Recently, attention has been paid to the convolutional neural network (CNN) based synthetic aperture radar (SAR) target recognition method. Because of its advantages of automatic feature extraction and the preservation of translation invariance, the recognition accuracies are stronger than traditional methods. However, similar to other deep learning models, CNN is a “black-box” model, whose working process is vague. It is difficult to locate the decision reasons. Because of this, we focus on the process analysis of a pre-trained CNN model. The role of the processing to feature extraction and final recognition decision is discussed. The discussed components of CNN models are convolution, activation function, and full connection. Here, the convolution processing can be deemed as image filtering. The activation function provides a nonlinear element of processing. Moreover, the fully connected layers can also further extract features. In the experiment, four classical CNN models, i.e., AlexNet, VGG16, GoogLeNet, and ResNet-50, are trained by public MSTAR data, which can realize ten-category SAR target recognition. These pre-trained CNN models are processing objects of the proposed process analysis method. After the analysis, the content of the SAR image target features concerned by these pre-trained CNN models is further clarified. In summary, we provide a paradigm to process the analysis of pre-trained CNN models used for SAR target recognition in this paper. To some degree, the adaptability of these models to SAR images is verified.

Intelligent fault diagnosis of rolling-element bearings is essential for predictive maintenance of rotating machinery. Although deep convolutional neural networks (CNNs) achieve high accuracy on public benchmarks, their large parameter counts and computational costs hinder deployment on resource-constrained edge devices. This paper proposes a frequency-aware lightweight CNN (FAL-CNN) that reconciles competitive accuracy with extreme efficiency. A fixed, non-trainable pipeline (fast Fourier transform, log compression, linear resampling, and min–max normalization) converts each 1024-sample vibration segment into a compact 128-dimensional spectrum, reducing the input eightfold. A frequency-aware module applies three parallel one-dimensional convolutions with kernels of 3, 7, and 15, matched to single-peak, harmonic-pair, and harmonic-family fault signatures, followed by a depthwise-separable backbone that sharply reduces parameters and floating-point operations (FLOPs). All Case Western Reserve University (CWRU) experiments use a leakage-controlled protocol with time-blocked splits, a sample-isolation gap, and five random seeds. Across four operating loads, FAL-CNN reaches a 99.78% mean accuracy with 65,122 parameters and 3.88 M multiply-accumulate operations (MACs) (7.76 M FLOPs), inferring in 0.17 ms on a single CPU thread (0.27 ms end-to-end) within a 288.8 KB Open Neural Network Exchange (ONNX) footprint. Because full-data accuracy saturates for all compact models, we evaluate where architecture matters: under operating-load shift, FAL-CNN outperforms a parameter-matched plain CNN by 13.27 percentage points (88.42% vs. 75.15%); frequency-masking and attribution analyses confirm that the multi-scale filters concentrate on fault frequency bands (a 21.91-percentage-point targeted-masking gap, roughly twice that of structural controls); under 10:1 training class imbalance, it maintains a macro-F1 above 99.2%; and on the Paderborn real damage benchmark with bearing-level nested validation, it attains (59.86 ± 18.86)% accuracy on unseen bearings, the best mean among the compared pipelines, although the difference from the parameter-matched baseline is not statistically significant. These results position FAL-CNN as a compact, edge-oriented classifier whose measured feasibility is currently limited to the development CPU; physical edge-device profiling remains required. Its demonstrated value lies in reliability under the evaluated distribution shifts rather than in saturated benchmark accuracy.