Maintenance Optimization Approaches for Condition Based Maintenance: a review and analysis

Machado, Mario Marcondes; Haskins, Cecilia

doi:10.1002/j.2334-5837.2016.00171.x

Cited by 3 publications

(1 citation statement)

References 18 publications

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“…There are also some existing surveys covering different aspects of the domain. The surveys and reviews being conducted in this domain are either generically relevant to CBM processes in multiple domains and industries [3] or specifically related to a single domain like wind power turbines [4]. For instance, Quatrini et.…”

Section: B Related Surveysmentioning

confidence: 99%

Artificial Intelligence Based Prognostic Maintenance of Renewable Energy Systems: A Review of Techniques, Challenges, and Future Research Directions

Afridi¹,

Ahmad²,

Hassan³

2021

Preprint

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Section: B Related Surveysmentioning

confidence: 99%

Artificial Intelligence Based Prognostic Maintenance of Renewable Energy Systems: A Review of Techniques, Challenges, and Future Research Directions

Afridi¹,

Ahmad²,

Hassan³

2021

Preprint

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Artificial intelligence based prognostic maintenance of renewable energy systems: A review of techniques, challenges, and future research directions

Afridi

Ahmad

Hassan

2021

Intl J of Energy Research

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Summary Since the depletion of fossil fuels, the world has started to rely heavily on renewable sources of energy. With every passing year, our dependency on renewable sources of energy is increasing exponentially. As a result, complex and hybrid generation systems are being developed to meet the energy demands and ensure energy security in a country. The continual improvement in the technology and an effort toward the provision of uninterrupted power to the end‐users is strongly dependent on an effective and fault‐resilient Operation & Maintenance (O&M) system. Ingenious algorithms and techniques are hence been introduced aiming to minimize equipment and plant downtime. Efforts are being made to develop robust prognostic maintenance systems that can identify the faults before they occur. To this aim, complex Data Analytics and Artificial Intelligence (AI) algorithms are being used to increase the overall efficiency of these prognostic maintenance systems. This paper provides an overview of the predictive/prognostic maintenance frameworks reported in the literature. We pay a particular focus to the approaches, challenges, including data‐related issues, such as the availability of quality data and data auditing, feature engineering, interpretability, and security issues. Being a key aspect of ML‐based solutions, we also discuss some of the commonly used publicly available datasets in the domain. The paper also identifies the key future research directions to further enhance the prognostics maintenance procedures.

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Diamond-Coated Mechanical Seal Remaining Useful Life Prediction Based on Convolution Neural Network

Lin

Gao

Zhang

et al. 2019

Int. J. Patt. Recogn. Artif. Intell.

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Reliable remaining useful life (RUL) prediction of industrial equipment key components is of considerable importance in condition-based maintenance to avoid catastrophic failure, promote reliability and reduce cost during the production. Diamond-coated mechanical seal is one of the most critical wearing components in petroleum chemical, nuclear power and other process industries. Estimating the RUL is of critical importance. We consider the data-driven approaches for diamond-coated mechanical seal RUL estimation based on AE sensor data, since it is difficult to construct an explicit mathematical degradation model of seal. The challenges of this work are dealing with the noisy AE sensor data and modeling the degradation process with fluctuation. Faced with these challenges, we propose a pipeline method CDF-CNN to estimate the RUL for mechanical seal: WPD-KLD to raise the signal-to-noise ratio, novel CDF-based statistics to represent seal degradation process and CNN structure to estimate RUL. To acquire AE sensor data, several diamond-coated seals are tested from new to failure in three working conditions. Experimental results demonstrate that the proposed method can accurately predict the RUL of diamond-coated mechanical seal based on AE signals. The proposed prediction method can be generalized to other various mechanical assets.

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Maintenance Optimization Approaches for Condition Based Maintenance: a review and analysis

Cited by 3 publications

References 18 publications

Artificial Intelligence Based Prognostic Maintenance of Renewable Energy Systems: A Review of Techniques, Challenges, and Future Research Directions

Artificial Intelligence Based Prognostic Maintenance of Renewable Energy Systems: A Review of Techniques, Challenges, and Future Research Directions

Artificial intelligence based prognostic maintenance of renewable energy systems: A review of techniques, challenges, and future research directions

Diamond-Coated Mechanical Seal Remaining Useful Life Prediction Based on Convolution Neural Network

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