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Along with the evolution of the technique and use of aspectoriented programming (AOP), the difficulty of testing the aspectoriented programs is now receiving much attention. In this position paper, we describe an AspectJ program testing method based on fault model with the help of dependency model and interaction model.

The intelligent fault diagnosis of a planetary gearbox under variable speed is still a challenging topic. Due to the similar spectrum structure, overlapping features occur and result in decreasing diagnosis accuracy. Autoencoder-based methods can extract features adaptively but few studies proposed approaches to enhance the discriminability of features from different classes under variable speeds. Besides, the adverse variability of encoder weights may result in an adverse effect on the decoder. Adversarially learned inference (ALI) trains the encoder and decoder independently, but it is time-consuming to reach Nash equilibrium. To address the issues, a parallel adversarial learning inference model (PALI) is proposed, which aims at validating the parallel training of encoder and decoder and enhancing the discriminability of features. Specifically, time-frequency analysis is utilized to reveal the time-varying characteristics of raw signals and obtain time-frequency images as input for the encoder. Then, an explicit multi-dimensional uniform distribution is used for the merit of a simple probability density function to construct visualized and well-classified samples as input for the decoder. After that, a parallel adversarial game is explored to train the encoder and decoder simultaneously and independently, which will reduce computing interference and make the extracted features similar to the well-classified samples and reconstruct the raw signals. Finally, a Softmax classifier is trained and tested by the features. This method and its generability are validated via a planetary gearbox data set and a public bearing data under variable speed. The results indicate that the proposed parallel adversarial game is valid for training encoder and decoder independently, and PALI works as well as adversarial autoencoder (AAE) and outperforms ALI, variational autoencoder (VAE) in obtaining well-clustered features over different training data. Compared to Wigner-Ville distribution (WVD) and continuous wavelet transform (CWT), PALI based on short-time Fourier transformation (STFT) works better over different training data.

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