An integrated approach to planetary gearbox fault diagnosis using deep belief networks

The diagnosis performance of Deep Convolutional Neural Network (DCNN) method is closely related to the generalization ability of the training model. An empirical training strategy is to randomly disperse the training samples and train the model with mini-batch training samples. But there are still two problems in the empirical method that need to be solved urgently. Firstly, what is the theoretical basis for random discretization of samples? Secondly, how to scientifically quantify batch division? Aiming at these two problems, the theoretical basis of sample random discretization has been deduced and proved, furthermore, a scientific quantitative batch division method is proposed based on the proved thesis. The fault diagnosis results of the planetary gearbox show that: (1) The model obtained by the training guide proposed in this paper has stronger generalization ability; (2) The DCNN with the training guide can accurately and effectively diagnose the faults of planetary gearbox and obtain ideal diagnosis results.

show abstract

“…When there exist K , according to "borel strong law of large numbers" theorem, the formula (15) holds…”

Section: Conclusion: the Generalization Error Of The Dcnn Model Is Pomentioning

confidence: 99%

“…In recent years, Deep Learning method has been widely used in Computer Vision [10,11] and Speech Recognition [12]. Due to its powerful data processing and pattern recognition capabilities, some Deep Learning algorithms have been used for the mechanical equipment health monitoring too [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

DCNN With Explicable Training Guide and its Application to Fault Diagnosis of the Planetary Gearboxes

Luo

Shen

et al. 2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Deep belief networks (DBN) consist of an unsupervised restricted Boltzmann machine (RBM) and a supervised network, as shown in Figure 2. Each RBM has a layer of input neurons and a single hidden layer with hidden-to-all-visible connections [34,35]. The DBN model can achieve a better performance with the initialization weight than that of the ANN model with random weights [36,37].…”

Section: Deep Belief Network (Dbn)mentioning

confidence: 99%

Monthly Streamflow Forecasting Using EEMD-Lasso-DBN Method Based on Multi-Scale Predictors Selection

Chen

Wei

Qiu

2018

Water

View full text Add to dashboard Cite

For the inherent characteristics of a raw streamflow times series and the complicated relationship between multi-scale predictors and streamflow, monthly streamflow forecasting is very difficult. In this paper, an method was proposed integrating the ensemble empirical mode decomposition (EEMD), least absolute shrinkage and selection operator (Lasso) with deep belief networks (DBN) for forecasting monthly streamflow time series, which is EEMD-Lasso-DBN (ELD) method. To develop the ELD model, the raw streamflow time series was resolved into different elements, including intrinsic mode functions (IMFs) and residue series, using the EEMD technique. The predictors of each IMF element and residue were screened using the Lasso technique from a large number of candidate predictors, respectively. Then, the DBN models were built to simulate the complex relationship between the resolved elements and the selected predictors, respectively. The predicted results of the IMFs and residual series were assembled as an ensemble forecast for the raw streamflow time series and were compared with the other models. The monthly streamflow series from Tennessee, in the USA, were investigated using the ELD method. It was found that each IMF has different characteristics and physical meaning, corresponding to different predictors. The proposed ELD model can significantly improve the accuracy of monthly streamflow forecasting.

show abstract

“…Specifically, deep belief networks (DBNs) [21], a typical class of deep learning methods, are used in this study, which are naturally probabilistic generative models with multilayered architecture. Compared with shallow neural network methods, DBNs can capture complex nonlinear features, have a powerful modeling capacity and are quite suitable for modeling complex SCADA data [22]. DBNs have received attention in the fields of wind speed prediction [23], mechanical engineering fault diagnosis [24] and complex system fault detection [25].…”

Section: Introductionmentioning

confidence: 99%

Early Fault Detection of Wind Turbines Based on Operational Condition Clustering and Optimized Deep Belief Network Modeling

Wang¹,

Wang²,

Jiang³

et al. 2019

Energies

View full text Add to dashboard Cite

Health monitoring and early fault detection of wind turbines have attracted considerable attention due to the benefits of improving reliability and reducing the operation and maintenance costs of the turbine. However, dynamic and constantly changing operating conditions of wind turbines still pose great challenges to effective and reliable fault detection. Most existing health monitoring approaches mainly focus on one single operating condition, so these methods cannot assess the health status of turbines accurately, leading to unsatisfactory detection performance. To this end, this paper proposes a novel general health monitoring framework for wind turbines based on supervisory control and data acquisition (SCADA) data. A key feature of the proposed framework is that it first partitions the turbine operation into multiple sub-operation conditions by the clustering approach and then builds a normal turbine behavior model for each sub-operation condition. For normal behavior modeling, an optimized deep belief network is proposed. This optimized modeling method can capture the sophisticated nonlinear correlations among different monitoring variables, which is helpful to enhance the prediction performance. A case study of main bearing fault detection using real SCADA data is used to validate the proposed approach, which demonstrates its effectiveness and advantages.

show abstract

An integrated approach to planetary gearbox fault diagnosis using deep belief networks

Cited by 39 publications

References 41 publications

DCNN With Explicable Training Guide and its Application to Fault Diagnosis of the Planetary Gearboxes

DCNN With Explicable Training Guide and its Application to Fault Diagnosis of the Planetary Gearboxes

Monthly Streamflow Forecasting Using EEMD-Lasso-DBN Method Based on Multi-Scale Predictors Selection

Early Fault Detection of Wind Turbines Based on Operational Condition Clustering and Optimized Deep Belief Network Modeling

Contact Info

Product

Resources

About