Autocorrelation Aided Random Forest Classifier-Based Bearing Fault Detection Framework

Roy, Sayanjit Singha; Dey, Sayantan; Chatterjee, Soumya

doi:10.1109/jsen.2020.2995109

Cited by 114 publications

(34 citation statements)

References 24 publications

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“…Roy et al propose an autocorrelation-based methodology for feature extraction from a raw signal and then use the random forest classifier for fault classification. They achieve comparable accuracies to the deep learning methods discussed earlier [27].…”

Section: Introductionmentioning

confidence: 76%

FaultNet: A Deep Convolutional Neural Network for Bearing Fault Classification

Magar

Ghule

et al. 2021

IEEE Access

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The increased presence of advanced sensors on the production floors has led to the collection of datasets that can provide significant insights into machine health. An important and reliable indicator of machine health, vibration signal data can provide us a greater understanding of different faults occurring in mechanical systems. In this work, we analyze vibration signal data of mechanical systems with bearings by combining different signal processing methods and coupling them with machine learning techniques to classify different types of bearing faults. We also highlight the importance of using different signal processing methods and their effect on accuracy for bearing fault detection. Apart from the traditional machine learning algorithms we also propose a convolutional neural network FaultNet which can effectively determine the type of bearing fault with a high degree of accuracy. The distinguishing factor of this work is the idea of channels proposed to extract more information from the signal, we have stacked the 'Mean' and 'Median' channels to raw signal to extract more useful features to classify the signals with greater accuracy.

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Section: Introductionmentioning

confidence: 76%

FaultNet: A Deep Convolutional Neural Network for Bearing Fault Classification

Magar

Ghule

et al. 2021

IEEE Access

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“…to be selected when debugging the model, and the selection of the hyper-parameters is often complicated, and it usually requires experienced engineers to be able to produce better prediction results. RF, on the other hand, as an ensemble learning algorithm based on decision trees, is widely used not only to solve classification and regression problems, [23][24][25][26][27] but also to handle anomaly detection and clustering problems. [28][29][30][31][32] Relative to artificial neural network modeling, RF modeling is much simpler and only requires to adjust the number of learners, the depth of the tree and the maximum number of features to be able to adjust the better prediction results.…”

mentioning

confidence: 99%

An improved random forest algorithm and its application to wind pressure prediction

Lang

Tiancai

et al. 2021

Int J Intell Syst

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When making regression predictions, the traditional random forest (RF) algorithm can only make predictions within the training set, which can easily lead to overfitting when modeling data have some specific noise. To solve the problem of over‐fitting, an improved RF method is proposed in this paper for wind pressure prediction. With the aim to verify the prediction performance of the improved RF algorithm, this paper predicts the wind pressure coefficients of a high‐rise building model without wind pressure measurement points. The results show that the improved RF can achieve good results in predicting the mean and fluctuating wind pressure coefficients of high‐rise buildings, and its relative error for each measurement point is basically controlled at 5%, which is acceptable in engineering terms. Further applications show that this improved RF can be used for wind pressure distribution prediction in other large‐span building type wind tunnel tests.

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“…Although the artificial neural network (ANN) is a popular supervised learning algorithm, it is vulnerable to causing over-fitting and affecting the generalization ability and the diagnosis results. Therefore, the random forest (RF) algorithm is adopted to train the CSFDD classifier, which is not easy to fall into over-fitting due to the introduction of two randomness (random samples, random features) (Roy et al, 2020;Fezai et al, 2021). Meanwhile, the current fault texture features are proposed to train the RF CSFDD classifier, which can improve the feature diversity and the diagnosis accuracy.…”

Section: Introductionmentioning

confidence: 99%

A Random Forest and Current Fault Texture Feature–Based Method for Current Sensor Fault Diagnosis in Three-Phase PWM VSR

et al. 2021

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Three-phase PWM voltage-source rectifier (VSR) systems have been widely used in various energy conversion systems, where current sensors are the key component for state monitoring and system control. The current sensor faults may bring hidden danger or damage to the whole system; therefore, this paper proposed a random forest (RF) and current fault texture feature–based method for current sensor fault diagnosis in three-phase PWM VSR systems. First, the three-phase alternating currents (ACs) of the three-phase PWM VSR are collected to extract the current fault texture features, and no additional hardware sensors are needed to avoid causing additional unstable factors. Then, the current fault texture features are adopted to train the random forest current sensor fault detection and diagnosis (CSFDD) classifier, which is a data-driven CSFDD classifier. Finally, the effectiveness of the proposed method is verified by simulation experiments. The result shows that the current sensor faults can be detected and located successfully and that it can effectively provide fault locations for maintenance personnel to keep the stable operation of the whole system.

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Autocorrelation Aided Random Forest Classifier-Based Bearing Fault Detection Framework

Cited by 114 publications

References 24 publications

FaultNet: A Deep Convolutional Neural Network for Bearing Fault Classification

FaultNet: A Deep Convolutional Neural Network for Bearing Fault Classification

An improved random forest algorithm and its application to wind pressure prediction

A Random Forest and Current Fault Texture Feature–Based Method for Current Sensor Fault Diagnosis in Three-Phase PWM VSR

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