Predictive maintenance applications for machine learning

Cline, Brad; Niculescu, Radu Stefan; Huffman, Duane L.; Deckel, Bob

doi:10.1109/ram.2017.7889679

Cited by 40 publications

(14 citation statements)

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“…PdM also supports data-driven strategies (Zhang et al, 2019) that can deal with sensible manufacturing and industrial huge information, particularly for acting health perception (e.g., fault diagnosis and remaining life assessment) (Farrar & Worden, 2012). It tends to classify the precise industrial applications supporting six algorithms of ML (Cline et al, 2017) and deep learning (DL), and compare five performance metrics for every classification. The accuracy (a metric to judge the formula performance) of those PdM applications is analyzed.…”

Section: Related Workmentioning

confidence: 99%

Automobile Predictive Maintenance Using Deep Learning

Dash

Raj

Agarwal

et al. 2021

International Journal of Artificial Intelligence and Machine Learning

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There are three types of maintenance management policy Run-tofailure (R2F), Preventive Maintenance (PvM) and Predictive Maintenance (PdM). In both R2F and PdM we have the data related to the maintenance cycle. In case of Preventive Maintenance (PvM) complete information about maintenance cycle is not available. Among these three maintenance policies, predictive Maintenance (PdM) is becoming a very important strategy as it can help us to minimize the repair time and the associated cost with it. In this paper we have proposed PdM, which allows the dynamic decision rules for the maintenance management. PdM is achieved by training the machine learning model with the datasets. It also helps in planning of maintenance schedules. We specially focused on two models that are Binary Classification and Recurrent Neural Network. In Binary Classification we classify whether our data belongs to the failure class or the non failure class. In Binary Classification the number of cycles is entered and classification model predicts whether it belongs to the failure/non failure class.

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Section: Related Workmentioning

confidence: 99%

Automobile Predictive Maintenance Using Deep Learning

Dash

Raj

Agarwal

et al. 2021

International Journal of Artificial Intelligence and Machine Learning

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“…Fleet level data is used to learn probability distributions over HC variation. [Cline et al, 2017] review 19 years of inspection data for swivels and valves on oil and gas equipment. Authors noted that they were unable to compute residual life of the majority of components due to the fact that most components never failed.…”

Section: Othermentioning

confidence: 99%

System-Level Predictive Maintenance: Review of Research Literature and Gap Analysis

Miller¹,

Dubrawski²

2020

Preprint

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This paper reviews current literature in the field of predictive maintenance from the system point of view. We differentiate the existing capabilities of condition estimation and failure risk forecasting as currently applied to simple components, from the capabilities needed to solve the same tasks for complex assets. System-level analysis faces more complex latent degradation states, it has to comprehensively account for active maintenance programs at each component level and consider coupling between different maintenance actions, while reflecting increased monetary and safety costs for system failures. As a result, methods that are effective for forecasting risk and informing maintenance decisions regarding individual components do not readily scale to provide reliable sub-system or system level insights. A novel holistic modeling approach is needed to incorporate available structural and physical knowledge and naturally handle the complexities of actively fielded and maintained assets.

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“…These failures resulted due to the lack of technology that could support real-time monitoring, data collection, and decision making, but also due to a failure to make real changes in the workplace so that these technologies could be used to the fullest extent possible [14,[16][17][18]. In order to obtain the maximum result from these solutions, radical changes need to be made to the way maintenance is applied in todays' companies [19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

A Predictive Maintenance System for Reverse Supply Chain Operations

et al. 2022

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Background: Reverse supply chains of machinery and equipment face significant challenges, and overcoming them is critical for effective customer service and sustainable operation. Maintenance and repair services, strongly associated with the reverse movement of equipment, are among the most demanding reverse supply chain operations. Equipment is scattered in various locations, and multiple suppliers are involved in its maintenance, making it challenging to manage the related reverse supply chain operations. Effective maintenance is essential for businesses-owners of the equipment, as reducing costs while improving service quality helps them gain a competitive advantage. Methods: To enhance reverse supply chain operations related to equipment maintenance, this paper presents the operational framework, the methodological approach, and the architecture for developing a system that covers the needs for predictive maintenance in the service supply chain. It is based on Industry 4.0 technologies, such as the Internet of things, machine learning, and cloud computing. Results: As a result of the successful implementation of the system, effective equipment maintenance and service supply chain management is achieved supporting the reverse supply chain. Conclusions: This will eventually lead to fewer good-conditioned spare part replacements, just in time replacements, extended equipment life cycles, and fewer unnecessary disposals.

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Predictive maintenance applications for machine learning

Cited by 40 publications

References 0 publications

Automobile Predictive Maintenance Using Deep Learning

Automobile Predictive Maintenance Using Deep Learning

System-Level Predictive Maintenance: Review of Research Literature and Gap Analysis

A Predictive Maintenance System for Reverse Supply Chain Operations

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