Gear pitting severity level identification using binary segmentation methodology

Kundu, Pradeep; Darpe, Ashish K.; Kulkarni, Makarand S.

doi:10.1002/stc.2478

Cited by 18 publications

(12 citation statements)

References 29 publications

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“…This quantified fatigue damage of gear effectively reflects the performance degradation of gear. Darpe and coauthors [32] proposed a binary segmentation method to identify different pitting stages of the spur gear. The relevant degradation parameters are extracted from the residual vibration signal to represent the progress of gear damage, and then according to the changes in the distribution characteristics of the degradation parameters, different pitting stages of the gear are identified.…”

Section: Digital Twin-driven Physical Model-based Methods Ofmentioning

confidence: 99%

Digital Twin-Driven Remaining Useful Life Prediction for Gear Performance Degradation: A Review

Liu

Dong

2021

Journal of Computing and Information Science in Engineering

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As a transmission component, the gear has been obtained widespread attention. The remaining useful life (RUL) prediction of gear is critical to the prognostics health management (PHM) of gear transmission systems. The digital twin (DT) provides support for gear RUL prediction with the advantages of rich health information data and accurate health indicators (HI). This paper reviews digital twin-driven RUL prediction methods for gear performance degradation, from the view of digital twin-driven physical model-based and virtual model-based prediction method. From the view of physical model-based one, it includes prediction model based on gear crack, gear fatigue, gear surface scratch, gear tooth breakage, and gear permanent deformation. From the view of digital twin-driven virtual model-based one, it includes non-deep learning methods, and deep learning methods. Non-deep learning methods include wiener process, gamma process, hidden Markov model (HMM), regression-based model, proportional hazard model. Deep learning methods include deep neural networks (DNN), deep belief networks (DBN), convolutional neural networks (CNN), and recurrent neural networks (RNN), etc. It mainly summarizes the performance degradation and life test of various models in gear, and evaluates the advantages and disadvantages of various methods. In addition, it encourages future works.

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Section: Digital Twin-driven Physical Model-based Methods Ofmentioning

confidence: 99%

Digital Twin-Driven Remaining Useful Life Prediction for Gear Performance Degradation: A Review

Liu

Dong

2021

Journal of Computing and Information Science in Engineering

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“…For a lab device, various experiments under various fault and operating conditions can be conducted to collect a large dataset with abundant fault information and knowledge. 51 It is indicated as fx s i , y s i g n s i¼1 , where n s means the number of samples, y s i Y s is the health label of sample x s i , Y s is the label space with k health conditions, that is, Y s ¼ f1, 2, …,kg, and sample x s i obeys the probability distribution PðX s Þ, where X s is the feature space of original samples. With a huge amount of correctly labeled data, it is certainly possible to train a deep neural network for intelligently diagnosing machine health conditions.…”

Section: Cross-machine Transfer Diagnosis Frameworkmentioning

confidence: 99%

Cross‐machine intelligent fault diagnosis of gearbox based on deep learning and parameter transfer

Han

Zhou

Xiang

et al. 2021

Structural Contr & Hlth

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With the rapid development of artificial intelligence technologies, data-driven methods have significantly contributed to the intelligent monitoring and diagnosis of mechanical systems. However, the state-of-the-art approaches, especially the deep learning-based ones, implicitly assume the availability of large amounts of labeled fault data for supervised training, which is often infeasible due to the highly reliable system design in the field. In this research, a deep transfer convolutional neural network (CNN) scheme is proposed to enhance the diagnosis performance when dealing with insufficient training data in the target domain. By utilizing transfer learning, rich but relevant feature representation can be learnt from massive data in the source domain. The learnt weights and biases in the source domain are transferred to the target task as the initial parameter values. Then, the transferred parameters are properly fine-tuned with the small labeled datasets in the target domain. To avoid overfitting in the case of scarcely labeled samples in the target domain, global average pooling (GAP) is introduced to replace the fully-connected layers, and the traditional architecture in CNN is modified, to reduce the number of trainable parameters. Finally, by fully considering the transfer scenarios between diverse operating conditions and diverse machines, the cross-machine transfer experiments are designed with three gearbox datasets provided by the Prognostic and Health Management (PHM) 2009 conference, the Tsinghua University, and the University of Alberta. The results demonstrate the effectiveness of the proposed method with scarce labeled samples in the target domain. K E Y W O R D S convolutional neural network, deep transfer learning, gearbox fault diagnosis, global average pooling, scarcely labeled samples 1 | INTRODUCTION Machinery condition monitoring and fault diagnosis is a classic problem in the manufacturing industry. 1-4 Generally, the machinery fault diagnosis methods can be categorized into three classes, including model-based methods, knowledge-based methods, and data-driven methods. The model-based methods, such as finite element simulation and system identification, can perform fault diagnosis by monitoring the consistency between the real-time variables of the practical machine and the model-predicted values. However, due to the complex structure and working mechanism,

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“…Typically, the raw sensor data are first preprocessed to enhance the data quality, then identify the fault-relevant features, and utilize techniques such as machine learning-based approaches to formulate a predictive model for diagnostic and prognostic purposes. Most PHM studies focus on critical machinery components and infrastructures including bearings, 4,5 gears, 6,7 batteries, 8,9 bridges, 10,11 and railway. 12 However, these studies are mainly developed based on conventional machine learning models with shallow configurations.…”

Section: Introductionmentioning

confidence: 99%

A probabilistic Bayesian recurrent neural network for remaining useful life prognostics considering epistemic and aleatory uncertainties

Caceres

González

Zhou

et al. 2021

Struct Control Health Monit

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Deep learning-based approach has emerged as a promising solution to handle big machinery data from multi-sensor suites in complex physical assets and predict their remaining useful life (RUL). However, most recent deep learningbased approaches deliver a single-point estimate of RUL as these models represent the weights of a neural network as a deterministic value and hence cannot convey uncertainty in the RUL prediction. This practice usually provides overly confident predictions that might cause severe consequences in safetycritical industries. To address this issue, this paper proposes a probabilistic Bayesian recurrent neural network (RNN) for RUL prognostics considering epistemic and aleatory uncertainties. The epistemic uncertainty is handled by Bayesian RNN layers as extensions from the Frequentist RNN layers using the Flipout method. The aleatory uncertainty is covered by a probabilistic output that follows a Gaussian distribution parameterized by the two neurons in the output layer. The network is trained using Bayes by backprop with the Flipout method. The proposed model is demonstrated by the open-access Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset of turbofan engines and a comparative study of the Frequentist RNN counterparts, the Monte Carlo Dropout-based RNN, and the state-of-the-art models for C-MAPSS datasets. The results demonstrate the promising performance and robustness of the proposed model in RUL prognostics.

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Gear pitting severity level identification using binary segmentation methodology

Cited by 18 publications

References 29 publications

Digital Twin-Driven Remaining Useful Life Prediction for Gear Performance Degradation: A Review

Digital Twin-Driven Remaining Useful Life Prediction for Gear Performance Degradation: A Review

Cross‐machine intelligent fault diagnosis of gearbox based on deep learning and parameter transfer

A probabilistic Bayesian recurrent neural network for remaining useful life prognostics considering epistemic and aleatory uncertainties

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