A BMFO-KNN based intelligent fault detection approach for reciprocating compressor

Patil, Amitkumar; Soni, Gunjan; Prakash, Anuj

doi:10.1007/s13198-021-01395-2

Cited by 4 publications

(2 citation statements)

References 56 publications

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“…Here are the specifics of the MDBN-IBSSA configuration: There is a 250 iteration limit and a 20 population limit. In order to evaluate the improved IBSSA algorithm's performance in feature selection, we employed the following comparative algorithms: Binomial Moth Flame Optimizer (BMFO) [40], Quantum Gaussian Dragonfly Algorithm (QGDA) [41], Binary Spread Strategy with Chaotic Local Search Grey Wolf Optimization (BSCGWO) [43], and BQGWO [42]. Table 3 provides a list of parameter combinations for three competing intelligent optimization techniques.…”

Section: Resultsmentioning

confidence: 99%

Developing a Modified Deep Belief Network with metaheuristic optimization Algorithm for predicting Alzheimer disease using Electroencephalogram

Jayagopal,

Mohan,

Rajasekar

et al. 2024

Preprint

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A neurological brain disorder that progresses over time is Alzheimer's disease. Alzheimer's disease can take years to identify, comprehend, and manifest—even in cases where signs are obvious. On the other hand, technological developments like imaging methods aid in early detection. But frequently, the results are unreliable, which delays the course of treatment. By dividing resting-state electroencephalography (EEG) signals into three groups—AD, healthy controls, and mild cognitive impairment (MCI)—this work offers a novel perspective on the diagnosis of Alzheimer's disease (AD). In order to overcome data limits and the over-fitting issue with deep learning models, we looked at augmenting the one-dimensional EEG data of 100 patients (49 AD participants, 37 MCI subjects, and 14 HC subjects) with overlapping sliding windows. Better results and early intervention could arise from this for persons afflicted with the illness. This research has the potential to significantly advance the early diagnosis of Alzheimer's disease and lay the groundwork for the creation of more precise and trustworthy diagnostic instruments for this debilitating condition. This study presents a Modified Deep Belief Network (MDBN) with a metaheuristic optimization method for detecting face expression and Alzheimer's disease using EEG inputs. The recommended method extracts significant features from EEG data in a novel way by applying the Improved Binary Salp Swarm Algorithm (IBSSA), which combines the MDBN and the metaheuristic optimization algorithm. The performance of the suggested technique MDBN-IBSSA for Alzheimer's disease diagnosis is evaluated using two publicly available datasets. The proposed technique's capacity to discriminate between healthy and ill patients is proved by the MDBN-IBSSA accuracy of 98.13%, f-Score of 96.23%, sensitivity of 95.89%, precision of 95.671%, and specificity of 97.13%. The experimental results of this study show that the MDBN-IBSSA algorithm proposed for AD diagnosis is effective, superior, and applicable.

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Section: Resultsmentioning

confidence: 99%

Developing a Modified Deep Belief Network with metaheuristic optimization Algorithm for predicting Alzheimer disease using Electroencephalogram

Jayagopal,

Mohan,

Rajasekar

et al. 2024

Preprint

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“…The fundamental of KNN is that similar data points are near each other, and outliers are away from similar data points clusters. The technique has been applied, for instance, in the slot milling cutting tool (Liu et al 2022 ), semiconductor manufacturing process (Subbaraj and Kannapiran 2010 ), motor bearing (Tian et al 2015 ), combustion engine (Jafarian et al 2018 ), gas sensor arrays (J. Yang et al 2016 ), power transformers (Islam et al 2017 ), reciprocating compressor (Patil et al 2022 ) among others.…”

Section: Condition-based Maintenance (Cbm)mentioning

confidence: 99%

Condition-based maintenance using machine learning and role of interpretability: a review

Sharma

Mittal

Soni

2022

Int J Syst Assur Eng Manag

Self Cite

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This article aims to review the literature on condition-based maintenance (CBM) by analyzing various terms, applications, and challenges. CBM is a maintenance technique that monitors the existing condition of an industrial asset to determine what maintenance needs to be performed. This article enlightens the readers with research in condition-based maintenance using machine learning and artificial intelligence techniques and related literature. A bibliometric analysis is performed on the data collected from the Scopus database. The foundation of a CBM is accurate anomaly detection and diagnosis. Several machine-learning approaches have produced excellent results for anomaly detection and diagnosis. However, due to the black-box nature of the machine learning models, the level of their interpretability is limited. This article provides insight into the existing maintenance strategies, anomaly detection techniques, interpretable models, and model-agnostic methods that are being applied. It has been found that significant work has been done towards condition based-maintenance using machine learning, but explainable artificial intelligence approaches got less attention in CBM. Based on the review, we contend that explainable artificial intelligence can provide unique insights and opportunities for addressing critical difficulties in maintenance leading to more informed decision-making. The analysis put forward encouraging research directions in this area.

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A Machine Learning Approach for Fault Detection and Diagnosis in Four-legged Inverters

Peykarporsan,

Heidary,

Oshnoei

et al. 2023

2023 IEEE Kansas Power and Energy Conference (KPEC)

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A BMFO-KNN based intelligent fault detection approach for reciprocating compressor

Cited by 4 publications

References 56 publications

Developing a Modified Deep Belief Network with metaheuristic optimization Algorithm for predicting Alzheimer disease using Electroencephalogram

Developing a Modified Deep Belief Network with metaheuristic optimization Algorithm for predicting Alzheimer disease using Electroencephalogram

Condition-based maintenance using machine learning and role of interpretability: a review

A Machine Learning Approach for Fault Detection and Diagnosis in Four-legged Inverters

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