Robust Fault Detection to Determine Compressor Surge Point Via Dynamic Neural Network-Based Subspace Identification Technique

2014 ◽  
Vol 136 (8) ◽  
Author(s):  
Sayyid Mahdi Alavinia ◽  
Mohammad Ali Sadrnia ◽  
Mohammad Javad Khosrowjerdi ◽  
Mohammad Mehdi Fateh
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Future Research ◽  
Subspace Identification ◽  
Hurwitz Matrix ◽  
Dynamic Neural Network ◽  
Compressor Surge ◽  
Identification Technique ◽  
Sensor Fault Detection ◽  
And Performance

In this paper, a dynamic neural network (DNN) based on robust identification scheme is presented to determine compressor surge point accurately using sensor fault detection (FD). The main innovation of this paper is to present different and complementary technique for surge suppressing studies within sensor FD. The proposed method aims to utilize the embedded analytical redundancies for sensor FD, even in the presence of uncertainty in the compressor and sensor noise. The robust dynamic neural network is developed to learn the input–output map of the compressor for residual generation and the required data is obtained from the compressor Moore–Greitzer simulated model. Generally, the main drawback of DNN method is the lack of systematic law for selecting of initial Hurwitz matrix. Therefore, the subspace identification method is proposed for selecting this matrix. A number of simulation studies are carried out to demonstrate the advantages, capabilities, and performance of our proposed FD scheme and a worthwhile direction for future research is also presented.

Sensor Fault Detection and Validation For Chemical Process Using A Neural Network Model

1997 ◽  
Vol 30 (11) ◽  
pp. 561-566 ◽  
Author(s):  
Koji Morinaga ◽  
Michael E. Sugars ◽  
Koji Muteki ◽  
Haruo Takada
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Network Model ◽  
Neural Network Model ◽  
Chemical Process ◽  
Sensor Fault ◽  
Sensor Fault Detection

A Neural Network Ensemble-Based Approach to Sensor Fault Detection

2011 ◽  
Vol 467-469 ◽  
pp. 923-927
Author(s):  
Ai She Shui ◽  
Wei Min Chen ◽  
Li Chuan Liu ◽  
Yong Hong Shui
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Rbf Neural Network ◽  
Sensor Faults ◽  
Neural Network Ensemble ◽  
Sensor Fault ◽  
Detection Approach ◽  
Tank System ◽  
Sensor Fault Detection ◽  
Feedback Control Systems

This paper focuses on the problem of detecting sensor faults in feedback control systems with multistage RBF neural network ensemble-based estimators. The sensor fault detection framework is introduced. The modeling process of the estimator is presented. Fault detection is accomplished by evaluating residuals, which are the differences between the actual values of sensor outputs and the estimated values. The particular feature of the fault detection approach is using the data sequences of multi-sensor readings and controller outputs to establish the bank of estimators and fault-sensitive detectors. A detectability study has also been done with the additive type of sensor faults. The effectiveness of the proposed approach is demonstrated by means of three tank system experiment results.

scholarly journals Convolutional-Neural Network-Based Image Crowd Counting: Review, Categorization, Analysis, and Performance Evaluation

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 43 ◽  
Author(s):  
Naveed Ilyas ◽  
Ahsan Shahzad ◽  
Kiseon Kim
Keyword(s):  
Neural Network ◽  
Performance Evaluation ◽  
Convolutional Neural Network ◽  
Smart Cities ◽  
Future Research ◽  
Crowd Counting ◽  
Crowd Management ◽  
Adaptive Monitoring ◽  
Potential Applications ◽  
And Performance

Traditional handcrafted crowd-counting techniques in an image are currently transformed via machine-learning and artificial-intelligence techniques into intelligent crowd-counting techniques. This paradigm shift offers many advanced features in terms of adaptive monitoring and the control of dynamic crowd gatherings. Adaptive monitoring, identification/recognition, and the management of diverse crowd gatherings can improve many crowd-management-related tasks in terms of efficiency, capacity, reliability, and safety. Despite many challenges, such as occlusion, clutter, and irregular object distribution and nonuniform object scale, convolutional neural networks are a promising technology for intelligent image crowd counting and analysis. In this article, we review, categorize, analyze (limitations and distinctive features), and provide a detailed performance evaluation of the latest convolutional-neural-network-based crowd-counting techniques. We also highlight the potential applications of convolutional-neural-network-based crowd-counting techniques. Finally, we conclude this article by presenting our key observations, providing strong foundation for future research directions while designing convolutional-neural-network-based crowd-counting techniques. Further, the article discusses new advancements toward understanding crowd counting in smart cities using the Internet of Things (IoT).

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