scholarly journals Distributed Kernel Extreme Learning Machines for Aircraft Engine Failure Diagnostics

2019 ◽  
Vol 9 (8) ◽  
pp. 1707 ◽  
Author(s):  
Junjie Lu ◽  
Jinquan Huang ◽  
Feng Lu
Keyword(s):  
Computational Complexity ◽  
Real Time ◽  
Evidence Theory ◽  
Aircraft Engine ◽  
Training Sample ◽  
Performance Estimation ◽  
Support Vector ◽  
Extreme Learning Machines ◽  
The Real ◽  
Learning Machines

Kernel extreme learning machine (KELM) has been widely studied in the field of aircraft engine fault diagnostics due to its easy implementation. However, because its computational complexity is proportional to the training sample size, its application in time-sensitive scenarios is limited. Therefore, in the case of largescale samples, the original KELM is difficult to meet the real-time requirements of aircraft engine onboard condition. To address this shortcoming, a novel distributed kernel extreme learning machines (DKELMs) algorithm is proposed in this paper. The distributed subnetwork is adopted to reduce the computational complexity, and then the likelihood probability and Dempster-Shafer (DS) evidence theory is used to design the fusion scheme to ensure the accuracy after fusion is not reduced. Afterwards, the verification on the benchmark datasets shows that the algorithm can greatly reduce the computational complexity and improve the real-time performance of the original KELM algorithm without sacrificing the accuracy of the model. Finally, the performance estimation and fault pattern recognition experiments of an aircraft engine show that, compared with the original KELM algorithm and support vector machine (SVM) algorithm, the proposed algorithm has the best performance considering both real-time capability and model accuracy.

Improving Real-Time Drilling Data Quality Using Artificial Intelligence and Machine Learning Techniques

2021 ◽  
Author(s):  
S. H. Al Gharbi ◽  
A. A. Al-Majed ◽  
A. Abdulraheem ◽  
S. Patil ◽  
S. M. Elkatatny
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Quality ◽  
Real Time ◽  
Input Data ◽  
Support Vector ◽  
The Real ◽  
Drilling Data ◽  
Drilling Operations

Abstract Due to high demand for energy, oil and gas companies started to drill wells in remote areas and unconventional environments. This raised the complexity of drilling operations, which were already challenging and complex. To adapt, drilling companies expanded their use of the real-time operation center (RTOC) concept, in which real-time drilling data are transmitted from remote sites to companies’ headquarters. In RTOC, groups of subject matter experts monitor the drilling live and provide real-time advice to improve operations. With the increase of drilling operations, processing the volume of generated data is beyond a human's capability, limiting the RTOC impact on certain components of drilling operations. To overcome this limitation, artificial intelligence and machine learning (AI/ML) technologies were introduced to monitor and analyze the real-time drilling data, discover hidden patterns, and provide fast decision-support responses. AI/ML technologies are data-driven technologies, and their quality relies on the quality of the input data: if the quality of the input data is good, the generated output will be good; if not, the generated output will be bad. Unfortunately, due to the harsh environments of drilling sites and the transmission setups, not all of the drilling data is good, which negatively affects the AI/ML results. The objective of this paper is to utilize AI/ML technologies to improve the quality of real-time drilling data. The paper fed a large real-time drilling dataset, consisting of over 150,000 raw data points, into Artificial Neural Network (ANN), Support Vector Machine (SVM) and Decision Tree (DT) models. The models were trained on the valid and not-valid datapoints. The confusion matrix was used to evaluate the different AI/ML models including different internal architectures. Despite the slowness of ANN, it achieved the best result with an accuracy of 78%, compared to 73% and 41% for DT and SVM, respectively. The paper concludes by presenting a process for using AI technology to improve real-time drilling data quality. To the author's knowledge based on literature in the public domain, this paper is one of the first to compare the use of multiple AI/ML techniques for quality improvement of real-time drilling data. The paper provides a guide for improving the quality of real-time drilling data.

scholarly journals An emotion classification method from electroencephalogram based on 1/f fluctuation theory

2020 ◽  
Vol 53 (5-6) ◽  
pp. 824-832
Author(s):  
Hao Li ◽  
Xia Mao ◽  
Lijiang Chen
Keyword(s):  
Real Time ◽  
Classification Accuracy ◽  
Support Vector Machine Classifier ◽  
Fluctuation Theory ◽  
Classification Method ◽  
Support Vector ◽  
Signal Acquisition ◽  
Emotion Classification ◽  
The Real ◽  
Time Performance

Electroencephalogram data are easily affected by artifacts, and a drift may occur during the signal acquisition process. At present, most research focuses on the automatic detection and elimination of artifacts in electrooculograms, electromyograms and electrocardiograms. However, electroencephalogram drift data, which affect the real-time performance, are mainly manually calibrated and abandoned. An emotion classification method based on 1/f fluctuation theory is proposed to classify electroencephalogram data without removing artifacts and drift data. The results show that the proposed method can still achieve a great classification accuracy of 75% in cases in which artifacts and drift data exist when using the support vector machine classifier. In addition, the real-time performance of the proposed method is guaranteed.

STRUCTURE-EMBEDDED AUC-SVM

2010 ◽  
Vol 24 (05) ◽  
pp. 667-690 ◽  
Author(s):  
YUNYUN WANG ◽  
SONGCAN CHEN ◽  
HUI XUE
Keyword(s):  
Computational Complexity ◽  
Training Sample ◽  
Decision Function ◽  
Information Loss ◽  
Global Structure ◽  
Support Vector ◽  
Structure Information ◽  
Hinge Loss ◽  
Novel Structure ◽  
High Computational Complexity

AUC-SVM directly maximizes the area under the ROC curve (AUC) through minimizing its hinge loss relaxation, and the decision function is determined by those support vector sample pairs playing the same roles as the support vector samples in SVM. Such a learning paradigm generally emphasizes more on the local discriminative information just associated with these support vectors whereas hardly takes the overall view of data into account, thereby it may incur loss of the global distribution information in data favorable for classification. Moreover, due to the high computational complexity of AUC-SVM induced by the large number of training sample pairs quadratic in the number of samples, sampling is usually adopted, incurring a further loss of the distribution information in data. In order to compensate the distribution information loss and simultaneously boost the AUC-SVM performance, in this paper, we develop a novel structure-embedded AUC-SVM (SAUC-SVM for short) through embedding the global structure information in the whole data into AUC-SVM. With such an embedding, the proposed SAUC-SVM incorporates the local discriminative information and global structure information in data into a uniform formulation and consequently guarantees better generalization performance. Comparative experiments on both synthetic and real datasets confirm its effectiveness.

Online Real-Time Quality Monitoring in Additive Manufacturing Processes Using Heterogeneous Sensors

2015 ◽  
Vol 137 (6) ◽  
Author(s):  
Prahalad K. Rao ◽  
Jia (Peter) Liu ◽  
David Roberson ◽  
Zhenyu (James) Kong ◽  
Christopher Williams
Keyword(s):  
Additive Manufacturing ◽  
Real Time ◽  
Dirichlet Process ◽  
Failure Modes ◽  
Evidence Theory ◽  
Sensor Data ◽  
Support Vector ◽  
Fused Filament Fabrication ◽  
Detection Approach ◽  
Real Time Identification

The objective of this work is to identify failure modes and detect the onset of process anomalies in additive manufacturing (AM) processes, specifically focusing on fused filament fabrication (FFF). We accomplish this objective using advanced Bayesian nonparametric analysis of in situ heterogeneous sensor data. Experiments are conducted on a desktop FFF machine instrumented with a heterogeneous sensor array including thermocouples, accelerometers, an infrared (IR) temperature sensor, and a real-time miniature video borescope. FFF process failures are detected online using the nonparametric Bayesian Dirichlet process (DP) mixture model and evidence theory (ET) based on the experimentally acquired sensor data. This sensor data-driven defect detection approach facilitates real-time identification and correction of FFF process drifts with an accuracy and precision approaching 85% (average F-score). In comparison, the F-score from existing approaches, such as probabilistic neural networks (PNN), naïve Bayesian clustering, support vector machines (SVM), and quadratic discriminant analysis (QDA), was in the range of 55–75%.

Real-time optimization using gradient adaptive selection and classification from infrared sensors measurement for esterification oleic acid with glycerol

2017 ◽  
Vol 10 (2) ◽  
pp. 130-144 ◽  
Author(s):  
Iwan Aang Soenandi ◽  
Taufik Djatna ◽  
Ani Suryani ◽  
Irzaman Irzaman
Keyword(s):  
Real Time ◽  
Sensor Data ◽  
Necessary Condition ◽  
Support Vector ◽  
Adaptive Selection ◽  
Content Type ◽  
The Real ◽  
Time Optimization ◽  
Starting Point ◽  
Real Time Optimization

Purpose The production of glycerol derivatives by the esterification process is subject to many constraints related to the yield of the production target and the lack of process efficiency. An accurate monitoring and controlling of the process can improve production yield and efficiency. The purpose of this paper is to propose a real-time optimization (RTO) using gradient adaptive selection and classification from infrared sensor measurement to cover various disturbances and uncertainties in the reactor. Design/methodology/approach The integration of the esterification process optimization using self-optimization (SO) was developed with classification process was combined with necessary condition optimum (NCO) as gradient adaptive selection, supported with laboratory scaled medium wavelength infrared (mid-IR) sensors, and measured the proposed optimization system indicator in the batch process. Business Process Modeling and Notation (BPMN 2.0) was built to describe the tasks of SO workflow in collaboration with NCO as an abstraction for the conceptual phase. Next, Stateflow modeling was deployed to simulate the three states of gradient-based adaptive control combined with support vector machine (SVM) classification and Arduino microcontroller for implementation. Findings This new method shows that the real-time optimization responsiveness of control increased product yield up to 13 percent, lower error measurement with percentage error 1.11 percent, reduced the process duration up to 22 minutes, with an effective range of stirrer rotation set between 300 and 400 rpm and final temperature between 200 and 210°C which was more efficient, as it consumed less energy. Research limitations/implications In this research the authors just have an experiment for the esterification process using glycerol, but as a development concept of RTO, it would be possible to apply for another chemical reaction or system. Practical implications This research introduces new development of an RTO approach to optimal control and as such marks the starting point for more research of its properties. As the methodology is generic, it can be applied to different optimization problems for a batch system in chemical industries. Originality/value The paper presented is original as it presents the first application of adaptive selection based on the gradient value of mid-IR sensor data, applied to the real-time determining control state by classification with the SVM algorithm for esterification process control to increase the efficiency.

scholarly journals Research on a Fast Human-Detection Algorithm for Unmanned Surveillance Area in Bulk Ports

2014 ◽  
Vol 2014 ◽  
pp. 1-17 ◽  
Author(s):  
Chao Mi ◽  
Xin He ◽  
Haiwei Liu ◽  
Youfang Huang ◽  
Weijian Mi
Keyword(s):  
Real Time ◽  
Computing Time ◽  
Training Sample ◽  
Detection Algorithm ◽  
Human Detection ◽  
Security Requirements ◽  
Support Vector ◽  
Svm Classifier ◽  
Histograms Of Oriented Gradients ◽  
Heavy Equipment

With the development of port automation, most operational fields utilizing heavy equipment have gradually become unmanned. It is therefore imperative to monitor these fields in an effective and real-time manner. In this paper, a fast human-detection algorithm is proposed based on image processing. To speed up the detection process, the optimized histograms of oriented gradients (HOG) algorithm that can avoid the large number of double calculations of the original HOG and ignore insignificant features is used to describe the contour of the human body in real time. Based on the HOG features, using a training sample set consisting of scene images of a bulk port, a support vector machine (SVM) classifier combined with the AdaBoost classifier is trained to detect human. Finally, the results of the human detection experiments on Tianjin Port show that the accuracy of the proposed optimized algorithm has roughly the same accuracy as a traditional algorithm, while the proposed algorithm only takes 1/7 the amount of time. The accuracy and computing time of the proposed fast human-detection algorithm were verified to meet the security requirements of unmanned port areas.

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