As　an　important　driving　device,　the　permanent　magnet　synchronous　motor　(PMSM)　plays　a　critical　role　in　modern　industrial　fields.　Given　the　harsh　working　environment,　research　into　accurate　PMSM　fault　diagnosis　methods　is　of　practical　significance.　Time-frequency　analysis　captures　the　rich　features　of　PMSM　operating　conditions,　and　convolutional　neural　networks　(CNNs)　offer　excellent　feature　extraction　capabilities.　This　study　proposes　an　intelligent　fault　diagnosis　method　based　on　continuous　wavelet　transform　(CWT)　and　CNNs.　Initially,　a　mechanism　analysis　is　conducted　on　the　inter-turn　short-circuit　and　demagnetization　faults　of　PMSMs,　identifying　and　displaying　the　key　feature　frequency　range　in　a　time-frequency　format.　Subsequently,　a　CNN　model　is　developed　to　extract　and　classify　these　time-frequency　images.　The　feature　extraction　and　diagnosis　results　are　visualized　with　t-distributed　stochastic　neighbor　embedding　(t-SNE).　The　results　demonstrate　that　our　method　achieves　an　accuracy　rate　of　over　98.6%　for　inter-turn　short-circuit　and　demagnetization　faults　in　PMSMs　of　various　severities.