A generic framework for decision fusion in Fault Detection and Diagnosis

Tidriri, Khaoula; Tiplica, Téodor; Chatti, Nizar; Verron, Sylvain

doi:10.1016/j.engappai.2018.02.014

Cited by 41 publications

(16 citation statements)

References 40 publications

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“…Data decision fusion is a higher level of data fusion [33]. It aims to improve the classification results and compensate the individual classifier weakness.…”

Section: Majority Vote Fusionmentioning

confidence: 99%

Classification using semantic feature and machine learning: Land-use case application

Elmannai

Algarni

2021

TELKOMNIKA

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Land cover classification has interested recent works especially for deforestation, urban are monitoring and agricultural land use. Traditional classification approaches have limited accuracy especially for nonheterogeneous land cover. Thus, using machine may improve the classification accuracy. The presented paper deals with the land-use scene recognition on very high-resolution remote sensing imagery. We proposed a new framework based on semantic features, handcrafted features and machine learning classifiers decisions. The method starts by semantic feature extraction using a convolutional neural network. Handcraft features are also extracted based on color and multi-resolution characteristics. Then, the classification stage is processed by three learning machine algorithms. The final classification result performed by majority vote algorithm. The idea behind is to take advantages from semantic features and handcrafted features. The second scope is to use the decision fusion to enhance the classification result. Experimentation results show that the proposed method provides good accuracy and trustable tool for land use image identification.

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“…Data decision fusion is a higher level of data fusion [33]. It aims to improve the classification results and compensate the individual classifier weakness.…”

Section: Majority Vote Fusionmentioning

confidence: 99%

Classification using semantic feature and machine learning: Land-use case application

Elmannai

Algarni

2021

TELKOMNIKA

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“…In [4], a hybrid diagnosis system design is proposed which combines model-based fault isolation with support vector data description anomaly classifiers to rank the different fault hypotheses. In [20], model-based residuals and sensor data are used as inputs to a Bayesian network to perform fault classification and in [21] model data features are extracted and fed into a neural network classifier. In [22], a hybrid approach combining model-based residuals with hidden Markov models and Bayesian methods is used to classify unknown faults.…”

Section: B Related Researchmentioning

confidence: 99%

Data-Driven Open-Set Fault Classification of Residual Data Using Bayesian Filtering

Jung

2020

IEEE Trans. Contr. Syst. Technol.

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Data-driven fault classification in industrial applications is complicated by unknown fault classes and limited training data. In addition, different faults can have similar effects on sensor outputs resulting in fault classification ambiguities, i.e. multiple fault hypotheses can explain the data. One solution is to identify and rank all plausible fault classes which give useful information, for example at a workshop when performing troubleshooting. A probabilistic fault classification algorithm is proposed for residual data classification combining Weibullcalibrated one-class support vector machines for fault class modeling and Bayesian filtering for time-series analysis. The fault classifier ranks different fault classes and can identify sequences from unknown fault realizations, i.e. faults not represented in training data. Real residual data computed from sensor data and model analysis of an internal combustion engine are used as a case study illustrating the usefulness of the proposed method.

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“…The TE process is a simulated industrial process created by TE Chemical Company to evaluate fault detection and fault diagnosis methods. TE process data, as the standard data for evaluating fault detection and fault diagnosis, has been widely used in process monitoring, fault detection, and fault diagnosis . The simplified process flow chart of the TE process is shown in Figure .…”

Section: Example Analysis: Intelligent Early Warning Of the Te Processmentioning

confidence: 99%

An improved intelligent early warning method based on MWSPCA and its application in complex chemical processes

et al. 2019

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With the development of industrial automation, the requirement of abnormal early warning in the industrial production process is getting higher and higher.Facing complex chemical processes, traditional fault detection and abnormal early warning methods have low detection efficiency and poor real-time performance. Therefore, this paper analyzes and studies fault detection and abnormal early warning methods, and puts forward an improved intelligent early warning method based on the moving window sparse principal component analysis (MWSPCA) suitable for complex chemical processes. The sparse principal component analysis algorithm is used to establish the initial early warning model, and then the moving window is used to update the early warning model, which makes the early warning model more suitable for the characteristics of time-varying data. Furthermore, the proposed method reduces the false alarm rate and missed alarm rate of the early warning, and improves the real-time performance of the early warning model. Finally, the feasibility and the validity of the proposed method are verified by the TE process and the oil drilling process. The experiment results show that the proposed method can reduce the risk of complex chemical processes. K E Y W O R D Scomplex chemical processes, fault detection, intelligent early warning, MWSPCA

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A generic framework for decision fusion in Fault Detection and Diagnosis

Cited by 41 publications

References 40 publications

Classification using semantic feature and machine learning: Land-use case application

Classification using semantic feature and machine learning: Land-use case application

Data-Driven Open-Set Fault Classification of Residual Data Using Bayesian Filtering

An improved intelligent early warning method based on MWSPCA and its application in complex chemical processes

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