an AE signal is detected when the signal level is above threshold. This approach may cause high hit-rate for noisy applications, such as gearbox monitoring. Another approach is to collect long duration waveforms (100-200 ms) with high sampling rate (1-2 MHz) at every time interval (2-5 s), and analyze the waveforms in post-processing. As crack growth is a stochastic process, it is considered that while some data will be lost due to the idle time of data acquisition system between waveform recording intervals, crack information will be stochastically detected. However, it is important to identify how to analyze long duration signals in order to reduce the influence of background noise from the extracted features. In this study, the streamed AE signals are recorded from the helicopter gearbox test rig at the Naval Air Systems Command (NAVAIR) facility. The AE signature of the fatigue crack growth is obtained from the scaled laboratory experiments. The spline section of the gearbox is modeled as a planar geometry, such that the crack growth direction is similar to the expected crack growth in a realistic condition. The streamed AE signal and the crack AE signal are merged with different signal to noise ratios in order to determine the minimum detectable AE signal amplitude due to the crack growth as compared to the background noise level; therein, the streamed signals are selected within the acoustically stable regions and in the early stage of testing as no crack is expected. A decomposition approach of the streamed AE signal to extract the crack information is developed. Additionally, the developed methodology is demonstrated on detecting burst signals that are embedded into the streamed signals recorded in the spline testing of the helicopter gearbox test rig located at the NavaIR facility. The organization of this paper is as follows. Section 2 describes the experimental designs at the NavaIR facility and the laboratory.