scholarly journals Fault Detection in the MSW Incineration Process Using Stochastic Configuration Networks and Case-Based Reasoning

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7356
Author(s):  
Chenxi Ding ◽  
Aijun Yan
Keyword(s):  
Fault Detection ◽  
Back Propagation ◽  
Waste Incineration ◽  
Training Sample ◽  
Support Vector ◽  
Case Based Reasoning ◽  
Detection Model ◽  
Msw Incineration ◽  
Incineration Process ◽  
Case Based

Fault detection in the waste incineration process depends on high-temperature image observation and the experience of field maintenance personnel, which is inefficient and can easily cause misjudgment of the fault. In this paper, a fault detection method is proposed by combining stochastic configuration networks (SCNs) and case-based reasoning (CBR). First, a learning pseudo metric method based on SCNs (SCN-LPM) is proposed by training SCN learning models using a training sample set and defined pseudo-metric criteria. Then, the SCN-LPM method is used for the case retrieval stage in CBR to construct the fault detection model based on SCN-CBR, and the structure, algorithmic implementation, and algorithmic steps are given. Finally, the performance is tested using historical data of the MSW incineration process, and the proposed method is compared with typical classification methods, such as a Back Propagation (BP) neural network, a support vector machine, and so on. The results show that this method can effectively improve the accuracy of fault detection and reduce the time complexity of the task and maintain a certain application value.

A case-based reasoning system for fault detection and isolation: a case study on complex gearboxes

2019 ◽  
Vol 25 (2) ◽  
pp. 213-235 ◽  
Author(s):  
Soumava Boral ◽  
Sanjay Kumar Chaturvedi ◽  
V.N.A. Naikan
Keyword(s):  
Decision Making ◽  
Fault Detection ◽  
Large Scale ◽  
Fault Detection And Isolation ◽  
Decision Making Process ◽  
Case Based Reasoning ◽  
Content Type ◽  
Complex Decision Making ◽  
Complex Decision ◽  
Case Based

Purpose Usually, the machinery in process plants is exposed to harsh and uncontrolled environmental conditions. Even after taking different types of preventive measures to detect and isolate the faults at the earliest possible opportunity becomes a complex decision-making process that often requires experts’ opinions and judicious decisions. The purpose of this paper is to propose a framework to detect, isolate and to suggest appropriate maintenance tasks for large-scale complex machinery (i.e. gearboxes of steel processing plant) in a simplified and structured manner by utilizing the prior fault histories available with the organization in conjunction with case-based reasoning (CBR) approach. It is also demonstrated that the proposed framework can easily be implemented by using today’s graphical user interface enabled tools such as Microsoft Visual Basic and similar. Design/methodology/approach CBR, an amalgamated domain of artificial intelligence and human cognitive process, has been applied to carry out the task of fault detection and isolation (FDI). Findings The equipment failure history and actions taken along with the pertinent health indicators are sufficient to detect and isolate the existing fault(s) and to suggest proper maintenance actions to minimize associated losses. The complex decision-making process of maintaining such equipment can exploit the principle of CBR and overcome the limitations of the techniques such as artificial neural networks and expert systems. The proposed CBR-based framework is able to provide inference with minimum or even with some missing information to take appropriate actions. This proposed framework would alleviate from the frequent requirement of expert’s interventions and in-depth knowledge of various analysis techniques expected to be known to process engineers. Originality/value The CBR approach has demonstrated its usefulness in many areas of practical applications. The authors perceive its application potentiality to FDI with suggested maintenance actions to alleviate an end-user from the frequent requirement of an expert for diagnosis or inference. The proposed framework can serve as a useful tool/aid to the process engineers to detect and isolate the fault of large-scale complex machinery with suggested actions in a simplified way.

scholarly journals Research on the model for tobacco disease prevention and control based on case-based reasoning and knowledge graph

Filomat ◽  
2018 ◽  
Vol 32 (5) ◽  
pp. 1947-1952 ◽  
Author(s):  
Lichuan Gu ◽  
Yingchun Xia ◽  
Xiaohui Yuan ◽  
Chao Wang ◽  
Jun Jiao
Keyword(s):  
Disease Prevention ◽  
Prevention And Control ◽  
Principal Component ◽  
Support Vector ◽  
Knowledge Graph ◽  
Case Based Reasoning ◽  
Long Time ◽  
And Control ◽  
Tobacco Disease ◽  
Case Based

Tobacco is one of the most important economic crops in China. The yield and quality of tobacco reduce severely because of long-time disease invasion. Currently, the main focus of researches on tobacco disease prevention and control is the diagnosis of disease that has occurred, which ignores to predict disease before it outbreaks. Therefore, in this paper, we follow the idea that prediction is used before disease prevention and control and study the model for tobacco disease prevention and control by using knowledge graph and case-based reasoning (CBR). In order to implement the model, we choose tobacco mosaic virus (TMV) as research object and follow the following methods to prevent occurrence of that. At first, a method to predicting environmental factors by using principal component analysis (PCA) and support vector machine (SVM) is proposed. According to the prediction result, knowledge graph and CBR are used to retrieve the most similarity case and finally determine the best solution. Experimental results demonstrate that our model can achieve high accuracy and give the most appropriate scheme for disease prevention and control.

scholarly journals Fault Detection for Turbine Engine Disk Based on Adaptive Weighted One-Class Support Vector Machine

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Jiusheng Chen ◽  
Xingkai Xu ◽  
Xiaoyu Zhang
Keyword(s):  
Time Series ◽  
Support Vector Machine ◽  
Fault Detection ◽  
Data Stream ◽  
Time Series Data ◽  
Series Data ◽  
Support Vector ◽  
Turbine Engine ◽  
Detection Model ◽  
Redundant Data

Fault detection for turbine engine components is becoming increasingly important for the efficient running of commercial aircraft. Recently, the support vector machine (SVM) with kernel function is the most popular technique for monitoring nonlinear processes, which can better handle the nonlinear representation of fault detection of turbine engine disk. In this paper, an adaptive weighted one-class SVM-based fault detection method coupled with incremental and decremental strategy is proposed, which can efficiently solve the time series data stream drifting problem. To update the efficient training of the fault detection model, the incremental strategy based on the new incoming data and support vectors is proposed. The weight of the training sample is updated by the variations of the decision boundaries. Meanwhile, to increase the calculating speed of the fault detection model and reduce the redundant data, the decremental strategy based on the k-nearest neighbor (KNN) is adopted. Based on time series data stream, numerical simulations are conducted and the results validated the superiority of the proposed approach in terms of both the detection performance and robustness.

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