Multi-state Nodes Research Articles

A novel approach based on fault tree analysis and Bayesian network for multi-state reliability analysis of complex equipment systems

Due to the complex structure of multi-state complex systems and the lack of data, information, and knowledge, the uncertainty of the logical relationship between the failure states of systems and components and the uncertainty of related failure data become the key issues in the reliability analysis of multi-state complex systems. In this paper, combined with multi-state fault tree (MSFT), a multi-state reliability assessment framework for complex systems considering uncertainty based on multi-source information fusion and multi-state Bayesian network (MSBN) is proposed. The multi-source information fusion method combines historical data and experts’ opinions to solve the uncertainty problem of multi-state failure data in complex equipment systems effectively. Based on the multi-source information fusion method, the calculation method of multi-state prior probability and the construction method of conditional probability are given. By constructing the conditional probability table (CPT), the uncertain logic relationship between the multi-state nodes is effectively expressed, which effectively improves the efficiency of CPT acquisition for MSBN and reduces the workload of experts scoring. Finally, a mud circulating system is taken as an example to prove the proposed method, and the specific calculation process, evaluation results, and some discussions are given. The results show that the proposed method is an effective multi-state reliability analysis method for complex uncertain multi-state systems.

On Area Coverage Reliability of Mobile Wireless Sensor Networks With Multistate Nodes

Wireless sensor networks (WSNs) are a special type of infrastructure-less network made up of a large number of tiny sensor nodes with limited energy, processing, and communication capabilities. WSNs have applications in health care, home security, environment monitoring, etc., with research challenges in energy efficiency, network lifetime, and network reliability. One of the major research challenges lies in providing application-specific coverage of the region of interest and reliable transmission of the gathered data to the mobile sink in the presence of multi-state sensor nodes. To quantify such a capability, this paper proposes a quantitative measure, called Area Coverage Reliability (ACR) for WSNs. ACR brings together WSN reliability, area coverage, energy efficiency, mobility of data collector or sink, random duty cycle of nodes, and multi-state nature of sensor nodes under a common umbrella. This paper proposes a Monte Carlo simulation approach that utilizes an energy matrix to evaluate the effect of energy-depleted nodes and energy-oriented data transfer capability on ACR. The energy matrix reflects the residual energy of sensors, the energy required to transmit data to the neighboring nodes, connectivity, and the multi-state nature of the sensors. The proposed approach is illustrated through a series of random examples. The ACR information allows the network designers to achieve a better understanding of the impact of random duty cycle, node energy, node/link reliability, and randomly deployed sensors on reliability.

Minimal Path-Based Reliability Model for Wireless Sensor Networks With Multistate Nodes

Wireless sensor networks (WSNs) find application in various fields like environmental monitoring, health-care, land security, and many more. To ease our day-to-day activity, WSNs have become an integral tool for complex data gathering tasks. Monitoring a phenomenon by a WSN depends on the collective data provided by the sensor nodes. To ensure reliable operation of WSNs, it is important to quantify the performance of such networks in terms of network reliability measures. This article studies the reliability of WSNs with multistate nodes and proposes an approach to evaluate the flow-oriented network reliability of WSNs consisting of multistate sensor nodes. The proposed method takes into account the dynamic state of the network due to multistate sensor nodes. The proposed approach includes enumeration of shortest minimal paths from application-specific flow satisfying sensor nodes (source nodes) to the sink node. It then proposes a modified sum-of-disjoint products approach to evaluate WSN reliability in the presence of multistate nodes from the enumerated shortest minimal paths. Simulations are performed on WSNs of various sizes to show the applicability of the proposed approach on arbitrary WSNs.

Intelligent System for Gearbox Fault Detection & Diagnosis Based on Vibration Analysis using Bayesian Networks

Moving energy from one machine to another and functioning to reduce speed while increasing torque is the ability of the gearbox. Due to many components and the structure between the components is fairly complex, thus to be able to detect the initial damage, sophisticated methods is needed. Vibration analysis is a method that has been effective in detecting the initial damage that occurs in machine. But it takes time and costs are not small to implement. The purpose of this study is to create an intelligent system capable of detecting gearbox damage based on data obtained from vibration measurements. Merging of two methods of vibration analysis and Bayesian networks is done to be able to design the system with the expected results. A series of multistate nodes are applied to the network and a system review is performed. Results are given and compared with results provided by the manual analysis. The results indicate that the system is feasible and reasonable which can assist inidentifying gearbox damages. This study definitively answers the problem of how to design an expert system capable of replacing the work of an expert on vibration analysis services.

A Monte-Carlo Markov chain approach for coverage-area reliability of mobile wireless sensor networks with multistate nodes

A mobile Wireless Sensor Network (mWSN) is composed of a large number of tiny, inexpensive resource-constrained sensors scattered in the field of interest, with the sink node or the data collector moving around the field. One fundamental concern of an mWSN is to provide application-specific coverage of the area under surveillance. The reliability of an mWSN depends on sensing area coverage, network connectivity, and data handling capacity of the mWSN in the presence of multi-state sensors. To mention here, each sensor node during its life cycle may exist in ACTIVE, SLEEP, RELAY, IDLE or FAIL states due to hardware failure, random duty cycle and/or energy limitations. Under such constraints, to quantify application-specific coverage oriented reliability, a new coverage-reliability index, CORE, is introduced. CORE gives a measure of the ability of a sensor network with multi-state nodes to satisfy the application-specific coverage area requirement with reliable data delivery to the mobile sink. A Monte-Carlo Markov Chain simulation approach is proposed for evaluating CORE. The conducted computational experiments are carried on mWSNs of various sizes to demonstrate the versatility of the proposed approach.

Improved compression inference algorithm for reliability analysis of complex multistate satellite system based on multilevel Bayesian network

Bayesian Network (BN) is a powerful tool for analyzing system reliability. However, for the complex multistate satellite system, the state combination explosion makes the reliability analysis based on BN computationally unaffordable. To solve this problem, compression algorithm and inference algorithm have been proposed. The original compression algorithm classifies all the phrases with same composition into the same type. The original compression algorithm classifies all the phrases with same composition into the same type. However, cases tested in this paper show that the results of inference can be incorrect when some node probability tables have special formulations. To solve the above defects, the compression algorithm and sequential inference algorithm are improved in this paper. Based on improved compression algorithm and improved inference algorithm, the improved compression inference algorithm (ICIA) is formed and extended to the application of multistate nodes with independent binary parent nodes. Besides, the multilevel BN (MBN) models are used to construct the BN models of complex multistate satellite systems so as to further improve inference efficiency. Cases tested in this paper show that the proposed algorithms are more efficient than the Bayes Net Toolbox and the AgenaRisk software for the reliability analysis of complex multistate satellite systems.

A method for modelling operational risk with fuzzy cognitive maps and Bayesian belief networks

A main concern of risk management in financial institutions is measurement of operational risk and its value at risk as a requirement of Basel II accord. Besides risk quantification, identifying causal mechanism leading to operational loss is necessary to plan risk mitigation activities. Bayesian belief networks (BBN) is a causal modelling method able to achieve both goals simultaneously. Eliciting BBN causal model and its parameters from expert knowledge is an alternative to data driven models in case of data scarcity. However, there is still a problem with parameter extraction for complex models with a number of multi parent and multi state nodes. In this paper, we proposed a method combining fuzzy cognitive maps (FCM) and BBN in order to improve BBN capability in modelling operational risks. In the first phase, a causal model is constructed by applying FCM and then a new migration method is proposed to translate FCM parameters to BBN ones. A case study of an Iranian private bank is then given to examine and validate the proposed method.

A Hierarchical Model for Mobile Ad Hoc Network Performability Assessment

Dynamic topology is one of the main influence factors on network performability. However, it was always ignored by the traditional network performability assessment methods when analyzing large-scale mobile ad hoc networks (MANETs) because of the state explosion problem. In this paper, we address this problem from the perspective of complex network. A two-layer hierarchical modeling approach is proposed for MANETs performability assessment, which can take both the dynamic topology and multi-state nodes into consideration. The lower level is described by Markov reward chains (MRC) to capture the multiple states of the nodes. The upper level is modeled as a small-world network to capture the characteristic path length based on different mobility and propagation models. The hierarchical model can promote the MRC of nodes into a state matrix of the whole network, which can avoid the state explosion in large-scale networks assessment from the perspective of complex network. Through the contrast experiments with OPNET simulation based on specific cases, the method proposed in this paper shows satisfactory performance on accuracy and efficiency.

Open Press Electrical Accident Risk Assessment based on Bayesian Network

Since multiple state events could not be described and accident probability could not be calculated by fault tree quantitatively for safety assessment, applications of Bayesian network (BN) in safety and reliability fields for the past few years were introduced and basic principle and inference algorithm of Bayesian network were presented. A Bayesian network was developed to model open press electric shock accident as a result of charged press enclosure. The multi-state nodes of the network were illustrated and accident probability was computed finally. The results show that Residual Current Operated Circuit-Breaker with Integral Overcurrent Protection (RCBO) installation can reduce accidents probability of phase-voltage and under-phase-voltage electric shock sharply for open press working in T - T system of low voltage power supply system. In a word, Bayesian network can model system which contains multiple state events, furthermore, accidents probability of that system can be computed correctly, in addition, it offers a comparative tool for various safety design of machine system to guarantee machine inherent safety.

A simple universal generating function method for estimating the reliability of general multi-state node networks

Many real-world systems (such as cellular telephones, transportation, etc.) are Multi-state Node Networks (MNNs) that are composed of multi-state nodes with different states determined by a set of nodes receiving the signal directly from these nodes without satisfying the conservation law. Current methods for evaluating MNN reliability are all derived from Universal Generating Function Methods (UGFMs). Unfortunately, UGFMs are only effective for special MNNs without any cycle, i.e. acyclic MNNs. A very simple revised UGFM is developed for the general MNN reliability problem. The proposed UGFM allows cycles with the same time complexity as the best-known UGFM. The correctness and computational complexity of the proposed UGFM are analyzed and proven. One example is given to illustrate how MNN reliability is evaluated using the proposed UGFM.

Reliability of acyclic multi-state networks with constant transmission characteristics of lines

Abstract Multi-state acyclic networks (MANs) consist of a number of independent multi-state nodes (MNs). Each network has a root MN (source), a number of intermediate MNs and a sink node. All the nodes are ordered and each node (except the sink) is connected by edges with a set of nodes with greater numbers. Each MN can have different states determined by a subset of available edges. Probability of each MN state is assumed to be known for every MN. The aim of the MAN is transmission of information or material flow from source to sink. The transmission process intensity depends on transmission characteristics (TCs) of network elements. In this paper we suggest a method for determining TC of the entire MAN based on TCs and availability of its elements. The MAN reliability is defined as probability that its TC meets some specified demand. A universal generating function approach is suggested for estimating reliability of MANs of different physical nature. In order to demonstrate the flexibility of the approach two different types of MANs are considered: minimal transmission time networks and maximal flow path networks. Illustrative examples are presented.