scholarly journals Sensitivity Analysis of Sensors in a Hydraulic Condition Monitoring System Using CNN Models

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3307 ◽  
Author(s):  
Caroline König ◽  
Ahmed Mohamed Helmi
Keyword(s):  
Deep Learning ◽  
Condition Monitoring ◽  
Efficient Solutions ◽  
Sensor Data ◽  
Learning Models ◽  
Attribution Analysis ◽  
Level Information ◽  
Condition Monitoring System ◽  
Computational Intelligence Methods ◽  
High Level

Condition monitoring (CM) is a useful application in industry 4.0, where the machine’s health is controlled by computational intelligence methods. Data-driven models, especially from the field of deep learning, are efficient solutions for the analysis of time series sensor data due to their ability to recognize patterns in high dimensional data and to track the temporal evolution of the signal. Despite the excellent performance of deep learning models in many applications, additional requirements regarding the interpretability of machine learning models are getting relevant. In this work, we present a study on the sensitivity of sensors in a deep learning based CM system providing high-level information about the relevance of the sensors. Several convolutional neural networks (CNN) have been constructed from a multisensory dataset for the prediction of different degradation states in a hydraulic system. An attribution analysis of the input features provided insights about the contribution of each sensor in the prediction of the classifier. Relevant sensors were identified, and CNN models built on the selected sensors resulted equal in prediction quality to the original models. The information about the relevance of sensors is useful for the system’s design to decide timely on the required sensors.

scholarly journals A Sensor Data-Based Approach for the Definition of Condition Taxonomies for a Hydraulic Pump

2020 ◽  
Vol 2 (1) ◽  
pp. 82
Author(s):  
Carlos Gil Buiges ◽  
Caroline König
Keyword(s):  
Condition Monitoring ◽  
Principal Component ◽  
Efficient Solutions ◽  
Point Of View ◽  
Sensor Data ◽  
Hydraulic Pump ◽  
Process Data ◽  
Data Ingestion ◽  
Condition Monitoring System ◽  
Computational Intelligence Methods

Condition monitoring (CM) is an important application in industry for detecting machine failures in an incipient stage. Based on sensor data, computational intelligence methods provide efficient solutions for the analysis of high dimensional process data with the ability to detect and predict complex condition states. IOT gateways are affordable devices with the ability to implement data ingestion and data analytics tasks on an edge device providing the possibility to implement condition monitoring in real-time on the device. In this work, we present an experimental bench for the sensorization of a hydraulic installation based on IoT gateways in order to detect several blocking states in a hydraulic pump and to avoid the cavitation problem. The experiments of 15 different blocking conditions yield a novel dataset with process sensor information for the described problem. The dataset is analyzed from a data quality point of view to find a meaningful categorization of fault conditions, which are feasible concerning implementation in a condition monitoring system. We use an exploratory data analysis approach, which is based on principal component analysis, provides data visualization of the different blocking conditions of the experiment, and allows us to decide on a proper fault categorization by detecting clearly separated data groups.

scholarly journals HRNN4F: HYBRID DEEP RANDOM NEURAL NETWORK FOR MULTI-CHANNEL FALL ACTIVITY DETECTION

2019 ◽  
pp. 1-14 ◽  
Author(s):  
Ahsen Tahir ◽  
Jawad Ahmad ◽  
Gordon Morison ◽  
Hadi Larijani ◽  
Ryan M. Gibson ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Health Care Services ◽  
Fall Detection ◽  
Sensor Data ◽  
Health Concern ◽  
Learning Models ◽  
Sensor Position ◽  
Random Neural Network ◽  
High Level

Falls are a major health concern in older adults. Falls lead to mortality, immobility and high costs to social and health care services. Early detection and classification of falls is imperative for timely and appropriate medical aid response. Traditional machine learning models have been explored for fall classification. While newly developed deep learning techniques have the ability to potentially extract high-level features from raw sensor data providing high accuracy and robustness to variations in sensor position, orientation and diversity of work environments that may skew traditional classification models. However, frequently used deep learning models like Convolutional Neural Networks (CNN) are computationally intensive. To the best of our knowledge, we present the first instance of a Hybrid Multichannel Random Neural Network (HMCRNN) architecture for fall detection and classification. The proposed architecture provides the highest accuracy of 92.23% with dropout regularization, compared to other deep learning implementations. The performance of the proposed technique is approximately comparable to a CNN yet requires only half the computation cost of the CNN-based implementation. Furthermore, the proposed HMCRNN architecture provides 34.12% improvement in accuracy on average than a Multilayer Perceptron.

scholarly journals A Total Crop-Diagnosis Platform Based on Deep Learning Models in a Natural Nutrient Environment

2018 ◽  
Vol 8 (10) ◽  
pp. 1992 ◽  
Author(s):  
YiNa Jeong ◽  
SuRak Son ◽  
SangSik Lee ◽  
ByungKwan Lee
Keyword(s):  
Deep Learning ◽  
Data Storage ◽  
Good Condition ◽  
Sensor Data ◽  
Weather Data ◽  
Learning Models ◽  
Weather Information ◽  
Nutrient Environment ◽  
Total Crop ◽  
Crop Diagnosis

This paper proposes a total crop-diagnosis platform (TCP) based on deep learning models in a natural nutrient environment, which collects the weather information based on a farm’s location information, diagnoses the collected weather information and the crop soil sensor data with a deep learning technique, and notifies a farm manager of the diagnosed result. The proposed TCP is composed of 1 gateway and 2 modules as follows. First, the optimized farm sensor gateway (OFSG) collects data by internetworking sensor nodes which use Zigbee, Wi-Fi and Bluetooth protocol and reduces the number of sensor data fragmentation times through the compression of a fragment header. Second, the data storage module (DSM) stores the collected farm data and weather data in a farm central server. Third, the crop self-diagnosis module (CSM) works in the cloud server and diagnoses by deep learning whether or not the status of a farm is in good condition for growing crops according to current weather and soil information. The TCP performance shows that the data processing rate of the OFSG is increased by about 7% compared with existing sensor gateways. The learning time of the CSM is shorter than that of the long short-term memory models (LSTM) by 0.43 s, and the success rate of the CSM is higher than that of the LSTM by about 7%. Therefore, the TCP based on deep learning interconnects the communication protocols of various sensors, solves the maximum data size that sensor can transfer, predicts in advance crop disease occurrence in an external environment, and helps to make an optimized environment in which to grow crops.

scholarly journals Inference of Brain States under Anesthesia with Meta Learning Based Deep Learning Models

2021 ◽  
Author(s):  
Qihang Wang ◽  
Feng Liu ◽  
Guihong Wan ◽  
Ying Chen
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Short Term Memory ◽  
Signal To Noise Ratio ◽  
General Anesthetics ◽  
Learning Models ◽  
Multi Stage ◽  
Meta Learning ◽  
Brain States ◽  
High Level

AbstractMonitoring the depth of unconsciousness during anesthesia is useful in both clinical settings and neuroscience investigations to understand brain mechanisms. Electroencephalogram (EEG) has been used as an objective means of characterizing brain altered arousal and/or cognition states induced by anesthetics in real-time. Different general anesthetics affect cerebral electrical activities in different ways. However, the performance of conventional machine learning models on EEG data is unsatisfactory due to the low Signal to Noise Ratio (SNR) in the EEG signals, especially in the office-based anesthesia EEG setting. Deep learning models have been used widely in the field of Brain Computer Interface (BCI) to perform classification and pattern recognition tasks due to their capability of good generalization and handling noises. Compared to other BCI applications, where deep learning has demonstrated encouraging results, the deep learning approach for classifying different brain consciousness states under anesthesia has been much less investigated. In this paper, we propose a new framework based on meta-learning using deep neural networks, named Anes-MetaNet, to classify brain states under anesthetics. The Anes-MetaNet is composed of Convolutional Neural Networks (CNN) to extract power spectrum features, and a time consequence model based on Long Short-Term Memory (LSTM) Networks to capture the temporal dependencies, and a meta-learning framework to handle large cross-subject variability. We used a multi-stage training paradigm to improve the performance, which is justified by visualizing the high-level feature mapping. Experiments on the office-based anesthesia EEG dataset demonstrate the effectiveness of our proposed Anes-MetaNet by comparison of existing methods.

Design a degradation condition monitoring system scheme for rolling bearing using EMD and PCA

2017 ◽  
Vol 117 (4) ◽  
pp. 713-728 ◽  
Author(s):  
Jun Wu ◽  
Chaoyong Wu ◽  
Yaqiong Lv ◽  
Chao Deng ◽  
Xinyu Shao
Keyword(s):  
Monitoring System ◽  
Condition Monitoring ◽  
Rolling Bearing ◽  
Sensor Data ◽  
Health Index ◽  
Rolling Bearings ◽  
Content Type ◽  
Bearing Condition Monitoring ◽  
Condition Monitoring System ◽  
Monotonicity Analysis

Purpose Rolling bearings based on rotating machinery are one of the most widely used in industrial applications because of their low cost, high performance and robustness. The purpose of this paper is to describe how to identify degradation condition of rolling bearing and predict its fault time in big data environment in order to achieve zero downtime performance and preventive maintenance for the rolling bearing. Design/methodology/approach The degradation characteristic parameters of rolling bearings including intrinsic mode energy and failure frequency were, respectively, extracted from the pre-processed original vibration signals using EMD and Hilbert transform. Then, Spearman’s rank correlation coefficient and PCA were used to obtain the health index of the rolling bearing so as to detect the appearance of degradations. Furthermore, the degradation condition of the rolling bearings might be identified through implementing the monotonicity analysis, robustness analysis and degradation analysis of the health index. Findings The effectiveness of the proposed method is verified by a case study. The result shows that the proposed method can be applied to monitor the degradation condition of the rolling bearings in industrial application. Research limitations/implications Further experiment remains to be done so as to validate the effectiveness of the proposed method using Apache Hadoop when massive sensor data are available. Practical implications The paper proposes a methodology for rolling bearing condition monitoring representing the steps that need to be followed. Real-time sensor data are utilized to find the degradation characteristics. Originality/value The result of the work presented in this paper form the basis for the software development and implementation of condition monitoring system for rolling bearings based on Hadoop.

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 Development of Platooning Technology with Road Condition Monitoring

2021 ◽  
Vol 9 (10) ◽  
pp. 515-518
Author(s):  
Srinivaas A
Keyword(s):  
Time Delay ◽  
Monitoring System ◽  
Condition Monitoring ◽  
Sensor Data ◽  
Automated Driving ◽  
The Road ◽  
Road Condition ◽  
Road Condition Monitoring ◽  
Condition Monitoring System ◽  
Lead Vehicle

Abstract: In this paper, we present a complete platooning system using a time-delay algorithm. The platooning is achieved by measuring the driver inputs from the lead vehicle and sending these inputs to the trail vehicle with a time-delay so that the trail vehicle can exactly mimic the motion of the lead vehicle. This system also does a road condition monitor as an add-on benefit which will help in assisting the driver of the trail vehicle/vehicle which takes the same path. The function of this monitoring system is to analyse the road surface using a lead vehicle and acquire sensor data, this acquired sensor data helps in assisting drivers who take the same track. The combination of both this platooning method and road condition monitoring system could potentially reduce the current risk of utilising this semi-automated driving system. Index terms: Platooning, Semi-automated driving, Road condition monitoring, Time-delay algorithm.

scholarly journals On Determining Suitable Embedded Devices for Deep Learning Models

2021 ◽  
Author(s):  
Daniel Padilla ◽  
Hatem A. Rashwan ◽  
Domènec Savi Puig
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Embedded Systems ◽  
State Of The Art ◽  
Image Data ◽  
Important Change ◽  
Learning Models ◽  
Embedded Devices ◽  
And Performance ◽  
High Level

Deep learning (DL) networks have proven to be crucial in commercial solutions with computer vision challenges due to their abilities to extract high-level abstractions of the image data and their capabilities of being easily adapted to many applications. As a result, DL methodologies had become a de facto standard for computer vision problems yielding many new kinds of research, approaches and applications. Recently, the commercial sector is also driving to use of embedded systems to be able to execute DL models, which has caused an important change on the DL panorama and the embedded systems themselves. Consequently, in this paper, we attempt to study the state of the art of embedded systems, such as GPUs, FPGAs and Mobile SoCs, that are able to use DL techniques, to modernize the stakeholders with the new systems available in the market. Besides, we aim at helping them to determine which of these systems can be beneficial and suitable for their applications in terms of upgradeability, price, deployment and performance.

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 Accurate Prediction of Earthquake-Induced Landslides Based on Deep Learning Considering Landslide Source Area

2021 ◽  
Vol 13 (17) ◽  
pp. 3436
Author(s):  
Yao Li ◽  
Peng Cui ◽  
Chengming Ye ◽  
José Marcato Junior ◽  
Zhengtao Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Prediction Accuracy ◽  
Large Scale ◽  
Source Area ◽  
Spatial Prediction ◽  
Disaster Mitigation ◽  
Support Vector ◽  
Learning Models ◽  
Elevation Model ◽  
High Level

An earthquake-induced landslide (EQIL) is a rapidly changing process occurring at the Earth’s surface that is strongly controlled by the earthquake in question and predisposing conditions. Predicting locations prone to EQILs on a large scale is significant for managing rescue operations and disaster mitigation. We propose a deep learning framework while considering the source area feature of EQIL to model the complex relationship and enhance spatial prediction accuracy. Initially, we used high-resolution remote sensing images and a digital elevation model (DEM) to extract the source area of an EQIL. Then, 14 controlling factors were input to a stacked autoencoder (SAE) to search for robust features by sparse optimization, and the classifier took advantage of high-level abstract features to identify the EQIL spatially. Finally, the EQIL inventory collected from the Wenchuan earthquake was used to validate the proposed model. The results show that the proposed method significantly outperformed conventional methods, achieving an overall accuracy (OA) of 91.88%, while logistic regression (LR), support vector machine (SVM), and random forest (RF) achieved 80.75%, 82.22%, and 84.16%, respectively. Meanwhile, this study reveals that shallow machine learning models only take advantage of significant factors for EQIL prediction, but deep learning models can extract more effective information related to EQIL distribution from low-value density data, which is why its prediction accuracy is growing with increasing input factors. There is hope that new knowledge of EQILs can be represented by high-level abstract features extracted by hidden layers of the deep learning model, which are typically acquired by statistical methods.

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