A deep learning approach to condition monitoring of cantilever beams via time-frequency extended signatures

2019 ◽  
Vol 105 ◽  
pp. 177-181 ◽  
Author(s):  
Habil. Darian M. Onchis
Keyword(s):  
Deep Learning ◽  
Condition Monitoring ◽  
Learning Approach ◽  
Cantilever Beams ◽  
Time Frequency

scholarly journals application based comparison of statistical versus deep learning approaches to reciprocating compressor valve condition monitoring

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Jason Kolodziej ◽  
Jacob Chesnes
Keyword(s):  
Deep Learning ◽  
Condition Monitoring ◽  
Frequency Analysis ◽  
Reciprocating Compressor ◽  
Learning Approaches ◽  
Early Assessment ◽  
Valve Seat ◽  
Repeated Impact ◽  
Time Frequency Analysis ◽  
Time Frequency

This paper presents a vibration-based condition monitoring approach for early assessment of valve wear in an industrial reciprocating compressor. Valve seat  wear is a common fault mode that is caused by repeated impact and accelerated by chatter. Seeded faults consistent with valve seat wear are installed on the head-side discharge valves of a Dresser-Rand ESH-1 industrial reciprocating compressor. Due to the cyclostationary nature of these units a time-frequency analysis is employed where targeted crank angle positions can isolate externally mounted, non-invasive, vibration measurements. A region-of-interest (ROI) is then extracted from the time-frequency analysis and used to train a suitably sized convolutional neural network (CNN). The proposed deep learning method is then compared against a similarly trained discriminant classifier using the same ROIs where features are extracted using texture and shape image statistics. Both methods achieve > 90% success with the CNN classification strategy nearing a perfect result.

A Simulation-Driven Deep Learning Approach for Condition Monitoring of Hydrodynamic Journal Bearings. Part I: Diagnostics of Wear Faults

2019 ◽  
Author(s):  
Diogo Stuani Alves ◽  
Tiago Machado ◽  
Katia Lucchesi Cavalca Dedini ◽  
Ozhan Gecgel ◽  
João Paulo Dias ◽  
...  
Keyword(s):  
Deep Learning ◽  
Condition Monitoring ◽  
Journal Bearings ◽  
Learning Approach

scholarly journals Deep PPG: Large-Scale Heart Rate Estimation with Convolutional Neural Networks

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3079 ◽  
Author(s):  
Attila Reiss ◽  
Ina Indlekofer ◽  
Philip Schmidt ◽  
Kristof Van Laerhoven
Keyword(s):  
Heart Rate ◽  
Deep Learning ◽  
Large Scale ◽  
Learning Approach ◽  
Frequency Spectra ◽  
Time Frequency ◽  
Rate Estimation ◽  
Wide Range ◽  
Public Datasets ◽  
Heart Rate Estimation

Photoplethysmography (PPG)-based continuous heart rate monitoring is essential in a number of domains, e.g., for healthcare or fitness applications. Recently, methods based on time-frequency spectra emerged to address the challenges of motion artefact compensation. However, existing approaches are highly parametrised and optimised for specific scenarios of small, public datasets. We address this fragmentation by contributing research into the robustness and generalisation capabilities of PPG-based heart rate estimation approaches. First, we introduce a novel large-scale dataset (called PPG-DaLiA), including a wide range of activities performed under close to real-life conditions. Second, we extend a state-of-the-art algorithm, significantly improving its performance on several datasets. Third, we introduce deep learning to this domain, and investigate various convolutional neural network architectures. Our end-to-end learning approach takes the time-frequency spectra of synchronised PPG- and accelerometer-signals as input, and provides the estimated heart rate as output. Finally, we compare the novel deep learning approach to classical methods, performing evaluation on four public datasets. We show that on large datasets the deep learning model significantly outperforms other methods: The mean absolute error could be reduced by 31 % on the new dataset PPG-DaLiA, and by 21 % on the dataset WESAD.

Fault severity classification of ball bearing using SinGAN and deep convolutional neural network

2021 ◽  
pp. 095440622110431
Author(s):  
P Akhenia ◽  
K Bhavsar ◽  
J Panchal ◽  
V Vakharia
Keyword(s):  
Deep Learning ◽  
Condition Monitoring ◽  
Data Augmentation ◽  
Ball Bearing ◽  
Rolling Bearing ◽  
Generative Adversarial Network ◽  
Time Frequency ◽  
Adversarial Network ◽  
Mode Decomposition ◽  
Fault Severity

Condition monitoring and diagnosis of a bearing are very important for any rotating machine as it governs the safety while the machine is in operating condition. To construct a feature vector selection of suitable signal processing techniques is a challenge for vibration-based condition monitoring techniques. In the methodology proposed, Short Time Fourier Transform (STFT), Walsh Hadamard Transform (WHT) and Variable Mode Decomposition (VMD) are used to generate 2-D time-frequency spectrograms from the various fault conditions of bearing. When Deep learning techniques apply for fault diagnosis, a large amount of dataset is required for training of machine learning model. To overcome this issue single image Generative Adversarial Network (SinGAN) as a data augmentation technique, utilized for generating additional 2-D time-frequency spectrograms from various fault conditions of ball bearing. To detect fault severity, four deep learning algorithms, ResNet 34, ResNet50, VGG16, and MobileNetV2 are used as a classifier. Experiments are conducted on a rolling bearing dataset provided by the bearing data center of Case Western Reserve University (CWRU) for validating the utility of methodology proposed. Results show that the proposed methodology enables to detect fault severity level with high classification accuracy.

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