Zengkai Liu

Using Bayesian networks in reliability evaluation for subsea blowout preventer control system

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.

Application of Bayesian Networks in Quantitative Risk Assessment of Subsea Blowout Preventer Operations

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.

Development of an automatic subsea blowout preventer stack control system using PLC based SCADA.

An extremely reliable remote control system for subsea blowout preventer stack is developed based on the off-the-shelf triple modular redundancy system. To meet a high reliability requirement, various redundancy techniques such as controller redundancy, bus redundancy and network redundancy are used to design the system hardware architecture. The control logic, human-machine interface graphical design and redundant databases are developed by using the off-the-shelf software. A series of experiments were performed in laboratory to test the subsea blowout preventer stack control system. The results showed that the tested subsea blowout preventer functions could be executed successfully. For the faults of programmable logic controllers, discrete input groups and analog input groups, the control system could give correct alarms in the human-machine interface.

Real-time reliability evaluation methodology based on dynamic Bayesian networks: A case study of a subsea pipe ram BOP system.

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.

Performance evaluation of subsea BOP control systems using dynamic Bayesian networks with imperfect repair and preventive maintenance

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.

A Fault Diagnosis Methodology for Gear Pump Based on EEMD and Bayesian Network.

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.

An approach for developing diagnostic Bayesian network based on operation procedures

A method to develop Bayesian network based on operation procedures is proposed.The diagnostic model consists of faults, symptoms and operation procedures.The presented approach is applied to a case study of closing BOP. 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.

Experimental study of galvanic corrosion behaviour of carbon fibre composite coupled to aluminium in artificial seawater

Abstract The galvanic corrosion behaviours of carbon fibre composite coupled to aluminium are studied when the galvanic couple is just immersed in artificial seawater or connected to a closed electric circuit. The effects of grinding condition, concentration of artificial seawater, applied torque, applied current and experimental time are studied. The roughness average, weight gain of carbon fibre composite and weight loss of aluminium are investigated as a function of the above variables. The results show that the applied current can accelerate the galvanic corrosion greatly. With improving grinding condition and increasing applied torque, the roughness average, weight gain of carbon fibre specimen and weight loss of aluminium specimen increase, reach maximum and then decrease. With increasing concentration of artificial seawater, applied current and experimental time, these measured values increase. Corroded surface morphology is also investigated using scanning electron microscopy.

Application of Petri nets to performance evaluation of subsea blowout preventer system

This paper presents an application of deterministic and stochastic Petri nets (DSPN) to evaluate the performance of subsea blowout preventer (BOP) system. The overall subsea BOP system is comprised of five mechanical subsystems and five electrical subsystems, which can be viewed as a series-parallel system. In regard to common cause failures, TimeNET 4.0 toolkit is utilized to develop and analyze the DSPN models. Availability and reliability of the subsea BOP system and its subsystems are obtained. Besides, the effects of failure rate and repair time of each component on system performance are researched.

Sensitivity thermal analysis of electrical discharge machining process based on probabilistic design system

The probabilistic design system is introduced for the sensitivity analysis to analyze the effects of machining parameters during electrical discharge machining process. An axisymmetric finite element thermal model is presented to investigate the electrical discharge machining process. By comparing the discharge crater geometry for the finite element method results and experiment results under different conditions, the deterministic thermal model is proved to be validated. Monte Carlo simulation method and response surface method are both used in the sensitivity analysis. Parameters of discharge voltage, peak current, pulse-on time and discharge channel radius are selected as the design variables. The sensitivity analysis results meet the confidence limit of 0.95. It is concluded that the discharge voltage and peak current have significant influences on the electrical discharge machining process, whereas the pulse-on time and discharge channel radius have little influence. Moreover, the increase in discharge channel radius can reduce the material removal rate. The increase in other parameters can increase the material removal rate.