As an important part of the internal combustion engine, the piston-liner assembly is subjected to high temperature and pressure and prone to failure during operation. Condition monitoring of the piston-liner is crucial for the normal operation and maintenance of the engine. The key parameters of the variational mode decomposition algorithm were determined based on block vibration characteristics, and the algorithm was then used to decompose the block vibration into six intrinsic mode functions (IMFs). Continuous wavelet transform was employed for the time-frequency analysis of block vibration. Time-frequency results indicated that IMF1 and IMF6 were closely associated with combustion and piston slap, respectively. Based on this, multiple evaluation criteria were utilized to confirm the characterization parameters of IMFs linked to combustion and piston slap. The support vector machine model was developed through input vector selection, training and test set construction, and kernel function choice. Subsequently, a genetic algorithm was employed to optimize the key parameters of the penalty factor and the kernel width parameter. The optimized support vector machine model was trained and tested. The diagnosis model achieved a 96.9% classification accuracy and met the piston-liner monitoring requirements.