The increasing range of faults encountered by mechanical systems has brought great challenges for conducting intelligent fault diagnosis based on insufficient samples, in recent years. To tackle the issue of unbalanced samples, an improved methodology based on a generative adversarial network that uses sample generation and classification is proposed. First, 1D vibration signals are transformed into 2D images considering the features of the vibrating signals. Next, the optimized generation adversarial network is constructed for adversarial training to synthesize diverse fake 2D images according to actual sample characteristics with the generative model as a generator and the discriminative model as a discriminator. Our model uses an attenuated learning rate with a cross-iteration batch normalization layer to enhance the validity of the generator. Last, the discriminative model as a classifier is used to identify the fault states. The experimental results demonstrate that the proposed strategy efficiently improves fault identification accuracy in the two cases of sample imbalance.

In this paper, a MANet-based image detection approach is designed to inspect crystal defects during the cooling crystallization process, like that involving β-form L-glutamic acid (LGA), utilizing an online imaging device. The steps in the presented strategy encompass crystal image preprocessing, crystal image segmentation, and crystal classification. Firstly, the guided image filter is introduced to preprocess the collected crystallization images for offline training and online detection. Then, by using an image augmentation strategy to enlarge the number of crystal image samples for training, the MANet-based network is improved for crystal image segmentation. Accordingly, by defining some features, needle-like crystals can be categorized into four types with an efficient classifier for the detection of normal and defective crystals. The experimental results for the batch crystallization of β-form LGA are provided to illustrate the validity of the presented detection methodology.