Predictive Maintenance Neural Control Algorithm for Defect Detection of the Power Plants Rotating Machines Using Augmented Reality Goggles

Lalik, Krzysztof; Wątorek, Filip

doi:10.3390/en14227632

Cited by 15 publications

(9 citation statements)

References 37 publications

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“…AR content is being continuously developed and used in various industrial fields. Lalick and Watorek developed an algorithm that can collect data and detect mechanical defects in wind turbines using AR goggles [4]. This algorithm simplifies the maintenance process of a wind turbine and provides an accurate monitoring system.…”

Section: A Related Workmentioning

confidence: 99%

Development and Evaluation of Augmented Reality Learning Content for Pneumatic Flow: Case Study on Brake Operating Unit of Railway Vehicle

Kwon

Kim

2023

IEEE Access

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The Brake operating unit (BOU) of a railway vehicle is one of the important systems for controlling the braking of the train. Because this system uses compressed air, it is difficult to understand and train the system. The existing education method involves learning the pneumatic flow of various control air in a 2D pneumatic circuit diagram based on a maintenance manual. However, in the actual braking system, it was difficult to learn effectively because the air flows in 3D. In order to solve these problems, the improvement of the training technique using the new 3D augmented reality (AR) was performed. In this study, to increase the learning effect of air brake flow, a technique for simultaneously displaying the pneumatic flow in 2D circuit diagram and 3D model was proposed. First, the distance ratio for simultaneous display can be determined using the proposed streamline matching variable calculation algorithm (SLMVC) that uses position and animation duration as input variables. Second, to avoid the complexity of using the 24 variables of the Particle System module in Unity, an existing universal 3D platform, a continuously emission property correction algorithm (CEPC) that can output particle objects as a streamline using only 4 properties (e.g., start lifetime, start speed, emission rate over time, start delay). As a result, the following 6 different types of BOU air pressure could be simultaneously displayed in 2D and 3D (e.g., AC, BC, SR, SBR, AS1, AS2). Therefore, maintenance staff can effectively learn complex pneumatic flow. To verify the usability of the developed content, a survey using the NASA-TLX technique was conducted targeting 60 maintenance staff. As a result of the comparison between Group A using the existing maintenance manual and Group B using the developed AR content, the perceived workload decreased by 28%. In particular, the frustration part decreased by 64% and the performance part decreased by 62%, indicating that the usability of AR content was very good.INDEX TERMS Railway vehicle, augmented reality, brake operating unit, pnematic flow, particle effect.The associate editor coordinating the review of this manuscript and approving it for publication was Jesus Felez .

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Section: A Related Workmentioning

confidence: 99%

Development and Evaluation of Augmented Reality Learning Content for Pneumatic Flow: Case Study on Brake Operating Unit of Railway Vehicle

Kwon

Kim

2023

IEEE Access

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“…In the manufacturing industry, many sensor devices are interconnected to collect operational data from machines on a continuous basis and feed it to backend computers for control and predictive analysis (Gopalakrishnan and Kumaran, 2022;Masero et al, 2018). These techniques can be used to monitor different parts of a manufacturing process (He et al, 2017), including belt drives (Pollak et al, 2021), bearings (Pichler et al, 2020;Lalik and Wa ˛torek, 2021), fleet (Ioanna et al, 2021), boiler feed pumps (Moleda et al, 2020), bi-directional JQME 29,2 control valve (Khadim et al, 2021), rotating machinery (Torres-Contreras et al, 2021;Lis et al, 2021), conveyor motors (Kiangala and Wang, 2020), injection moulding machines (Rousopoulou et al, 2020) and steel plate systems (Chong et al, 2021). In addition to all these, failures can happen in key components such as barrier machines in railroad crossings (Grzechca et al, 2021a) and heavy Earth moving machinery.…”

Section: Fault Detection Techniquesmentioning

confidence: 99%

“…ANN exhibits higher efficiency in binary and multi-class problems compared to other machine learning models (Karabacak and € Ozmen, 2022). ANNs are a good option when dealing with problems related to predictive maintenance of power plants (Lalik and Wa ˛torek, 2021), wind turbines (Santolamazza et al, 2021) and belt drives of manufacturing systems (Pollak et al, 2021).…”

Section: Supervised Learningmentioning

confidence: 99%

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Review of fault detection techniques for predictive maintenance

Divya

Bhasi

Kumar

2022

JQME

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PurposeThis study aims to bring awareness to the developing of fault detection systems using the data collected from sensor devices/physical devices of various systems for predictive maintenance. Opportunities and challenges in developing anomaly detection algorithms for predictive maintenance and unexplored areas in this context are also discussed.Design/methodology/approachFor conducting a systematic review on the state-of-the-art algorithms in fault detection for predictive maintenance, review papers from the years 2017–2021 available in the Scopus database were selected. A total of 93 papers were chosen. They are classified under electrical and electronics, civil and constructions, automobile, production and mechanical. In addition to this, the paper provides a detailed discussion of various fault-detection algorithms that can be categorised under supervised, semi-supervised, unsupervised learning and traditional statistical method along with an analysis of various forms of anomalies prevalent across different sectors of industry.FindingsBased on the literature reviewed, seven propositions with a focus on the following areas are presented: need for a uniform framework while scaling the number of sensors; the need for identification of erroneous parameters; why there is a need for new algorithms based on unsupervised and semi-supervised learning; the importance of ensemble learning and data fusion algorithms; the necessity of automatic fault diagnostic systems; concerns about multiple fault detection; and cost-effective fault detection. These propositions shed light on the unsolved issues of predictive maintenance using fault detection algorithms. A novel architecture based on the methodologies and propositions gives more clarity for the reader to further explore in this area.Originality/valuePapers for this study were selected from the Scopus database for predictive maintenance in the field of fault detection. Review papers published in this area deal only with methods used to detect anomalies, whereas this paper attempts to establish a link between different industrial domains and the methods used in each industry that uses fault detection for predictive maintenance.

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“…Indeed, the precise closed-loop control plant can be obtained without any knowledge related to the dynamic properties of the analyzed system. Consequently, the discussed solutions are applied in a number of engineering applications, where there is no possibility to receive an analytical model of the entire investigated process [16][17][18]. In the paper, the original approaches derived from the artificial intelligence phenomenon are proposed and compared with respect to classical performance indices.…”

Section: Introductionmentioning

confidence: 99%

Toward Optimal Control of a Multivariable Magnetic Levitation System

et al. 2022

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In the paper, a comparative case study covering different control strategies of unstable and nonlinear magnetic levitation process is investigated. Three control procedures are examined in order to fulfill the specified performance indices. Thus, a dedicated PD regulator along with the hybrid fuzzy logic PID one as well as feed-forward neural network regulator are respected and summarized according to generally understood tuning techniques. It should be emphasized that the second PID controller is strictly derived from both arbitrary chosen membership functions and those ones selected through the genetic algorithm mechanism. Simulation examples have successfully confirmed the correctness of obtained results, especially in terms of entire control process quality of the magnetic levitation system. It has been observed that the artificial-intelligence-originated approaches have outperformed the classical one in the context of control accuracy and control speed properties in contrary to the energy-saving behavior whereby the conventional method has become a leader. The feature-related compromise, which has never been seen before, along with other crucial peculiarities, is effectively discussed within this paper.

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Predictive Maintenance Neural Control Algorithm for Defect Detection of the Power Plants Rotating Machines Using Augmented Reality Goggles

Cited by 15 publications

References 37 publications

Development and Evaluation of Augmented Reality Learning Content for Pneumatic Flow: Case Study on Brake Operating Unit of Railway Vehicle

Development and Evaluation of Augmented Reality Learning Content for Pneumatic Flow: Case Study on Brake Operating Unit of Railway Vehicle

Review of fault detection techniques for predictive maintenance

Toward Optimal Control of a Multivariable Magnetic Levitation System

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