An adaptive threshold algorithm for sensor fault based on the grey theory

Wu, Lifeng; Yao, Beibei; Peng, Zhen; Guan, Yong

doi:10.1177/1687814017693193

Cited by 11 publications

(5 citation statements)

References 17 publications

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“…[33] e paper studies different hardware redundancy techniques e traditional approach for detecting faults. It involves extra cost, space, weight, and maintenance for redundant devices An adaptive threshold algorithm for sensor fault based on the grey theory [34] An adaptive dynamic threshold adjustment algorithm based on the grey theory. In this method, the threshold value can be dynamically adjusted according to the real-time mean and variance of the residual Factors affecting the current consumption of the device were not considered like drift, spikes, frequency of repairs, unplugging of the device not considered most active exploitation of physical infrastructure and IT resources [39,40].…”

Section: Proposed Approachmentioning

confidence: 99%

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SWOT: A Hybrid Hardware-Based Approach for Robust Fault-Tolerant Framework in a Smart Day Care

Sharma¹,

Gupta

et al. 2022

Security and Communication Networks

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Internet of Things (IoT) has made its imprint on every part of the globe today. Offices, households, factories, industries, agriculture, and day cares, among other places, have all upgraded to their innovative version. It has propounded great potential in various real-world applications. The topical trends in the adoption of IoT have also highlighted the challenges associated with the performance of IoT devices. IoT devices require continuous monitoring for any performance degradation. A bibliometric analysis of 587 papers is undertaken on the Scopus database to spot the increasing interest of researchers in fault detection in IoT. A smart system’s or an IoT-based space’s usability, efficiency, and performance are all built on a fault-tolerant approach and interruption-free smooth operations. The investigation was carried out based on the literature to determine the need for a pro-fault detection system in an IoT-enabled day care. Kids’ security and safety are highly dependent on the hassle-free working of smart devices. It’s overhead to carry out device tracking manually along with demanding kids. This issue needs to be addressed to uphold the smart day care’s trustworthiness. A pro-fault detection approach can be applied to resolve the aforementioned issue to enhance the smart day care’s performance and efficiency. This paper proposes SWOT, a novel hybrid hardware-based approach in an IoT-based day care to safeguard the proper working of all IoT devices deployed. It screens every single appliance associated with a smart day care to detect the faulty appliance beforehand. The solution will assist the day care staff in providing the best care and security to their kids without any overhead. SWOT evaluation proved that it is an economical and efficient approach in detecting faults, accurately, swiftly, and with a low false alarm ratio.

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Section: Proposed Approachmentioning

confidence: 99%

“…Comparison with existing work on hardware-based approaches of fault detection[30][31][32][33][34][35][36].…”

mentioning

confidence: 99%

SWOT: A Hybrid Hardware-Based Approach for Robust Fault-Tolerant Framework in a Smart Day Care

Sharma¹,

Gupta

et al. 2022

Security and Communication Networks

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“…Rather, they deploy various hardware-based techniques as mentioned in Fig. 11, to diagnose abnormalities in data [104,[137][138][139][140][141][142][143][144][145]. Combinations of various hardware techniques can also be used.…”

Section: Hardware-based Techniquesmentioning

confidence: 99%

An Insight Survey on Sensor Errors and Fault Detection Techniques in Smart Spaces

Sharma,

Gupta,

Gupta

et al. 2024

CMES

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The widespread adoption of the Internet of Things (IoT) has transformed various sectors globally, making them more intelligent and connected. However, this advancement comes with challenges related to the effectiveness of IoT devices. These devices, present in offices, homes, industries, and more, need constant monitoring to ensure their proper functionality. The success of smart systems relies on their seamless operation and ability to handle faults. Sensors, crucial components of these systems, gather data and contribute to their functionality. Therefore, sensor faults can compromise the system's reliability and undermine the trustworthiness of smart environments. To address these concerns, various techniques and algorithms can be employed to enhance the performance of IoT devices through effective fault detection. This paper conducted a thorough review of the existing literature and conducted a detailed analysis. This analysis effectively links sensor errors with a prominent fault detection technique capable of addressing them. This study is innovative because it paves the way for future researchers to explore errors that have not yet been tackled by existing fault detection methods. Significant, the paper, also highlights essential factors for selecting and adopting fault detection techniques, as well as the characteristics of datasets and their corresponding recommended techniques. Additionally, the paper presents a methodical overview of fault detection techniques employed in smart devices, including the metrics used for evaluation. Furthermore, the paper examines the body of academic work related to sensor faults and fault detection techniques within the domain. This reflects the growing inclination and scholarly attention of researchers and academicians toward strategies for fault detection within the realm of the Internet of Things.

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“…Since we only focus on the cases where the monitored value is greater than the threshold value, we consider the upper thresholdμ i + nσ i as the threshold T i . For updating purposes, means and variances are subsequently derived to further reduce the amount of calculation and reduce the data storage space (Feng and Han, 2013;Wu et al, 2017).…”

Section: Adaptive Thresholdmentioning

confidence: 99%

Condition monitoring and early fault warning of power plant auxiliary equipment using LSTM-SDAE model with an adaptive threshold

Hong

Liang

et al. 2023

Front. Energy Res.

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With the increasing penetration of renewable energy in the power grid, which makes power plant equipment is always in changing operating conditions. The correlation between the main and auxiliary equipment of the unit is easy to lead a potential fault, therefore, the safety and reliability of the auxiliary equipment of thermal power units have become a more challenging issue in the case of large-scale renewable energy. Adaptive condition monitoring of the auxiliary equipment can reduce maintenance costs and improve reliability in the thermal power units. Most existing studies perform poorly at extracting features from distributed control systems data and make less use of time series data. A novel adaptive condition monitoring framework and early fault warning method based on long short-term memory and stack denoising auto-encoder network has been proposed for auxiliary equipment of power plant unit. The proposed framework has two main parts, which contain condition monitoring and adaptive early fault warning. The Mahalanobis distance of a reconstruction error is defined as the monitoring indicator to reflect the condition of the equipment. The Chebyshev inequality determines an adaptive threshold for early anomaly detection that applies to changeable working conditions. The effectiveness of the proposed method was verified by the actual case of the coal mill. The adaptive threshold method can obtain the advance time of 42s and 108s, respectively.

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An adaptive threshold algorithm for sensor fault based on the grey theory

Cited by 11 publications

References 17 publications

SWOT: A Hybrid Hardware-Based Approach for Robust Fault-Tolerant Framework in a Smart Day Care

SWOT: A Hybrid Hardware-Based Approach for Robust Fault-Tolerant Framework in a Smart Day Care

An Insight Survey on Sensor Errors and Fault Detection Techniques in Smart Spaces

Condition monitoring and early fault warning of power plant auxiliary equipment using LSTM-SDAE model with an adaptive threshold

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