Vibration signals under the same health state often have large differences due to changes in operating conditions. Likewise, the differences among vibration signals under different health states can be small under some operating conditions. Traditional deep learning methods apply fixed nonlinear transformations to all the input signals, which has a negative impact on the discriminative feature learning ability, i.e., projecting the intra-class signals into the same region and the inter-class signals into distant regions. Aiming at this issue, this paper develops a new activation function, i.e., adaptively parametric rectifier linear units, and inserts the activation function into deep residual networks to improve the feature learning ability, so that each input signal is trained to have its own set of nonlinear transformations. To be specific, a sub-network is inserted as an embedded module to learn slopes to be used in the nonlinear transformation. The slopes are dependent on the input signal, and thereby the developed method has more flexible nonlinear transformations than the traditional deep learning methods. Finally, the improved performance of the developed method in learning discriminative features has been validated through fault diagnosis applications.

In recent years, increasing numbers of deep learning methods for fault diagnosis of rolling element bearings (REBS) have been proposed. However, in industry, the scarcity of available data to monitor the health condition of REBS leads to low recognition accuracy of the trained intelligent diagnostic models. To solve this problem, we propose a simulation-data-driven subdomain adaptation adversarial transfer learning (TL) network (SAATLN). Firstly, a defect vibration model is introduced to simulate vibration signals of different types of REBS faults. And the real signal and simulated signal are used as the target domain and source domain of the TL fault diagnosis methods, respectively. Secondly, SAATLN uses the designed residual squeeze-and-excitation (Re-SE) blocks to extract transfer features between different domains. Meanwhile, it combines adversarial learning and subdomain adaptation to adapt the marginal distribution and conditional distribution discrepancies of high-level features. Also, the local maximum mean discrepancy is introduced as the subdomain adaptation metric criterion. Finally, different transfer tasks are performed on the artificially damaged and run-to-failure REBS data sets. The results demonstrate the effectiveness and superiority of the SAATLN in the simulation-data-driven REBS fault diagnosis.

Aiming at the difficulty in evaluating the degradation performance of rolling bearing and recognizing bearing running state, a new method based on feature-to-noise energy ratio (FNER) indicator and improved deep residual shrinkage network (IDRSN) model is proposed. First, Hilbert transform and fast Fourier transform are used to obtain the envelope spectrum of bearing run-to-failure test signal and the fault feature energy of the envelope spectrum amplitude is calculated. Subsequently, the total energy of the envelope signal is obtained according to the autocorrelation function (ACF). After that the ratio of the fault feature energy and the noise energy is used as the bearing performance degradation indicator. Moreover, bearing running states are divided according to FNER indicator and tagged bearing samples realized. Then, tagged samples are used to train the IDRSN, which introduces the densely connected network (DenseNet) to obtain the bearing running state recognition model. In order to improve the anti-interference ability, the DropBlock layer is introduced into the first large convolution kernel and the Dropout technique is introduced before the global average pooling layer. Finally, a failure test data is used to verify the feasibility of the FNER indicator and two bearing run-to-failure test data are used to verify the validity of the FNER indicator and the IDRSN recognition model. Through comparison and verification, the results show that the proposed FNER indicator is better than other indicators and IDRSN can more accurately recognize bearing running state recognition.

Predicting the degradation process of bearings before they reach the failure threshold is extremely important in industry. This paper proposed a novel method based on the support vector machine (SVM) and the Markov model to achieve this goal. Firstly, the features are extracted by time and time-frequency domain methods. However, the extracted original features are still with high dimensional and include superfluous information, and the nonlinear multifeatures fusion technique LTSA is used to merge the features and reduces the dimension. Then, based on the extracted features, the SVM model is used to predict the bearings degradation process, and the CAO method is used to determine the embedding dimension of the SVM model. After the bearing degradation process is predicted by SVM model, the Markov model is used to improve the prediction accuracy. The proposed method was validated by two bearing run-to-failure experiments, and the results proved the effectiveness of the methodology.

A novel method to solve the rotating machinery fault diagnosis problem is proposed, which is based on principal components analysis (PCA) to extract the characteristic features and the Morlet kernel support vector machine (MSVM) to achieve the fault classification. Firstly, the gathered vibration signals were decomposed by the empirical mode decomposition (EMD) to obtain the corresponding intrinsic mode function (IMF). The EMD energy entropy that includes dominant fault information is defined as the characteristic features. However, the extracted features remained high‐dimensional, and excessive redundant information still existed. So, the PCA is introduced to extract the characteristic features and reduce the dimension. The characteristic features are input into the MSVM to train and construct the running state identification model; the rotating machinery running state identification is realized. The running states of a bearing normal inner race and several inner races with different degree of fault were recognized; the results validate the effectiveness of the proposed algorithm.

Automated crack detection technologies based on deep learning have been extensively used as one of the indicators of performance degradation of concrete structures. However, there are numerous drawbacks of existing methods in crack segmentation due to the fine and microscopic properties of cracks. Aiming to address this issue, a crack segmentation method is proposed. First, a pyramidal residual network based on encoder–decoder using Omni-Dimensional Dynamic Convolution is suggested to explore the network suitable for the task of crack segmentation. Additionally, the proposed method uses the mean intersection over union as the network evaluation index to lessen the impact of background features on the network performance in the evaluation and adopts a multi-loss calculation of positive and negative sample imbalance to weigh the negative impact of sample imbalance. As a final step in performance evaluation, a dataset for concrete cracks is developed. By using our dataset, the proposed method is validated to have an accuracy of 99.05% and an mIoU of 87.00%. The experimental results demonstrate that the concrete crack segmentation method is superior to the well-known networks, such as SegNet, DeeplabV3+, and Swin-unet.