scholarly journals Integrating Physics and Data Driven Cyber-Physical System for Condition Monitoring of Critical Transmission Components in Smart Production Line

2021 ◽  
Vol 11 (19) ◽  
pp. 8967
Author(s):  
Lin Song ◽  
Liping Wang ◽  
Jun Wu ◽  
Jianhong Liang ◽  
Zhigui Liu
Keyword(s):  
Signal Processing ◽  
Deep Learning ◽  
Condition Monitoring ◽  
Physical System ◽  
Production Line ◽  
Learning Algorithm ◽  
Data Driven ◽  
Equation Modeling ◽  
Cyber Physical System ◽  
Deep Learning Algorithm

In response to the lack of a unified cyber–physical system framework, which combined the Internet of Things, industrial big data, and deep learning algorithms for the condition monitoring of critical transmission components in a smart production line. In this study, based on the conceptualization of the layers, a novel five-layer cyber–physical systems framework for smart production lines is proposed. This architecture integrates physics and is data-driven. The smart connection layer collects and transmits data, the physical equation modeling layer converts low-value raw data into high-value feature information via signal processing, the machine learning modeling layer realizes condition prediction through a deep learning algorithm, and scientific decision-making and predictive maintenance are completed through a cognition layer and a configuration layer. Case studies on three critical transmission components—spindles, bearings, and gears—are carried out to validate the effectiveness of the proposed framework and hybrid model for condition monitoring. The prediction results of the three datasets show that the system is successful in distinguishing condition, while the short time Fourier transform signal processing and deep residual network deep learning algorithm is superior to that of other models. The proposed framework and approach are scalable and generalizable and lay the foundation for the extension of the model.

Detecting faults in VSC-HVDC systems by deep learning and K-means

2020 ◽  
Vol 13 ◽  
Author(s):  
Roohollah Sadeghi Goughari ◽  
Mehdi Jafari Shahbazzadeh ◽  
Mahdiyeh Eslami
Keyword(s):  
Signal Processing ◽  
Deep Learning ◽  
Wavelet Transform ◽  
Transmission Lines ◽  
Fault Location ◽  
Learning Algorithm ◽  
Hvdc Transmission ◽  
Deep Learning Algorithm ◽  
Processing Techniques ◽  
Signal Processing Techniques

Background: In this paper, two methods and their comparison used to determine the fault locaton in VSC-HVDC transmission lines. Fast and reliable control are features of these systems. Methods: Additionally, wavelet transform from advanced techniques of signal processing is employed for the purpose of extracting important characteristics of fault signal from both sides of the line by PMU. To do so, Deep learning is used to identify the relation between the extracted features from wavelet analysis of the fault current and variations under fault conditions. As such, wavelet transform and advanced signal processing techniques are used to extract important features of fault signal from both sides of the line by the PMU. Results: The results show the high accuracy of finding fault location by the deep learning algorithm method compared to the k-means algorithm with an error rate of <1%. Conclusion: Studies on the 50 kV VSC-HVDC transmission line with a length of 25 km in MATLAB have been simulated.

scholarly journals A Novel Classification Method for Flutter Signals Based on the CNN and STFT

2019 ◽  
Vol 2019 ◽  
pp. 1-8 ◽  
Author(s):  
Shiqiang Duan ◽  
Hua Zheng ◽  
Junhao Liu
Keyword(s):  
Signal Processing ◽  
Deep Learning ◽  
Wind Tunnel ◽  
Learning Algorithm ◽  
Wind Tunnel Test ◽  
Structural Response ◽  
Classification Method ◽  
Time Frequency ◽  
Deep Learning Algorithm ◽  
Aeroelastic Model

Necessary model calculation simplifications, uncertainty in actual wind tunnel test, and data acquisition system error altogether lead to error between a set of actual experimental results and a set of theoretical design results; wind tunnel test flutter data can be utilized to feedback this error. In this study, a signal processing method was established to use the structural response signals from an aeroelastic model to classify flutter signals via deep learning algorithm. This novel flutter signal processing and classification method works by combining a convolutional neural network (CNN) with time-frequency analysis. Flutter characteristics are revealed in both time and frequency domains, which are harmonic or divergent in the time series; the flutter model energy is singular and significantly increases in the frequency view, so the features of the time-frequency diagram can be extracted from the dataset-trained CNN model. As the foundation of the subsequent deep learning algorithm, the datasets are placed into a collection of time-frequency diagrams calculated by short-time Fourier transform (STFT) and labeled with two artificial states, flutter or no flutter, depending on the source of the signal measured from a wind tunnel test on the aeroelastic model. After preprocessing, a cross-validation schedule is implemented to update (and optimize) CNN parameters though the trained dataset. The trained models were compared against test datasets to validate their reliability and robustness. Our results indicate that the accuracy rate of test datasets reaches 90%. The trained models can effectively and automatically distinguish whether or not there is flutter in the measured signals.

scholarly journals Novel deep learning algorithm predicts the status of molecular pathways and key mutations in colorectal cancer from routine histology images

2021 ◽  
Author(s):  
Mohsin Bilal ◽  
Shan E Ahmed Raza ◽  
Ayesha Azam ◽  
Simon Graham ◽  
Muhammad Ilyas ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Data Driven ◽  
Molecular Pathways ◽  
Cellular Composition ◽  
Genetic Pathways ◽  
Histological Features ◽  
Deep Learning Algorithm ◽  
The Status

SummaryBackgroundDetermining molecular pathways involved in the development of colorectal cancer (CRC) and knowing the status of key mutations are crucial for deciding optimal target therapy. The goal of this study is to explore machine learning to predict the status of the three main CRC molecular pathways – microsatellite instability (MSI), chromosomal instability (CIN), CpG island methylator phenotype (CIMP) – and to detect BRAF and TP53 mutations as well as to predict hypermutated (HM) CRC tumors from whole-slide images (WSIs) of colorectal cancer (CRC) slides stained with Hematoxylin and Eosin (H&E).MethodsWe propose a novel iterative draw-and-rank sampling (IDaRS) algorithm to select representative sub-images or tiles from a WSI given a single WSI-level label, without needing any detailed annotations at the cell or region levels. IDaRS is used to train a deep convolutional network for predicting key molecular parameters in CRC (in particular, prediction of HM tumors and the status of three main CRC molecular pathways – MSI, CIN, CIMP – as well as the detection of two key mutations, BRAF and TP53) from digitized images of routine H&E stained tissue slides of CRC patients (n=497 for TCGA cohort and n=47 cases for the Pathology AI Platform or PAIP cohort). Visual fields most predictive of each pathway and HM tumors identified by IDaRS are analyzed for verification of known histological features for the first time to reveal novel histological features. This is achieved by systematic, data-driven analysis of the cellular composition of strongly predictive tiles.FindingsIDaRS yields high prediction accuracy for prediction of the three main CRC genetic pathways and key mutations by deep learning based analysis of the WSIs of H&E stained slides. It achieves the state-of-the-art AUROC values of 0.90, 0.83, and 0.81 for prediction of the status of MSI, CIN, and HM tumors for the TCGA cohort, which is significantly higher than any other currently published methods on that cohort. We also report prediction of status of CIMP pathway (CIMP-High and CIMP-Low) from H&E slides, with an AUROC of 0.79. We analyzed key discriminative histological features associated with HM tumors and each molecular pathway in a data-driven manner, via an automated quantitative analysis of the cellular composition of tiles strongly predictive of the corresponding molecular status. A key feature of the proposed method is that it enables a systematic and data-driven analysis of the cellular composition of image tiles strongly predictive of the various molecular parameters. We found that relatively high proportion of tumor infiltrating lymphocytes and necrosis are found to be strongly associated with HM and MSI, and moderately associated with CIMP-H and genome-stable (GS) cases, whereas relatively high proportions of neoplastic epithelial type 2 (NEP2), mesenchymal and neoplastic epithelial type 1 (NEP1) cells are found to be associated with CIN cases.InterpretationAutomated prediction of genetic pathways and key mutations from image analysis of simple H&E stained sections with a high accuracy can provide time and cost-effective decision support. This work shows that a deep learning algorithm can mine both visually recognizable as well as sub-visual histological patterns associated with molecular pathways and key mutations in CRC in a data-driven manner.FundingThis study was funded by the UK Medical Research Council (award MR/P015476/1).

Simulation-Driven Deep Learning Approach for Wear Diagnostics in Hydrodynamic Journal Bearings

2020 ◽  
pp. 1-26
Author(s):  
Ozhan Gecgel ◽  
Joao Paulo Dias ◽  
Stephen Ekwaro-Osire ◽  
Diogo Alves ◽  
Tiago H. Machado ◽  
...  
Keyword(s):  
Deep Learning ◽  
Condition Monitoring ◽  
Learning Algorithm ◽  
Journal Bearings ◽  
Operating Conditions ◽  
Intelligent Machines ◽  
The Past ◽  
Deep Learning Algorithm ◽  
Promising Tool ◽  
Rotating Systems

Abstract Early diagnosis in rotating machinery has been a challenge when looking towards the concept of intelligent machines. A crucial and critical component in these systems is the lubricated journal bearing, subjected to wear fault by abrasive removing of material in its inner wall, mainly during run-ups and run-downs. In extreme conditions, wear faults can cause unexpected shutdowns in rotating systems. Consequently, advanced condition monitoring is an essential procedure in the wear diagnosis of journal bearings. Although an increasing number of data-driven condition monitoring approaches for rotating machines have been proposed in the past decade, they mostly rely on substantial amounts of experimental data for training, which is expensive and time-consuming to obtain. The objective of this work is to develop a framework using a deep learning algorithm to classify wear faults in hydrodynamic journal bearings using simulated vibrations signals. Numerically simulated datasets under different wear severity levels and operating conditions were used to train and test the diagnostics framework. The results show that the proposed framework can be a promising tool to wear fault diagnostics in journal bearings.

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