scholarly journals Deep Learning Model for Industrial Leakage Detection Using Acoustic Emission Signal

Informatics ◽  
2020 ◽  
Vol 7 (4) ◽  
pp. 49
Author(s):  
Masoumeh Rahimi ◽  
Alireza Alghassi ◽  
Mominul Ahsan ◽  
Julfikar Haider
Keyword(s):  
Deep Learning ◽  
Wavelet Transforms ◽  
Detection Efficiency ◽  
Sensor Data ◽  
Inspection System ◽  
Leakage Detection ◽  
Emission Signal ◽  
Clear Cut ◽  
Manual Inspection ◽  
Deep Learning Model

Intelligent fault diagnosis methods have replaced time consuming and unreliable human analysis, increasing anomaly detection efficiency. Deep learning models are clear cut techniques for this purpose. This paper’s fundamental purpose is to automatically detect leakage in tanks during production with more reliability than a manual inspection, a common practice in industries. This research proposes an inspection system to predict tank leakage using hydrophone sensor data and deep learning algorithms after production. In this paper, leak detection was investigated using an experimental setup consisting of a plastic tank immersed underwater. Three different techniques for this purpose were implemented and compared with each other, including fast Fourier transform (FFT), wavelet transforms, and time-domain features, all of which are followed with 1D convolution neural network (1D-CNN). Applying FFT and converting the signal to a 1D image followed by 1D-CNN showed better results than other methods. Experimental results demonstrate the effectiveness and the superiority of the proposed methodology for detecting real-time leakage inaccuracy.

scholarly journals Intelligent System for Railway Wheelset Press-Fit Inspection Using Deep Learning

2021 ◽  
Vol 11 (17) ◽  
pp. 8243
Author(s):  
Jung-Sing Jwo ◽  
Ching-Sheng Lin ◽  
Cheng-Hsiung Lee ◽  
Li Zhang ◽  
Sin-Ming Huang
Keyword(s):  
Deep Learning ◽  
Intelligent System ◽  
Working Hours ◽  
Inspection System ◽  
Practical Case ◽  
Assembly Quality ◽  
Press Fit ◽  
Manual Inspection ◽  
Testing Dataset ◽  
Traditional Manufacturing

Railway wheelsets are the key to ensuring the safe operation of trains. To achieve zero-defect production, railway equipment manufacturers must strictly control every link in the wheelset production process. The press-fit curve output by the wheelset assembly machine is an essential indicator of the wheelset’s assembly quality. The operators will still need to manually and individually recheck press-fit curves in our practical case. However, there are many uncertainties in the manual inspection. For example, subjective judgment can easily cause inconsistent judgment results between different inspectors, or the probability of human misinterpretation can increase as the working hours increase. Therefore, this study proposes an intelligent railway wheelset inspection system based on deep learning, which improves the reliability and efficiency of manual inspection of wheelset assembly quality. To solve the severe imbalance in the number of collected images, this study establishes a predicted model of press-fit quality based on a deep Siamese network. Our experimental results show that the precision measurement is outstanding for the testing dataset contained 3863 qualified images and 28 unqualified images of press-fit curves. The proposed system will serve as a successful case of a paradigm shift from traditional manufacturing to digital manufacturing.

scholarly journals Elderly Fall Detection using Lightweight Convolution Deep Learning Model

2021 ◽  
Vol 12 (2) ◽  
pp. 2097-2103
Author(s):  
Neeraj Varshney
Keyword(s):  
Deep Learning ◽  
Daily Life ◽  
Fall Detection ◽  
Living Alone ◽  
Sensor Data ◽  
Routine Activity ◽  
Old People ◽  
Accurate Identification ◽  
Daily Life Activities ◽  
Deep Learning Model

Old people, who are living alone at home face serious problem of Falls while moving from one place to another and sometime life threading also. In order to prevent this situation, several fall monitoring systems based on sensor data were proposed. However, there was an issue of misclassification to identify the fall as daily life activities and also routine activity as fall. Towards this end, a deep learning based model is proposed in this paper by using the data of heart rate, BP and sugar level to identify fall along with other daily life activities like walking, running jogging etc. For accurate identification of fall accidents, a publicly accessible data collection and a lightly weighted CNN model are used. The model reports proposed and 98.21 % precision.

scholarly journals SmartFall: A Smartwatch-Based Fall Detection System Using Deep Learning

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3363 ◽  
Author(s):  
Taylor Mauldin ◽  
Marc Canby ◽  
Vangelis Metsis ◽  
Anne Ngu ◽  
Coralys Rivera
Keyword(s):  
Support Vector Machine ◽  
Deep Learning ◽  
Naive Bayes ◽  
Fall Detection ◽  
Learning Model ◽  
Naïve Bayes ◽  
Sensor Data ◽  
Support Vector ◽  
Accelerometer Data ◽  
Deep Learning Model

This paper presents SmartFall, an Android app that uses accelerometer data collected from a commodity-based smartwatch Internet of Things (IoT) device to detect falls. The smartwatch is paired with a smartphone that runs the SmartFall application, which performs the computation necessary for the prediction of falls in real time without incurring latency in communicating with a cloud server, while also preserving data privacy. We experimented with both traditional (Support Vector Machine and Naive Bayes) and non-traditional (Deep Learning) machine learning algorithms for the creation of fall detection models using three different fall datasets (Smartwatch, Notch, Farseeing). Our results show that a Deep Learning model for fall detection generally outperforms more traditional models across the three datasets. This is attributed to the Deep Learning model’s ability to automatically learn subtle features from the raw accelerometer data that are not available to Naive Bayes and Support Vector Machine, which are restricted to learning from a small set of extracted features manually specified. Furthermore, the Deep Learning model exhibits a better ability to generalize to new users when predicting falls, an important quality of any model that is to be successful in the real world. We also present a three-layer open IoT system architecture used in SmartFall, which can be easily adapted for the collection and analysis of other sensor data modalities (e.g., heart rate, skin temperature, walking patterns) that enables remote monitoring of a subject’s wellbeing.

scholarly journals Optimising Deep Learning at the Edge for Accurate Hourly Air Quality Prediction

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1064
Author(s):  
I Nyoman Kusuma Wardana ◽  
Julian W. Gardner ◽  
Suhaib A. Fahmy
Keyword(s):  
Deep Learning ◽  
Air Quality ◽  
Short Term Memory ◽  
Model Performance ◽  
Learning Model ◽  
Sensor Data ◽  
Raspberry Pi ◽  
Learning Models ◽  
Proposed Model ◽  
Deep Learning Model

Accurate air quality monitoring requires processing of multi-dimensional, multi-location sensor data, which has previously been considered in centralised machine learning models. These are often unsuitable for resource-constrained edge devices. In this article, we address this challenge by: (1) designing a novel hybrid deep learning model for hourly PM2.5 pollutant prediction; (2) optimising the obtained model for edge devices; and (3) examining model performance running on the edge devices in terms of both accuracy and latency. The hybrid deep learning model in this work comprises a 1D Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) to predict hourly PM2.5 concentration. The results show that our proposed model outperforms other deep learning models, evaluated by calculating RMSE and MAE errors. The proposed model was optimised for edge devices, the Raspberry Pi 3 Model B+ (RPi3B+) and Raspberry Pi 4 Model B (RPi4B). This optimised model reduced file size to a quarter of the original, with further size reduction achieved by implementing different post-training quantisation. In total, 8272 hourly samples were continuously fed to the edge device, with the RPi4B executing the model twice as fast as the RPi3B+ in all quantisation modes. Full-integer quantisation produced the lowest execution time, with latencies of 2.19 s and 4.73 s for RPi4B and RPi3B+, respectively.

scholarly journals Automatic Template Images Setting Method Based on the Pix2pix Deep Learning Model for Visual Inspection System

2020 ◽  
Vol 86 (9) ◽  
pp. 714-719
Author(s):  
Hajime OSADA ◽  
Kimiya AOKI ◽  
Hayata KATAYAMA ◽  
Makoto IKENO ◽  
Mitsuyasu FUKUSAWA
Keyword(s):  
Deep Learning ◽  
Visual Inspection ◽  
Learning Model ◽  
Inspection System ◽  
Deep Learning Model

scholarly journals AUTOMATING POWERLINE INSPECTION: A NOVEL MULTISENSOR SYSTEM FOR DATA ANALYSIS USING DEEP LEARNING

2020 ◽  
Vol XLIII-B4-2020 ◽  
pp. 747-754
Author(s):  
O. Kähler ◽  
S. Hochstöger ◽  
G. Kemper ◽  
J. Birchbauer
Keyword(s):  
Deep Learning ◽  
Sensor Data ◽  
Inspection System ◽  
Electricity Supply ◽  
Automated Inspection ◽  
The Status ◽  
Recorded Data ◽  
Inspection Tasks ◽  
High Level ◽  
Review Current

Abstract. Powerline infrastructure provides the backbone for the electricity supply of industrial, administrative and private sectors. Its maintenance requires regular inspections, that are still largely carried out manually. In this work, we propose an automated inspection system instead. We review current inspection processes as a baseline, give an overview of relevant inspection criteria, propose a suitable multi-modal sensor system, and discuss methods to automate the inspection tasks. In our system, we particularly focus on the high-level organization of the sensor data and inspection results to form a Digital Twin of the power line, that allows operators to browse through the recorded data in a meaningful way and review the status of their powerline from the desk.

scholarly journals Enhanced Hand-Oriented Activity Recognition Based on Smartwatch Sensor Data Using LSTMs

Symmetry ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 1570
Author(s):  
Sakorn Mekruksavanich ◽  
Anuchit Jitpattanakul ◽  
Phichai Youplao ◽  
Preecha Yupapin
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Activity Recognition ◽  
Learning Model ◽  
Sensor Data ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Deep Learning Model ◽  
F Measure

The creation of the Internet of Things (IoT), along with the latest developments in wearable technology, has provided new opportunities in human activity recognition (HAR). The modern smartwatch offers the potential for data from sensors to be relayed to novel IoT platforms, which allow the constant tracking and monitoring of human movement and behavior. Recently, traditional activity recognition techniques have done research in advance by choosing machine learning methods such as artificial neural network, decision tree, support vector machine, and naive Bayes. Nonetheless, these conventional machine learning techniques depend inevitably on heuristically handcrafted feature extraction, in which human domain knowledge is normally limited. This work proposes a hybrid deep learning model called CNN-LSTM that employed Long Short-Term Memory (LSTM) networks for activity recognition with the Convolution Neural Network (CNN). The study makes use of HAR involving smartwatches to categorize hand movements. Using the study based on the Wireless Sensor Data Mining (WISDM) public benchmark dataset, the recognition abilities of the deep learning model can be accessed. The accuracy, precision, recall, and F-measure statistics are employed using the evaluation metrics to assess the recognition abilities of LSTM models proposed. The findings indicate that this hybrid deep learning model offers better performance than its rivals, where the achievement of 96.2% accuracy, while the f-measure is 96.3%, is obtained. The results show that the proposed CNN-LSTM can support an improvement of the performance of activity recognition.

scholarly journals Robust Human Activity Recognition by Integrating Image and Accelerometer Sensor Data Using Deep Fusion Network

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 174
Author(s):  
Junhyuk Kang ◽  
Jieun Shin ◽  
Jaewon Shin ◽  
Daeho Lee ◽  
Ahyoung Choi
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Input Signal ◽  
Short Term Memory ◽  
Learning Model ◽  
Sensor Data ◽  
Accelerometer Data ◽  
Accelerometer Sensor ◽  
Skeleton Image ◽  
Deep Learning Model

Studies on deep-learning-based behavioral pattern recognition have recently received considerable attention. However, if there are insufficient data and the activity to be identified is changed, a robust deep learning model cannot be created. This work contributes a generalized deep learning model that is robust to noise not dependent on input signals by extracting features through a deep learning model for each heterogeneous input signal that can maintain performance while minimizing preprocessing of the input signal. We propose a hybrid deep learning model that takes heterogeneous sensor data, an acceleration sensor, and an image as inputs. For accelerometer data, we use a convolutional neural network (CNN) and convolutional block attention module models (CBAM), and apply bidirectional long short-term memory and a residual neural network. The overall accuracy was 94.8% with a skeleton image and accelerometer data, and 93.1% with a skeleton image, coordinates, and accelerometer data after evaluating nine behaviors using the Berkeley Multimodal Human Action Database (MHAD). Furthermore, the accuracy of the investigation was revealed to be 93.4% with inverted images and 93.2% with white noise added to the accelerometer data. Testing with data that included inversion and noise data indicated that the suggested model was robust, with a performance deterioration of approximately 1%.

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