Sensor Fault Diagnosis Method Based on <i>α</i>‐Grey Wolf Optimization‐Support Vector Machine

Cheng, Xuezhen; Wang, Dafei; Xu, Chuannuo; Li, Jiming

doi:10.1155/2021/1956394

Cited by 5 publications

(4 citation statements)

References 24 publications

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“…Cheng et al 20 proposed an approach for diagnosing sensor faults called α ‐GWO‐SVM, which is based on a combination of intelligent and data‐driven diagnosis to improve diagnosing accuracy. The fusions of KPCA and time‐domain variables are utilized to extract fault data from sensors.…”

Section: Literature Surveymentioning

confidence: 99%

Chaos quantum optimization‐based layered diagnosis framework for faulty sensor node diagnosis and classification in wireless sensor networks

Babu,

Santhosh Kumar

2024

Int J Communication

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SummaryNode faults are often influenced by factors such as physical component failure, communication module errors, battery exhaustion, and environmental factors. Various researchers have contributed to addressing the problem of detecting, diagnosing, and classifying faults in sensor networks. However, computational complexity and fault occurrence probability increase with the size of the network. In this context, an enhanced optimized layered diagnosis framework (OLDF) is proposed for detecting, diagnosing, and classifying sensor node faults. OLDF utilizes a deep belief network (DBN) with chaos quantum‐behaved particle swarm optimization (CQ‐PSO) for efficient diagnosis and classification of various sensor faults in the network. The proposed OLDF consists of two layers. In the first layer, optimized DBN with CQ‐PSO algorithm is employed to determine the fault type of the sensor nodes. Then, the fault type of sensor nodes that are classified in the first layer is given as input into the second layer of the OLDF algorithm to determine the fault severity in the network. Experimentation has been carried out by using the NS3 simulator for the OLDF framework with performance metrics such as classification accuracy, diagnosis accuracy, false positives, fault alarm rate, and average energy consumption. The validity of the proposed framework has been verified through comparisons with existing works such as MFD, INSA, FDRFC, and HFD. From the comparative analysis carried out, it is apparent that the proposed framework achieves a high classification accuracy of 89.94% and diagnosis accuracy of 93.21%, with less false positive rate, and utilizes minimum energy when compared with its counterparts.

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Section: Literature Surveymentioning

confidence: 99%

Chaos quantum optimization‐based layered diagnosis framework for faulty sensor node diagnosis and classification in wireless sensor networks

Babu,

Santhosh Kumar

2024

Int J Communication

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“…Time-frequency-based empirical mode decomposition was used as a feature extraction method, and an SVM was used for sensor fault diagnosis in a gas turbine system [27]. In some recent work, various optimization techniques, such as α-Gray Wolf Optimization [28] and the Baum-Welch algorithm [29] were integrated with an SVM to make fault diagnosis more robust. Moreover, previously practiced model-based approaches are sometimes combined with datadriven methods in a hybrid model for fault analysis.…”

Section: Introductionmentioning

confidence: 99%

Wasserstein GAN-Based Digital Twin-Inspired Model for Early Drift Fault Detection in Wireless Sensor Networks

2023

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In this Internet of Things (IoT) era, the number of devices capable of sensing their surroundings is increasing day by day. Based on the data from these devices, numerous services and systems are now offered where critical decisions depend on the data collected by sensors. Therefore, error-free data are most desirable, but due to extreme operating environments, the possibility of faults occurring in sensors is high. So, detecting faults in data obtained by sensors is important. In this paper, a digital twin inspired detection approach is proposed, and its ability to detect a single type of fault in several sensor is analyzed. The digital equivalent of the sensor is developed using a Generative Adversarial Network (GAN). As GANs inherently performs well with images, Gramian Angular Field (GAF) encoding is used to convert timeseries data to image. The GAF encoding preserves the temporal relations of the timeseries data. The GAN is trained with the GAF images. The trained GAN model acts as the virtual representation of the sensor, and the discriminator network of the GAN model, once it has learned the pattern of normal data, is used as the fault detector. The performance of the virtual sensor is promising because it successfully generates data for normal conditions. The best fault detection accuracy achieved by the proposed model is 98.7%, which makes this GAN-based digital twin inspired approach a promising candidate for sensor fault detection.

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“…A plethora of conventional algorithms, such as Support Vector Machine (SVM), Random Forest, Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and others [16,17,20,21], have been employed in fault diagnosis. Moreover, the integration of biologically-inspired optimization techniques, such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Grey Wolf Optimization (GWO), has been pursued to enhance the precision of fault diagnosis and prediction [22][23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Progressive Classifier Mechanism for Bridge Expansion Joint Health Status Monitoring System Based on Acoustic Sensors

Zhang

Cheng

et al. 2023

Sensors

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The application of IoT (Internet of Things) technology to the health monitoring of expansion joints is of great importance in enhancing the efficiency of bridge expansion joint maintenance. In this study, a low-power, high-efficiency, end-to-cloud coordinated monitoring system analyzes acoustic signals to identify faults in bridge expansion joints. To address the issue of scarce authentic data related to bridge expansion joint failures, an expansion joint damage simulation data collection platform is established for well-annotated datasets. Based on this, a progressive two-level classifier mechanism is proposed, combining template matching based on AMPD (Automatic Peak Detection) and deep learning algorithms based on VMD (Variational Mode Decomposition), denoising, and utilizing edge and cloud computing power efficiently. The simulation-based datasets were used to test the two-level algorithm, with the first-level edge-end template matching algorithm achieving fault detection rates of 93.3% and the second-level cloud-based deep learning algorithm achieving classification accuracy of 98.4%. The proposed system in this paper has demonstrated efficient performance in monitoring the health of expansion joints, according to the aforementioned results.

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Sensor Fault Diagnosis Method Based on α‐Grey Wolf Optimization‐Support Vector Machine

Cited by 5 publications

References 24 publications

Chaos quantum optimization‐based layered diagnosis framework for faulty sensor node diagnosis and classification in wireless sensor networks

Chaos quantum optimization‐based layered diagnosis framework for faulty sensor node diagnosis and classification in wireless sensor networks

Wasserstein GAN-Based Digital Twin-Inspired Model for Early Drift Fault Detection in Wireless Sensor Networks

Progressive Classifier Mechanism for Bridge Expansion Joint Health Status Monitoring System Based on Acoustic Sensors

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