Vibration analysis in bearings for failure prevention using CNN

Pinedo-Sanchez, Luis A.; Mercado-Ravell, Diego Alberto; Carballo-Monsivais, Carlos A.

doi:10.1007/s40430-020-02711-w

Cited by 28 publications

(9 citation statements)

References 42 publications

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“…RMS is widely used among these features due to its practical significance. Table 1 presents the formulas for TSFs [21,23,24]:…”

Section: Time Domain Analysismentioning

confidence: 99%

“…However, obtaining suitable features may require a long period of recorded signals, making it expensive, time-consuming, or even impossible for certain fault types or with complex equipment [5]. RMS and kurtosis are commonly used in the time domain, especially kurtosis, which is highly effective in early fault detection [24].…”

Section: State-of-the-art and Research Gapsmentioning

confidence: 99%

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A Novel Customised Load Adaptive Framework for Induction Motor Fault Classification Utilizing MFPT Bearing Dataset

Hejazi,

Packianather,

Liu

2023

Preprint

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This research presents a novel Customised Load Adaptive Framework (CLAF) for fault classifica-tion in Induction Motors (IMs), utilizing the Machinery Fault Prevention Technology (MFPT) Bearing Dataset. CLAF represents a pioneering approach that extends traditional fault classification methodologies by accounting for load variations and dataset customization. Through a meticulous two-phase process, it unveils load-dependent fault subclasses that have not been readily identified in traditional approaches. Additionally, new classes are created to accommodate the dataset's unique characteristics. Phase 1 involves the exploration of load-dependent patterns in time and frequency domain features using one-way Analysis of Variance (ANOVA) ranking and validation via bagged tree classifiers. In Phase 2, CLAF is applied to identify mild, moderate, and severe load-dependent fault subclasses through optimal Continuous Wavelet Transform (CWT) selection through Wavelet Singular Entropy (WSE) and CWT energy analysis. The results are compelling, with a 96.3% classification accuracy achieved when employing a wide neural network to classify proposed load-dependent fault subclasses. This underscores the practical value of CLAF in en-hancing fault diagnosis in IMs and its future potential in advancing IM condition monitoring.

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“…RMS is widely used among these features due to its practical significance. Table 1 presents the formulas for TSFs [21,23,24]:…”

Section: Time Domain Analysismentioning

confidence: 99%

Section: State-of-the-art and Research Gapsmentioning

confidence: 99%

A Novel Customised Load Adaptive Framework for Induction Motor Fault Classification Utilizing MFPT Bearing Dataset

Hejazi,

Packianather,

Liu

2023

Preprint

View full text Add to dashboard Cite

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“…Orrù et al [18] presented a machine learning model with two different algorithms such as SVM and multi-layer perceptron for early fault prediction of a centrifugal pump in the oil and gas industry. Pinedo et al [19] used machine learning techniques for analyzing vibration signals and feature extraction to classify the wear level of bearings, essentially aiming for fault identification and classification. In view of the fault diagnosis of industrial process equipment, the authors introduced a convolutional neural network identification method with continuous wavelet transform on the vibration signal of the equipment and achieved good diagnosis results [20].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Approach for Automatic Fault Classification Based on Data Balance and One-Dimensional Deep Learning

Ileri,

Altun,

Narin

2024

Applied Sciences

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Predictive maintenance (PdM) is implemented to efficiently manage maintenance schedules of machinery and equipment in manufacturing by predicting potential faults with advanced technologies such as sensors, data analysis, and machine learning algorithms. This paper introduces a study of different methodologies for automatically classifying the failures in PdM data. We first present the performance evaluation of fault classification performed by shallow machine learning (SML) methods such as Decision Trees, Support Vector Machines, k-Nearest Neighbors, and one-dimensional deep learning (DL) techniques like 1D-LeNet, 1D-AlexNet, and 1D-VGG16. Then, we apply normalization, which is a scaling technique in which features are shifted and rescaled in the dataset. We reapply classification algorithms to the normalized dataset and present the performance tables in comparison with the first results we obtained. Moreover, in contrast to existing studies in the literature, we generate balanced dataset groups by randomly selecting normal data and all faulty data for all fault types from the original dataset. The dataset groups are generated with 100 different repetitions, recording performance scores for each one and presenting the maximum scores. All methods utilized in the study are similarly employed on these groups. From these scores, the use of 1D-LeNet deep learning classifiers and feature normalization resulted in achieving the highest overall accuracy and F1-score performance of 98.50% and 98.32%, respectively. As a result, the goal of this study was to develop an efficient approach for automatic fault classification, leveraging data balance, and additionally, to provide an analysis of one-dimensional deep learning and shallow machine learning-based classification methods. In light of the experimentation and comparative analysis, this study successfully achieves its stated goal by demonstrating that one-dimensional deep learning and data balance collectively emerge as the optimal approach, offering good prediction accuracy.

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“…However, it is difficult to stabilize the training of GANs and teach them to generate discrete data. Due to their ability to detect important features, convolutional neural networks (CNNs) have also found applications in predicting remaining useful life (RUL) [ 13 , 14 ] and fault diagnosis [ 15 , 16 , 17 ]. However, CNNs can be easily overfit on time series, so recurrent neural networks (RNNs), which are good at capturing consistent dependencies over time, are mostly used in the same tasks of fault diagnosis [ 18 , 19 , 20 ] and RUL prediction [ 21 , 22 , 23 ].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Technical State of Mechanical Systems Based on Interpretive Neural Network Model

Kononov

Kliuev

Tashkinov

2023

Sensors

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A classic problem in prognostic and health management (PHM) is the prediction of the remaining useful life (RUL). However, until now, there has been no algorithm presented to achieve perfect performance in this challenge. This study implements a less explored approach: binary classification of the state of mechanical systems at a given forecast horizon. To prove the effectiveness of the proposed approach, tests were conducted on the C-MAPSS sample dataset. The obtained results demonstrate the achievement of an almost maximal performance threshold. The explainability of artificial intelligence (XAI) using the SHAP (Shapley Additive Explanations) feature contribution estimation method for classification models trained on data with and without a sliding window technique is also investigated.

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Vibration analysis in bearings for failure prevention using CNN

Cited by 28 publications

References 42 publications

A Novel Customised Load Adaptive Framework for Induction Motor Fault Classification Utilizing MFPT Bearing Dataset

A Novel Customised Load Adaptive Framework for Induction Motor Fault Classification Utilizing MFPT Bearing Dataset

An Efficient Approach for Automatic Fault Classification Based on Data Balance and One-Dimensional Deep Learning

Prediction of Technical State of Mechanical Systems Based on Interpretive Neural Network Model

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