This paper suggests the correlation information, hidden in spatial configuration and temporal dynamic of frequencies, is an important indication for bearing health condition. To consider this information, we extend graph-modeling strategy, and introduce a bearing fault detection and diagnosis technique based on temporal-spatio graph. First, short-time periodogram is extracted from vibration signal, and then modeled by a temporal-spatio graph. In fault detection phase, the spectrum of temporal channel graph is used to map short-time periodogram to acquire the so-called graph-mapped spectrum (GMS). The principal frequency in resulting GMS is found highly related with the health condition of monitored bearing. Thus, any change of health condition can be detected by checking this principal frequency over time. Once a fault is detected, the spatio channel graph is fed to K-Nearest Neighbor classifier, coupled with a specific graph distance metric, for fault type identification. Comprehensive experiments on two benchmarking data sets along with theoretical interpretation demonstrate the superiority of proposed method over state-of-the-arts. The proposed temporal-spatio graph provides a signif- icant extension of existing spectrum analysis for fault detection and diagnosis.

Pipelines are economical and efficient modes of transporting oil and gas. Pipelines will inevitably confront various risk factors through their lifespan, which could lead to defects. Defects in pipelines can compromise the integrity of the pipeline systems and may result in catastrophic accidents. Thus, it is vital to conduct the integrity assessment of pipelines so that the safe operation of the pipelines can be ensured. Up to the present, widely used approaches for pipeline integrity assessment include defect characterization, growth rate prediction, and failure pressure evaluation. Although the theoretical developments of pipeline integrity assessment methods have yielded fruitful achievements and significantly benefit the industry practices, there is still a lack of a systematic review covering the whole process from data collection to model establishment of the pipeline integrity assessment. Therefore, a comprehensive review is conducted in this paper on the pipeline defect integrity assessment from the data modeling perspective. Firstly, the description of data required to construct pipeline defect integrity assessment models is presented, where the required data for modeling can be obtained from pipeline inspection measurements, monitoring sensors, testing experiments, etc. Then, different modeling techniques applied to pipeline integrity assessment are reviewed, which are classified into physics-based models, data-driven models, and multi-model fusion. Also, the advantages and limitations of these techniques are discussed. Finally, the possibility of applying the existing models to a digital twin of pipeline defect is explored. This paper aims to provide a guideline for researchers to select optimal models according to data availability and research requirements, which can benefit the research community, as well as, the industry.

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.

The intelligent fault diagnosis powered deep learning (DL) is widely applied in various practical industries, but the conventional intelligent fault diagnosis methods cannot fully juggle the manifold structure information with multiple-order similarity from the massive unlabeled industrial data. Thus, a new Multiple-Order Graphical Deep Extreme Learning Machine (MGDELM) algorithm for unsupervised fault diagnosis of rolling bearing is proposed in this study. Specifically, the developed MGDELM algorithm mainly contains two parts: i). one is unsupervised multiple-order feature extraction, the first-order proximity with Cauchy graph embedded is applied to extract the local structural information, and the second-order proximity is simultaneously employed for mining global structural information; ii). the other is used the unsupervised Fuzzy-C-Mean (FCM) into fault clustering built on the extracted multiple-order graph embedding features. Empirically, two cases of rolling bearing failure data validate the effectiveness of the proposed algorithm and fault diagnosis method. By jointly optimizing the multiple-order objective function, the proposed MGDELM algorithm can synchronously extract local and global structural information from the raw industrial data. This study also provides a novel promising approach for unsupervised fault diagnosis.