Predictive maintenance strategy of running fault based on ELM algorithm for power transformer

Wu, Qian; Xinfeng, Yang; Deng, Renming

doi:10.1504/ijims.2018.091999

Cited by 3 publications

(6 citation statements)

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“…, 2018), faults in microchips (Lughofer et al. , 2018), power failure in transformers (Wu et al ., 2018), failure in an automatic washing machine (Zhang et al. , 2019) could cause a vast loss in maintenance value.…”

Section: Rq1: What Are the Domains That Use Anomaly Detection For Pre...mentioning

confidence: 99%

“…maintenance can be performed early on using threat detection for reducing the potential risk of failures in such a network. In electrical and electronic trade, an anomaly in photovoltaic systems (Benedetti et al, 2018), faults in microchips (Lughofer et al, 2018), power failure in transformers (Wu et al, 2018), failure in an automatic washing machine (Zhang et al, 2019) could cause a vast loss in maintenance value. In such scenarios, predictive maintenance can reduce costs and improve the quality of the product.…”

Section: Fault Detection Techniquesmentioning

confidence: 99%

“…(2) Hidden Markov Models (HMM) Hidden Markov Models (HMM) (Casado-Vara et al, 2019) can be either supervised or unsupervised. The method to be modelled here assumes that it is a hidden Markov mechanism that can be used to detect imprecise or malfunctioning IoT nodes within a cloud network architecture (Wu et al, 2018). Degradation of system output from production is a stochastic process that can be represented via gamma processes (Herranz et al, 2019;Susto et al, 2018).…”

Section: Unsupervised Learningmentioning

confidence: 99%

“…In the case of point anomalies, an attempt from the algorithm is to identify points of fault in the system, whereas in the latter, faults appear in a subregion that forms a fault cluster in that region (Unal et al, 2021). In both these systems, the detection of anomalies leads to the identification of faults within the system (Wu et al, 2018). This capability aids the practitioners to apply anomaly detection algorithms in predictive maintenance.…”

Section: Introductionmentioning

confidence: 99%

“…, 2021). In both these systems, the detection of anomalies leads to the identification of faults within the system (Wu et al. , 2018).…”

Section: Introductionmentioning

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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Section: Rq1: What Are the Domains That Use Anomaly Detection For Pre...mentioning

confidence: 99%

Section: Fault Detection Techniquesmentioning

confidence: 99%

Section: Unsupervised Learningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…, 2021). In both these systems, the detection of anomalies leads to the identification of faults within the system (Wu et al. , 2018).…”

Section: Introductionmentioning

confidence: 99%

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Review of fault detection techniques for predictive maintenance

Divya

Bhasi

Kumar

2022

JQME

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A scoping review on multi-fault diagnosis of industrial rotating machines using multi-sensor data fusion

Gawde

Patil²,

Kumar³

et al. 2022

Artif Intell Rev

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A comprehensive literature review of the applications of AI techniques through the lifecycle of industrial equipment

Elahi,

Afolaranmi,

Martinez Lastra

et al. 2023

Discov Artif Intell

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Driven by the ongoing migration towards Industry 4.0, the increasing adoption of artificial intelligence (AI) has empowered smart manufacturing and digital transformation. AI enhances the migration towards industry 4.0 through AI-based decision-making by analyzing real-time data to optimize different processes such as production planning, predictive maintenance, quality control etc., thus guaranteeing reduced costs, high precision, efficiency and accuracy. This paper explores AI-driven smart manufacturing, revolutionizing traditional approaches and unlocking new possibilities throughout the major phases of the industrial equipment lifecycle. Through a comprehensive review, we delve into a wide range of AI techniques employed to tackle challenges such as optimizing process control, machining parameters, facilitating decision-making, and elevating maintenance strategies within the major phases of an industrial equipment lifecycle. These phases encompass design, manufacturing, maintenance, and recycling/retrofitting. As reported in the 2022 McKinsey Global Survey (https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai-in-2022-and-a-half-decade-in-review), the adoption of AI has witnessed more than a two-fold increase since 2017. This has contributed to an increase in AI research within the last six years. Therefore, from a meticulous search of relevant electronic databases, we carefully selected and synthesized 42 articles spanning from 01 January 2017 to 20 May 2023 to highlight and review the most recent research, adhering to specific inclusion and exclusion criteria, and shedding light on the latest trends and popular AI techniques adopted by researchers. This includes AI techniques such as Convolutional Neural Networks (CNN), Generative Adversarial Networks (GAN), Bayesian Networks, Support Vector Machines (SVM) etc., which are extensively discussed in this paper. Additionally, we provide insights into the advantages (e.g., enhanced decision making) and challenges (e.g., AI integration with legacy systems due to technical complexities and compatibilities) of integrating AI across the major stages of industrial equipment operations. Strategically implementing AI techniques in each phase enables industries to achieve enhanced productivity, improved product quality, cost-effectiveness, and sustainability. This exploration of the potential of AI in smart manufacturing fosters agile and resilient processes, keeping industries at the forefront of technological advancements and harnessing the full potential of AI-driven solutions to improve manufacturing processes and products.

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Predictive maintenance strategy of running fault based on ELM algorithm for power transformer

Cited by 3 publications

References 0 publications

Review of fault detection techniques for predictive maintenance

Review of fault detection techniques for predictive maintenance

A scoping review on multi-fault diagnosis of industrial rotating machines using multi-sensor data fusion

A comprehensive literature review of the applications of AI techniques through the lifecycle of industrial equipment

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