Automated Fault Diagnosis in Wireless Sensor Networks: A Comprehensive Survey

Swain, Rakesh Ranjan; Dash, Tirtharaj; Khilar, Pabitra Mohan

doi:10.1007/s11277-022-09916-3

Cited by 18 publications

(7 citation statements)

References 161 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The adapter circuit is an important part of this automatic test system. The actual interface of the tested object is various, and most test instruments are 3 difficult to connect directly with the signal port of the tested object [8][9]. At this time, it is necessary to make the adapter circuit, through the interface circuit, adapt to the port of the tested object, so that the instrument can correctly connect the signal to the tested circuit and read the response signal of the circuit.…”

Section: Hardware Overall Connectionmentioning

confidence: 99%

Overall Scheme Design of Fault Diagnosis for Complex Electronic Systems

Mao,

Pang,

Jin

et al. 2024

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

Traditional manual testing methods are no longer able to meet the maintenance needs of modern shipboard fire control system board-level circuits. Therefore, the development of intelligent and universal automatic test systems has become an important issue for shipboard fire control system fault detection. This article analyzes the performance and functional requirements of the automatic test system, develops the automatic test system based on PXI bus instruments, and designs an overall solution with signal time-frequency analysis capabilities that can carry out intelligent fault diagnosis for a variety of board-level circuits. On this basis, the design of hardware circuit and software solution is completed. The automatic test system was analyzed on hardware indicators and software functions. On the hardware side, the hardware resource selection was completed based on the hardware indicator requirements. On the software side, the overall software architecture design and the design of each functional module were completed based on the system software functional requirements. Finally, the composition and functions of each module of the system software and the overall operation process are introduced.

show abstract

Section: Hardware Overall Connectionmentioning

confidence: 99%

Overall Scheme Design of Fault Diagnosis for Complex Electronic Systems

Mao,

Pang,

Jin

et al. 2024

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

show abstract

“…Deep Learning (DL) is a subset of AI with automated feature learning capability, in contrast to classical ML, further reducing human or machine-assisted tuning as illustrated in Figure 2 [3], [8], [10]. The DL's advantage over ML is due to the rise of Big Data, Internet of Things (IoT), Wireless Sensor Networks (WSN), and Computer Processors [1], [6], [16]. Conversely, DL, in general, struggles in situations with limited training data (especially faulty data) and computational hardware due to financial and energy efficiency limitations [3], [4], [6].…”

Section: Deep Learning In Fddmentioning

confidence: 99%

A Comparative Study and Improved Bearing Fault Classifier Using Raw Vibration Data Under Limited Training Samples

Yap,

Lim,

Leong

2024

Int. J. Automot. Mech. Eng.

View full text Add to dashboard Cite

Artificial intelligence is gaining traction in bearing fault detection and diagnosis. Generally, signal processing and feature selection are carried out to facilitate the fault classification process; however, classification accuracy tends to degrade under limited training data. In this paper, various artificial intelligence (AI) classification models are studied and compared using raw vibration data without signal processing and feature engineering. A Cosine k-Nearest Neighbours (CosKNN)-based classification model is optimized by integrating a Segmentive Mechanism, resulting in an overall classification F1-score improvement to 90.8% compared to the original classifier's 76.9%. The comparative findings show that the proposed model is suitable for circumstances with limited availability of training data, signal processing tools, and feature engineering tuning.

show abstract

“…Different productive applications provided by VANET are environmental parameter monitoring through VANET [39,40], secure transaction during automatic toll tax collection [41], etc. Optimization of various vehicular parameters [42] and self fault detection [43][44][45][46] in VANET [47][48][49] encourage to use VANET for the wide range of applications. Autonomous applications is successful through VANET due to efficient transmission of physical parameters measured through various sensors.…”

Section: Consideration Of Vanet Based On Applicationsmentioning

confidence: 99%

AI-assisted Emergency Healthcare using Vehicular Network and Support Vector Machine

Senapati

Khilar

Dash

et al. 2023

Wireless Pers Commun

Self Cite

View full text Add to dashboard Cite

The COVID-19 pandemic has created an emergency across the globe. The number of corona positive and death cases is still rising worldwide. All countries’ governments are taking various steps to control the infection of COVID-19. One step to control the coronavirus’s spreading is to quarantine. But the number of active cases at the quarantine center is increasing daily. Also, the doctors, nurses, and paramedical staff providing service to the people at the quarantine center are getting infected. This demands the automatic and regular monitoring of people at the quarantine center. This paper proposed a novel and automated method for monitoring people at the quarantine center in two phases. These are the health data transmission phase and health data analysis phase. The health data transmission phase proposed a geographic-based routing that involves components like Network-in-box, Roadside-unit, and vehicles. An effective route is determined using route value to transmit data from the quarantine center to the observation center. The route value depends on the factors such as density, shortest path, delay, vehicular data carrying delay, and attenuation. The performance metrics considered for this phase are E2E delay, number of network gaps, and packet delivery ratio, and the proposed work performs better than the existing routing like geographic source routing, anchor-based street traffic aware routing, Peripheral node based GEographic DIstance Routing . The analysis of health data is done at the observation center. In the health data analysis phase, the health data is classified into multi-class using a support vector machine. There are four categories of health data: normal, low-risk, medium-risk, and high-risk. The parameters used to measure the performance of this phase are precision, recall, accuracy, and F-1 score. The overall testing accuracy is found to be 96.8%, demonstrating strong potential for our technique to be adopted in practice.

show abstract

Automated Fault Diagnosis in Wireless Sensor Networks: A Comprehensive Survey

Cited by 18 publications

References 161 publications

Overall Scheme Design of Fault Diagnosis for Complex Electronic Systems

Overall Scheme Design of Fault Diagnosis for Complex Electronic Systems

A Comparative Study and Improved Bearing Fault Classifier Using Raw Vibration Data Under Limited Training Samples

AI-assisted Emergency Healthcare using Vehicular Network and Support Vector Machine

Contact Info

Product

Resources

About