Freight rail airbrakes need improvement in reliability to reduce failures, which can lead to harm to the environment, loss of human life, and a negative impact on the economy. This research focuses on identifying the failures of airbrakes using a Reliability Centered Maintenance (RCM) framework, which uses Failure Modes and Effects Analysis (FMEA) to identify and rank failures. For the purpose of our research, the FMEA result of railroad operating companies was used to research on condition-monitoring techniques for airbrake leakages. Ultrasound Leakage Detection (ULD) is presented as an alternative to the soap and bubble test, as it is a more effective, proactive method to locate and quantify leakages. Experiments were conducted both in the field and in laboratory with simulated and original components to qualitatively and quantitatively evaluate and verify the implications of ULD in cold weather conditions. Correlations between operating pressure, temperature, leakage orifice size, flow-rate and ultrasound intensity were performed to analyze interdependencies. Principal Component Analysis was applied on the spectral features of dynamic sound signatures to reduce the number of variables and find the correlation between them. The contribution that the frequency ranges made to the factors was estimated to find those having significant impact on spectral feature value for different levels of a particular operating variable. For the sum of contributions from individual spectral features, the frequency range of 2400 – 2500 Hz has the maximum contribution. Frequency range, 1100 – 1200 Hz can be used as a feature for discriminating orifice size, as it has 46% contribution for the Root Mean Square (RMS) value of Power Spectral Density (PSD). The results of this research compares the contribution values of frequency ranges, but it does not state the value at which the readings become significant. With extensive quantitative research on the contribution of frequency ranges and an operational inspection strategy, ULD will result in an effective detection method for airbrake leakages in rail service that will be useful for assessing leakage location and severity with more accuracy. This will facilitate reliable airbrakes operations at severe weather and topographical conditions.