Improve System Reliability Research Articles

Rational Selection of Rail Transit Emergency Site Using Complex Network Topology and Genetic Algorithm

Rail transit network is a convenient and reliable transportation mode in urban areas. With every surge in transportation demand, the network undergoes operational changes like restructuring and making of multiline tracks. Urban rail transit network is a complex network and needs proper emergency services and controlling mechanisms. Unexpected mishaps in rail transit will cause more hazard and panic than the other transportation systems. Therefore, a reliable emergency service station is required to ensure the safety and security of passengers. In the proposed method, the basic statistical properties of complex network topology are considered to construct a P-center site selection model for urban rail transit emergency service stations. The P-center site selection model is solved by genetic algorithm. Validity and reasonableness of the model are demonstrated by implementing it in the Hangzhou emergency service stations for rail transportation. The results show that the P-center site selection model based on statistical properties of complex network topologies can better solve the urban rail transit emergency service station site selection problems. The model minimizes the number of emergency service stations while satisfying the optimal objective function and reduces the construction cost of emergency service stations. The approach has a significant effect on improving system reliability and reducing the risk of emergencies.

A proposed artificial intelligence workflow to address application challenges leveraged on algorithm uncertainty

SummaryArtificial Intelligence (AI) has achieved state-of-the-art performance in medical imaging. However, most algorithms focused exclusively on improving the accuracy of classification while neglecting the major challenges in a real-world application. The opacity of algorithms prevents users from knowing when the algorithms might fail. And the natural gap between training datasets and the in-reality data may lead to unexpected AI system malfunction. Knowing the underlying uncertainty is essential for improving system reliability. Therefore, we developed a COVID-19 AI system, utilizing a Bayesian neural network to calculate uncertainties in classification and reliability intervals of datasets. Validated with four multi-region datasets simulating different scenarios, our approach was proved to be effective to suggest the system failing possibility and give the decision power to human experts in time. Leveraging on the complementary strengths of AI and health professionals, our present method has the potential to improve the practicability of AI systems in clinical application.

Multi-objective optimisation of in-service asphalt pavement maintenance schedule considering system reliability estimated via LSTM neural networks

This article presents a novel system reliability-based framework for multi-objective optimisation of preventive maintenance (PM) management of in-service asphalt pavement. To accurately predict the international roughness index (IRI) sequence of pavement sections, a long short-term memory (LSTM) neural network that considers the spatiotemporal correlations between IRI sequences is trained with data retrieved from the long-term pavement performance (LTPP) program. Based on time-dependent limit-state functions (LSFs) incorporating the uncertainty associated with LSTM neural network prediction and the observational error involved in IRI measurement, Monte Carlo simulation (MCS) with importance sampling (IS) is adopted to calculate the reliability of pavement sections. Pavement sections located in New Mexico and Montana are selected as illustrative examples. Tri-objective optimisation processes are investigated by maximising user benefits (i.e. improved system reliability) and agency benefits (i.e. extended service life) while minimising the associated life-cycle cost (LCC) (i.e. user and agency costs) with multi-objective genetic algorithms (GAs). The obtained Pareto solution sets may assist decision-makers in the selection of well-balanced solutions to identify the optimal timing for applying PM treatments to in-service asphalt pavement.

Node-based valid inequalities for the optimal transmission switching problem

The benefits of transmission line switching are well-known in terms of reducing operational cost and improving system reliability of power systems. However, finding the optimal power network configuration is a challenging task due to the combinatorial nature of the underlying optimization problem. In this work, we identify a certain “node-based” set that appears as substructure of the optimal transmission switching problem and then conduct a polyhedral study of this set. We construct an extended formulation of the integer hull of this set and present the inequality description of the integer hull in the original space in some cases. These inequalities in the original space can be used as cutting-planes for the transmission line switching problem. Finally, we present the results of our computational experiments using these cutting-planes on difficult test cases from the literature.

Velostat Sensor Array for Object Recognition

This paper presents a cost-effective pressure sensing system for object detection and identification. The pressure sensing system consists of a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$27\times 27$ </tex-math></inline-formula> piezoresistive sensor array made of carbon composite Velostat, a signal processing subsystem for signal scanning, amplification, registration, and enhancement. A convolutional neural network is used to classify various objects through the pressure signals produced and processed by the sensing array. Based on systematic characterizations and calibrations of sensing materials and system sensitivity, three experiment setups are established to recognize 10 objects to be detected. In series of experiments, a pressure image data set consisting of 32264 frames of images is first assembled to represent the 10 objects. Contrast enhancement algorithm was used to process the pressure image data set and combined with a convolutional neural network ResNet-PI to classify the 10 objects. For pressure images collected with the preestablished three experiment setups, an overall accuracy of 0.9854 is achieved. Compared with other systems based on Velostat sensor array, the system demonstrated in this study features improvements in structural robustness, detection repeatability and system reliability, suggesting its potential applications in emerging areas including human-computer interaction and smart health monitoring.

A generalized model selection framework for multi-state failure data analysis

PurposeThe purpose of this paper is to carry out a reliability analysis of a mechanical system considering the degraded states to get a proper understanding of system behavior and its propagation towards complete failure.Design/methodology/approachThe reliability analysis of computerized numerical control machine tools (CNCMTs) using a multi-state system (MSS) approach that considers various degraded states rather than a binary approach is carried out. The failures of the CNCMT are classified into five states: one fully operational state, three degraded states and one failed state.FindingsThe analysis of failure data collected from the field and tests conducted in the laboratory provided detailed understandings about the quality of the material and its failure behavior used in designing and the capability of the manufacturing system. The present work identified that Class II (major failure) is critical from a maintainability perspective whereas Class III (moderate failure) and Class IV (minor failure) are critical from a reliability perspective.Research limitations/implicationsThis research applies to reliability data analysis of systems that consider various degraded states.Practical implicationsMSS reliability analysis approach will help to identify various degraded states of the system that affect the performance and productivity and also to improve system reliability, availability and performance.Social implicationsIndustrial system designers recognized that reliability and maintainability is a critical design attribute. Reliability studies using the binary state approach are insufficient and incorrect for the systems with degraded failures states, and such analysis can give incorrect results, and increase the cost. The proposed MSS approach is more suitable for complex systems such as CNCMT rather than the binary-state system approach.Originality/valueThis paper presents a generalized framework MSS's failure and repair data analysis has been developed and applied to a CNCMT.

A Multiobjective Approach for the Optimal Placement of Protection and Control Devices in Distribution Networks With Microgrids

Protection and control systems represent an essential part of distribution networks by ensuring the physical integrity of components and by improving system reliability. Protection devices isolate a portion of the network affected by a fault, while control devices reduce the number of de-energized loads by transferring loads to neighboring feeders. The integration of distributed generation has the potential to enhance the continuity of energy services through islanding operation during outage conditions. In this context, this study presents a multi-objective optimization approach for sizing and allocating protection and control devices in distribution networks with microgrids supplied by renewable energy sources. Reclosers, fuses, remote-controlled switches, and directional relays are considered in the formulation. Demand and generation uncertainties define the islanding operation and the load transfer possibilities. A non-dominated sorting genetic algorithm is applied in the solution of the allocation problem considering two conflicting objectives: cost of energy not supplied and equipment cost. The compromise programming is then performed to achieve the best solution from the Pareto front. The results show interesting setups for the protection system and viability of islanding operation.

Fault Diagnosis of Exhaust Gas Treatment System Combining Physical Insights and Neural Networks

Fault diagnosis is important for automotive systems, e.g., to reduce emissions and improve system reliability. Developing diagnosis systems is complicated by model inaccuracies and limited training data from relevant operating conditions, especially for new products and models. One solution is the use of hybrid fault diagnosis techniques combining model-based and data-driven methods. In this work, data-driven residual generation for fault detection and isolation is investigated for a system injecting urea into the aftertreatment system of a heavy-duty truck. A set of recurrent neural network-based residual generators is designed using a structural model of the system. The performance of this approach is compared to a baseline model-based approach using data collected from a heavy-duty truck during different fault scenarions with promising results.

The role of sustainable oil maintenance on lubrication system reliability

This article discusses the value of sustainable oil maintenance by using submicron high-tech offline filtration based on oil condition monitoring to reduce wear particles and improve system reliability. These activities incorporate broader goals of establishing a proactive maintenance approach, defined as continuous monitoring and controlling of the root causes of machine failure. Among these causes, contamination is the industry's most severe, popular, and most recognized failure cause. In order to ensure all necessary proactive maintenance activities, this paper uses practical analysis for oil contamination monitoring based on ISO 4405. The author applied a 0.1-micron high-tech offline filtration for lubrication systems of ball mills in the Sungun copper mine as an oil maintenance activity to control the system lubrication contamination as a root cause of failures.

Dynamic Reliability and Availability Allocation of Wind Turbine Subassemblies Through Importance Measures

This paper illustrates the impact of wind turbine (WT) subassemblies’ availability on WTs’ performance. Complete and detailed reliability, availability, and maintainability (RAM) analysis of various subassemblies of WTs utilizing the Weibull probability density function (PDF) is also introduced. A modified dynamic important measures-based reliability and availability are presented to show the significant impact of various WT subassemblies on the overall system performance. This method is mainly utilized to rank the WT subassemblies regarding their impact on the system reliability and availability, identifying the subassemblies that the planned maintenance should focus on. Dynamic ranking of WT subassemblies is obtained to achieve the desired level of reliability and availability. The obtained results demonstrate the effectiveness and efficiency of the proposed approach that achieves the system’s secure operation and improves system reliability and availability.

A Collaborative optimization approach for grid equipment overhaul/retirement strategy considering system effectiveness and risk

In order to improve the science of grid equipment overhaul and decommissioning decisions, a collaborative optimization method of equipment overhaul/decommissioning strategy considering system effectiveness and risk is proposed. The interaction effect between overhaul and decommissioning is analysed from the perspective of system effectiveness, and 2 attributes of integrated cost input and system reliability improvement are proposed, and then a prospect model with cost/benefit attributes is established based on the prospect theory. Then, with the maximum integrated prospect value as the optimization objective, a collaborative optimization model of overhaul/decommissioning strategy is established with the system risk level as the constraint. Finally, the effectiveness of the proposed method is verified by taking the transformer equipment of an urban area distribution network as an example.

Reliability Assessment and Improvement of Electrical Distribution Systems by Using Multinomial Monte Carlo Simulations and a Component Risk Priority Index

In this paper, a method of assessing and improving the reliability of power distribution systems based on Monte Carlo simulation and a novel risk priority index is proposed. The initialization of the assessment process is carried out by using Multinomial Monte Carlo simulation with a nonsequential technique to assess system reliability in the form of SAIFI and SAIDI indices. Then, the novel per-time-based component reliability indices representing the insights obtained from root-cause analysis for each component in the system are evaluated to make suitable decisions on improvement measures. The proposed indices are derived as a component risk priority index based on the principle of the failure mode and an effect analysis to prioritize and select the implementation points by the Pareto principle. By applying the proposed method, a reliability improvement should be achieved at the correct point with minimal operations. In addition, the proposed method can be used to study the effect of uncertainty regarding some device operations on the system reliability. To verify the performance of the proposed method and demonstrate its application, three case studies were performed on the IEEE RBTS Bus-2 test system. From the first case study, the results of the proposed assessment process were validated by comparison with a standard benchmark. The second case study showed the performance of applying the entire process to improve system reliability, and the results showed that system reliability can be improved significantly. The third case study was performed to determine the effect of uncertainty in protective device operations. The results of the third case showed that there was a significant decrease in overall reliability in terms of a higher level of power outages, while the performance of the protective components was slightly reduced.

Optimal Planning of Parking Lots of PEVs Incorporating V2G for Reliability Improvement of Distribution Systems

The number of plug-in electric vehicles (PEV) increased significantly over the past few years and is expected to increase further in the next years due to environmental concerns and fossil fuel depletion. Distribution systems were not originally designed to accommodate this continuous prevalence of PEVs, challenging system planners to install parking lots (PLs) that support PEV charging. The literature has presented many studies for planning PLs, although it did not consider the vehicle-to-grid (V2G) contribution to improving system reliability. This paper proposes an optimal planning framework for allocating and sizing the PLs of PEVs incorporating V2G to improve the reliability of distribution systems. A combined probabilistic model of Markov Chain Monte Carlo simulation (MCMCS) and Monte Carlo simulation (MCS) is applied to capture the stochastic existence of PEVs in PLs. Reliability is evaluated based on a group of energy-oriented indices and the customer’s annual cost of interruption. A comprehensive investigation is presented to test the proposed framework on a distribution system for grid-to-vehicle (G2V) and V2G case studies. The results show the efficiency of the proposed framework in improving the reliability indices by 12% and reducing the customer cost of interruption by 10.06% while prioritizing critical customers.

A Concurrent Fault Diagnosis Model via the Evidential Reasoning Rule

Fault diagnosis is an important tool to improve system reliability, and multiple faults always occur simultaneously in a complex engineering system. In most of the current methods for fault diagnosis, single-fault is mainly involved and concurrent faults are usually ignored, which may cause hidden danger to the safe and reliable operation of the system. Thus, it is significant to diagnose concurrent faults. However, both multiple indexes of complex systems and harsh environmental interference bring a great challenge to this task. Therefore, in this paper, a new concurrent fault diagnosis model is proposed based on the evidential reasoning (ER) rule. Specifically, multiple sub-ER models are established to form a parallel diagnosis mode. A parameter optimization model is proposed to update evidence weights and referential values. By synthesizing the outputs of all the sub-ER models, the single fault or concurrent fault can be diagnosed. The performance of the proposed diagnosis model under disturbance is analyzed theoretically, which can provide some guidance for its applications in engineering practice. A case study of the machine rotor system is carried out to validate the effectiveness of the proposed ER rule-based model.

Reliability optimization using hybrid genetic and particle swarm optimization algorithm

Redundancy-allocation problem i.e. RAP is among the reliability optimization problems which make use of non-linear programming method to improve the reliability of complex system. The objective of this research paper is reliability optimization through the application of Genetic Algorithm i.e. GA and Hybrid Genetic &amp; Particle Swarm Optimization (H-GAPSO) on a RAP. Certain shortcomings have been seen when results are obtained by application of single algorithms. In order to get rid of these shortcomings, HGA-PSO is introduced where attractive properties of GA and PSO are combined. This hybrid method makes use of iterative process of GA after obtaining initial best population from PSO. Comparative Analysis of results of GA and H-GAPSO is done with respect to reliability and computation (CPU) time and it is observed that H-GAPSO improved system reliability up to maximum by 63.10%. MATLprogramming has been used for computation of results from GA and HGA-PSO algorithms.

Performance Analysis of Wireless Power Transfer Enabled Dual Hop Relay System Under Generalised Fading Scenarios

Dual-hop relay communication systems have received extensive research attention due to their property of improving system reliability, spectral and power efficiency. RF wireless power transfer has also become a popular green technology for remote supply of energy to communication devices. In this paper a RF wireless power transfer enabled dual hop relay system is analysed over generalised fading scenarios. A closed form expression is derived for the outage probability of the decode and forward as well as amplify and forward systems valid for all fading scenarios. Based on this analysis, the optimum design parameter settings for the system are also presented which serve as reference point for measuring the outage performance of other variant of the generalised system. The numerical results obtained from derived expressions are compared with Monte-Carlo simulations and are found to be in excellent agreement with each other.

Distribution Line Fault Location With Unknown Fault Impedance Based on Electromagnetic Time Reversal

Rapid and accurate location of power distribution line faults is essential for power system reliability improvement. Based on the electromagnetic time reversal (EMTR), an improved expression of fault current considering the influence of both sides of the transverse branch in the reversed time was derived in this research. In the reversed time, the transient voltage signals were injected into both ends of the line and multiplied one signal by −1, as suggested in An <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> 's work, and an assumed impedance of the transverse branch was set to 1000 Ω. A new criterion of the mirrored minimum fault current signal energy was proposed to determine the fault location. In the direct time, by setting different ground fault impedances, the distribution of the fault current signal energy in the frequency domain was calculated. Furthermore, the time-domain simulation verification was performed in the power systems computer-aided design software. The results indicate that when the actual fault impedance was unknown, the accurate fault location of the distribution line can be achieved by the proposed EMTR-based method, and it is less affected by time synchronization.

Prescheduled switching time: A new strategy for systems with standby components

Reliability improvement is a major industrial concern. A commonly known approach to improve the reliability of a system is the so-called ‘redundancy strategy’ in which redundant components are used in the subsystems to achieve considerable enhancements in total system reliability. Generally, the two active and standby strategies are the ones most used in addressing reliability optimization problems (ROPs). The present article proposes a new structure for the standby strategy that revolutionizes its conventional structure into one that considers a predefined time at which a component starts operation without the need for a switching system to activate it. The standby component actively enters the system at the predefined time; this naturally improves system reliability by relaxing the need for the switch to a considerable extent, which is especially useful in cases of a poor switching system. The objective in this problem is to determine the best time for the component to join the system. According to the new strategy, the system typically uses its switching system whenever there is a need for a component to join the subsystem but the component may also join the system automatically at a predefined time in order to reduce the likelihood of system failure.

A New Method for Auto-Inspecting Both Opposite Surfaces of Tec Components Based on Equal-Optical-Path Imaging System

An new optical method for simultaneously inspecting opposite surfaces defects of thermoelectric cooler (TEC) components that meets condition of equal optical paths for both surfaces imaging has been proposed. The optical apparatus for surfaces defects inspection has been designed and established with “confocal” imaging system consisting of two trapzoid reflection prisms and one optical images combiner. Experimental investigations on defects inspection with the “confocal” imaging system have been carried out. The results showed that the proposed optical method can be used to simultaneously inspect the defects of opposite surfaces of TEC components without need to employ a tele-centric imaging lens with large depth of focus. It was concluded that the optical inspection method can meet the technical requirements for inspecting opposite surfaces (both side surfaces, or both top and bottom surfaces) defects of TEC components and has advantages of equal good imaging quality, increased inspection accuracy and throughput, simplified system configuration and improved system reliability etc.

Research on operation strategy of radiant cooling system based on intermittent operation characteristics

Radiant cooling systems have become increasingly prevalent due to their great potential in comfort and energy saving. Nevertheless, their applications are limited by condensation and slow response. This study aims to explore the intermittent operation characteristics of radiant cooling systems and propose an effective operation strategy to improve system reliability. An operation strategy called standby cooling was proposed, and a none standby cooling scheme and four standby cooling schemes were simulated with the transient system simulation tool (TRNSYS). The results show that the none standby cooling scheme was difficult to satisfy the indoor comfort of the rooms with long shutdown periods. The indoor dry-bulb temperature and dew-point temperature when the air-conditioning mode is turned on have the approximate monotonic relationships with the maximum predicted mean vote (PMV) during the occupied period, and the Spearman correlation coefficient were 0.8809 and 0.8019, respectively. By contrast, with increased energy consumption, the standby cooling schemes can satisfy the comfort well and ensure the smooth operation of the radiant cooling system. With the same radiant system setting, the extra energy consumption of the schemes with continuous fresh air supply is 67.71%∼157.42% more than that of schemes that supply fresh air intermittently. With the same fresh air system setting, the schemes turning off the radiant cooling system need 11.67%∼71.41% more extra energy consumption than the schemes turning on the radiant system. Accordingly, from the perspective of energy consumption, one should turn off the fresh air system and turn on the radiant system as much as possible. This study provides a theoretical basis for understanding intermittent operation characteristics of radiant cooling systems and can direct the design and operation to enhance system reliability with less energy consumption.