Rotary Machines Fault Diagnosis based on Principal Component Analysis

Elsamanty, Mahmoud; Salman, Wan Syaza Ainaa Wan; Ibrahim, Abdelrahman

doi:10.21608/erj.2021.193822

Cited by 3 publications

(2 citation statements)

References 22 publications

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“…These challenges include data cleaning, where we handle missing values, outliers and inconsistencies in the data [200]. Next is normalization, where numerical features are scaled to a standard range to ensure consistency [201]. The core of data processing is feature extraction that identify relevant feature or extract informative characteristics from the data [202].…”

Section: Classification Of Intelligent Fault Diagnosis (Ifd) Methodsmentioning

confidence: 99%

Deep Learning based Approaches for Intelligent Industrial Machinery Health Management & Fault Diagnosis in Resource-Constrained Environments

Khan,

Akram,

Khattak

et al. 2024

Preprint

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Industry 4.0 represents the fourth industrial revolution, which is characterized by the incorporation of digital technologies, the Internet of Things (IoT), artificial intelligence, big data, and other advanced technologies into industrial processes.Industrial Machinery Health Management (IMHM) is a crucial element, based on the Industrial Internet of Things (IIoT), which focuses on monitoring the health and condition of industrial machinery. The academic community has focused on various aspects of IMHM, such as prognostic maintenance, condition monitoring, estimation of remaining useful life (RUL), intelligent fault diagnosis (IFD), and architectures based on edge computing. Each of these categories holds its own significance in the context of industrial processes. In this survey, we specifically examine the research on RUL prediction, edge-based architectures, and intelligent fault diagnosis, with a primary focus on the domain of intelligent fault diagnosis. The importance of IFD methods in ensuring the smooth execution of industrial processes has become increasingly evident. However, most methods are formulated under the assumption of complete, balanced, and abundant data, which often does not align with real-world engineering scenarios. The difficulties linked to these classifications of IMHM have received noteworthy attention from the research community, leading to a substantial number of published papers on the topic. While there are existing comprehensive reviews that address major challenges and limitations in this field, there is still a gap in thoroughly investigating research perspectives across RUL prediction, edge-based architectures, and complete intelligent fault diagnosis processes. To fill this gap,we undertake a comprehensive survey that reviews and discusses research achievements in this domain, specifically focusing on IFD. Initially, we classify the existing IFD methods into three distinct perspectives: the method of processing data, which aims to optimize inputs for the intelligent fault diagnosis model and mitigate limitations in the training sample set; the method of constructing the model, which involves designing the structure and features of the model to enhance its resilience to challenges; and the method of optimizing training, which focuses on refining the training process for intelligent fault diagnosis models and emphasizes the importance of ideal data in the training process. Subsequently, the survey covers techniques related to RUL prediction and edge-cloud architectures for resource-constrained environments. Finally, this survey consolidates the outlook on relevant issues in IMHM, explores potential solutions, and offers practical recommendations for further consideration.

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Section: Classification Of Intelligent Fault Diagnosis (Ifd) Methodsmentioning

confidence: 99%

Deep Learning based Approaches for Intelligent Industrial Machinery Health Management & Fault Diagnosis in Resource-Constrained Environments

Khan,

Akram,

Khattak

et al. 2024

Preprint

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“…A singular spectrum analysis was proposed as a method for bearing fault detection (Bugharbee and Trendafilova 2018). Principal components was proposed to identify the healthy and different faulty bearing (Elsamanty, S. Salman, and A. Ibrahim 2021). Multivariate statistical process was proposed as bearing fault detection framework (Jin, Fan, and Chow 2019).…”

Section: Introductionmentioning

confidence: 99%

Exploring Pattern Recognition for Bearing Fault Diagnosis

Darwis

Hajarisman²,

Suliadi³

et al. 2022

statistika

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Traditional bearing sensory diagnostic include touching and hearing rely on personal experience, and for more complex system are unable to meet the needs of equipment fault diagnosis. The research on bearing fault diagnosis is developing significantly. Bearings are used in rotating machinery and most machinery failures are caused by bearing failures. The fault diagnosis of bearings is an important research area. The core of bearing fault diagnosis is the pattern recognition of fault features. The key of pattern recognition is to develop a reasonable classifier. Intelligent pattern recognition has been developed such as principal components, support vector machine, neural network. In this study, a bearing fault diagnosis based on exploring pattern recognition is proposed. The key to pattern recognition is to design a significant classifier. A number of features from bearing vibration of normal and fault bearing are extracted and processed using principal components of correlation matrix. Plot of principal components shows the visualization of normal and fault bearing and the classifier is chosen subjectively. The principal components exploration will be confirmed using least squares support vector machine. The parameter of support vector machine estimated using heuristic optimization particle swarm optimization. The proposed method can be applied in the detection of faults of bearing

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Principal component analysis approach for detecting faults in rotary machines based on vibrational and electrical fused data

Elsamanty

Ibrahim

Salman

2023

Mechanical Systems and Signal Processing

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Rotary Machines Fault Diagnosis based on Principal Component Analysis

Cited by 3 publications

References 22 publications

Deep Learning based Approaches for Intelligent Industrial Machinery Health Management & Fault Diagnosis in Resource-Constrained Environments

Deep Learning based Approaches for Intelligent Industrial Machinery Health Management & Fault Diagnosis in Resource-Constrained Environments

Exploring Pattern Recognition for Bearing Fault Diagnosis

Principal component analysis approach for detecting faults in rotary machines based on vibrational and electrical fused data

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Rotary Machines Fault Diagnosis based on Principal Component Analysis

Cited by 3 publications

References 22 publications

Deep Learning based Approaches for Intelligent Industrial Machinery Health Management &amp; Fault Diagnosis in Resource-Constrained Environments

Deep Learning based Approaches for Intelligent Industrial Machinery Health Management &amp; Fault Diagnosis in Resource-Constrained Environments

Exploring Pattern Recognition for Bearing Fault Diagnosis

Principal component analysis approach for detecting faults in rotary machines based on vibrational and electrical fused data

Contact Info

Product

Resources

About

Deep Learning based Approaches for Intelligent Industrial Machinery Health Management & Fault Diagnosis in Resource-Constrained Environments

Deep Learning based Approaches for Intelligent Industrial Machinery Health Management & Fault Diagnosis in Resource-Constrained Environments