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Structural Prognostics and Health Management in Power & Energy Systems

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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F: Electrical Engineering".

Deadline for manuscript submissions: closed (30 June 2019) | Viewed by 64444

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Special Issue Editors

Dr. Dong Wang

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The State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: intelligent operation and maintenance; mathematical basis of fault feature extraction and sparse measure; prognostic and health management
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Prof. Dr. Shun-Peng Zhu

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Guest Editor

Center for System Reliability and Safety, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: structural integrity and reliability analysis; damage tolerance design and life prediction; artificial intelligence and health assessment
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Prof. Dr. Xiancheng Zhang

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Key Laboratory of Pressure Systems and Safety, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China
Interests: multi-physics damage modeling; high temperature fatigue; fatigue-creep interaction; life design and prediction; structural integrity; damage tolerance
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Prof. Dr. Gang Chen

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Guest Editor

School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, China
Interests: reliability testing and statistics; life prediction; advanced testing techniques; chemical equipment; power plant technologies; damage modeling; fracture mechanics; fatigue; damage tolerance; structural integrity assessment

Prof. Dr. José António Correia

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Department of Civil Engineering, University of Porto, 4200-465 Porto, Portugal
Interests: numerical modeling of engineering structures and structural components (offshore applications, steel bridges, pressure vessels, pipelines, wind turbine towers, etc.); mathematical problems in fatigue and fracture; mechanics of solids and structures; metals materials and structures; numerical fracture mechanics and crack growth; local approaches; finite element methods in structural mechanics applications; computer-aided structural integrity
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Dr. Guian Qian

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Guest Editor

Laboratory for Nuclear Materials, Paul Scherrer Institute (PSI), Switzerland
Interests: steam turbine; power plant technologies; failure mechanisms; probabilistic damage tolerance; structural integrity; fatigue and fracture analysis of nuclear components and structures; nuclear energy and safety; pressurized thermal shock analysis of reactor pressure vessels; leak-before-break analysis of nuclear piping; nuclear materials

Special Issue Information

Dear Colleagues,

In order to ensure the safety and reliability of power and energy systems, including wind turbines, gas/steam turbines, power plants, etc., failure mechanism, reliability assessment, prognostics, and health management (PHM) have becoming recent developments in integrity analysis of these systems. For many countries, such as the European countries, England and the USA, currently facing a potential future mismatch from energy production and transformation, currently increasing interests are being paid on new techniques to discover and understand the remaining life and integrity assessment of power and energy systems.

To prevent any unexpected machine breakdowns and accidents, early faults of critical components in these systems should be detected as soon as possible. Once early faults of critical components are diagnosed, their performance degradation assessment and remaining useful life estimation should be conducted to maximize lifetime of power and energy systems. Moreover, due to unexpected ageing related degradations/damaging, mechanical properties, microstructures and structural resistance of systems/components often require stochastic considerations related to failure mechanism modeling and analysis. In addition, various sources of uncertainty/variability arising from a simpliﬁed representation of the actual physical process (often through semi-empirical or empirical models) and/or sparse information on manufacturing, material properties, and loading proﬁles contribute to stochastic behavior under operation.

Accordingly, continued improvements on PHM have been possible through advanced signature analysis, performance degradation assessment, as well as accurate modeling of failure mechanisms by introducing advanced mathematical approaches/tools. Through combining the deterministic and probabilistic modeling techniques, researches on PHM and structural health monitoring (SHM) can provide assurance for new structures at the design stage and ensure the integrity in the construction at the fabrication phase. Specifically, power and energy system failure occurs under multi-sources of uncertainty/variability, resulting from load variation in usages, material properties, geometry variations within tolerances, and other uncontrolled variations. Thus, advanced methods and applications for theoretical, numerical, and experimental contributions that address these issues on PHM are desired and expected, which attempts to prevent over-design and unnecessary inspection and provide the tools to enable a balance between safety and economy to be achieved.

The aim of this Special Issue would be to provide the data, models and tools necessary to performing PHM from structural to the system, resulting in the use of advanced mathematical, numerical and experimental techniques. Therefore, researchers are invited to provide original research and review articles that seek for accurate and efficient machine fault diagnosis and prognosis, remaining life assessment, condition-based maintenance, and so forth. Potential topics include, but are not limited to:

wind/gas/steam turbine technologies
power plant technologies
failure mechanisms
damage/degradation
digital/adaptive signal processing
statistical signal processing
prognostics and health management
probabilistic damage tolerance
probabilistic physics of failure
structural integrity assessment
structural reliability
reliability testing and statistics
life prediction
degradation modeling and analysis
structural health monitoring
system diagnostics

Dr. Dong Wang
Dr. Shun-Peng Zhu
Prof. Dr. Xiancheng Zhang
Prof. Dr. Gang Chen
Dr. José A.F.O. Correia
Dr. Guian Qian
Guest Editors

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Published Papers (11 papers)

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Research

12 pages, 5334 KiB

Open AccessArticle

An Improved Signal Processing Approach Based on Analysis Mode Decomposition and Empirical Mode Decomposition

by Zhongzhe Chen, Baqiao Liu, Xiaogang Yan and Hongquan Yang

Energies 2019, 12(16), 3077; https://doi.org/10.3390/en12163077 - 9 Aug 2019

Cited by 29 | Viewed by 3626

Abstract

Empirical mode decomposition (EMD) is a widely used adaptive signal processing method, which has shown some shortcomings in engineering practice, such as sifting stop criteria of intrinsic mode function (IMF), mode mixing and end effect. In this paper, an improved sifting stop criterion based on the valid data segment is proposed, and is compared with the traditional one. Results show that the new sifting stop criterion avoids the influence of end effects and improves the correctness of the EMD. In addition, a novel AEMD method combining the analysis mode decomposition (AMD) and EMD is developed to solve the mode-mixing problem, in which EMD is firstly applied to dispose the original signal, and then AMD is used to decompose these mixed modes. Then, these decomposed modes are reconstituted according to a certain principle. These reconstituted components showed mode mixing phenomena alleviated. Model comparison was conducted between the proposed method with the ensemble empirical mode decomposition (EEMD), which is the mainstream method improved based on EMD. Results indicated that the AEMD and EEMD can effectively restrain the mode mixing, but the AEMD has a shorter execution time than that of EEMD. Full article

(This article belongs to the Special Issue Structural Prognostics and Health Management in Power & Energy Systems)

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14 pages, 1544 KiB

Open AccessArticle

A Lithium-ion Battery RUL Prediction Method Considering the Capacity Regeneration Phenomenon

by Xiaoqiong Pang, Rui Huang, Jie Wen, Yuanhao Shi, Jianfang Jia and Jianchao Zeng

Energies 2019, 12(12), 2247; https://doi.org/10.3390/en12122247 - 12 Jun 2019

Cited by 91 | Viewed by 7028

Abstract

Prediction of Remaining Useful Life (RUL) of lithium-ion batteries plays a significant role in battery health management. Battery capacity is often chosen as the Health Indicator (HI) in research on lithium-ion battery RUL prediction. In the rest time of batteries, capacity will produce a certain degree of regeneration phenomenon, which exists in the use of each battery. Therefore, considering the capacity regeneration phenomenon in RUL prediction of lithium-ion batteries is helpful to improve the prediction performance of the model. In this paper, a novel method fusing the wavelet decomposition technology (WDT) and the Nonlinear Auto Regressive neural network (NARNN) model for predicting the RUL of a lithium-ion battery is proposed. Firstly, the multi-scale WDT is used to separate the global degradation and local regeneration of a battery capacity series. Then, the RUL prediction framework based on the NARNN model is constructed for the extracted global degradation and local regeneration. Finally, the two parts of the prediction results are combined to obtain the final RUL prediction result. Experiments show that the proposed method can not only effectively capture the capacity regeneration phenomenon, but also has high prediction accuracy and is less affected by different prediction starting points. Full article

(This article belongs to the Special Issue Structural Prognostics and Health Management in Power & Energy Systems)

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16 pages, 6985 KiB

Open AccessArticle

Weighted Regression-Based Extremum Response Surface Method for Structural Dynamic Fuzzy Reliability Analysis

by Cheng Lu, Yun-Wen Feng and Cheng-Wei Fei

Energies 2019, 12(9), 1588; https://doi.org/10.3390/en12091588 - 26 Apr 2019

Cited by 19 | Viewed by 3070

Abstract

The parameters considered in structural dynamic reliability analysis have strong uncertainties during machinery operation, and affect analytical precision and efficiency. To improve structural dynamic fuzzy reliability analysis, we propose the weighted regression-based extremum response surface method (WR-ERSM) based on extremum response surface method (ERSM) and weighted regression (WR), by considering the randomness of design parameters and the fuzziness of the safety criterion. Therein, we utilize the ERSM to process the transient to improve computational efficiency, by transforming the random process of structural output response into a random variable. We employ the WR to find the efficient samples with larger weights to improve the calculative accuracy. The fuzziness of the safety criterion is regarded to improve computational precision in the WR-ERSM. The WR-ERSM is applied to perform the dynamic fuzzy reliability analysis of an aeroengine turbine blisk with the fluid-structure coupling technique, and is verified by the comparison of the Monte Carlo (MC) method, equivalent stochastic transformation method (ESTM) and ERSM, with the emphasis on model-fitting property and simulation performance. As revealed from this investigation, (1) the ERSM has the capacity of processing the transient of the structural dynamic reliability evaluation, and (2) the WR approach is able to improve modeling accuracy, and (3) regarding the fuzzy safety criterion is promising to improve the precision of structural dynamic fuzzy reliability evaluation, and (4) the change rule of turbine blisk structural stress from start to cruise for the aircraft is acquired with the maximum value of structural stress at t = 165 s and the reliability degree (Pr = 0.997) of turbine blisk. The proposed WR-ERSM can improve the efficiency and precision of structural dynamic reliability analysis. Therefore, the efforts of this study provide a promising method for structural dynamic reliability evaluation with respect to working processes. Full article

(This article belongs to the Special Issue Structural Prognostics and Health Management in Power & Energy Systems)

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15 pages, 3980 KiB

Open AccessArticle

GA-BP Neural Network-Based Strain Prediction in Full-Scale Static Testing of Wind Turbine Blades

by Zheng Liu, Xin Liu, Kan Wang, Zhongwei Liang, José A.F.O. Correia and Abílio M.P. De Jesus

Energies 2019, 12(6), 1026; https://doi.org/10.3390/en12061026 - 15 Mar 2019

Cited by 46 | Viewed by 4695

Abstract

This paper proposes a strain prediction method for wind turbine blades using genetic algorithm back propagation neural networks (GA-BPNNs) with applied loads, loading positions, and displacement as inputs, and the study can be used to provide more data for the wind turbine blades’ health assessment and life prediction. Among all parameters to be tested in full-scale static testing of wind turbine blades, strain is very important. The correlation between the blade strain and the applied loads, loading position, displacement, etc., is non-linear, and the number of input variables is too much, thus the calculation and prediction of the blade strain are very complex and difficult. Moreover, the number of measuring points on the blade is limited, so the full-scale blade static test cannot usually provide enough data and information for the improvement of the blade design. As a result of these concerns, this paper studies strain prediction methods for full-scale blade static testing by introducing GA-BPNN. The accuracy and usability of the GA-BPNN prediction model was verified by the comparison with BPNN model and the FEA results. The results show that BPNN can be effectively used to predict the strain of unmeasured points of wind turbine blades. Full article

(This article belongs to the Special Issue Structural Prognostics and Health Management in Power & Energy Systems)

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22 pages, 4553 KiB

Open AccessArticle

Early Fault Detection of Wind Turbines Based on Operational Condition Clustering and Optimized Deep Belief Network Modeling

by Hong Wang, Hongbin Wang, Guoqian Jiang, Jimeng Li and Yueling Wang

Energies 2019, 12(6), 984; https://doi.org/10.3390/en12060984 - 13 Mar 2019

Cited by 48 | Viewed by 4090

Abstract

Health monitoring and early fault detection of wind turbines have attracted considerable attention due to the benefits of improving reliability and reducing the operation and maintenance costs of the turbine. However, dynamic and constantly changing operating conditions of wind turbines still pose great challenges to effective and reliable fault detection. Most existing health monitoring approaches mainly focus on one single operating condition, so these methods cannot assess the health status of turbines accurately, leading to unsatisfactory detection performance. To this end, this paper proposes a novel general health monitoring framework for wind turbines based on supervisory control and data acquisition (SCADA) data. A key feature of the proposed framework is that it first partitions the turbine operation into multiple sub-operation conditions by the clustering approach and then builds a normal turbine behavior model for each sub-operation condition. For normal behavior modeling, an optimized deep belief network is proposed. This optimized modeling method can capture the sophisticated nonlinear correlations among different monitoring variables, which is helpful to enhance the prediction performance. A case study of main bearing fault detection using real SCADA data is used to validate the proposed approach, which demonstrates its effectiveness and advantages. Full article

(This article belongs to the Special Issue Structural Prognostics and Health Management in Power & Energy Systems)

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21 pages, 5446 KiB

Open AccessArticle

A Data-Driven Predictive Prognostic Model for Lithium-ion Batteries based on a Deep Learning Algorithm

by Phattara Khumprom and Nita Yodo

Energies 2019, 12(4), 660; https://doi.org/10.3390/en12040660 - 18 Feb 2019

Cited by 255 | Viewed by 15970

Abstract

Prognostic and health management (PHM) can ensure that a lithium-ion battery is working safely and reliably. The main approach of PHM evaluation of the battery is to determine the State of Health (SoH) and the Remaining Useful Life (RUL) of the battery. The advancements of computational tools and big data algorithms have led to a new era of data-driven predictive analysis approaches, using machine learning algorithms. This paper presents the preliminary development of the data-driven prognostic, using a Deep Neural Networks (DNN) approach to predict the SoH and the RUL of the lithium-ion battery. The effectiveness of the proposed approach was implemented in a case study with a battery dataset obtained from the NASA Ames Prognostics Center of Excellence (PCoE) database. The proposed DNN algorithm was compared against other machine learning algorithms, namely, Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), Artificial Neural Networks (ANN), and Linear Regression (LR). The experimental results reveal that the performance of the DNN algorithm could either match or outweigh other machine learning algorithms. Further, the presented results could serve as a benchmark of SoH and RUL prediction using machine learning approaches specifically for lithium-ion batteries application. Full article

(This article belongs to the Special Issue Structural Prognostics and Health Management in Power & Energy Systems)

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28 pages, 5612 KiB

Open AccessArticle

Initial Design Phase and Tender Designs of a Jacket Structure Converted into a Retrofitted Offshore Wind Turbine

by Lorenzo Alessi, José A. F. O. Correia and Nicholas Fantuzzi

Energies 2019, 12(4), 659; https://doi.org/10.3390/en12040659 - 18 Feb 2019

Cited by 26 | Viewed by 5937

Abstract

Jackets are the most common structures in the Adriatic Sea for extracting natural gas. These structural typologies are suitable for relative low water depths and flat sandy sea floors. Most of them have been built in the last 50 years. When the underground source finishes, these structures should be moved to another location or removed if they have reached their design life. Nevertheless, another solution might be considered: change the future working life of these platforms by involving renewable energy and transforming them into offshore wind towers. The present research proposal aims to investigate the possibility of converting actual structures for gas extraction into offshore platforms for wind turbine towers. This simplified analysis is useful for initial design phases and tender design, or generally when available information is limited. The model proposed is a new simplified tool used to study the structural analysis of the jacket structure, developed and summarized in 10 steps, firstly adopted to study the behavior of the oil and gas structure and then for the retrofitted wind tower configuration. Full article

(This article belongs to the Special Issue Structural Prognostics and Health Management in Power & Energy Systems)

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21 pages, 39943 KiB

Open AccessArticle

Dynamic Study of a Rooftop Vertical Axis Wind Turbine Tower Based on an Automated Vibration Data Processing Algorithm

by Ying Wang, Wensheng Lu, Kaoshan Dai, Miaomiao Yuan and Shen-En Chen

Energies 2018, 11(11), 3135; https://doi.org/10.3390/en11113135 - 13 Nov 2018

Cited by 11 | Viewed by 4653

Abstract

When constructed on tall building rooftops, the vertical axis wind turbine (VAWT) has the potential of power generation in highly urbanized areas. In this paper, the ambient dynamic responses of a rooftop VAWT were investigated. The dynamic analysis was based on ambient measurements of the structural vibration of the VAWT (including the supporting structure), which resides on the top of a 24-story building. To help process the ambient vibration data, an automated algorithm based on stochastic subspace identification (SSI) with a fast clustering procedure was developed. The algorithm was applied to the vibration data for mode identification, and the results indicate interesting modal responses that may be affected by the building vibration, which have significant implications for the condition monitoring strategy for the VAWT. The environmental effects on the ambient vibration data were also investigated. It was found that the blade rotation speed contributes the most to the vibration responses. Full article

(This article belongs to the Special Issue Structural Prognostics and Health Management in Power & Energy Systems)

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22 pages, 4359 KiB

Open AccessArticle

Study on Vibration Transmission among Units in Underground Powerhouse of a Hydropower Station

by Jijian Lian, Hongzhen Wang and Haijun Wang

Energies 2018, 11(11), 3015; https://doi.org/10.3390/en11113015 - 2 Nov 2018

Cited by 7 | Viewed by 3762

Abstract

Research on the safety of powerhouse in a hydropower station is mostly concentrated on the vibration of machinery structure and concrete structure within a single unit. However, few studies have been focused on the vibration transmission among units. Due to the integrity of the powerhouse and the interaction, it is necessary to study the vibration transmission mechanism of powerhouse structure among units. In this paper, field structural vibration tests are conducted in an underground powerhouse of a hydropower station on Yalong River. Additionally, the simplified mechanical models are established to explain the transmission mechanism theoretically. Moreover, a complementary finite element (FE) model is built to replicate the testing conditions for comprehensive analysis. The field tests results show that: (1) the transmission of lateral-river vibration is greater than those of longitude-river vibration and vertical vibration; (2) the vibration transmission of the vibrations that is caused by the low frequency tail fluctuation is basically equal to that of the vibrations caused by rotation of hydraulic generator. The transmission mechanism is demonstrated by the simplified mechanical models and is verified by the FE results. This study can provide guidance for further research on the vibration of underground powerhouse structure. Full article

(This article belongs to the Special Issue Structural Prognostics and Health Management in Power & Energy Systems)

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17 pages, 1456 KiB

Open AccessArticle

A Non-Probabilistic Solution for Uncertainty and Sensitivity Analysis on Techno-Economic Assessments of Biodiesel Production with Interval Uncertainties

by Zhang-Chun Tang, Yanjun Xia, Qi Xue and Jie Liu

Energies 2018, 11(3), 588; https://doi.org/10.3390/en11030588 - 8 Mar 2018

Cited by 13 | Viewed by 3554

Abstract

Techno-economic assessments (TEA) of biodiesel production may comply with various economic and technical uncertainties during the lifespan of the project, resulting in the variation of many parameters associated with biodiesel production, including price of biodiesel, feedstock price, and rate of interest. Engineers may only collect very limited information on these uncertain parameters such as their variation intervals with lower and upper bound. This paper proposes a novel non-probabilistic strategy for uncertainty analysis (UA) in the TEA of biodiesel production with interval parameters, and non-probabilistic reliability index (NPRI) is employed to measure the economically feasible extent of biodiesel production. A sensitivity analysis (SA) indicator is proposed to assess the sensitivity of NPRI with regard to an individual uncertain interval parameter. The optimization method is utilized to solve NPRI and SA. Results show that NPRI in the focused biodiesel production of interest is 0.1211, and price of biodiesel, price of feedstock, and cost of operating can considerably affect TEA of biodiesel production. Full article

(This article belongs to the Special Issue Structural Prognostics and Health Management in Power & Energy Systems)

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2080 KiB

Open AccessArticle

Remaining Useful Life Estimation of Aircraft Engines Using a Modified Similarity and Supporting Vector Machine (SVM) Approach

by Zhongzhe Chen, Shuchen Cao and Zijian Mao

Energies 2018, 11(1), 28; https://doi.org/10.3390/en11010028 - 23 Dec 2017

Cited by 80 | Viewed by 6363

Abstract

As the main power source for aircrafts, the reliability of an aero engine is critical for ensuring the safety of aircrafts. Prognostics and health management (PHM) on an aero engine can not only improve its safety, maintenance strategy and availability, but also reduce its operation and maintenance costs. Residual useful life (RUL) estimation is a key technology in the research of PHM. According to monitored performance data from the engine’s different positions, how to estimate RUL of an aircraft engine by utilizing these data is a challenge for ensuring the engine integrity and safety. In this paper, a framework for RUL estimation of an aircraft engine is proposed by using the whole lifecycle data and performance-deteriorated parameter data without failures based on the theory of similarity and supporting vector machine (SVM). Moreover, a new state of health indicator is introduced for the aircraft engine based on the preprocessing of raw data. Finally, the proposed method is validated by using 2008 PHM data challenge competition data, which shows its effectiveness and practicality. Full article

(This article belongs to the Special Issue Structural Prognostics and Health Management in Power & Energy Systems)

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