Artificial intelligence (AI) models are widely used in every field of life to classify and predict the abnormal conditions. The key to activating AI models is to obtain enough training samples. The lumped parameter model is the one of the best choices to provide sufficient samples. In this study, the lumped parameter model is used in the numerical simulation of gears to obtain enough training samples to detect their running state. The aim is to replace the traditional sample generation method with the modified lumped parameter numerical method to realize the fault diagnosis of gears. Firstly, the lumped parameter model of gears is constructed and further updated. Secondly, failure types are inserted into the updated lumped parameter model and generate samples. The set composed of measured signals and simulated signals is equally divided, and the corresponding time-domain signal indexes are calculated to form a sample matrix for AI training and classification. Finally, CNN, RNN and LSTM are selected as the representatives of AI model, and then the trained models are used to classify the unknown measurement fault samples. The classification model trained by the simulation fault samples has almost 100% accuracy for the current gear fault classification, which shows that it is a feasible way to generate sample data for artificial intelligence fault classification by using the lumped parameter models.