Efficient information extraction enhances condition monitoring and fault diagnosis for bearings. The graph model (GM) has been proven to be a practical approach to extracting signal information within the temporal dynamic of frequencies. This paper proposes an early fault detection method based on graph entropy (GE) for the dynamical bearing degradation process, considering the structure differences between graph dynamic changes. First, the complete graph model (CGM) is constructed by a short-time spectrum generated from the original signal. In the fault detection phase, the GE, highly correlated with the health condition, is extracted from the GM to check any change in the machine state. Subsequently, the adaptive threshold of short-term month-over-month is used to judge the final decision-making in an automated way. Finally, the validation experiment on the XJTU-SY dataset and FEMTO-ST dataset, as well as compared with the state-of-the-art demonstrates its excellent detection performance. The proposed method extracts an effective one-dimensional index, which affords an excellent detection ability on early fault occurring in noisy environments, indicating a good potential for identification in the practical dynamic operation of engineering applications.

Adapting detection to different shot types is a significant challenge for video summarization methods based on shot boundary detection. In our recent work, a new graph model was introduced in the feature modelling of frames and analysed for changes in graph structure to improve the detection of shot boundaries. In this paper, we further explore the potential of graph models and propose a more general framework for online, real-time automatic video summarization. The framework develops a novel adaptive multiview graph difference analysis method to improve the algorithm’s robustness in detecting different shot transitions. Previous fusion methods typically used a priori knowledge to assign weights to the various feature differences from videos. In contrast, our framework can weigh and fuse the resulting differences by learning the importance of various video features from the structural changes of the corresponding multiview graphs. Additionally, we propose a new threshold-based adaptive decision method which can dynamically select the most accurate shot boundary decision threshold by analysing a small number of historical frames and learning the tolerance factor in the current shot. The experimental results show that the proposed method outperforms state-of-the-art methods in terms of precision and F-score on the VSUMM and YouTube datasets.

Although the importance of remaining useful life (RUL) prediction is widely recognized in industries, its implementation in real scenarios is highly restricted by the complexity of the degradation mechanism, uncertainty of machinery, and insufficiency of prior knowledge. To address such a challenge, this article proposes a model-based framework, which has the capability to integrate multiple predictive models via a probabilistic mechanism. When a new observation is fed into each predictive model, the posterior distribution of each model will be updated via Bayesian inference. Then, a grid-sampling strategy is applied to their posterior distributions for identifying the “peak” and “profile,” which are used for RUL prediction and reliability assessment, respectively. The effectiveness of this framework is validated with the experiments on a set of steel tension specimens. Theoretical interpretations and comparative studies demonstrate the superiority of the proposed framework. Besides, the proposed framework can not only reduce human workload on trivial parameter setting but also be effective with insufficient prior knowledge, making the intelligent RUL prediction easier.

Early detection of changes, that are from normal to abnormal in dynamical degradation process, is essential to enhance the function of bearings during their long-term service time. This article presents a new and practical method for this problem based on hierarchical graph model (HGM) coupled with adaptive inputs weighting (AIW) fusion. Hierarchical decomposition (HD) is first adopted on the raw vibration signal, such that the inspection of bearing health condition can be allowed over different multiple scales. HGMs are then constructed from each decomposed components that are resulted from HD. AIW is employed to aggregate all constructed HGMs by taking into account their difference in characterization of dynamical bearing health condition. A common hypothesis testing is finally adopted to make decision. Experiments are conducted on two publicly available datasets. Results demonstrate the effectiveness of the proposed method; meanwhile, comparisons with benchmarking methods suggest its good potential in real applications.

To address the problem that many existing approaches are not appropriate for action recognition in low-resolution (LR) videos, this paper presents a framework based on the Dempster–Shafer (DS) theory for this purpose. In the framework, artificial neural networks (ANNs) are firstly trained for every class with training samples, and then basic belief assignments (BBAs) for underlying classes are computed with the trained ANNs. The resulted BBAs are fused from all frames in the whole video sequentially by frame-by-frame based on DS’s rule of fusion. Action recognition is last performed with a threshold-based decision making. We conducted experiments on extensive testing data with various levels of video resolution. Results reveal that the proposed framework: (1) shows outperforming recognition performances compared with state-of-the-art classifications, respectively, such as sequence matching, voting-based strategy and bag-of-words (BoW) method; and (2) can achieve a low observational latency in recognition.

Detection of structural changes in machine state attracts increasing attention for the monitoring and prognosis of rotating machinery. Very recently, the graph model has been introduced and adopted for modeling of normal machine state so as to investigate the likelihood of potential changes by the use of Martingale-test. This paper expands the potential of the graph model toward correlation-analysis-based monitoring applications, where the problem of measuring graph similarity is the main challenge due to domain specificity. We first investigated six typical schemes taken from other areas for this purpose, and found that they show discriminative capacities when dealing with changes with different types in the machine condition monitoring scenarios. Subsequently, based on a procedure called “method of ranks,” we have chosen four schemes among them which are potentially promising for our usage. Meanwhile, based on these metrics, two machine learning based similarity metrics are proposed to further improve their practical values by combining them. At last, comprehensive theoretical interpretations and comparisons of presented methods are made both in simulated scenarios and real-engineering monitoring applications.

Current QoS-aware automatic service composition queries over a network of web services are often one-time in nature. After a network of web services is built, such queries are issued once, and answers are found from the scratch. The underlying assumption is that the participating web services are rather static, which means that their functional and non-functional parameters seldom change. However, such an assumption is often baseless. New services come and go, service APIs change gradually, and QoS values fluctuate in practice. Therefore, a support for efficiently adapting service composition is desired. In this paper, we propose an event driven continuous query algorithm to intelligently cope with different types of dynamic services, and thus enable evolution of service composition. Evaluation using both real QoS data and synthetic web service data shows its superior performance, compared with the state-of-the-art solution which won the performance championship of Web Service Challenge in 2009 and 2010. Moreover, we generalize a new graph problem: dynamic single-source optimal-Directed Acyclic Graphs (DAG) problem based on the above work. It is argued that API recommendation for framework evolution, as another typical application, could also be modeled and addressed efficiently using the proposed new graph approach.