A Systematic Mapping Study of Predictive Maintenance in SMEs

Khan, Maqbool; Ahmad, Arshad; Sobieczky, Florian; Pichler, Mario; Moser, Bernhard; Bukovský, Ivo

doi:10.1109/access.2022.3200694

Cited by 15 publications

(6 citation statements)

References 66 publications

(100 reference statements)

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“…The comparative investigation of the high-frequency asymptotic scattering model for the acoustic target strength of the underwater submarine vehicle yields interesting results as depicted in Table 1 to reference [1], [35] and [37]. First, in terms of acoustic pressure, the model shows good agreement with experimental data at high frequencies, demonstrating its accuracy in forecasting the acoustic pressure response.…”

Section: Consistent With the Reference Modelmentioning

confidence: 88%

“…The Helmholtz equation and the wave equation can be used to generate simple acoustic fields, but a more complex geometrical model that accounts for the boundary conditions of underwater habitats is required to produce an accurate underwater environment [34][35][36]. This research employs the FEM with the governing equation in acousticsolid connected modules [37] to do the required complex calculation. The target strength (TS) is computed by the following Equation 3:…”

Section: Theoretical Basis Of the Proposed Modelmentioning

confidence: 99%

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Submarine Acoustic Target Strength Modeling at High-Frequency Asymptotic Scattering

Kumar,

Chinthaginjala,

Anbazhagan

et al. 2024

IEEE Access

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The ability to identify asymptotic scattering is critical for the continuous operation of modern combat systems. In the active sonar equation, acoustic target strength is crucial. The plane-wave propagation target strength equation predicts far-field reradiated intensity. Submarines defend themselves by being invisible. Sonar can be used to detect submarines. Saltwater readily absorbs radio waves. Commercial fishing and scientific ocean exploration both make use of sonar technology. Acoustic wave reflection reduces the area requirements of ocean depth sections, therefore submarine designers take this into consideration. The Target Strength (TS) of a sonar target is used to assess its size. The Pressure Acoustic-Boundary Element Model (PA-BEM) of the High-Frequency Boundary Model (HFB) illuminates and simplifies the TS analysis of the Benchmark Target Echo Strength Simulation (BeTTSi) benchmark submarine. We explore how subsea shape, material qualities, and operation frequency affect acoustic target strength using comprehensive models. The findings underline the need of correctly characterising the structural sections of the submarine and their impact on dispersion. They also help with research into the submarine's acoustic signature, detectability, and potential detection mitigation measures.

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Section: Consistent With the Reference Modelmentioning

confidence: 88%

Section: Theoretical Basis Of the Proposed Modelmentioning

confidence: 99%

Submarine Acoustic Target Strength Modeling at High-Frequency Asymptotic Scattering

Kumar,

Chinthaginjala,

Anbazhagan

et al. 2024

IEEE Access

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show abstract

“…The overall principle of Prognostics and Health Management is to transform a set of raw data collected on the monitored equipment into health indicators, whose extrapolation over time makes it possible to define detailed decision support. There are several architectures in the field of Industry 4.0 among those that are developed and the best known are Open System Architecture for Condition-Based-Maintenance [5][6][7][8][9]. An overall view of this PHM architecture is given in Figure 6.…”

Section: Prognostics and Health Management (Phm) As A New Methods Of ...mentioning

confidence: 99%

“…Even before the pandemic, Industry 4.0 technologies were transforming the way manufacturers work. Today, the effectiveness of leaders in using technology is essential [5][6][7][8]. With the advent of Industry 4.0 [9,10] in manufacturing, businesses can leverage new technologies to monitor and better understand their operations in real time, transforming a typical manufacturing facility into a smart factory.…”

Section: Industrial Maintenance Technologiesmentioning

confidence: 99%

Contribution of artificial intelligence to industrial maintenance in the field of mechanics

El Khaili,

Rafik,

Fila

et al. 2024

Recent Topics in Maintenance Management

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The global industry is in continuous technological evolution, which aims for reliability, efficiency, availability, and safety while reducing maintenance costs. Modern maintenance follows change, which can no longer be limited to being corrective or preventive, but must be proactive involving the continuous monitoring and verification of the root causes of failure; it must also be predictive which makes it possible to anticipate breakdowns and increase equipment usage time based on the Prognosis and Health Management (PHM), which transforms raw data into indicators and makes it possible to define the Residual Life (RUL) and its extrapolation as a decision-making tool. Our chapter consists of presenting the contribution of AI to industrial maintenance in the field of mechanics. It focuses on industrial maintenance through its concepts, technologies, and methods used. So, the presentation of artificial intelligence and its algorithms applied toward maintenance 4.0 are to show the contribution of AI to maintenance.

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“…Predictive maintenance is a well-described topic in the literature, and it has many cases of successful implementation in several different industries [5][6][7]. Predictive maintenance typically consists of two main elements: a prediction model itself and a service planning model.…”

Section: Introductionmentioning

confidence: 99%

Cost-Effectiveness of Predictive Maintenance for Offshore Wind Farms: A Case Study

Frederiksen,

Bocewicz,

Radzki

et al. 2024

Energies

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The successful implementation of predictive maintenance for offshore wind farms suffers from a poor understanding of the consequential short-term impacts and a lack of research on how to evaluate the cost-efficiency of such efforts. This paper aims to develop a methodology to explore the short-term marginal impacts of predictive maintenance applied to an already existing preventive maintenance strategy. This method will be based on an analysis of the performance of the underlying predictive model and the costs considered under specific maintenance services. To support this analysis, we develop a maintenance efficiency measure able to estimate the efficiency of both the underlying prediction model used for predictive maintenance and the resulting maintenance efficiency. This distinction between the efficiency of the model and the service results will help point out insufficiencies in the predictive maintenance strategy, as well as facilitate calculations on the cost–benefits of the predictive maintenance implementation. This methodology is validated on a realistic case study of an annual service mission for an offshore wind farm and finds that the efficiency metrics described in this paper successfully support cost–benefit estimates.

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A Systematic Mapping Study of Predictive Maintenance in SMEs

Cited by 15 publications

References 66 publications

Submarine Acoustic Target Strength Modeling at High-Frequency Asymptotic Scattering

Submarine Acoustic Target Strength Modeling at High-Frequency Asymptotic Scattering

Contribution of artificial intelligence to industrial maintenance in the field of mechanics

Cost-Effectiveness of Predictive Maintenance for Offshore Wind Farms: A Case Study

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