Extracting intuitive operating state features from vibration signals without prior knowledge is a prospective requirement for health monitoring and fault diagnosis in bearings. In this paper, a visualized stacked denoising auto-encoder (VSDAE) model is proposed for the unsupervised extraction and quantitative evaluation of bearings’ state features. First, the stacked denoising auto-encoder (SDAE) was used to reconstruct vibration signals. The intermediate vector of the SDAE, which is a high-information-density representation of vibration signals, was regarded as the pending state feature. Then, the dimension of the intermediate vector was reduced by the t-distributed stochastic neighbor embedding (t-SNE) method to the two-dimensional visualization space. Finally, the silhouette coefficient of feature distribution was calculated to quantitatively evaluate the extracted features. The proposed model was evaluated using experimental bearing signals simulating various operating states. The results proved that the features, extracted and evaluated by the VSDAE, allowed the recognition of the operating states of the examined bearings.

As a kind of thermal control device, fluid loop systems must operate with the demands of high safety, high reliability, and long life. In order to accurately assess the health status of fluid loop systems, a hierarchical multi-information fusion (HMIF) method is proposed in this paper. Considering that fluid loop systems generally have distinct structural hierarchies, the health evaluation process in this method is divided into three levels, which are the indicator level, the component level, and the system level. In the evaluation process, the health indices are, respectively, constructed to quantify the health status at the three levels. At the indicator level, One-Class support vector machine algorithm is used to obtain the distribution space of each state monitoring indicator under a normal state. The indicator-level health indices are evaluated by calculating the ratio of the data located in the distribution space to the overall data. At the component level, a fuzzy theory is used to calculate the health indices of the component level. Health indices at the indicator level are first converted to membership degree by membership degree function. Then, the evaluation fusion strategy is used to deduce the membership degree of the component level. The health indices at the component level are obtained from the mapping relationship between the membership degree and the health index. At the system level, an adaptive weight adjustment strategy is proposed to fuze all component-level health indices. Taking a practical fluid loop system as an example, health indices at the three levels evaluated by the HMIF method are compared with the actual status. The results indicate that the proposed method can correctly judge the health state of the system and provide a reference for the maintenance and fault diagnosis of fluid loop systems.

Cross-condition fault diagnosis of rotating machinery remains challenging under variable speed and load because vibration signals have strong non-stationarity and exhibit distribution shifts across operating conditions. To address this problem, a tacholess diagnosis framework combining time–frequency ridge extraction with an uncertainty-guided distribution-regularised convolutional Wasserstein autoencoder (UDR-CWAE) is proposed. Firstly, instantaneous rotational frequency is estimated directly from vibration signals using harmonic-amplitude-based ridge initialisation, edge-constrained search, and cost-function-based tracking. Then, the estimated rotational frequency is integrated to construct single-rotation-cycle vibration samples, which are normalised to reduce the discrepancy of amplitude scale among samples. Finally, UDR-CWAE regularises the aggregated latent distribution using maximum mean discrepancy, while uncertainty weighting adaptively balances reconstruction, distribution-regularisation, and classification losses. Cross-condition experiments on the Ottawa bearing dataset and the SQI test-rig dataset achieved classification accuracies of 99.98% and 99.23%, respectively. These results demonstrate that the proposed framework provides accurate tacholess speed estimation and robust fault recognition under unseen rotational-frequency conditions.