The invention discloses an increment compensation dynamic adaptive enhancement-based fault diagnosis method. The method comprises the following steps of 1, collecting vibration signals in various states for a vibration sensor at a driving end of a motor; 2, preprocessing fault data of a bearing device; 3, adding a training sample into a random noise to serve as an input of a denoising autocoder to perform unsupervised greedy layer-by-layer pre-training; 4, when new device state data exists, performing new fault mode extraction by using an existing trained DAE model, performing similar mode comparison by utilizing a mode similarity algorithm, performing increment combination on new fault modes by adopting an increment active fusion algorithm, and calculating dynamic weighting by utilizing a weight dynamic compensation algorithm; 5, training an SVM classifier by taking labeled fault data and unlabeled fault data subjected to dynamic deep learning training weighting as input vectors; 6, performing global fine adjustment on related parameters in the whole model by utilizing a BP algorithm; and 7, performing classification diagnosis of fault types.