A Fault Diagnosis Method for Multi-Condition System Based on Random Forest

Shi, Junyou; Niu, Nanpo; Zhu, Xianjie

doi:10.1109/phm-paris.2019.00066

Cited by 4 publications

(3 citation statements)

References 6 publications

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“…A large number of Regression trees can be combined and trained into one final result called Random decision forest, and such an approach is often encountered, as it does not require too many computer resources to analyse larger databases (Shi et al, 2019). This approach includes multiple independent classes of Regression trees, which jointly decide what the final output value or category will be.…”

Section: Random Forestmentioning

confidence: 99%

Selection of an algorithm for the prediction of stoppages and/or failure of excavation units using supervised machine learning

Misita,

Spasojević-Brkić,

Mihajlović

et al. 2024

XIX International May Conference on Strategic Management – IMCSM24 Proceedings - Zbornik Radova

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The paper presents research into the possibility of applying machine learning algorithms in the prediction of stoppages and/or failure of excavator units. Regression trees, Random Forest and Support Vector Machine (SVM) algorithms were tested with different hyperparameter variations on the collected set of data on the causes and downtime of stoppages of the observed excavator units. The result indicates that the trained SVM algorithm with sufficient accuracy (MSE 0.106) can predict the stoppages of the observed excavator units. Further research is aimed at expanding the database and further improving the possibility of predicting the level of danger for various causes of stoppages and/or failure of the observed excavator units.

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Section: Random Forestmentioning

confidence: 99%

Selection of an algorithm for the prediction of stoppages and/or failure of excavation units using supervised machine learning

Misita,

Spasojević-Brkić,

Mihajlović

et al. 2024

XIX International May Conference on Strategic Management – IMCSM24 Proceedings - Zbornik Radova

View full text Add to dashboard Cite

show abstract

“…In the arena of ML techniques, Artificial Neuronal Networks (ANN) [26] and decision trees [27] (including the Random Forest algorithm [28]) have been used, as well as Support Vector Machines (SVM) in terms of both supervised [29] and unsupervised [30] learning. Finally, the k-Nearest Neighbor Technique (k-NN) is one of the most common for fault classification [31], for prediction of useful life time (RUL) [32] and early detection [33].…”

Section: State Of the Artmentioning

confidence: 99%

SOPRENE: Assessment of the Spanish Armada’s Predictive Maintenance Tool for Naval Assets

et al. 2021

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Predictive maintenance has lately proved to be a useful tool for optimizing costs, performance and systems availability. Furthermore, the greater and more complex the system, the higher the benefit but also the less applied: Architectural, computational and complexity limitations have historically ballasted the adoption of predictive maintenance on the biggest systems. This has been especially true in military systems where the security and criticality of the operations do not accept uncertainty. This paper describes the work conducted in addressing these challenges, aiming to evaluate its applicability in a real scenario: It presents a specific design and development for an actual big and diverse ecosystem of equipment, proposing an semi-unsupervised predictive maintenance system. In addition, it depicts the solution deployment, test and technological adoption of real-world military operative environments and validates the applicability.

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“…Furthermore, a set of DTs can be trained and assembled to a Random Forest (RF). According to recent literature on fault diagnosis and prognosis [163][164][165][166][167], RF-based approaches are widely employed due to its low computational cost with large data and stable results.…”

Section: B Decision Tree (Dt)mentioning

confidence: 99%

A Survey of Predictive Maintenance: Systems, Purposes and Approaches

Ran,

Zhou,

Lin

et al. 2019

Preprint

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This paper provides a comprehensive literature review on Predictive Maintenance (PdM) with emphasis on system architectures, purposes and approaches. In industry, any outages and unplanned downtime of machines or systems would degrade or interrupt a company's core business, potentially resulting in significant penalties and unmeasurable reputation loss. Existing traditional maintenance approaches suffer from some assumptions and limits, such as high prevent/repair costs, inadequate or inaccurate mathematical degradation processes and manual feature extraction. With the trend of smart manufacturing and development of Internet of Things (IoT), data mining and Artificial Intelligence (AI), etc., PdM is proposed as a novel type of maintenance paradigm to perform maintenances only after the analytical models predict certain failures or degradations. In this survey, we first provide a high-level view of the PdM system architectures including the Open System Architecture for Condition Based Monitoring (OSA-CBM), cloud-enhanced PdM system and PdM 4.0, etc. Then, we make clear the specific maintenance purposes/objectives, which mainly comprise cost minimization, availability/reliability maximization and multiobjective optimization. Furthermore, we provide a review of the existing approaches for fault diagnosis and prognosis in PdM systems that include three major subcategories: knowledge based, traditional Machine Learning (ML) based and DL based approaches. We make a brief review on the knowledge based and traditional ML based approaches applied in diverse industrial systems or components with a complete list of references, while providing a comprehensive review of DL based approaches. Finally, important future research directions are introduced.

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A Fault Diagnosis Method for Multi-Condition System Based on Random Forest

Cited by 4 publications

References 6 publications

Selection of an algorithm for the prediction of stoppages and/or failure of excavation units using supervised machine learning

Selection of an algorithm for the prediction of stoppages and/or failure of excavation units using supervised machine learning

SOPRENE: Assessment of the Spanish Armada’s Predictive Maintenance Tool for Naval Assets

A Survey of Predictive Maintenance: Systems, Purposes and Approaches

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