pdf/a-comprehensive-review-on-convolutional-neural-network-in-1f58bn8ncl.pdf

A comprehensive review on convolutional neural network in machine fault diagnosis

A systematic review of deep transfer learning for machinery fault diagnosis

Deep learning for prognostics and health management: State of the art, challenges, and opportunities

Data-driven methods provided smart manufacturing with unprecedented opportunities to facilitate the transition toward Industry 4.0–based production. Machine learning and deep learning play a critical role in developing intelligent systems for descriptive, diagnostic, and predictive analytics for machine tools and process health monitoring. This paper reviews the opportunities and challenges of deep learning (DL) for intelligent machining and tool monitoring. The components of an intelligent monitoring framework are introduced. The main advantages and disadvantages of machine learning (ML) models are presented and compared with those of deep models. The main DL models, including autoencoders, deep belief networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs), were discussed, and their applications in intelligent machining and tool condition monitoring were reviewed. The opportunities of data-driven smart manufacturing approach applied to intelligent machining were discussed to be (1) automated feature engineering, (2) handling big data, (3) handling high-dimensional data, (4) avoiding sensor redundancy, (5) optimal sensor fusion, and (6) offering hybrid intelligent models. Finally, the data-driven challenges in smart manufacturing, including the challenges associated with the data size, data nature, model selection, and process uncertainty, were discussed, and the research gaps were outlined.

A review on deep learning in machining and tool monitoring: methods, opportunities, and challenges

Intelligent monitoring and diagnostics using a novel integrated model based on deep learning and multi-sensor feature fusion

Data-driven methods with multi-sensor time series data are the most promising approaches for monitoring machine health Extracting fault-sensitive features from multi-sensor time series is a daunting task for both traditional data-driven methods and current deep learning models A novel hybrid end-to-end deep learning framework named Time-distributed ConvLSTM model (TDConvLSTM) is proposed in the paper for machine health monitoring, which works directly on raw multi-sensor time series In TDConvLSTM, the normalized multi-sensor data is first segmented into a collection of subsequences by a sliding window along the temporal dimension Time-distributed local feature extractors are simultaneously applied to each subsequence to extract local spatiotemporal features Then a holistic ConvLSTM layer is designed to extract holistic spatiotemporal features between subsequences At last, a fully-connected layer and a supervised learning layer are stacked on the top of the model to obtain the target TDConvLSTM can extract spatiotemporal features on different time scales without any handcrafted feature engineering The proposed model can achieve better performance in both time series classification tasks and regression prediction tasks than some state-of-the-art models, which has been verified in the gearbox fault diagnosis experiment and the tool wear prediction experiment

https://www.mdpi.com/1424-8220/18/9/2932/pdf

A Time-Distributed Spatiotemporal Feature Learning Method for Machine Health Monitoring with Multi-Sensor Time Series.

Extracting robust fault sensitive features of vibration signals remains a challenge for rotating machinery fault diagnosis under variable operating conditions. Most existing fault diagnosis methods based on the convolutional neural network (CNN) can only extract single-scale features, which not only loss fault sensitive information on other scales, but also suffer from the domain shift problem. In this work, a novel end-to-end deep learning network named adaptive weighted multiscale convolutional neural network (AWMSCNN) is proposed to adaptively extract robust and discriminative multiscale fusion features from raw vibration signals. The AWMSCNN consists of three main components: the denoising layer, the adaptive weighted multiscale convolutional (AWMSC) block, and the multiscale feature fusion layer. The AWMSC block can learn rich and complementary features on multiple scales in parallel. Then, an adaptive weight vector is introduced to modulate multiscale features to emphasize fault sensitive features and suppress features that are sensitive to operating conditions. The train wheelset bearing dataset and the bearing dataset provided by Case Western Reserve University (CWRU) are used to verify the superiority of the proposed model over the basic CNN and other multiscale CNN models. The experiment results show that the proposed model has strong fault discriminative ability and domain adaptive ability against variable operating conditions.

/pdf/an-adaptive-weighted-multiscale-convolutional-neural-network-4jvza0w139.pdf

An Adaptive Weighted Multiscale Convolutional Neural Network for Rotating Machinery Fault Diagnosis Under Variable Operating Conditions

The invention relates to a bearing fault diagnosis method under a strong noise variable speed condition based on an energy weight method. The method comprises the steps: extracting a vibration signalorder through employing a time-frequency ridge feature point linear interpolation and masking algorithm method according to a time-frequency representation graph based on Gabor transformation; performing instantaneous frequency estimation and secondary fitting on the vibration signal by using a local extremum search algorithm and the extracted order; carrying out equal-angle resampling on the vibration signal by utilizing a key phase time scale method according to the fitted instantaneous frequency; performing Hilbert-Huang transformation of CEEMDAN on the resampled isometric domain signal toobtain an order-frequency spectrum of the signal; extracting an impact energy occurrence position in the order-frequency spectrum, and then carrying out binaryzation on the order-frequency spectrum; acquiring an energy weight order sequence capable of reflecting impacts through multi-scale binary spectrum analysis, and carrying out power spectrum analysis on the energy weight order sequence to obtain fault-related impact components. The influence of strong noise and variable rotating speed on vibration signal analysis can be eliminated, and the accuracy of rolling bearing fault diagnosis is improved.

Bearing fault diagnosis method under strong noise variable speed condition based on energy weight method

Due to the disadvantages that rely on prior knowledge and expert experience in traditional order analysis methods and deep learning cannot accurately extract the features in time-varying conditions. A fault diagnosis method for rotating machinery under time-varying conditions based on tacholess order tracking (TOT) and deep learning is proposed in this paper. Firstly, frequency domain periodic signals and estimated speed information are obtained by order tracking. Secondly, the frequency domain periodic signal is speed normalized using the estimated speed information. Finally, normalized features are extracted by deep learning network to form feature vector. The feature vector is fed into a softmax layer to complete fault diagnosis of the gearbox. The fault diagnosis of the gearbox results are compared with other traditional methods and show that the proposed fault diagnosis method can effectively identify the faults and obtain higher fault diagnosis accuracy under time-varying speed.

/pdf/fault-diagnosis-of-rotating-machinery-under-time-varying-3ohlvsj35y.pdf

Fault diagnosis of rotating machinery under time-varying speed based on order tracking and deep learning

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Papers

A Time-Distributed Spatiotemporal Feature Learning Method for Machine Health Monitoring with Multi-Sensor Time Series.

An Adaptive Weighted Multiscale Convolutional Neural Network for Rotating Machinery Fault Diagnosis Under Variable Operating Conditions

Bearing fault diagnosis method under strong noise variable speed condition based on energy weight method

Fault diagnosis of rotating machinery under time-varying speed based on order tracking and deep learning