Independent component analysis (ICA) is an excellent latent variables (LVs) extraction method that can maximize the non-Gaussianity between LVs to extract statistically independent latent variables and which has been widely used in multivariate statistical process monitoring (MSPM). The underlying assumption of ICA is that the observation data are composed of linear combinations of LVs that are statistically independent. However, the assumption is invalid because the observation data are always derived from the nonlinear mixture of LVs due to the nonlinear characteristic in industrial processes. Under this circumstance, the ICA-based fault detection is unable to provide accurate detection for specific faults of industrial processes. Since the observation data come from the nonlinear mixing of LVs, this makes the observation data change faster than the intrinsic LVs on the time scale. The temporal slowness can be regarded as an additional criterion in the extraction of LVs. The slow feature analysis (SFA) derived from the temporal slowness has received extensive attention and application in MSPM in recent years. Simultaneously, the temporal slowness is expected to make up for the problem that the LVs extracted by ICA have difficulty accurately describing the characteristics of the process. To solve the above problems, this work proposes to monitor non-Gaussian and nonlinear processes using the independent slow feature analysis (ISFA) that combines statistical independence and temporal slowness in extracting the LVs. When the observation data are composed of a nonlinear mixture of LVs, the extracted LVs of ISFA can describe the characteristics of the processes better than ICA, thereby improving the accuracy of fault detection for the non-Gaussian and nonlinear processes. The superiority of the proposed method is verified by a numerical example design and the Tennessee–Eastman process.

Expression recognition is a very important direction for computers to understand human emotions and human-computer interaction. However, for 3D data such as video sequences, the complex structure of traditional convolutional neural networks, which stretch the input 3D data into vectors, not only leads to a dimensional explosion, but also fails to retain structural information in 3D space, simultaneously leading to an increase in computational cost and a lower accuracy rate of expression recognition. This paper proposes a video sequence face expression recognition method based on Squeeze-and-Excitation and 3DPCA Network (SE-3DPCANet). The introduction of a 3DPCA algorithm in the convolution layer directly constructs tensor convolution kernels to extract the dynamic expression features of video sequences from the spatial and temporal dimensions, without weighting the convolution kernels of adjacent frames by shared weights. Squeeze-and-Excitation Network is introduced in the feature encoding layer, to automatically learn the weights of local channel features in the tensor features, thus increasing the representation capability of the model and further improving recognition accuracy. The proposed method is validated on three video face expression datasets. Comparisons were made with other common expression recognition methods, achieving higher recognition rates while significantly reducing the time required for training.

Deep learning has been widely applied in the field of rotating machinery fault diagnosis. Existing deep learning-based fault diagnosis methods commonly utilize deep neural networks to extract features from raw data and employ fully connected layers for classification of the extracted features. However, in existing methods, fault type-related information is coupled with irrelevant information (e.g., load variations, fault sizes, and system intrinsic properties) at the fully connected layer. The lack of decoupling may result in fault type-related information being overwhelmed by irrelevant factors, thereby hindering accurate fault type determination. We propose that there exists an intrinsic feature in the data, where information is interrelated and closely tied to the fault type. This feature remains highly stable under changes in irrelevant factors, such as load and fault size, and provides an accurate representation of the fault type. By extracting and analyzing fault-type intrinsic features, the interference caused by irrelevant factors on fault type determination can be effectively eliminated. To address this, this article proposes a novel decoupling generative adversarial network for decoupling the intrinsic feature of fault type. The proposed method effectively extracts the intrinsic features of fault types that are unaffected by irrelevant factors, significantly improving fault diagnosis accuracy and robustness, and, to some extent, endowing the fault-type intrinsic features with interpretability and physical rationality.