A distance-to-fault (DTF) measurement method for intermittent faults in aging aircraft wiring is described. Multiple technologies are available for identifying and locating wiring faults in aircraft wiring harnesses. One particularly difficult type of fault to identify and locate is intermittent faults that occur only under flight loads, where vibration and airframe flexure can locally stress wiring. Such dynamic failures are less likely to measure direct faults on the ground under static maintenance conditions. Low-Energy High-Voltage (LEHV) failure analysis systems like Astronics' Wire Fault Tester (WIFT) apply a highvoltage, limited energy test stimulus to aircraft wiring. The high voltage will induce a momentary arc-to-ground event at the site of insulation damage, then the resulting voltage waveform at the test interface may be analyzed to locate the fault. Under the conditions of such a momentary fault, the spark arcing to ground launches a waveform away from the arc toward the instrumentation interface. The propagating wave-front reflects between the arc location and the instrument multiple times before dissipating completely, resulting in a waveform, as measured at the instrument, similar to the step-response of a transmission line with an open load. Conventionally, "bounce diagrams" are used to analyze this type of dynamic behavior, such as in high-speed digital circuits. Certain features of the waveform give indication of the fault location, however any variation in impedance along the length of the wiring section under test produces small-scale local reflections, resulting in waveform distortion that hampers identification of the fault location. A waveform analysis method has been developed that is less sensitive to impedance variations, and accurately estimates distance-to-fault. Repeatable accuracy of +/-10% DTF estimation over actual DTF lengths ranging from 5 to 95 ft has been demonstrated with conventional controlledimpedance wiring types, and with discrete, uncontrolled impedance conductors. The core of the analysis consists of a correlation-based method that estimates the periodicity of the waveform as a starting point for local feature identification.