Enabling Cognitive Predictive Maintenance Using Machine Learning: Approaches and Design Methodologies

Poosapati, Vijayaramaraju; Katneni, Vedavathi; Manda, Vijaya Killu; Ramesh, T.

doi:10.1007/978-981-13-3393-4_5

Cited by 7 publications

(4 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As previously mentioned, in predictive maintenance, as a general rule, the most important is the number of real failures that the model is capable of predicting, that is, the value of the model's Recall parameter [28]. This parameter becomes even more important as the consequences of false negatives, that is, true failures that the model was unable to predict, exceed the consequences of false positives, that is, a false prediction of a failure [33,34].…”

Section: Test Set Behaviormentioning

confidence: 99%

Application of Predictive Maintenance Concepts Using Artificial Intelligence Tools

Cardoso

Ferreira

2020

Applied Sciences

View full text Add to dashboard Cite

The growing competitiveness of the market, coupled with the increase in automation driven with the advent of Industry 4.0, highlights the importance of maintenance within organizations. At the same time, the amount of data capable of being extracted from industrial systems has increased exponentially due to the proliferation of sensors, transmission devices and data storage via Internet of Things. These data, when processed and analyzed, can provide valuable information and knowledge about the equipment, allowing a move towards predictive maintenance. Maintenance is fundamental to a company’s competitiveness, since actions taken at this level have a direct impact on aspects such as cost and quality of products. Hence, equipment failures need to be identified and resolved. Artificial Intelligence tools, in particular Machine Learning, exhibit enormous potential in the analysis of large amounts of data, now readily available, thus aiming to improve the availability of systems, reducing maintenance costs, and increasing operational performance and support in decision making. In this dissertation, Artificial Intelligence tools, more specifically Machine Learning, are applied to a set of data made available online and the specifics of this implementation are analyzed as well as the definition of methodologies, in order to provide information and tools to the maintenance area.

show abstract

Section: Test Set Behaviormentioning

confidence: 99%

Application of Predictive Maintenance Concepts Using Artificial Intelligence Tools

Cardoso

Ferreira

2020

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…The adoption of AI-based solutions for predictive maintenance perfectly fits the vision of the fourth industrial revolution [45]. It is not a coincidence that all of the biggest IT companies started to propose their PdM solutions based on ML and cloud technologies: Amazon with their AWS Solutions [46], Microsoft with Azure [47], and Google with Google Cloud Platform [48].…”

Section: Maintenance Decision-makingmentioning

confidence: 99%

A Model for Predictive Maintenance Based on Asset Administration Shell

Cavalieri

Salafia

2020

Sensors

View full text Add to dashboard Cite

Maintenance is one of the most important aspects in industrial and production environments. Predictive maintenance is an approach that aims to schedule maintenance tasks based on historical data in order to avoid machine failures and reduce the costs due to unnecessary maintenance actions. Approaches for the implementation of a maintenance solution often differ depending on the kind of data to be analyzed and on the techniques and models adopted for the failure forecasts and for maintenance decision-making. Nowadays, Industry 4.0 introduces a flexible and adaptable manufacturing concept to satisfy a market requiring an increasing demand for customization. The adoption of vendor-specific solutions for predictive maintenance and the heterogeneity of technologies adopted in the brownfield for the condition monitoring of machinery reduce the flexibility and interoperability required by Industry 4.0. In this paper a novel approach for the definition of a generic and technology-independent model for predictive maintenance is presented. Such model leverages on the concept of the Reference Architecture Model for Industry (RAMI) 4.0 Asset Administration Shell, as a means to achieve interoperability between different devices and to implement generic functionalities for predictive maintenance.

show abstract

“…Condition estimation [Poosapati et al, 2019] proposes a rule-based strategy for processing anomalies in predictive maintenance applications. Their goal is to develop cognitive reasoning capabilities that can recognize patterns and suggest courses of action.…”

Section: Othermentioning

confidence: 99%

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

Miller¹,

Dubrawski²

2020

Preprint

View full text Add to dashboard Cite

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.

show abstract

Enabling Cognitive Predictive Maintenance Using Machine Learning: Approaches and Design Methodologies

Cited by 7 publications

References 18 publications

Application of Predictive Maintenance Concepts Using Artificial Intelligence Tools

Application of Predictive Maintenance Concepts Using Artificial Intelligence Tools

A Model for Predictive Maintenance Based on Asset Administration Shell

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

Contact Info

Product

Resources

About