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As commonly used components in rotating machinery, rolling element bearings (REBs) can fail due to complex working conditions and high-speed rotation. The failure of bearings may cause great damage. It is necessary to identify the faults of bearings to prevent property losses and heavy casualties. This paper proposes a fault diagnosis approach based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and Bayesian network. The intrinsic mode functions (IMFs) extracted by ICEEMDAN algorithm are applied to construct feature vectors based on the energy entropy, and then the fault diagnosis model of the bearing is constructed by Bayesian network. The influence of load and sampling frequency on diagnostic accuracy of the bearing with different fault types is studied in this paper. And the research results show that the ICEEMDAN-BN method can improve the uncertainty reasoning ability and accuracy of the developed fault diagnosis model. [ABSTRACT FROM AUTHOR]

Oil spills are serious threats to the marine ecosystem. Especially when an oil spill is faced with extreme weather, the consequences might be more severe. Until now, no such researches focus on the risk of these extreme scenarios. This paper proposes a novel dynamic assessment method to quantify the risk of oil spills in extreme winds based on dynamic Bayesian networks (DBNs). The physical models of advection, spreading, evaporation, and dispersion are transformed into DBNs, and the vulnerability model is established according to coastline types and socio-economic resources. By integrating all the sub-models, the overall DBN to quantify the dynamic risk of oil spills occurring in extreme winds is obtained. The proposed method is demonstrated by the Laizhou Bay. The developed model is validated by a three-axiom-based approach. Temporal and spatial dynamics of risk caused by oil spills in potential locations could be calculated. Based on the developed DBN, the risk of the Laizhou Bay coast caused by oil spills in annual extreme wind speeds corresponding to different mean recurrence intervals is studied. In addition, the effects of the occurrence time of annual extreme winds are also researched. [Display omitted] • A dynamic and quantitative risk assessment method for oil spills in extreme winds is proposed. • DBNs of advection, spreading, evaporation and dispersion are developed. • Vulnerability model is built based on coastline types and socio-economic resources. • Temporal and spatial dynamics of oil spill risk in annual extreme winds are explored. • Risks caused by annual extreme wind speeds of different mean recurrence intervals are researched. [ABSTRACT FROM AUTHOR]

Bayesian network (BN) is a widely used formalism for representing uncertainty in probabilistic systems and it has become a popular tool in reliability engineering. The GO-FLOW method is a success-oriented system analysis technique and capable of evaluating system reliability and risk. To overcome the limitations of GO-FLOW method and add new method for BN model development, this paper presents a novel approach on constructing a BN from GO-FLOW model. GO-FLOW model involves with several discrete time points and some signals change at different time points. But it is a static system at one time point, which can be described with BN. Therefore, the developed BN with the proposed method in this paper is equivalent to GO-FLOW model at one time point. The equivalent BNs of the fourteen basic operators in the GO-FLOW methodology are developed. Then, the existing GO-FLOW models can be mapped into equivalent BNs on basis of the developed BNs of operators. A case of auxiliary feedwater system of a pressurized water reactor is used to illustrate the method. The results demonstrate that the GO-FLOW chart can be successfully mapped into equivalent BNs. [ABSTRACT FROM AUTHOR]

A framework for the reliability evaluation of grid-connected PV (photovoltaic) systems with intermittent faults is proposed using DBNs (dynamic Bayesian networks). A three-state Markov model is constructed to represent the state transition relationship of no faults, intermittent faults, and permanent faults for PV components. The model is subsequently fused into the DBNs. The reliability and availability of three simple PV systems with centralized, string, and multistring configurations, as well as a complex PV system, are analyzed through the proposed framework. The sequence of the degree of importance of PV components is investigated using mutual information. The effects of intermittent fault parameters, including the coefficients of intermittent fault, permanent fault, and intermittent repair, on the reliability and availability are explored. Results show that the reliability and availability of the PV system with centralized configuration rapidly decrease, compared with those of the PV systems with string and multistring configurations. The sequence of the degree of importance of PV components is DC/AC inverter, DC/DC converter, DC combiner, and PV module arranged from the largest to the smallest. The finding indicates that the DC/AC inverter should be given considerable attention to improve the reliability and availability and to prevent their possible failures. [ABSTRACT FROM AUTHOR]

A novel real-time reliability evaluation methodology is proposed by combining root cause diagnosis phase based on Bayesian networks (BNs) and reliability evaluation phase based on dynamic BNs (DBNs). The root cause diagnosis phase exactly locates the root cause of a complex mechatronic system failure in real time to increase diagnostic coverage and is performed through backward analysis of BNs. The reliability evaluation phase calculates the real-time reliability of the entire system by forward inference of DBNs. The application of the proposed methodology is demonstrated using a case of a subsea pipe ram blowout preventer system. The value and the variation trend of real-time system reliability when the faults of components occur are studied; the importance degree sequence of components at different times is also determined using mutual information and belief variance. [ABSTRACT FROM AUTHOR]

This paper proposes a fault diagnosis methodology for a gear pump based on the ensemble empirical mode decomposition (EEMD) method and the Bayesian network. Essentially, the presented scheme is a multi-source information fusion based methodology. Compared with the conventional fault diagnosis with only EEMD, the proposed method is able to take advantage of all useful information besides sensor signals. The presented diagnostic Bayesian network consists of a fault layer, a fault feature layer and a multi-source information layer. Vibration signals from sensor measurement are decomposed by the EEMD method and the energy of intrinsic mode functions (IMFs) are calculated as fault features. These features are added into the fault feature layer in the Bayesian network. The other sources of useful information are added to the information layer. The generalized three-layer Bayesian network can be developed by fully incorporating faults and fault symptoms as well as other useful information such as naked eye inspection and maintenance records. Therefore, diagnostic accuracy and capacity can be improved. The proposed methodology is applied to the fault diagnosis of a gear pump and the structure and parameters of the Bayesian network is established. Compared with artificial neural network and support vector machine classification algorithms, the proposed model has the best diagnostic performance when sensor data is used only. A case study has demonstrated that some information from human observation or system repair records is very helpful to the fault diagnosis. It is effective and efficient in diagnosing faults based on uncertain, incomplete information. [ABSTRACT FROM AUTHOR]

In this paper, a novel approach of developing the Bayesian network for fault diagnosis based on operation procedures is presented. The proposed Bayesian network consists of operation procedure layer, fault layer and fault symptom layer. First, operation procedure layer containing procedure nodes and state decision nodes is developed. Second, the fault layer is determined based on the state decision nodes in the operation procedure layer. Then fault symptom layer including symptoms sensitive to the concerned faults is developed. Finally, the entire Bayesian network is established by integrating the three layers. The presented approach is applied to hydraulic control system of subsea blowout preventer (BOP). Taking an example of closing the BOP, the operation procedures are illustrated. The entire Bayesian network for fault diagnosis of closing the BOP is established. Several cases possible to appear during the closing process are studied to evaluate the developed model. [ABSTRACT FROM AUTHOR]

Highlights: [•] A multi-source information fusion based fault diagnosis methodology is proposed. [•] The diagnosis model is obtained by combining two proposed Bayesian networks. [•] The proposed model can increase the fault diagnostic accuracy for single fault. [•] The model can correct the wrong results for multiple-simultaneous faults. [Copyright &y& Elsevier]

Highlights: [•] The reliability and availability of subsea BOP system are evaluated. [•] The fault tree is translated into dynamic Bayesian network directly. [•] The imperfect repair is modeled using multi-state degraded system. [•] The imperfect repair cannot degrade the system performances significantly. [•] Eight basic events contribute much to subsea BOP system failure. [ABSTRACT FROM AUTHOR]

Abstract: The work presents a dynamic Bayesian networks (DBN) modeling of series, parallel and 2-out-of-3 (2oo3) voting systems, taking account of common-cause failure, imperfect coverage, imperfect repair and preventive maintenance. Seven basic events of one, two or three component failure are proposed to model the common-cause failure of the three-components-systems. The imperfect coverage is modeled in the conditional probability table by defining a coverage factor. A multi-state degraded component is used to model the imperfect repair and preventive maintenance. Using the proposed method, a DBN modeling of a subsea blowout preventer (BOP) control system is built, and the reliability and availability are evaluated. The mutual information is researched in order to assess the important degree of basic events. The effects of degradation probability, failure rate and mean time to repair (MTTR) on the performances are studied. The results show that the repairs and maintenance can improve the system performance significantly, whereas the imperfect repair cannot degrade the system performance significantly in comparison with the perfect repair, and the preventive maintenance can improve the system performance slightly in comparison with the imperfect repair. In order to improve the performance of subsea BOP control system, the single surface components and the components with all-common-cause failure should given more attention. The influence of degradation probability on the performance is in the order of PLC, PC and ES. The influence of failure rate and MTTR on the performance is in the order of PLC, ES, PC, DO, DI and AI. [Copyright &y& Elsevier]

This article proposes a methodology for the application of Bayesian networks in conducting quantitative risk assessment of operations in offshore oil and gas industry. The method involves translating a flow chart of operations into the Bayesian network directly. The proposed methodology consists of five steps. First, the flow chart is translated into a Bayesian network. Second, the influencing factors of the network nodes are classified. Third, the Bayesian network for each factor is established. Fourth, the entire Bayesian network model is established. Lastly, the Bayesian network model is analyzed. Subsequently, five categories of influencing factors, namely, human, hardware, software, mechanical, and hydraulic, are modeled and then added to the main Bayesian network. The methodology is demonstrated through the evaluation of a case study that shows the probability of failure on demand in closing subsea ram blowout preventer operations. The results show that mechanical and hydraulic factors have the most important effects on operation safety. Software and hardware factors have almost no influence, whereas human factors are in between. The results of the sensitivity analysis agree with the findings of the quantitative analysis. The three-axiom-based analysis partially validates the correctness and rationality of the proposed Bayesian network model. [ABSTRACT FROM AUTHOR]

Abstract: The Bayesian network models of redundant systems including parallel system and voting system, taking account of common cause failure and imperfect coverage, are proposed. The Triple Modular Redundancy (TMR) and Double Dual Modular Redundancy (DDMR) control systems for subsea Blowout Preventer (BOP) are presented. By applying the proposed Bayesian network models, the reliability of subsea BOP control systems are evaluated at any given time, and the difference between posterior and prior probabilities of each single component given the system failure is obtained. The effects of coverage factor of redundant subsystem and failure rate of single component on reliability of systems are also researched. The results show that the DDMR control system has a little higher reliability than TMR system. To improve the reliability of subsea BOP control systems, the component failure rates of Ethernet switch (ES), programmable logic controller (PLC) and personal computer (PC) should be reduced for TMR system, whereas the failure rates of ES and PC should be reduced for DDMR system. The recovery mechanism of PLC, PC and ES subsystems, and PC and ES subsystems should be paid more attention for TMR and DDMR control systems, respectively. [Copyright &y& Elsevier]

• A novel method is proposed to reduces degradation prediction error. • A algorithm fully considering parameter uncertainties is developed. • The ability for parameter analysis and RUL updates has been strengthened. Remaining useful life (RUL) is the critical goal of fault prediction, which provides theoretical support for subsequent maintenance decisions of a system. The state measurement of industrial equipment is often accompanied by a large amount of random noise. In addition, the parameters of the degradation are often random. This kind of uncertainty makes carrying out RUL prediction difficult. To this end, a novel hybrid model-data-driven RUL prediction method based on a fusion of Kalman filter (KF) and dynamic Bayesian network (DBN) is proposed in this paper. The hybrid DBN-KF-based method provides a more comprehensive evaluation and improves accuracy compared with the traditional KF method. By enhancing the performance of observation values through DBN, the optimal estimation of the system state is implemented. Estimation error and observation error are fully considered. In addition, the uncertainty distribution of degradation parameters and environmental parameters is integrated into the state estimation model. RUL is determined based on the system state calculated by the proposed method and the failure threshold obtained by the system characteristics. Numerical simulation is carried out for a subsea Christmas tree valves to demonstrate the advantages of the proposed RUL prediction method. [ABSTRACT FROM AUTHOR]

Risk management is crucial for the safety of offshore platforms. With advancements in safety management, much data on hidden dangers has been generated. The data contains a wealth of risk information, which realistically reflects the current state of safety on offshore platforms. However, this data is unstructured natural language, which cannot be analyzed statistically or queried with structured fields. Moreover, the manual identification of risk factors from hidden danger data is time-consuming and subject to subjective biases. A method for risk identification and assessment for offshore platform equipment and operations based on text mining of hidden danger data is proposed. From a big data perspective, a framework for expressing risk factors is established, which completes the transition from an unstructured text about the hidden danger to a structured expression of risk information. An automated method for risk identification is developed that combines machine learning, deep learning, and natural language processing techniques. The identified risk factors are used to build the Bayesian network model for data-driven risk assessment. Data from China's Bohai oil field is used to demonstrate the effectiveness of this method. The result shows that it improves the identification of risk factors and the automation of the assessment. [ABSTRACT FROM AUTHOR]

Drilling well control is complex and dynamic with high uncertainty because of the complicated geological conditions and operational environments. Until now, quantitative risk assessment on drilling well control is not integrating human errors, equipment failure, and internal mechanisms together. To address the research gap, this paper presents a novel dynamic quantitative risk assessment method for drilling well control by integrating multi types of risk factors. The entire dynamic Bayesian network is composed of models of human factors, mechanical factors, and environmental factors. The presented method is demonstrated by a case of well section. The dynamic characteristics of the overall risk and individual risks caused by different types of factors are obtained. The influences of human, mechanical and environmental factors on drilling well control risk are researched. It shows that environmental factors have the highest influence on the whole drilling well control risk in Breccia formation. The parameters uncertainty analysis on collapse pressure and fracture pressure of three different rock formations are carried out. It is found out that in-situ stresses have the highest influence on the collapse and fracture pressure. [Display omitted] [ABSTRACT FROM AUTHOR]

Hydraulic control is a control pattern that uses compression fluid as the energy medium and information medium. Hydraulic system is widely used in the control of industrial systems because of its flexibility and reliability. Hydraulic systems have the characteristics of strong fault concealment, significant sensor delay, and a complex signal transmission mechanism. Hence, it is very difficult to identify the faults of systems under the influence of powerful nonlinear time-varying characteristic. The diagnosis of complex hydraulic control system is a problem facing the current research. In order to cope with the challenges caused by limited sensors and concealment of faults, a three-model-driven fault diagnosis method is proposed for complex hydraulic control system. The three hydraulic control system models with regard to energy, fluid and information are proposed and established to explain the work process of the system from different perspectives. The diagnostic model is completed by establishing a fault reasoning model based on the Bayesian network. A redundant control system for subsea blowout preventer is used as a case to demonstrate the proposed method, and the results show that the proposed method has high accuracy. [ABSTRACT FROM AUTHOR]

Electric-hydraulic control system is widely used in subsea oil and gas drilling and production equipment because of its excellent performance. Efficient fault diagnosis of electrical control system helps improve economic benefits and safety of operators. The multi-component characteristics of electro-hydraulic control system make concurrent fault diagnosis a challenging problem. A model-driven method using the Bayesian network and D-S evidence theory is proposed in this paper to diagnose concurrent faults of the electro-hydraulic control system. The electro-hydraulic control system diagnosis problem is divided into multiple fault diagnostic sub-models based on the analysis of system structure and work process. A sub-model is established based on OOBNs for preliminary diagnosis and reasoning. The concurrent fault diagnosis and reasoning model is established based on the D-S evidence theory. The evaluated fault probability and diagnostic belief degree of each fault are combined. Fault identification rules combined with fault probability and diagnostic belief are established. A control system for a subsea blowout preventer is used as a case to demonstrate the proposed method. • Preliminary diagnosis and reasoning sub-models are established based on OOBNs. • The D-S evidence theory is used for further evidence fusion. • The proposed method performs excellent accuracy on diagnosing concurrent faults. [ABSTRACT FROM AUTHOR]

Oil spills pose a serious threat to the ecological environment and human health. When the spilled oil enters seawater, oil weathering processes like evaporation, emulsification, biodegradation, photooxidation and dissolution will occur. These processes have high uncertainty and directly affect the spatial distribution and fate of oil spills. This paper presents a stochastic modeling method for weathering process of spilled oil on the sea surface based on Dynamic Bayesian Network (DBN). The physical equations of evaporation, emulsification, biodegradation, photooxidation and dissolution are converted into DBN, and then all the individual DBNs are integrated to obtain the entire DBN of the oil weathering process. The developed DBN could intuitively and graphically express the logical relations and conditional probabilities among physical variables. This method is helpful to find out the influencing mechanism of multi-random variables on the oil weathering process. The proposed method is applied to one type of light oil that is spilled on the sea surface of the Bohai Sea. Based on the developed DBN model, the oil weathering process in the first 18 h are studied by quantitative analysis. Through sensitivity analysis, it shows that the photooxidation and dissolution have greater impact on the heavy oil fraction than biodegradation. For oil-water mixture, it is greatly affected by evaporation in the first few hours, but emulsification becomes the leading weathering process in the 18th hour. The effects of dissolution are low, but higher than biodegradation and photooxidation. The key parameters affecting each oil weathering process are determined. Uncertainty analysis is carried out to reveal the possible upper and lower limit values of some physical quantities caused by the weathering process. • Modeling method for weathering process of spilled oil based on DBN is proposed. • DBNs of five types of weathering process are developed. • Individual DBNs are integrated to obtain the entire DBN of the weathering process. • Sensitivity analysis is used to find the key parameters for each weathering process. • Uncertainty analysis shows the possible upper and lower limit values of variables. [ABSTRACT FROM AUTHOR]

Leakages in subsea pipelines can have serious environmental and economic consequences, making it essential to develop reliable evaluation methods to prevent failures. Up to now, no comprehensive assessment methods focusing on parameter uncertainty of lifetime distribution functions in the dependent competing failure of subsea pipelines has been developed. This paper proposes a novel method for evaluating the reliability of subsea pipelines with dependent competing failure processes and parameter uncertainty, using dynamic Bayesian network (DBN). The independent variables and dependent variables of physical models are converted into parent nodes and child nodes of DBNs, respectively. Uncertain parameters of the degradation failure and sudden failure equations are denoted by root nodes and assigned to probability distributions in the DBN model. The interaction between degradation failure and sudden failure of subsea pipelines is studied, and two DBNs of competing failure based on variable degradation increment and variable degradation rate are established. Mutual information analysis finds out the important parameters affecting the reliability of subsea pipelines. The parameters in degradation failure model have little effects on the failure of subsea pipelines at first, but their impact is increasing over time. According to 3-sigma rule, three different situations are defined to perform uncertainty analysis of parameters in the developed DBN model. It shows that increasing the values of uncertain parameters, the reliability of the subsea pipelines would have obvious decrease earlier. • A DBN-based modeling method for dependent competition failure process is proposed. • The interaction between degradation failure and sudden failure of subsea gas pipelines is studied. • The effects of parameter uncertainty on dependent competing failures are studied. • Mutual information analysis finds out the important parameters affecting the reliability. [ABSTRACT FROM AUTHOR]

• A RUL prediction method for control systems based on statistical law is proposed. • DUKF can alleviate the instability problem and optimize the RUL prediction process. • The proposed method has high relative precision and can suppress process noise. With the improvement of control system composition and operation process complexity, the uncertainty in its operation process increases and real-time observation data is difficult to obtain, and the influence of noise also exists in the process of signal acquisition, which brings more difficulties to the prediction of the remaining useful life (RUL). To solve these problems, a hybrid multi-stage methodology for RUL prediction of control system is proposed. The variant of unscented Kalman filter (UKF) utilizes dynamic Bayesian networks (DBNs) for uncertainty analysis in the process of prediction using UKF, to analyze RUL of nonlinear degenerate systems. In the prophase of prediction, the dynamic unscented Kalman filter models calculate the distribution of random faults and process noise, match the degradation stage of the system and obtain the operation data. Then, optimize the degradation process of the system, and the covariance and the optimal estimate of the system are calculated by cyclic iteration. The real degradation process of control system is simulated by optimizing the results, so as to compensate for the lack of accurate measurement of the real degradation process. The proposed method can improve the accuracy of RUL prediction and enhance the robustness of the prediction model. The methodology is verified by subsea Christmas tree with electro-hydraulic compound control. [ABSTRACT FROM AUTHOR]

Interactions exist in the performance degradation of the systems because of uneven distribution of working pressures of components. Actually, even the same two components have different degradation processes under the natural degradation or external impact. For a complex system, even if single degradation process of components is known, it is still difficult to evaluate its overall performance. The remaining useful life (RUL) of the system is also tough to estimate due to the effects of interactions. This paper contributed a multi-stage model-based RUL estimation approach considering interactions. The framework of the approach is based on Bayesian networks (BNs). On the one hand, BNs is used to identify and match the system degradation stage, and on the other hand, it is used to deal with the uncertainty of parameters in the RUL estimation. Multi-stage degradation and interaction coefficients are the two most important tasks to complete the RUL estimation considering interactions. The RUL are obtained from the time difference from healthy operation to failure. An electro-hydraulic subsea Christmas tree is adopted to demonstrate the proposed approach. The results show that the approach can effectively evaluate the system performance and RUL under the influence of interactions. • A multi-stage model-based RUL estimation approach for subsea Christmas tree is proposed. • The multi-stage degradation model with an indicator of performance is developed. • The modeling process of influence relationship of interactions is researched. [ABSTRACT FROM AUTHOR]

Proper installation of deepwater oil and gas equipment is a prerequisite to ensure its service life. Deepwater equipment faces many challenges during installation, such as the complexity of the installation process and uncertainty of the environment. Thus, risk assessment is necessary to guide personnel in avoiding and addressing potential problems during the installation of deepwater equipment. This study proposes a quantitative risk assessment method of installation process for deepwater oil and gas equipment based on fuzzy Bayesian networks (FBNs). First, the flow chart of deepwater oil and gas equipment in installation process is translated into a main Bayesian networks (BNs). Second, risk factors in different stages of installation are determined from human, management, equipment, environment and third-party interference, and entire risk assessment model is established. Finally, the FBN model is quantitatively analyzed by introducing fuzzy set theory. An empirical study on the installation process of subsea blowout preventer (BOP) is conducted. The results show that the failure of the subsea BOP installation is a small probability event, and equipment factors have the most prominent effect on the installation process. The sensitivity analysis results are consistent with the quantitative analysis. The corresponding improvement measures are proposed according to the analysis results. [ABSTRACT FROM AUTHOR]