A systematic literature review of machine learning methods applied to predictive maintenance

Carvalho, Thyago Peres; Soares, Fabrízzio; Vita, Roberto; Francisco, Roberto da Piedade; Basto, João Pedro Tavares Vieira; Alcalá, Symone Gomes Soares

doi:10.1016/j.cie.2019.106024

Cited by 861 publications

(374 citation statements)

References 40 publications

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“…Thus, the purpose and novelty of this paper is the focus it provides on specific topic. Carvalho et al [13] presents a structured review of new literature presented in the years after 2014 and does so in a general manner, so the reader can get a general understanding of what moves within the field. Merkt et al [4] studies various maintenance approaches and the different applications that PdM might have.…”

Section: The Field Of Prognostics and Health Managementmentioning

confidence: 99%

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Predictive Maintenance for Pump Systems and Thermal Power Plants: State-of-the-Art Review, Trends and Challenges

Olesen

Shaker

2020

Sensors

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Thermal power plants are an important asset in the current energy infrastructure, delivering ancillary services, power, and heat to their respective consumers. Faults on critical components, such as large pumping systems, can lead to material damage and opportunity losses. Pumps plays an essential role in various industries and as such clever maintenance can ensure cost reductions and high availability. Prognostics and Health Management, PHM, is the study utilizing data to estimate the current and future conditions of a system. Within the field of PHM, Predictive Maintenance, PdM, has been gaining increased attention. Data-driven models can be built to estimate the remaining-useful-lifetime of complex systems that would be difficult to identify by man. With the increased attention that the Predictive Maintenance field is receiving, review papers become increasingly important to understand what research has been conducted and what challenges need to be addressed. This paper does so by initially conceptualising the PdM field. A structured overview of literature in regard to application within PdM is presented, before delving into the domain of thermal power plants and pump systems. Finally, related challenges and trends will be outlined. This paper finds that a large number of experimental data-driven models have been successfully deployed, but the PdM field would benefit from more industrial case studies. Furthermore, investigations into the scale-ability of models would benefit industries that are looking into large-scale implementations. Here, examining a method for automatic maintenance of the developed model will be of interest. This paper can be used to understand the PdM field as a broad concept but does also provide a niche understanding of the domain in focus.

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Section: The Field Of Prognostics and Health Managementmentioning

confidence: 99%

“…Year Focus [13] 2019 Structured PdM literature review of 30 papers and their algorithms. Applications for RF, ANN, SVM, k-means are presented.…”

Section: Referencementioning

confidence: 99%

Predictive Maintenance for Pump Systems and Thermal Power Plants: State-of-the-Art Review, Trends and Challenges

Olesen

Shaker

2020

Sensors

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“…Artificial intelligence can be useful to solve critical issue for sustainable manufacturing (e.g., optimization of energy resources, logistics, supply chain management, waste management, etc.). In this context, in smart production, there is a trend to incorporate AI into green manufacturing processes for stricter environmental policies [20]. In fact, as said in March 2019 by Hendrik Fink, head of Sustainability Services at PricewaterhouseCoopers, "If we properly incorporate artificial intelligence, we can achieve a revolution with regard to sustainability.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence and Machine Learning Applications in Smart Production: Progress, Trends, and Directions

et al. 2020

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Adaptation and innovation are extremely important to the manufacturing industry. This development should lead to sustainable manufacturing using new technologies. To promote sustainability, smart production requires global perspectives of smart production application technology. In this regard, thanks to intensive research efforts in the field of artificial intelligence (AI), a number of AI-based techniques, such as machine learning, have already been established in the industry to achieve sustainable manufacturing. Thus, the aim of the present research was to analyze, systematically, the scientific literature relating to the application of artificial intelligence and machine learning (ML) in industry. In fact, with the introduction of the Industry 4.0, artificial intelligence and machine learning are considered the driving force of smart factory revolution. The purpose of this review was to classify the literature, including publication year, authors, scientific sector, country, institution, and keywords. The analysis was done using the Web of Science and SCOPUS database. Furthermore, UCINET and NVivo 12 software were used to complete them. A literature review on ML and AI empirical studies published in the last century was carried out to highlight the evolution of the topic before and after Industry 4.0 introduction, from 1999 to now. Eighty-two articles were reviewed and classified. A first interesting result is the greater number of works published by the USA and the increasing interest after the birth of Industry 4.0.

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“…An equipment failure, and subsequent reparation, can occur into a high expense to the company. As such, there is a strong need for Predictive Maintenance (PdM) solutions [3], especially addressing the prevention of component failure, with impact in production quality and client satisfaction (among many other factors). Therefore, predictive maintenance refers to the ability to forecast failures, ensuring that important operations are not disrupted as actions can be taken to prevent them.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Appliances Failures: A Machine-Learning Approach to Predictive Maintenance

et al. 2020

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Heating appliances consume approximately 48 % of the energy spent on household appliances every year. Furthermore, a malfunctioning device can increase the cost even further. Thus, there is a need to create methods that can identify the equipment’s malfunctions and eventual failures before they occur. This is only possible with a combination of data acquisition, analysis and prediction/forecast. This paper presents an infrastructure that supports the previously mentioned capabilities and was deployed for failure detection in boilers, making possible to forecast faults and errors. We also present our initial predictive maintenance models based on the collected data.

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A systematic literature review of machine learning methods applied to predictive maintenance

Cited by 861 publications

References 40 publications

Predictive Maintenance for Pump Systems and Thermal Power Plants: State-of-the-Art Review, Trends and Challenges

Predictive Maintenance for Pump Systems and Thermal Power Plants: State-of-the-Art Review, Trends and Challenges

Artificial Intelligence and Machine Learning Applications in Smart Production: Progress, Trends, and Directions

Forecasting Appliances Failures: A Machine-Learning Approach to Predictive Maintenance

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