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24 pages, 5953 KiB

Open AccessArticle

Integrating Fuzzy FMEA and RAM Analysis for Evaluating Modernization Strategies in an LNG Plant Pumping and Vaporization Facility

by Orlando Durán, Fabián Orellana, Gabriel Lobos and Alexis Ibacache

Appl. Sci. 2024, 14(22), 10729; https://doi.org/10.3390/app142210729 - 20 Nov 2024

Viewed by 542

Abstract

In today’s competitive industrial landscape, Reliability Engineering plays a vital role in minimizing costs and expenses in energy projects. The main focus of this paper is to propose the integration of a fuzzy-based FMECA process into a RAM analysis to assess modernization and [...] Read more.

In today’s competitive industrial landscape, Reliability Engineering plays a vital role in minimizing costs and expenses in energy projects. The main focus of this paper is to propose the integration of a fuzzy-based FMECA process into a RAM analysis to assess modernization and reconfiguration strategies for LNG facilities. This approach estimates, through a systematic procedure, the system’s failure probabilities and gauges the impact of various maintenance and topological modification initiatives on the asset and the system’s availability as a driver of profitability. A methodology based on fuzzy-FMEA is proposed to collect and process imprecise data about reliability and maintainability of the components of the facility. Furthermore, Monte Carlo-based RAM experiments are performed. The selection of parameters for conducting Monte Carlo experiments is done after the defuzzification of MTBF and MTTR values defined in the FMEA stage. The proposed procedure allows for the prediction of the system’s reliability across hypothetical scenarios, incorporating design tweaks and potential improvements. As a case study, the proposed was applied to a Pumping and Vaporization facility in a Chilean LNG plant. Sensitivity analysis was performed on critical elements, leading to an optimization strategy for key components like Open Rack Vaporizers (ORV) and Submerged Combustion Vaporizers (SCV). The anticipated availability rate was found to be 99.95% over an 8760 h operating period. Final conclusions and managerial insights are discussed. Full article

(This article belongs to the Special Issue Advances and Challenges in Reliability and Maintenance Engineering)

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23 pages, 5505 KiB

Open AccessArticle

CEEMDAN-RIME–Bidirectional Long Short-Term Memory Short-Term Wind Speed Prediction for Wind Farms Incorporating Multi-Head Self-Attention Mechanism

by Wenlu Yang, Zhanqiang Zhang, Keqilao Meng, Kuo Wang and Rui Wang

Appl. Sci. 2024, 14(18), 8337; https://doi.org/10.3390/app14188337 - 16 Sep 2024

Viewed by 893

Abstract

Accurate wind speed prediction is extremely critical to the stable operation of power systems. To enhance the prediction accuracy, we propose a new approach that integrates bidirectional long short-term memory (BiLSTM) with fully adaptive noise ensemble empirical modal decomposition (CEEMDAN), the RIME optimization [...] Read more.

Accurate wind speed prediction is extremely critical to the stable operation of power systems. To enhance the prediction accuracy, we propose a new approach that integrates bidirectional long short-term memory (BiLSTM) with fully adaptive noise ensemble empirical modal decomposition (CEEMDAN), the RIME optimization algorithm (RIME), and a multi-head self-attention mechanism (MHSA). First, the historical data of wind farms are decomposed via CEEMDAN to extract the change patterns and features on different time scales, and different subsequences are obtained. Then, the parameters of the BiLSTM model are optimized using the frost ice optimization algorithm, and each subsequence is input into the neural network model containing the MHSA for prediction. Finally, the predicted values of each component are weighted and reconstructed to obtain the predicted values of wind speed time series. According to the experimental results, the method can predict the short-term wind speeds of wind farms more accurately. We verified the effectiveness of the method by comparing it with different models. Full article

(This article belongs to the Special Issue Advances and Challenges in Reliability and Maintenance Engineering)

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19 pages, 1466 KiB

Open AccessArticle

Evaluation of Spare Parts Support Capacity of Civil Aircrafts Based on Type-2 Hesitant Pythagorean Fuzzy Sets and Improved Technique for Order Preference by Similarity to Ideal Solution

by Liang You, Lili Wang, Xiaofan Lv, Huachun Xiang and Zheng Wang

Appl. Sci. 2024, 14(17), 7475; https://doi.org/10.3390/app14177475 - 23 Aug 2024

Viewed by 615

Abstract

To improve the spare parts support capacity of civil aircrafts and given the actual lack of evaluation methods at present, the evaluation problem of spare parts support capacity was solved in this study by proposing a multi-attribute decision method based on Type-2 hesitant [...] Read more.

To improve the spare parts support capacity of civil aircrafts and given the actual lack of evaluation methods at present, the evaluation problem of spare parts support capacity was solved in this study by proposing a multi-attribute decision method based on Type-2 hesitant Pythagorean fuzzy sets and an improved Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). First, the basic definitions of Type-2 hesitant Pythagorean fuzzy sets were given, which were further promoted to Type-n hesitant Pythagorean fuzzy sets, and the basic order relation criterion of Type-2 hesitant Pythagorean fuzzy sets was introduced. Second, a complete evaluation system for spare parts supply support capacity was established with the spare parts of civil aircrafts as the study objects, and each evaluation indicator was introduced in detail. Then, the spare parts support solutions were preferentially sorted using the correlation coefficient formula of Type-2 hesitant Pythagorean fuzzy sets and improved TOPSIS. Finally, the reliability and reasonability of the proposed method were verified through an example calculation and comparative analysis. The experimental results indicate that the proposed method can acquire the evaluation results of spare parts support capacity more scientifically and can be referenced by relevant studies. Full article

(This article belongs to the Special Issue Advances and Challenges in Reliability and Maintenance Engineering)

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15 pages, 4448 KiB

Open AccessArticle

A Multi-Performance Reliability Evaluation Approach Based on the Surrogate Model with Cluster Mixing Weight

by Xiaoduo Fan, Jiantai Wang, Jianguo Zhang and Ziqi Ni

Appl. Sci. 2024, 14(13), 5813; https://doi.org/10.3390/app14135813 - 3 Jul 2024

Viewed by 602

Abstract

Kriging surrogate model has extracted extensive attention in reliability evaluation, owing to its excellent applicability and operability nowadays, which confronts with difficulties in balancing the efficiency and accuracy for complicated mechanical assets with multiple failure modes. Consequently, this paper devises a multi-performance reliability [...] Read more.

Kriging surrogate model has extracted extensive attention in reliability evaluation, owing to its excellent applicability and operability nowadays, which confronts with difficulties in balancing the efficiency and accuracy for complicated mechanical assets with multiple failure modes. Consequently, this paper devises a multi-performance reliability analysis approach within the surrogate model framework, particularly innovative in its use of cluster mixing weight. Specifically, high-value test points are selected to fit the surrogate model after sorting the samples referring to the corresponding values; then, a cluster-based active learning strategy is employed to accomplish rapid convergence, and the particle swarm algorithm is utilized to optimize relevant parameters. Afterwards, the mixing weight for every performance referring to the contributions to the final reliability is determined, and the failure probability is subsequently predicted. Furthermore, the superiority of the proposed approach with the clustering surrogate model and mixing weight, compared with traditional sampling as well as other surrogate models, has been verified via case studies, contributing to overcoming the multi-performance reliability analysis oriented to complicated mechanical assets. Full article

(This article belongs to the Special Issue Advances and Challenges in Reliability and Maintenance Engineering)

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20 pages, 6265 KiB

Open AccessArticle

Design and Experimental Study of an Embedded Controller for a Model-Based Controllable Pitch Propeller

by Pan Su, Guanghui Chang, Jiechang Wu, Yuxin Wang and Xuejiao Feng

Appl. Sci. 2024, 14(10), 3993; https://doi.org/10.3390/app14103993 - 8 May 2024

Cited by 1 | Viewed by 910

Abstract

The controllable pitch propeller hydraulic system has high constraints and nonlinearity. Due to these inherent deficiencies, the proportional–integral–derivative (PID) control algorithm cannot meet the control accuracy requirements of nonlinear systems. A control law based on a model predictive control (MPC) algorithm is designed [...] Read more.

The controllable pitch propeller hydraulic system has high constraints and nonlinearity. Due to these inherent deficiencies, the proportional–integral–derivative (PID) control algorithm cannot meet the control accuracy requirements of nonlinear systems. A control law based on a model predictive control (MPC) algorithm is designed in this paper. The gain parameters of the predictive control are optimized. The MPC and PID control systems are compared and simulated to verify the MPC controller’s effectiveness. Subsequently, the embedded controller of a controllable pitch propeller is developed. The support package for the embedded circuit board target containing an underlying driver for each interface is written by introducing the C-MEX S-Function and TLC programming language. A semi-physical simulation experiment is performed. The results show that the established controllable pitch propeller with an embedded controller displays reliable running performance, good anti-interference, and the capacity to fulfill the control function of the pitch propeller under various working conditions. Full article

(This article belongs to the Special Issue Advances and Challenges in Reliability and Maintenance Engineering)

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23 pages, 11750 KiB

Open AccessArticle

Study on Ring Deformation and Contact Characteristics of Thin-Walled Bearing for RV Reducer

by Yanshuang Wang and Fangzheng Liu

Appl. Sci. 2024, 14(9), 3741; https://doi.org/10.3390/app14093741 - 27 Apr 2024

Viewed by 1214

Abstract

The thin-walled rings of the RV reducer main bearings are prone to structural elastic deformation, which can significantly change the bearing mechanical characteristics. According to the actual assembly state of the RV reducer, the simulation model of the planetary frame–main bearings–pin gear housing [...] Read more.

The thin-walled rings of the RV reducer main bearings are prone to structural elastic deformation, which can significantly change the bearing mechanical characteristics. According to the actual assembly state of the RV reducer, the simulation model of the planetary frame–main bearings–pin gear housing is established considering the ring deformation. The model was used to calculate and comparatively analyze the ring deformation and contact characteristics of thin-walled bearings under rigid and flexible conditions, on the basis of which the mechanism of ring deformation was described, and the effects of load conditions, ring thickness and radial clearance on ring deformation, flexible contact characteristics, and ultimate carrying capacity were analyzed. The results show that the distribution of contact loads is the main factor affecting the ring deformation. The ring deformation can optimize the bearing contact characteristics, and the greater the deformation, the more pronounced the optimization effect. However, excessive ring deformation makes the contact ellipse more susceptible to truncation, which, in turn, reduces the ultimate carrying capacity. This study indicates a 38.2% decrease in the carrying capacity of the flexible ring model compared to that of the rigid ring model. In this paper, the effect of ring deformation on bearing mechanical characteristics is deeply discussed. The research results have important guiding significance for the structural optimization design of thin-walled bearings. Full article

(This article belongs to the Special Issue Advances and Challenges in Reliability and Maintenance Engineering)

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19 pages, 4000 KiB

Open AccessArticle

Improved Bootstrap Method Based on RBF Neural Network for Reliability Assessment

by Houxiang Wang, Haitao Liu and Songshi Shao

Appl. Sci. 2024, 14(7), 2901; https://doi.org/10.3390/app14072901 - 29 Mar 2024

Viewed by 856

Abstract

The investigation of the reliability of long-life equipment is typically hindered by the lack of experimental data, which makes accurate assessments challenging. To address this problem, a bootstrap method based on the improved RBF (radial basis function) neural network is proposed. This method [...] Read more.

The investigation of the reliability of long-life equipment is typically hindered by the lack of experimental data, which makes accurate assessments challenging. To address this problem, a bootstrap method based on the improved RBF (radial basis function) neural network is proposed. This method utilizes the exponential function to modify the conventional empirical distribution function and fit right-tailed data. In addition, it employs the RBF radial basis neural network to obtain the distribution characteristics of the original samples and then constructs the neighborhood function to generate the input network. The expanded sample is used to estimate the scale and shape parameters of the Weibull distribution and obtain the estimated value of the MTBF (mean time between failures). The bias correction method is then used to obtain the interval estimate for the MTBF. Subsequently, a simulation experiment is conducted based on the failure data of a CNC (computer numerical control) machine tool to verify the effect of this method. The results show that the accuracy of the MTBF point estimation and interval estimation obtained using the proposed method is superior to those of the original and conventional bootstrap methods, which is of major significance to engineering applications. Full article

(This article belongs to the Special Issue Advances and Challenges in Reliability and Maintenance Engineering)

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19 pages, 5592 KiB

Open AccessArticle

A Bearing Fault Diagnosis Method Based on Improved Transfer Component Analysis and Deep Belief Network

by Dalin Li and Meiling Ma

Appl. Sci. 2024, 14(5), 1973; https://doi.org/10.3390/app14051973 - 28 Feb 2024

Cited by 1 | Viewed by 872

Abstract

Domain adaptation can handle data distribution in different domains and has been successfully applied to bearing fault diagnosis under variable working conditions. However, most of these methods ignore the influences of noise and data distribution discrepancy on marking pseudo labels. Additionally, most domain [...] Read more.

Domain adaptation can handle data distribution in different domains and has been successfully applied to bearing fault diagnosis under variable working conditions. However, most of these methods ignore the influences of noise and data distribution discrepancy on marking pseudo labels. Additionally, most domain adaptive methods require a large amount of data and training time. To overcome the aforementioned challenges, firstly, sample rejection and pseudo label correction using K-means (SRPLC-K-means) were developed and explored to filter the noisy samples and correct the pseudo labels to obtain pseudo labels with higher confidence. Furthermore, a bearing fault diagnosis method based on the improved transfer component analysis and deep belief network is proposed, which can achieve subdomain adaptation and improve the compactness of the samples, leading to a complete bearing fault diagnosis under variable working conditions that is faster and more accurate. Finally, the results of the comparative tests confirmed that the proposed method could boost the average accuracy of 0.73%, 0.99%, and 5.55% in the three tests than the state-of-the-art methods, respectively. Moreover, the comparison of the time required for a fault diagnosis using different methods shows that compared to the end-to-end models, the proposed method reduces the time required by 594.9 s and 1431.6 s, respectively. Full article

(This article belongs to the Special Issue Advances and Challenges in Reliability and Maintenance Engineering)

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15 pages, 566 KiB

Open AccessArticle

Hi-RCA: A Hierarchy Anomaly Diagnosis Framework Based on Causality and Correlation Analysis

by Jingjing Yang, Yuchun Guo, Yishuai Chen and Yongxiang Zhao

Appl. Sci. 2023, 13(22), 12126; https://doi.org/10.3390/app132212126 - 8 Nov 2023

Viewed by 1107

Abstract

Microservice architecture has been widely adopted by large-scale applications. Due to the huge amount of data and complex microservice dependency, it also poses new challenges in ensuring reliable performance and maintenance. Existing approaches still suffer from limitations of anomaly data, over-simplification of metric [...] Read more.

Microservice architecture has been widely adopted by large-scale applications. Due to the huge amount of data and complex microservice dependency, it also poses new challenges in ensuring reliable performance and maintenance. Existing approaches still suffer from limitations of anomaly data, over-simplification of metric relationships, and lack of diagnosing interpretability. To solve these issues, this paper builds a hierarchy root cause diagnosis framework, named Hi-RCA. We propose a global perspective to characterize different abnormal symptoms, which focuses on changes in metrics’ causation and correlation. We decompose the diagnosis task into two phases: anomalous microservice location and anomalous reason diagnosis. In the first phase, we use Kalman filtering to quantify microservice abnormality based on the estimation error. In the second phase, we use causation analysis to identify anomalous metrics and generate anomaly knowledge graphs; by correlation analysis, we construct an anomaly propagation graph and explain the anomaly symptoms via graph comparison. Our experimental evaluation on an open dataset shows that Hi-RCA can effectively locate root causes with 90% mean average precision, outperforming state-of-the-art methods. Full article

(This article belongs to the Special Issue Advances and Challenges in Reliability and Maintenance Engineering)

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22 pages, 5301 KiB

Open AccessArticle

Assessment Possibilities of the Quality of Mining Equipment and of the Parts Subjected to Intense Wear

by Vlad Alexandru Florea, Mihaela Toderaș and Răzvan-Bogdan Itu

Appl. Sci. 2023, 13(6), 3740; https://doi.org/10.3390/app13063740 - 15 Mar 2023

Cited by 4 | Viewed by 1846

Abstract

The equipment in underground mines provides a continuous production flow, depending on the way their quality is preserved during their operation. The TR-7A scraper conveyer subassemblies, which function in the Jiu Valley coal basin and are subjected to abrasion wear, showed a high [...] Read more.

The equipment in underground mines provides a continuous production flow, depending on the way their quality is preserved during their operation. The TR-7A scraper conveyer subassemblies, which function in the Jiu Valley coal basin and are subjected to abrasion wear, showed a high failure frequency (chains, chain elevators, and driving and turning drums), as well as the hydraulic couplings and certain electric equipment of the same machinery. The data collected following the TR-7A scraper conveyer at work allowed the parameters to be determined that characterise the reliability and maintainability of the above-mentioned components, the failure modes, and their effects. Using calculation methods, the interpretation of the results has been facilitated, with a view to reducing maintenance costs and obtaining an 80% reliability for the components with the most failures, in the case of the TR-7A scraper conveyer. Full article

(This article belongs to the Special Issue Advances and Challenges in Reliability and Maintenance Engineering)

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Review

Jump to: Research

23 pages, 8007 KiB

Open AccessReview

Monitoring and Leak Diagnostics of Sulfur Hexafluoride and Decomposition Gases from Power Equipment for the Reliability and Safety of Power Grid Operation

by Luxi Yang, Song Wang, Chuanmin Chen, Qiyu Zhang, Rabia Sultana and Yinghui Han

Appl. Sci. 2024, 14(9), 3844; https://doi.org/10.3390/app14093844 - 30 Apr 2024

Cited by 3 | Viewed by 1145

Abstract

Sulfur hexafluoride (SF₆) is a typical fluorine gas with excellent insulation and arc extinguishing properties that has been widely used in large-scale power equipment. The detection of SF₆ gas in high-power electrical equipment is a necessary measure to ensure the [...] Read more.

Sulfur hexafluoride (SF₆) is a typical fluorine gas with excellent insulation and arc extinguishing properties that has been widely used in large-scale power equipment. The detection of SF₆ gas in high-power electrical equipment is a necessary measure to ensure the reliability and safety of power grid operation. A failure of SF₆ insulated electrical equipment, such as discharging or overheating conditions, can cause SF₆ gas decomposition, resulting in various decomposition products. The decomposed gases inside the equipment decrease the insulating properties and are toxic. The leakage of SF₆ can also decrease the insulating properties. Therefore, it is crucial to monitor the leakage of SF₆ decomposed gases from electrical equipment. Quantitative testing of decomposition products allows us to assess the insulation state of the equipment, identify internal faults, and maintain the equipment. This review comprehensively introduces the decomposition formation mechanism of SF₆ gas and the current detection technology of decomposition products from the aspects of principle and structure, materials, test effect, and practicability. Finally, the development trends of SF₆ and decomposition gas detection technology for the reliability and safety of power grid operation are prospected. Full article

(This article belongs to the Special Issue Advances and Challenges in Reliability and Maintenance Engineering)

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