Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

A comprehensive review on convolutional neural network in machine fault diagnosis

Deep separable convolutional network for remaining useful life prediction of machinery

The degradation of bearings plays a key role in the failures of industrial machinery Prognosis of bearings is critical in adopting an optimal maintenance strategy to reduce the overall cost and to avoid unwanted downtime or even casualties by estimating the remaining useful life (RUL) of the bearings Traditional data-driven approaches of RUL prediction rely heavily on manual feature extraction and selection using human expertise This paper presents an innovative two-stage automated approach to estimate the RUL of bearings using deep neural networks (DNNs) A denoising autoencoder-based DNN is used to classify the acquired signals of the monitored bearings into different degradation stages Representative features are extracted directly from the raw signal by training the DNN Then, regression models based on shallow neural networks are constructed for each health stage The final RUL result is obtained by smoothing the regression results from different models The proposed approach has achieved satisfactory prediction performance for a real bearing degradation dataset with different working conditions

A Two-Stage Approach for the Remaining Useful Life Prediction of Bearings Using Deep Neural Networks

Rotating machinery intelligent diagnosis based on deep learning (DL) has gone through tremendous progress, which can help reduce costly breakdowns. However, different datasets and hyper-parameters are recommended to be used, and few open source codes are publicly available, resulting in unfair comparisons and ineffective improvement. To address these issues, we perform a comprehensive evaluation of four models, including multi-layer perception (MLP), auto-encoder (AE), convolutional neural network (CNN), and recurrent neural network (RNN), with seven datasets to provide a benchmark study. We first gather nine publicly available datasets and give a comprehensive benchmark study of DL-based models with two data split strategies, five input formats, three normalization methods, and four augmentation methods. Second, we integrate the whole evaluation codes into a code library and release it to the public for better comparisons. Third, we use specific-designed cases to point out the existing issues, including class imbalance, generalization ability, interpretability, few-shot learning, and model selection. Finally, we release a unified code framework for comparing and testing models fairly and quickly, emphasize the importance of open source codes, provide the baseline accuracy (a lower bound), and discuss existing issues in this field. The code library is available at: https://github.com/ZhaoZhibin/DL-based-Intelligent-Diagnosis-Benchmark.

Deep learning algorithms for rotating machinery intelligent diagnosis: An open source benchmark study.

Remaining useful life (RUL) prediction of rolling element bearings plays a pivotal role in reducing costly unplanned maintenance and increasing the reliability, availability, and safety of machines. This paper proposes a hybrid prognostics approach for RUL prediction of rolling element bearings. First, degradation data of bearings are sparsely represented using relevance vector machine regressions with different kernel parameters. Then, exponential degradation models coupled with the Frechet distance are employed to estimate the RUL adaptively. The proposed approach is evaluated using the vibration data from accelerated degradation tests of rolling element bearings and the public PRONOSTIA bearing datasets. Experimental results demonstrate the effectiveness of the proposed approach in improving the accuracy and convergence of RUL prediction of rolling element bearings.

A Hybrid Prognostics Approach for Estimating Remaining Useful Life of Rolling Element Bearings

Recurrent convolutional neural network: A new framework for remaining useful life prediction of machinery

To integrate the complete degradation information of machinery, deep learning-based prognostics approaches usually use monitoring data acquired by different sensors as the inputs of networks These approaches, however, lack an explicit learning mechanism to effectively identify the distinctions of different sensor data and highlight the important degradation information, thereby affecting the accuracy of deep prognostics networks and limiting their generalization To overcome the aforementioned weaknesses, a new deep prognostics framework named multiscale convolutional attention network (MSCAN) is proposed in this article for predicting the remaining useful life (RUL) of machinery In the proposed MSCAN, self-attention modules are first constructed to effectively fuse the input multisensor data Then, a multiscale learning strategy is developed to automatically learn representations from different temporal scales Finally, the learned high-level representations are fed into dynamic dense layers to perform regression analysis and RUL estimation The proposed MSCAN is evaluated using multisensor monitoring data from life testing of milling cutters, and also compared with some state-of-the-art prognostics approaches Experimental results demonstrate the effectiveness and superiority of the proposed MSCAN in fusing multisensor information and improving RUL prediction accuracy

Biao Wang

Papers

A Hybrid Prognostics Approach for Estimating Remaining Useful Life of Rolling Element Bearings

Deep separable convolutional network for remaining useful life prediction of machinery

Recurrent convolutional neural network: A new framework for remaining useful life prediction of machinery

Multiscale Convolutional Attention Network for Predicting Remaining Useful Life of Machinery

Degradation modeling and remaining useful life prediction for dependent competing failure processes