Multi-sensor Fusion Approach with Fault Detection and Isolation Based on two-state Probability Ratio

Qiang, Xingzi; Xue, Rui; Zhu, Yanbo

doi:10.1109/wispnet51692.2021.9419448

Cited by 2 publications

(1 citation statement)

References 7 publications

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“…[43], [44] Whenever an event is sent from the knowledge reasoning module, it is uniformly processed by the event description module into a simple form. In order to get the lower-level hardware devices to carry out the demand from the top layers, the event decomposition module is used to translate those requirements into commands the system can accept [45]. Fig.…”

Section: Figure 3: Algorithms For ML In the Iotmentioning

confidence: 99%

IoT-Based Patient Health Data Using Improved Context-Aware Data Fusion and Enhanced Recursive Feature Elimination Model

Saranya

Fatima

2022

IEEE Access

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The Internet of Things (IoT) in the healthcare market is propelled forward by the implementation of digital systems for monitoring and analysing health problems. IoT and smart devices can contribute to a highly smart environment. Smart medical devices interconnected with smartphone apps can collect medical and other required health data. "Data Fusion (DF)" refers to integrating data and knowledge from multiple sources. However, these techniques are also applied to other domains, including text processing. Using data from multiple distributed sources, the objective of DF in multisensory contexts is to reduce the chance of detection errors and increase their reliability. The objective is to increase scalability, performance efficiency, and identification. A medical device's ability to scale up or down demonstrates its capacity to respond to environmental factors. A more scalable system performs as expected, with no interruptions, and makes the best available use of the resource management it has. To ensure that these tracking devices all work the same way, it is essential to form a specialised group to develop uniformity in areas such as communication channels, aggregation of data, and smart interfaces. The main contribution of this research is pre-processing, DF using the Improved Context-aware Data Fusion (ICDF) algorithm, feature extraction via Improved Principal Component Analysis (IPCA), feature selection through the Enhanced Recursive Feature Elimination (ERFE) algorithm, and a classifier using an ensemble-based Machine Learning (ML) model. The Improved Dynamic Bayesian Network (IDBN) is a good trade-off for tractability, becoming a tool for ICDF operations. The simulation results show that the proposed ICDF model achieves higher performance in terms of 97% accuracy, 96% precision, 97% recall, and 97% F1 score in the healthcare system.

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Section: Figure 3: Algorithms For ML In the Iotmentioning

confidence: 99%

IoT-Based Patient Health Data Using Improved Context-Aware Data Fusion and Enhanced Recursive Feature Elimination Model

Saranya

Fatima

2022

IEEE Access

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Two-Step Neural-Network-Based Fault Isolation for Stochastic Systems

Yin

Liu

et al. 2022

Mathematics

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This paper studies a fault isolation method for an optical fiber vibration source detection and early warning system. We regard the vibration sources in the system as faults and then detect and isolate the faults of the system based on a two-step neural network. Firstly, the square root B-spline expansion method is used to approximate the output probability density functions. Secondly, the nonlinear weight dynamic model is established through a dynamic neural network. Thirdly, the nonlinear filter and residual generator are constructed to estimate the weight, analyze the residual, and estimate the threshold, so as to detect, diagnose, and isolate the faults. The feasibility criterion of fault detection and isolation is given by using some linear matrix inequalities, and the stability of the estimation error system is proven according to the Lyapunov theorem. Finally, simulation experiments based on a optical fiber vibration source system are given to verify the effectiveness of this method.

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Multi-sensor Fusion Approach with Fault Detection and Isolation Based on two-state Probability Ratio

Cited by 2 publications

References 7 publications

IoT-Based Patient Health Data Using Improved Context-Aware Data Fusion and Enhanced Recursive Feature Elimination Model

IoT-Based Patient Health Data Using Improved Context-Aware Data Fusion and Enhanced Recursive Feature Elimination Model

Two-Step Neural-Network-Based Fault Isolation for Stochastic Systems

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