Spectral coherence (SCoh) is a powerful tool to reveal the hidden periodicities of signals, which has been widely used for rolling bearing fault diagnosis. However, most SCoh-based methods focus on searching a single demodulation band, which results in their inability to compound fault diagnosis and discrete frequency band localization. Moreover, many studies are conducted based on prior fault characteristic frequencies (FCFs), which limits their application in limited vision cases. To solve such issues, a prior knowledge-needless method namely clustering weighted envelope spectrum (CWES) is proposed for rolling bearing fault diagnosis. Firstly, based on the algorithms of peak searching and multiple relation checking, the potential FCFs (PFCFs) of each spectral frequency slice (SFS) of SCoh are automatically identified without any prior knowledge. The PFCFs of each SFS are regarded as its fault type label and are used to design a weight to evaluate its fault information abundance. Then, the SFSs with similar labels are clustered and other SFSs are ignored. Each cluster is considered to be associated with a potential cyclostationary component, and the importance of all clusters is sorted based on their maximum weights. Finally, to further enhance the fault characteristics, CWESs are defined as the weighted average of the SFSs in each top-ranked cluster. By using this method, the discrete informative frequency bands of multiple faults can be quickly located without prior FCFs and iterative optimization. The advantages of CWES over the state-of-the-art methods are validated by the experimental data of bearing single and compound faults. The results indicate that CWES has the best completeness in fault information extraction and the highest accuracy of fault diagnosis compared with other methods. Moreover, the robustness and computational efficiency of the proposed method are also advantageous.Note to Practitioners—This paper is motivated by the problems of discrete frequency band localization and compound fault separation in the field of rolling bearing fault diagnosis. Different from other prior FCF-oriented methods, we design a prior knowledge-needless algorithm to identify the PFCFs of each SFS of the SCoh. The PFCFs of each SFS can not only indicate the fault type but also quantify the abundance of fault information. Based on the identified PFCFs, several CWESs can be generated for fault diagnosis through the clustering algorithm and the weighted mechanism. Our experimental results show the proposed method has higher diagnostic accuracy than the existing methods.

Landslide susceptibility prediction is critical in open pit mines and geotechnical fields. Prediction accuracy is very essential to reduce the risk of slope instability. Traditional statistical learning methods have been widely used in early warning systems, but they cannot thoroughly explore the coupling effect among related factors, which often results in low prediction accuracy. This paper establishes an ensemble learning prediction model optimized by a genetic algorithm (GA) to determine landslide susceptibility more quickly and efficiently. The model is based on 290 sets of slope cases containing height (H), slope angle (α), unit weight (γ), cohesion (c), friction angle (φ), and pore water pressure (ru). Two common algorithms are incorporated into the ensemble learning model: Xgboost and gradient boosting decision tree (GBDT). The area under the curve (AUC) of GA‐GBDT and GA‐Xgboost were found to be 0.928 and 0.933, respectively, both of which could predict landslide susceptibility better. Compared with multiple logistic regression and other machine learning algorithms, both GA‐GBDT and GA‐Xgboost models perform better in terms of accuracy and applicability. The study results demonstrate that the developed optimized machine learning model can accurately predict landslide susceptibility and that the parameters should be optimized on a case‐by‐case basis to achieve more accurate results after building a suitable machine model. The optimization model proposed in this paper can be an effective new method for the intelligent prediction of landslide susceptibility.

A milling cutter is one of the most important parts of machine tools. Its working status significantly influences the precision of workpiece. Due to the complex wear mechanism, the single sensor may be difficult to acquire the complete degradation information of milling cutters. Therefore, in this article, a feature learning based method is proposed to automatically extract features from multisource data and predict the remaining useful life of cutting tools in real time. First, a statistic-based method is constructed to detect and delete the outliers hidden in the monitoring data. Second, the clean data are input into a multiscale convolutional attention network (MSAN) to learn features and fuse multisource data. At last, the fused data are used to predict the remaining useful life of cutting tools in a regression layer. Compared with traditional tool life prediction methods, the proposed method is able to fuse multisource data through an attention feature learning model to conduct the life prediction of tools. Additionally, the data cleaning and model optimization methods are also proposed to promote engineering practicability. To validate the effectiveness of such method, the life testing experiments on milling cutters are conducted to obtain run-to-failure data. In those experiments, multisensor monitor data are acquired, which are used to conduct validation experiments testing the effectiveness of the proposed method. The results indicate the superiority of the proposed method in remaining useful life prediction milling cutters.

In the industrial applications of mechanical fault diagnosis, machines work in normal condition at most time. In other words, most of the collected datasets are highly imbalanced. Although deep learning has been widely applied in intelligent diagnosis, it is unsuitable for such imbalanced situation. In addition, few studies attempted to determine the parameters in the diagnosis models. For solving such problems, Pareto-optimal adaptive loss residual shrinkage network (PALRSN) is proposed. First, a fixed length-based encoding method is implemented to represent the candidate architectures of PALRSN. Then, multiply accumulate operations and Gmean value representing the model complexity and identification performance, respectively, on imbalanced datasets are selected as the optimization targets to search for the optimal PALRSN architecture. In the training process, an adaptive loss function assigns different misclassification costs on all categories according to their number discrepancy to highlight the minority samples. The proposed method is validated by bearing data and milling cutter data with different imbalanced ratio. The experimental results demonstrate that such approach outperforms the state-of-the-art methods in imbalanced classification.

Some recent work proposed differential private collaborative filtering (DPCF) recommender systems to protect user privacy from indirect access attacks, e.g., KNN attacks. As the cost of such protection, the MAE of recommendation was retained with insignificant increase but the more focused metrics, i.e., the precision and recall of top-k recommendations, degraded unacceptable. To address this problem, we propose a DPCF framework based on privacy-relevance of topics, named DPCFT. Firstly, DPCFT works on topic-preference level which highly aggregates user behaviors and keeps the precision and recall of top-k differential private recommendations acceptable. More importantly, considering the unnoticed fact that the information leakage of some special topics worries users much more than that of other ones in terms of privacy concerns, DPCFT introduces the topic privacy-relevance level in the similarity computation and neighbor selection to impose stronger privacy protection on higher privacy-relevance topics with overall differential privacy and recommendation performance reserved. Finally, to reduce the recommendation performance cost for differential privacy, DPCFT selects the top-k recommendation items at user side further with personal topic-preference without risk of data expose. Experimental results on the MovieLens dataset verify that the proposed framework DPCFT preserves differential privacy and top-k recommendation performance simultaneously.

In online video systems, viewer demographic information (gender, age, etc.) is of huge commercial value for delivering targeted advertising and video recommendations, but generally not available directly. This paper targets inferring viewers' gender based on implicit watching history in the large-scale online video systems. To tackle the sparsity problem without filtering out any cold users or videos, we not only introduce video tags as features, but also use an efficient Chinese word segmentation method to extract hot key-words from video titles as features. Moreover, users' viewing behavior distribute lognormally, hence we apply a logarithmic transformation on the inference matrixes and further find key features via principal components analysis (PCA). We then solve the gender inference as a classification problem and define some modified evaluation metrics adapt to the imbalance classification problem. We compare a set of classifiers including Class prior, EM, SVM, Logistic regression, Partially supervised soft-label and belief-based mixture and find that Logistic regression is the best. The inference results show that our algorithms can obtain high F̃1values for all classes. The highest value of PPTV dataset can reach nearly 0.75. And inference based on key-words results in a 14.63% increase of F̃1contrast to the ratings of MovieLens.