•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.
In order to increase the diagnostic accuracy of ground-source heat pump (GSHP) system, especially for multiple-simultaneous faults, the paper proposes a multi-source information fusion based fault diagnosis methodology by using Bayesian network, due to the fact that it is considered to be one of the most useful models in the filed of probabilistic knowledge representation and reasoning, and can deal with the uncertainty problem of fault diagnosis well. The Bayesian networks based on sensor data and observed information of human being are established, respectively. Each Bayesian network consists of two layers: fault layer and fault symptom layer. The Bayesian network structure is established according to the cause and effect sequence of faults and symptoms, and the parameters are studied by using Noisy-OR and Noisy-MAX model. The entire fault diagnosis model is established by combining the two proposed Bayesian networks. Six fault diagnosis cases of GSHP system are studied, and the results show that the fault diagnosis model using evidences from only sensor data is accurate for single fault, while it is not accurate enough for multiple-simultaneous faults. By adding the observed information as evidences, the probability of fault present for single fault of “Refrigerant overcharge” increases to 100% from 99.69%, and the probabilities of fault present for multiple-simultaneous faults of “Non-condensable gas” and “Expansion valve port largen” increases to almost 100% from 61.1% and 52.3%, respectively. In addition, the observed information can correct the wrong fault diagnostic results, such as “Evaporator fouling”. Therefore, the multi-source information fusion based fault diagnosis model using Bayesian network can increase the fault diagnostic accuracy greatly.
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.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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.
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.
► The common cause failure and imperfect coverage are modeled by Bayesian networks. ► The reliabilities of subsea BOP control systems are studied by Bayesian networks. ► The ES, PLC and PC have great effects on TMR control system. ► The ES and PC have great effects on DDMR control system.
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.
On the basis of the principal components analysis-particle swarm optimization-least squares support vector machine (PCA-PSO-LSSVM) algorithm, a fault diagnosis system is proposed for the compressor ...system. The relationship between the working principle of a compressor system, the fault phenomenon, and the root cause is analyzed. A fault diagnosis model is established based on the LSSVM optimized using PSO, the compressor fault diagnosis test experimental platform is used to obtain the fault signal of various fault occurrence states, and the PCA algorithm is employed to extract the characteristic data in the fault signal as input to the fault diagnosis model. The back-propagation neural network, the LSSVM algorithm, and the PSO-LSSVM algorithm are analyzed and compared with the proposed fault diagnosis model. Results show that the PCA-PSO-LSSVM fault diagnosis model has a maximum fault recognition efficiency that is 10.4% higher than the other three models, the test sample classification time is reduced by 0.025 s, the PCA algorithm can effectively reduce the input dimension, and the PSO-LSSVM fault diagnosis model based on the PCA algorithm for extracting features has a high recognition rate and accuracy. Therefore, the proposed fault diagnosis system can effectively identify the compressor fault and improve the efficiency of the compressor.
Reliability analysis of the electrical control system of a subsea blowout preventer (BOP) stack is carried out based on Markov method. For the subsea BOP electrical control system used in the current ...work, the 3-2-1-0 and 3-2-0 input voting schemes are available. The effects of the voting schemes on system performance are evaluated based on Markov models. In addition, the effects of failure rates of the modules and repair time on system reliability indices are also investigated.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Application of chemical dispersants is one option for combatting oil spills, dispersing oil into the water column and thereby reducing potential pollution to coastal areas. Efficiency of dispersant ...application depends on oil characteristics, sea and weather conditions. Potential environmental impacts must also be taken into account. Referring to the German Bight region (North Sea), we show how probabilistic Bayesian network (BN) technology can integrate all these aspects to support contingency planning. Expected effects of chemical dispersion on oil spill drift paths are quantified based on comprehensive numerical ensemble simulations. Ecological impacts are represented just in simplified terms focusing on nearshore seabird distributions. The intuitive and interactive BN summarizes expected benefits from chemical dispersion depending on where and under which weather conditions a hypothetical pollution occurs.
Display omitted
•A Bayesian network (BN) to assess potential benefits of using dispersants is proposed.•Expected effects of chemical dispersion on oil spill drift paths are quantified.•Ecological impacts are represented focusing on seabird distributions near the coast.•The BN is useful for contingency planning regarding oil spills in the German Bight.
This paper proposes a Bayesian network approach to assess potential benefits of using chemical dispersants for combatting oil spills in the German Bight.
This paper aims to eliminate nonlinear friction from the performance of the digital hydraulic cylinder to enable it to have good adaptive ability. First, a mathematical model of a digital hydraulic ...cylinder based on the LuGre friction model was established, and then a dual-observer structure was designed to estimate the unobservable state variables in the friction model. The Lyapunov method is used to prove the global asymptotic stability of the closed-loop system using the adaptive friction compensation method. Finally, Simulink is used to simulate the system performance. The simulation results indicate that the addition of adaptive friction compensation control can effectively reduce system static error, suppress system limit loop oscillation, “position decapitation,” “speed dead zone,” and low-speed creep phenomena, and improve the overall performance of the digital hydraulic cylinder. The control method has practical application value for improving the performance index of the digital hydraulic cylinder.
Oil spills are one of the major threats to the marine environment in the German Bight (North Sea). In case of an accident, application of chemical dispersants would be one response option among ...others. Dispersion breaks oil slicks into small droplets which get then mixed into the water column. Removal of the oil from the water surface may reduce contamination of the coast. However, the window of opportunity for effective dispersant application is short and there are concerns about potential effects to the marine life. We propose a Bayesian network (BN) as an interactive and intuitive tool for responders to justify decisions on using chemical dispersants and possibly the provision of appropriate assets. The BN combines detailed sub-BNs for different criteria that govern the decision process. Expected drift trajectories are estimated based on comprehensive numerical ensemble simulations of hypothetical oil spills. Ecological impacts are represented prototypically, focusing on vulnerability of seabird concentrations to pollution in coastal areas. Dispersant effectiveness is estimated considering oil properties and weather conditions. Decision making is supposed to be based on expected satisfaction. The definition of what is considered satisfactory is of central importance for the whole analysis.
Display omitted
•A Bayesian network (BN) for decision making on using chemical dispersants to combat oil spills is proposed.•The BN integrates key criteria of decision making such as drift behavior, dispersion efficacy and ecological impacts.•Based on expected satisfaction, the BN supports contingency planning regarding oil spills in the German Bight.
PDF
