The　burgeoning　field　of　the　Industrial　Internet　of　Things　(IIoT)　necessitates　advanced　fault　diagnosis　methods　capable　of　navigating　the　dual　challenges　of　high-predictive　accuracy　and　the　constraints　of　edge　computing　environments.　Our　study　introduces　Wave-ConvNeXt,　a　novel　fault　diagnosis　model　that　seamlessly　integrates　the　state-of-the-art　ConvNeXt　architecture　with　Wavelet　Transform.　This　innovative　model　stands　out　for　its　lightweight　design　yet　delivers　exceptional　accuracy　in　fault　diagnosis.　In　Wave-ConvNeXt,　we　re-engineer　the　ConvNeXt　model　for　IIoT　applications　by　adopting　1-D　convolution,　tailored　for　processing　high-frequency,　nonperiodic　inputs.　This　adaptation　is　complemented　by　replacing　the　traditional　＇patchify＇　layer　with　a　Wavelet　transform　layer,　which　simplifies　input　signals　into　subsignals,　thereby　easing　learning　complexities　and　diminishing　the　dependence　on　elaborate　deep　architectures.　Further　enhancing　this　model,　we　incorporate　a　squeeze-and-excitation　module,　enriching　its　ability　to　prioritize　channelwise　feature　relevance,　akin　to　self-attention　mechanisms.　This　integration　is　rigorously　validated　through　an　ablation　study.　Wave-ConvNeXt　epitomizes　a　holistic　approach,　enabling　an　end-to-end　optimization　of　feature　learning　and　fault　classification.　Our　empirical　analysis　on　two　real-world　IIoT　data　sets　demonstrates　Wave-ConvNeXt＇s　superiority　over　existing　models.　It　not　only　elevates　prediction　accuracy　but　also　significantly　curtails　computational　complexity.　Additionally,　our　exploration　into　the　impact　of　various　mother　wavelets　reveals　the　effectiveness　of　using　wavelet　basis　functions　with　smaller　support,　bolstering　diagnostic　precision.　The　source　code　of　Wave-ConvNeXt　is　available　at　https://github.com/leviszhang/waveConvNeXt.　©　2014　IEEE.