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Fault prognosis based on single model is generally inaccurate due to the varying working conditions. A multistage fault prognosis methodology combining stage identification with Bayesian networks (BNs) and time series approach with particular emphasis on the autoregressive moving average (ARMA) model is proposed to solve this problem. In the first stage, degradation data are identified, and outliers are marked by the Euclidean distance. Degenerate attributes of outliers are finely identified by BNs and matched to the corresponding model. In the second stage, the ARMA model is used for prognosis according to the results of the fine identification. Subsequently, the double-precision identification and ARMA submodel prognosis are carried out alternately throughout the prognosis process. Three degradation types of permanent magnet synchronous motor are simulated to verify the applicability of the method. Result shows that it can track the changes in the degradation in time and obtains better results. [ABSTRACT FROM AUTHOR]

Fault diagnosis for a solar assisted heat pump (SAHP) system in the presence of incomplete data and expert knowledge is discussed in this article. A method for parameter learning of Bayesian networks (BNs) from incomplete data based on the back-propagation (BP) neural network and maximum likelihood estimation (MLE), which is called BP-MLE method, is presented. The BP neural network is utilized to impute the missing data and the complete data sets are addressed with MLE to obtain the parameters of BN. A method for parameter estimation under incomplete expert knowledge based on BP neural networks and fuzzy set theory is also presented, which is called BP-FS method. Similarly, the missing information is imputed by the trained BP neural network. Fuzzy set theory is employed to quantify the parameters of BN based on complete qualitative expert knowledge. The presented methods are applied to parameter learning of diagnostic BN for a SAHP system with incomplete simulation data and expert knowledge. The developed BN can perform fault diagnosis with complete or incomplete symptoms. [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]

• A dynamic Bayesian network based fault diagnosis methodology of subsea Christmas tree considering component degradation and safety-fault is presented. • The diagnostic fault types consist of blocking, leakage and safety-fault. • The proposed methodology can aid engineers to identify the component faults and distinguish the fault types at different times accurately. A subsea Christmas tree (XT) is an extremely important part of a subsea production system. The safety-fault of subsea XT indicates that no major safety incidents are difficult to diagnose. To identify the faulty components and distinguishing the fault types, including the blocking, leakage, and especially safety-fault, we present a dynamic Bayesian networks (DBN)-based fault diagnosis methodology of subsea XT considering component degradation and safety-fault. As the performance of components degrades over time, the diagnosis results can differ at different times for the given identical fault symptoms. DBNs are established to model the dynamic degradation of components in a system under additional information by using the failure rate, and fault diagnosis is conducted through a backward analysis of DBNs. Three fault diagnosis cases of subsea XT system are investigated. In case 1, when safety-fault occur on surface control subsea safety valve (SCSSV) and production main valve (PWV) components, the absolute difference in the posterior and prior probabilities of safety-fault for SCSSV and PWV was >50%. In case 2, when the blocking and leakage occur in SCSSV and annular main valve (AMV) components, respectively, the absolute difference between the posterior and prior probabilities of blocking for the SCSSV was >30%, and the absolute difference between the posterior and prior probabilities of leakage for AMV was >30%. In case 3, when the fault occur in production control valve (PCV) and chemical injection valve 1 (CIV1) components, respectively, the absolute difference between the posterior and prior probabilities of leakage for PCV and CIV1 was >60%. Three fault diagnosis cases validate the accuracy and effectiveness of the proposed methodology. This method is appropriate in providing maintenance instructions to engineers. [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]

• A sensor placement methodology of hydraulic control system is proposed. • Discrete particle swarm algorithm is applied to optimize number and position of sensors. • The proposed methodology has an efficient convergence speed and high diagnostic efficiency. During the state monitoring and fault diagnosis of hydraulic control system, different kinds of sensors are used to collect fault signals. The arrangement of a limited number of sensors in the most reasonable positions of the hydraulic system, that is, to solve the problem on the optimal placement of sensors, is the key to improving efficiency of fault diagnosis. Aiming at fault diagnosis of hydraulic control system, this paper proposes a sensor placement methodology of hydraulic control system to determine the optimal number and position of sensors based on a discrete particle swarm algorithm. First, the model of fault propagation and sensor response time is evaluated by a simulation model. Second, a discrete optimization model for sensor placement is established. Finally, a discrete particle swarm optimization algorithm is used to calculate the optimal solution for the optimal placement of sensors. In the iterative process, a Monte Carlo simulation-based comparison algorithm is used for the evaluation and comparison of particle. The simulation case of typical multi-circuit hydraulic control systems proves that the proposed method has fast convergence speed and optimization results. A real case of a subsea blowout preventer control system shows that the proposed method reduces the number of sensors and data redundancy effectively. Compared with the traditional method, the robustness of the proposed system under the optimal solution is improved. [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]

• A novel resilience evaluation methodology for mechanical structure is developed. • The degradation model is established by combining finite element model and dynamic Bayesian networks. • Subsea wellhead connector demonstrates the application of the evaluation methodology. Resilience notionally means the ability to adapt changing conditions and recover rapidly from disruptions, which is vital for mechanical structure. Structure failure is usually caused by sudden changes of the fatigue mechanical property. Mechanical properties of structures should be considered when assessing resilience. The work proposes a general resilience assessment approach for mechanical structure through combining finite element models and dynamic Bayesian networks (DBNs). Resilience assessment process is divided into two parts, namely degradation process and recovery process. Degradation states in different time points can be analyzed by the finite element model, which can further provide data when establishing the DBN model of the degradation process. Recovery process is composed of fault diagnosis, resource allocation and maintenance. Fault diagnosis capability and resource allocation capability are calculated as quantitative coefficients, which can influence the maintenance activity. The maintenance capability is simulated by a DBN model through physical model mapping. The DBN model for the recovery process is finally established by integrating the quantitative coefficients and the maintenance model. Subsea wellhead connector attacked by the internal wave is adopted to demonstrate the application of the proposed assessment approach. [ABSTRACT FROM AUTHOR]

• A data-driven early fault diagnosis methodology of PMSM is proposed. • Bayesian networks are applied to identify the early, middle and permanent faults. • The proposed methodology has a good tolerance for different applied loads. Permanent magnet synchronous motor (PMSM) is one of the common core power components in modern industrial systems. Early fault diagnosis can avoid major accidents and plan maintenance in advance. However, the features of early faults are weak, and the symptoms are not obvious. Meanwhile, the fault signal is often overwhelmed by noise. Accordingly, fault diagnosis for early faults is difficult, and the diagnostic accuracy is generally low. A Bayesian-network-based data-driven early fault diagnostic methodology of PMSM is proposed with vibration and acoustic emission data. The wavelet threshold denoising and minimum entropy deconvolution methods are used to improve the signal-to-noise ratio. The complementary ensemble empirical mode decomposition method is used to extract signal eigenvalues, and Bayesian networks are applied to identify the early, middle, and permanent faults. Experimental data carried out with Tyco ST8N80P100V22E medium PMSM are used to train the fault diagnostic model and validate the proposed fault diagnostic methodology. Result shows that the accuracy for early faults is more than 90% when acoustic emission signal is used, and it is higher than the accuracy with vibration signal. The influence of load on diagnostic accuracy is also investigated, and it indicates that the accuracy with acoustic emission signal is higher than that with vibration signal under different loads. [ABSTRACT FROM AUTHOR]