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With the continuous development of artificial intelligence technology, the method based on deep learning has become a research hotspot in the field of engineering. However, the setting of super parameters and the amount of training datas are strictly required for the deep neural networks, which is difficult to meet the needs of rapid, accurate and stable diagnosis in practical industry. To solve above problems, a diagnosis method based on improved deep forest was proposed, which is applied to small-scale data sets. The cascade forest stage of traditional deep forest model has problems like high computational cost and redundant data processing, therefore, in the paper a deep forest model based on Linear Discriminant Analysis (LDA) feature extraction is designed. The improved deep forest model improves the data transmission and processing ability in multi-granularity scanning and cascade forest, and enhances the feature representation in the model while ensuring the data diversity, so as to improve the operation efficiency and diagnosis performance of the algorithm. Compared with the traditional machine learning methods, the experimental results show that the improved method has better accuracy.

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