This　paper　compares　and　analyzes　the　quantitative　diagnosis　methods　based　on　Lempel-Ziv　complexity　for　bearing　fault,　using　continuous　wavelet　transform　(CWT),　Empirical　Mode　Decomposition　(EMD)　method,　and　wavelet　packet　method　for　decomposition　of　vibration　signal　measured　by　acceleration　sensors,　respectively.　The　kurtosis　and　entropy　indices　are　also　analyzed　in　order　to　select　the　optimumanalysis　area　from　the　vibration　signal.　The　variation　trend　of　vibration　signal　Lempel-Ziv　complexity　of　bearing　inner　race　and　outer　race　with　the　varying　fault　severity　is　analyzed　and　predicted　based　on　the　generation　mechanism　and　characteristics　of　fault　vibration　signals.　Experimental　results　show　that　it　is　suitable　for　observing　the　fault　growing　trend　and　severity　by　examining　the　value　based　on　improved　Lempel-Ziv　complexity　algorithmusing　continuous　wavelet　transform(CWT),　Empirical　Mode　Decomposition　(EMD)　method,　and　wavelet　packet　method　for　signal　decomposition,　using　kurtosis　indices　for　optimal　analysis　area　selection,　and　using　the　optimal　weight　coefficients　for　complexity　calculation.