Abstract: In the field of agricultural machine and equipment, gearbox was a key mechanical part that was widely applied in speed regulation and power transmission. The gearbox had a high fault rate in actual operating process due to the severe working condition and its complex configuration. State monitoring and fault diagnosing were of great significance to guarantee the safety and stability of gearbox. A fault diagnosis method based on the improved fireworks algorithm (FWA) and probabilistic neural network (PNN) was proposed to overcome the shortcomings, such as the sensitivity to environmental noise and low fault recognition rate, when conducting the fault diagnosis system of gearbox under complex operating conditions. To enhance the pattern classifying performance of traditional methods based on PNN, a new similarity measure for samples was defined, which made the quality of PNN training data increase in modeling process. An improved FWA-PNN classification method was proposed by combining FWA optimization algorithm with PNN technology. The FWA was applied to optimize the smoothing parameters of PNN to determine the optimal values of network parameters, and thus in some way the pattern classification and identification accuracy of PNN could be improved. The proposed FWA-PNN classification method was applied in fault diagnosis modeling for gearbox under noisy environment, and the complex non-linear mapping relationship between fault characteristic parameters and equipment working conditions was constructed. Experiments were carried out on JZQ250 gearbox in laboratory and the process of the fault diagnosis modeling was summarized as follows: At first, 6 working states of the gearbox that included normal state and 5 typical fault states were simulated during the experiments. And then the vibration signals of the gearbox were gained by using the accelerometers of signal acquisition system under different working conditions. After the pretreatment, time and frequency domain analysis of vibration signals were carried out and 27 time and frequency parameters reflecting the working status of the gearbox were obtained. Kernel principal component analysis (KPCA) method was applied to extract the features of the original high-dimensional data, and 7 characteristic parameters were selected as the fault feature vectors of gearbox at last. As a result, the original fault samples were generated according to the fault feature vectors. Given that the fault vibration signals of gearbox were easy to be interfered by different noises in practice, the fault modeling sample sets were regenerated by adding random noises of different levels in the original fault samples. Two thirds of the samples were randomly selected as the training data and the remaining samples were as the test data to establish fault diagnosis model for gearbox based on FWA-PNN. Next, in order to validate the effectiveness and robustness of this new model, BP (back propagation) neural network (BP-NN), genetic algorithm based BP-NN (GABP-NN) and normal PNN methods were introduced to compare with the improved pattern classification method, and 4 different fault diagnosis models were built. The training results of different neural network models indicated that FWA-PNN had better performance in error convergence speed and precision than GABP-NN and BP-NN, which had an excellent fault tolerance and fault classification capability. Finally, 4 different models were applied in fault diagnosis and classification by using the noise samples as the test data. Comparison results indicated that FWA-PNN model could effectively improve the precision of fault detection due to the smooth parameters of all pattern categories optimized by FWA. Application results showed that by compared with the fault diagnosis models based on BPNN, GABPNN and traditional PNN, the FWA-PNN model had the highest fault recognition rate under different noise levels. In conclusion, a novel fault diagnosis program for nonlinear and complex mechanical systems is provided in this paper. It has good application prospects and popularized value in fault diagnosing for agricultural machinery and equipment.