Advanced engine test methods incorporate several different sensing and signal processing techniques for identifying and locating manufacturing or assembly defects of an engine. A successful engine test method therefore, requires advanced signal processing techniques. This paper introduces a novel signal processing technique to successfully detect a faulty internal combustion engine in a quantitative manner. Accelerometers are mounted on the cylinder head and lug surfaces while vibration signals are recorded during engine operation. Using the engine's cam angular position, the vibration signals are transformed from the time domain to the crank-angle domain. At the heart of the transformation lies interpolation. In this paper, linear, cubic spline and sinc interpolation methods are demonstrated for reconstructing vibration signals in the crank-angle domain. Finally, the ensemble-averaged mean squared-error criterion is introduced as the fault-detection metric to determine whether the engine under test is faulty or not. The proposed method is cost effective, non-intrusive and non-destructive. Moreover, the results have shown the crank-angle domain analysis using sinc interpolation for signal reconstruction to be more precise than that using linear or cubic spline interpolation. This improved precision enables better fault detection with a more accurate measure of the severity of the fault condition.