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Gear fault diagnosis is essential to the safe operation of cranes. This work proposes a new fault diagnosis method of crane gear based on Ensemble empirical mode decomposition (EEMD), Treelet transform, and Gaussian Process (GP). A Bacterial foraging optimization (BFO) algorithm is proposed to optimize the hyperparameters of the GP model. A multi-modal time-frequency distribution feature extraction method is developed based on ensemble empirical mode decomposition and Hilbert transform. Treelet transform is used to reduce the feature dimensionality of gear vibration signal. Finally, the fault location of the gear is identified by the BFO-GP model. Experimental results show that the EEMD-Treelet- GP diagnosis method can not only identify the best feature vector, but also identify the fault location.