In order to improve the accuracy of bearings fault diagnosis, one of the most crucial components of rotating machinery, a novel features extraction procedure incorporating an improved features dimensionality reduction method is proposed. In the first step, using the empirical mode decomposition method, the original statistical characteristics were calculated from intrinsic mode functions of the vibration signal. Due to information redundancy of the original statistical characteristics, this paper presents a novel features extraction method that combines K-means method and standard deviation to select the most sensitive characteristics. Furthermore, a modified features dimensionality reduction method is proposed, to realize the low-dimensional representations for high-dimensional feature space. Finally, the performance of the fault diagnosis model is evaluated by vibration signals with 12 bearing fault conditions, which are provided by Bearing Data Center of Case Western Reserve University. Experiment results show that the proposed fault diagnosis model can serve as an effective and adaptive bearing fault diagnosis system.

Intelligent fault diagnosis methods based on deep learning have attracted significant attention in recent years. However, it still faces many challenges, including complex and variable working conditions, noise interference, and insufficient valid data samples. Therefore, a novel deep transfer learning bearing fault diagnosis model is designed in this work by fusing time-frequency analysis, residual network (ResNet) and self-attention mechanism (SAM). A multiscale time-frequency feature map (MTFFM) and global statistical feature matrix (GSFM) of vibration signals are first constructed using wavelet packet transform (WPT). A deep feature extraction network combining ResNet and SAM networks is then designed to realize the fused extraction of local and global time-frequency features. Finally, we construct a joint loss function by the combination of multi-kernel maximum mean discrepancy (MK-MMD) and the domain adversarial neural network (DANN) to optimize the depth feature extraction network, which improves the cross-domain invariance and fault state discrimination of depth features. The proposed optimization method fully exploits the advantages of high-dimensional spatial distribution difference evaluation and gradient inversion adversarial strategy. Its effectiveness is demonstrated through variable working condition transfer fault diagnosis tasks, showing superior performance compared with other intelligent fault diagnosis methods.

In rotating machinery fault diagnosis, domain adaptation (DA) transfer learning-based framework has been attracting great attentions to tackle the problem of inconsistent feature distribution and insufficient labeled fault feature data. However, most of the existing approaches mainly focus on either the cross-domain distribution alignment or manifold subspace learning, which faces two critical limitations: 1) it is hard to overcome the feature distortions when aligning the distribution in the original feature space and 2) subspace learning is insufficient to decrease the distribution divergence. To address the above limitations, this work proposes an intelligent fault diagnosis scheme based on supervised DA with manifold embedding and key features selection. It first applies maximal overlap discrete wavelet packet transform (MODWPT) to process the vibration signals and performs the statistical feature extraction. In order to ensure that the selected key features are conductive to domain adaption, the fault discriminative ability and domain invariance of the features are investigated based on the domain differences and Laplacian score. Then, it presents a new supervised domain adaption with manifold embedding for the distribution alignment in manifold subspace by taking the class information and neighboring relationships into account. Finally, an intra classifier is learned to predict the unlabeled target domain. The proposed fault diagnosis scheme is evaluated using a set of practical data sets of motor and bearing. The extensive experimental results demonstrate that it significantly outperforms the comparative models and achieves much more effective fault diagnosis under different working conditions.

Life detection radar can detect human physiological signals (respiration, heartbeat, body movement, etc.) from a long distance away by penetrating nonmetal mediums (brick walls, ruins, etc.). However, interference is often caused by respiratory movements of the radar's operator when detecting vital signs of another human target. The detection accuracy can be significantly influenced by this kind of interference. In this letter, an experimental setup with a dual-frequency continuous-wave life detection radar is investigated. The system operates with different frequencies of 5.75 and 35 GHz. An adaptive filtering method is applied to suppress the interference caused by the operator's respiratory movements. Experimental results show that this method can effectively suppress respiratory interference and improve detection accuracy.

Cross-domain fault diagnosis based on transfer learning has been popularly developed to overcome inconsistent data distribution-caused degradation of diagnostic performance. However, the existing methods are typically suffering from a class imbalance of domains and lacking sufficient fault data because it is difficult to obtain the real industrial data of machinery under fault conditions. To address these problems, this work proposes a new cross-domain bearing diagnosis framework based on transferable features and manifold embedded discriminative distribution adaption. First, it applies the maximal overlap discrete wavelet packet transform to process the vibration data and extract different statistics-based features. Then, to enhance the domain adaptation performance, it designs a transferability evaluation based on the adjusted rand index and maximum mean discrepancy to quantify the fault discriminability and domain invariance of the features. After that, it proposes a novel manifold embedded discriminative joint distribution adaptation method to perform cross-domain feature discriminative joint distribution alignment in a Grassmann manifold subspace. Finally, it utilizes a random forest classifier to train the cross-domain fault diagnosis model. To verify the performances of the proposed methods, extensive experiments have been conducted on two real rolling bearing datasets. The results demonstrate that the proposed methods can achieve the desirable diagnosis results and significantly outperform comparative classical transfer learning-based models when there is the class imbalance between source and target domains.

In real industrial scenarios, with the use of conventional machine learning techniques, data‐driven diagnosis models have a limitation that it is difficult to achieve the desirable fault diagnosis performance, and the reason is that the training and testing datasets are assumed to have the same feature distributions. To address this problem, a novel bearing fault diagnosis framework based on domain adaptation and preferred feature selection is proposed, in that the model trained by the labeled data collected from a working condition can be applied to diagnose a new but similar target data collected from other working conditions. In this framework, an improved domain adaptation method, transfer component analysis with preserving local manifold structure (TCAPLMS), is proposed to reduce the differences in the data distributions between different domain datasets and, at the same time, take the label information of feature dataset and the local manifold structure of feature data into consideration. Furthermore, preferred feature selection by fault sensitivity and feature correlation (PSFFC) is embedded into this framework for selecting features which are more beneficial to fault pattern recognition and reduce the redundancy of feature set. Finally, vibration datasets collected from two test platforms are used for experimental analysis. The experimental results validate that the proposed method can obviously improve diagnosis accuracy and has significant potential benefits towards actual industrial scenarios.