Investigations on the effect of oil quality on gearboxes using neural network predictors

Kalkat, Menderes

doi:10.1108/ilt-02-2013-0020

Cited by 7 publications

(5 citation statements)

References 17 publications

(17 reference statements)

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“…CNN is a deep neural network, which consists of convolutional layer, pooling layer and fully connected layer (Jiao et al , 2018). A two-layers neural network is shown in Figure 1, where m is the number of nodes in l – layer; the output of l −1 layer is a l- 1 ; n is the number of nodes in l layer; the weight vector of l layer is the matrix W l of n ∗ m ; and the bias is the matrix b l of n ∗ 1.…”

Section: Methodsmentioning

confidence: 99%

Intelligent fault diagnosis method of rolling bearing based on stacked denoising autoencoder and convolutional neural network

Che¹,

Wang²,

Ni³

et al. 2020

ILT

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Purpose The purpose of this study is to analyze the intelligent fault diagnosis method of rolling bearing. Design/methodology/approach The vibration signal data of rolling bearing has long time series and strong noise interference, which brings great difficulties for the accurate diagnosis of bearing faults. To solve those problems, an intelligent fault diagnosis model based on stacked denoising autoencoder (SDAE) and convolutional neural network (CNN) is proposed in this paper. The SDAE is used to process the time series data with multiple dimensions and noise interference. Then the dimension-reduced samples can be put into CNN model, and the fault classification results can be obtained by convolution and pooling operations of hidden layers in CNN. Findings The effectiveness of the proposed method is validated through experimental verification and comparative experimental analysis. The results demonstrate that the proposed model can achieve an average classification accuracy of 96.5% under three noise levels, which is 3-13% higher than the traditional models and single deep-learning models. Originality/value The combined SDAE–CNN model proposed in this paper can denoise and reduce dimensions of raw vibration signal data, and extract the in-depth features in image samples of rolling bearing. Consequently, the proposed model has more accurate fault diagnosis results for the rolling bearing vibration signal data with long time series and noise interference. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-11-2019-0496/

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Section: Methodsmentioning

confidence: 99%

Intelligent fault diagnosis method of rolling bearing based on stacked denoising autoencoder and convolutional neural network

Che¹,

Wang²,

Ni³

et al. 2020

ILT

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“…The basic step of an ANN is presented in the below picture (Figure 7), where an artificial neural network scheme is shown for fuel consumption prediction based on various engine parameters such as torque, speed, intake air pressure, and EGR%. In a similar fashion, lubrication research was facilitated by ANN to predict lubricant performance or the need for maintenance by analyzing input parameters of the tribological system [51]. A neural network provides a layered representation of the input features in the latent space, with hidden layers and their corresponding activation functions.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Recent Progress of Machine Learning Algorithms for the Oil and Lubricant Industry

2023

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Machine learning (ML) algorithms have brought about a revolution in many industries where otherwise operation time, cost, and safety would have been compromised. Likewise, in lubrication research, ML has been utilized on many occasions. This review provides an in-depth understanding of seven ML algorithms from a tribological perspective. More specifically, it presents a comprehensive overview of recent advancements in ML applied to lubrication research, organized into four distinct categories. The first category, experimental parameter prediction, highlights the significant contributions of artificial neural networks (ANNs) in accurately forecasting operating conditions related to friction and wear. These predictions offer valuable insights that aid in forensic preparation. Discriminant analysis, Bayesian modeling, and transfer learning approaches have also been used to predict experimental parameters. Second, to predict the lubrication film thickness and identify the lubrication regime, algorithms such as logistic regression and ANN were useful. Such predictions provide up to 99.25% accuracy. Third, to predict the friction and wear for a given experimental condition, support vector machine (SVM), polynomial regression, and ANN offered an accuracy above 93%. Finally, for condition monitoring for bearings, gearboxes, gear trains, and similar critical situations where regular in-person inspection is difficult, Naïve Bayes, SVM, decision trees, and ANN were utilized to predict the safe life of lubricants. This review highlighted these four aspects with state-of-the-art examples and discussed the current situation and projected future possibilities of lubricant design facilitated by ML techniques.

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“…Therefore, this paper focuses on the vibrational aspects of the worm gear box. Kalkat (2015) investigated the effect on gearboxes due to the changes in the oil quality using neural network. Five different oil types were used to classify the faults in the gear box using MNN and RBNN.…”

Section: Introductionmentioning

confidence: 99%

Archives of Acoustics

Thamba

Venkata

Nutakki

et al. 2020

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This research highlights the vibration analysis on worm gears at various conditions of oil using the experimental set up. An experimental rig was developed to facilitate the collection of the vibration signals which consisted of a worm gear box coupled to an AC motor. The four faults were induced in the gear box and the vibration data were collected under full, half and quarter oil conditions. An accelerometer was used to collect the signals and for further analysis of the vibration signals, MATLAB software was used to process the data. Symlet wavelet transform was applied to the raw FFT to compare the features of the data. ANN was implemented to classify various faults and the accuracy is 93.3%.

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Investigations on the effect of oil quality on gearboxes using neural network predictors

Cited by 7 publications

References 17 publications

Intelligent fault diagnosis method of rolling bearing based on stacked denoising autoencoder and convolutional neural network

Intelligent fault diagnosis method of rolling bearing based on stacked denoising autoencoder and convolutional neural network

Recent Progress of Machine Learning Algorithms for the Oil and Lubricant Industry

Archives of Acoustics

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