From predictive to energy-based maintenance paradigm: Achieving cleaner production through functional-productiveness

Orošnjak, Marko; Brkljač, Nebojša; Šević, Dragoljub; Čavić, Maja; Oros, Dragana; Penčić, Marko

doi:10.1016/j.jclepro.2023.137177

Cited by 17 publications

(14 citation statements)

References 32 publications

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“…Except for the turbine parameters, all the parameters of the system are constant, i.e., they do not change with the operating conditions, as shown in Table 1. It is worth noting that these parameters are not static and can change to varying degrees due to usage time, fault maintenance, or other reasons [37]. When the parameters change significantly, it is necessary to redetermine the parameter values and make corrections to the results of the previous parameter sensitivity analysis.…”

Section: Hydraulic-mechanical-electrical Coupling Model Of Htrsmentioning

confidence: 99%

A Parameter Sensitivity Analysis of Hydropower Units under Full Operating Conditions Considering Turbine Nonlinearity

Liu,

Wei,

Zhang

et al. 2023

Sustainability

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A parameter sensitivity analysis is an important part of the stability study of hydro turbine regulation systems, which helps operators to deepen their understanding of the characteristics and connections among the various parts of these systems. Considering that large hydropower stations undertake an essential regulation task in the power grid, the safety and stability of their operation cannot be ignored. To this end, taking a unit in a giant hydropower station in China as an example, a hydraulic–mechanical–electrical coupling model of the hydraulic turbine regulation system is established. A comprehensive parameter sensitivity indicator and parameter sensitivity analysis framework are proposed. On this basis, the sensitivity of the main system variables to parameter changes under full operating conditions is investigated by considering two different control modes of the unit (i.e., corresponding to different grid types). The results show that the sensitivity of the system state to the mechanical parameters of the generator is the highest in the power control mode, while the sensitivity to the electrical parameters of the generator and excitation system is higher in the frequency control mode. The sensitivity of the system with these key parameters also shows different patterns of change with a change in the unit operating conditions. The relevant findings can provide some theoretical guidance for the operation of hydropower stations and help to reduce the risk of system instability.

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Section: Hydraulic-mechanical-electrical Coupling Model Of Htrsmentioning

confidence: 99%

A Parameter Sensitivity Analysis of Hydropower Units under Full Operating Conditions Considering Turbine Nonlinearity

Liu,

Wei,

Zhang

et al. 2023

Sustainability

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“…Given that the vibration signal is easily interfered with by factors such as the inherent mechanical vibration of the hydraulic servomotor and the strong noise background, and the fact that pressure signals play an increasingly important role in the field of the intelligent operation and maintenance of hydraulic systems [9], a specific description of the characteristics of the pressure signal can be formed through the extraction of multiple time-domain and frequency-domain features of the pressure signal that are of good physical significance and interpretability. However, the characteristic information obtained only from a single pressure signal is incomplete in describing the overall operating state of the adjustment hydraulic servomotor, and the Multi-source Information Fusion (MSIF) method can combine the complementary information in space.…”

Section: Introductionmentioning

confidence: 99%

Abnormal Detection and Fault Diagnosis of Adjustment Hydraulic Servomotor Based on Genetic Algorithm to Optimize Support Vector Data Description with Negative Samples and One-Dimensional Convolutional Neural Network

Yang,

Jiang,

Jiang

et al. 2024

Machines

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Because of the difficulty in fault detection for and diagnosing the adjustment hydraulic servomotor, this paper uses feature extraction technology to extract the time domain and frequency domain features of the pressure signal of the adjustment hydraulic servomotor and splice the features of multiple pressure signals through the Multi-source Information Fusion (MSIF) method. The comprehensive expression of device status information is obtained. After that, this paper proposes a fault detection Algorithm GA-SVDD-neg, which uses Genetic Algorithm (GA) to optimize Support Vector Data Description with negative examples (SVDD-neg). Through joint optimization with the Mutual Information (MI) feature selection algorithm, the features that are most sensitive to the state deterioration of the adjustment hydraulic servomotor are selected. Experiments show that the MI algorithm has a better performance than other feature dimensionality reduction algorithms in the field of the abnormal detection of adjustment hydraulic servomotors, and the GA-SVDD-neg algorithm has a stronger robustness and generality than other anomaly detection algorithms. In addition, to make full use of the advantages of deep learning in automatic feature extraction and classification, this paper realizes the fault diagnosis of the adjustment hydraulic servomotor based on 1D Convolutional Neural Network (1DCNN). The experimental results show that this algorithm has the same superior performance as the traditional algorithm in feature extraction and can accurately diagnose the known faults of the adjustment hydraulic servomotor. This research is of great significance for the intelligent transformation of adjustment hydraulic servomotors and can also provide a reference for the fault warning and diagnosis of the Electro-Hydraulic (EH) system of the same type of steam turbine.

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“…Conventional maintenance practices have primarily focused on reactive repairs and replacements, often leading to inefficient resource utilisation and environmental degradation. However, the paradigm is shifting towards predictive, energy-based [1], and sustainable maintenance (SM) practices [2]. These practices encompass strategies neglecting the sole aim of prolonging the lifespan for the sake of profitability, in fact, the environmental and social priorities are now at the forefront [3].…”

Section: Introduction 1backgroundmentioning

confidence: 99%

Identification of Criteria for Enabling the Adoption of Sustainable Maintenance Practice: An Umbrella Review

Vasić,

Orošnjak,

Brkljač

et al. 2024

Sustainability

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The evolution from traditional industrial maintenance to sustainable maintenance (SM) is pivotal within an existing industrial ecosystem. This study, utilising an umbrella review (UR), critically examines this transition, highlighting its increased importance in maintenance decision-making (MDM). Using a sample (n = 20) of reviews, we synthesised meta-, methodological-, and content-based evidence and performed bibliometric, thematic and statistical analyses. For the bibliometric and thematic/conceptual analyses, we used the R bibliometrix package. The results show that the early research focuses mainly on theoretical aspects, while recent studies examine the practical implications. Also, comprehensive studies evaluating the benefits of implementing environmental and social aspects within MDM are still lacking. For that reason, we switched the attention to content-based data, from which we identified 43 distinct criteria discussed. For the analysis of criteria, the Bayesian Network Analysis with Gaussian Copula Graphical Model (BNA-GCGM) method was used. Although the evidence shows that environmental pollution, energy consumption and health and safety of workers are the most discussed criteria, the BNA-GCGM suggests that labour costs, resource consumption, employee satisfaction and energy consumption, among others, are the most influential criteria in the network analysis. Interestingly, after distinguishing studies into pre- and post-2021 research, the results show that pre-2021 research is primarily focused on economic and technical factors, reflecting a profit-oriented approach. The post-2021 analysis suggests a discernible shift towards more balanced considerations by incorporating social and environmental factors, suggesting a more socially responsible approach. Finally, while SM is gaining momentum, further empirical and practical research are required to demonstrate the advantages that SM offers in the light of the upcoming Industry 5.0.

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From predictive to energy-based maintenance paradigm: Achieving cleaner production through functional-productiveness

Cited by 17 publications

References 32 publications

A Parameter Sensitivity Analysis of Hydropower Units under Full Operating Conditions Considering Turbine Nonlinearity

A Parameter Sensitivity Analysis of Hydropower Units under Full Operating Conditions Considering Turbine Nonlinearity

Abnormal Detection and Fault Diagnosis of Adjustment Hydraulic Servomotor Based on Genetic Algorithm to Optimize Support Vector Data Description with Negative Samples and One-Dimensional Convolutional Neural Network

Identification of Criteria for Enabling the Adoption of Sustainable Maintenance Practice: An Umbrella Review

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