Product quality prediction in pulsed laser cutting of silicon steel sheet using vibration signals and deep neural network

Structural health monitoring of mechanical systems is essential to avoid their catastrophic failure. In this article, an effective deep neural network is developed for extracting the damage-sensitive features from frequency data of vibration signals to damage detection of mechanical systems in the presence of the uncertainties such as modeling errors, measurement errors, and environmental noises. For this purpose, the finite element method is used to analyze a mechanical system (finite element model). Then, vibration experiments are carried out on the laboratory-scale model. Vibration signals of real intact system are used to updating the finite element model and minimizing the disparities between the natural frequencies of the finite element model and real system. Some parts of the signals that are not related to the nature of the system are removed using the complete ensemble empirical mode decomposition technique. Frequency domain decomposition method is used to extract frequency data. The proposed deep neural network is trained using frequency data of the finite element model and real intact state and then is tested using frequency data of the real system. The proposed network is designed in two stages, namely, the pre-training classification based on deep auto-encoder and Softmax layer (first stage), and the re-training classification based on backpropagation algorithm for fine tuning of the network (second stage). The proposed method is validated using a lab-scale offshore jacket structure. The results show that the proposed method can learn features from the frequency data and achieve higher accuracy than other comparative methods.

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A DBN-Based Deep Neural Network Model with Multitask Learning for Online Air Quality Prediction

Journal of Control Science and Engineering ◽

10.1155/2019/5304535 ◽

2019 ◽

Vol 2019 ◽

pp. 1-9 ◽

Cited By ~ 5

Author(s):

Jiangeng Li ◽

Xingyang Shao ◽

Rihui Sun

Keyword(s):

Neural Network ◽

Air Quality ◽

Network Model ◽

Neural Network Model ◽

Prediction Accuracy ◽

Deep Neural Network ◽

Multitask Learning ◽

Air Pollutant ◽

Quality Prediction ◽

Air Quality Prediction

To avoid the adverse effects of severe air pollution on human health, we need accurate real-time air quality prediction. In this paper, for the purpose of improve prediction accuracy of air pollutant concentration, a deep neural network model with multitask learning (MTL-DBN-DNN), pretrained by a deep belief network (DBN), is proposed for forecasting of nonlinear systems and tested on the forecast of air quality time series. MTL-DBN-DNN model can solve several related prediction tasks at the same time by using shared information contained in the training data of different tasks. In the model, DBN is used to learn feature representations. Each unit in the output layer is connected to only a subset of units in the last hidden layer of DBN. Such connection effectively avoids the problem that fully connected networks need to juggle the learning of each task while being trained, so that the trained networks cannot get optimal prediction accuracy for each task. The sliding window is used to take the recent data to dynamically adjust the parameters of the MTL-DBN-DNN model. The MTL-DBN-DNN model is evaluated with a dataset from Microsoft Research. Comparison with multiple baseline models shows that the proposed MTL-DBN-DNN achieve state-of-art performance on air pollutant concentration forecasting.

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DeepAir: Air Quality Prediction using Deep Neural Network

TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON) ◽

10.1109/tencon.2019.8929470 ◽

2019 ◽

Author(s):

Pratyush Singh ◽

T Lakshmi Narasimhan ◽

Chandra Shekar Lakshminarayanan

Keyword(s):

Neural Network ◽

Air Quality ◽

Deep Neural Network ◽

Quality Prediction ◽

Air Quality Prediction

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A domain adaptation model for early gear pitting fault diagnosis based on deep transfer learning network

Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability ◽

10.1177/1748006x19867776 ◽

2019 ◽

Vol 234 (1) ◽

pp. 168-182 ◽

Cited By ~ 4

Author(s):

Jialin Li ◽

Xueyi Li ◽

David He ◽

Yongzhi Qu

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Transfer Learning ◽

Working Conditions ◽

Deep Neural Network ◽

Domain Adaptation ◽

Diagnostic Model ◽

Target Domain ◽

Vibration Signals ◽

Learning Network

In recent years, research on gear pitting fault diagnosis has been conducted. Most of the research has focused on feature extraction and feature selection process, and diagnostic models are only suitable for one working condition. To diagnose early gear pitting faults under multiple working conditions, this article proposes to develop a domain adaptation diagnostic model–based improved deep neural network and transfer learning with raw vibration signals. A particle swarm optimization algorithm and L2 regularization are used to optimize the improved deep neural network to improve the stability and accuracy of the diagnosis. When using the domain adaptation diagnostic model for fault diagnosis, it is necessary to discriminate whether the target domain (test data) is the same as the source domain (training data). If the target domain and the source domain are consistent, the trained improved deep neural network can be used directly for diagnosis. Otherwise, the transfer learning is combined with improved deep neural network to develop a deep transfer learning network to improve the domain adaptability of the diagnostic model. Vibration signals for seven gear types with early pitting faults under 25 working conditions collected from a gear test rig are used to validate the proposed method. It is confirmed by the validation results that the developed domain adaptation diagnostic model has a significant improvement in the adaptability of multiple working conditions.

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Rolling bearings fault diagnosis and classification based on optimal wavelet subband selection and deep neural network

10.21203/rs.3.rs-342393/v1 ◽

2021 ◽

Author(s):

Rakesh Kumar Jha ◽

Preety D Swami

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Deep Neural Network ◽

Vital Role ◽

Fault Classification ◽

Vibration Signals ◽

Softmax Classifier ◽

Practical Applications ◽

Time Frequency ◽

Diagnosis And Classification

Abstract Time-frequency analysis plays a vital role in fault diagnosis of nonstationary vibration signals acquired from mechanical systems. However, the practical applications face the challenges of continuous variation in speed and load. Apart from this, the disturbances introduced by noise are inevitable. This paper aims to develop a robust method for fault identification in bearings under varying speed, load and noisy conditions. An Optimal Wavelet Subband Deep Neural Network (OWS-DNN) technique is proposed that automatically extracts features from an optimal wavelet subband selected on the basis of Shannon entropy. After denoising the optimal subband, the optimal subbands are dimensionally reduced by the encoder section of an autoencoder. The output of the encoder can be considered as data features. Finally, softmax classifier is employed to classify the encoder output. The vibration signals were recorded on a machinery fault simulator setup for various combinations of speed and load for healthy and faulty bearings. The signals were subjected to various noise levels and the deep neural network was trained. The achieved experimental results reveal high accuracy in fault classification as compared to other techniques under comparison.

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