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Dear Colleagues,

With the rapid development of various smart digital sensors, the Internet of Thing (IoT) is a swiftly evolving technology, which is contributing substantially to Industry 4.0 and the promotion of monitoring systems. The use of intelligent IoT sensors drives smart manufacturing and raises new requirements and challenges for fault diagnosis platforms on efficiency, reliability, and data security. In the era of IoT big data, the integration of cloud-edge computing technologies, cyberphysical systems, and artificial intelligence enables the full potential of accurate fault diagnosis and predictive maintenance in power transformer cyberattacks, battery lifetime predictions, rotating machine faults, etc.

This Special Issue seeks innovative works on a wide range of research topics, which include (but are not limited to) the following:

Industrial system security;
Advanced cloud-assisted intelligent architecture in smart factory;
End–edge–cloud-orchestrated fault diagnosis platforms;
Integrated framework employing IoT and big data techniques;
Artificial intelligence of things systems;
Fault diagnosis methods based on big data analysis;
Deep learning applications in industrial security challenges.

We would like to cordially invite you to submit an article to this Special Issue, including short communications, full research articles, and timely reviews.

Prof. Dr. Linbo Xie
Prof. Dr. Robert Hsu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

fault diagnosis
predictive maintenance
Industry 4.0
Internet of Things
cloud–edge computing
cyberphysical system
artificial intelligence

Published Papers (2 papers)

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Research

22 pages, 10942 KiB

Open AccessArticle

Interacting Multiple Model Estimators for Fault Detection in a Magnetorheological Damper

by Andrew Sanghyun Lee, Yuandi Wu, Stephen Andrew Gadsden and Mohammad AlShabi

Sensors 2024, 24(1), 251; https://0-doi-org.brum.beds.ac.uk/10.3390/s24010251 - 31 Dec 2023

Viewed by 629

Abstract

This paper proposes a novel estimator for the purpose of fault detection and diagnosis. The interacting multiple model (IMM) strategy is effective for estimating the behaviour of systems with multiple operating modes. Each mode corresponds to a distinct mathematical model and is subject to a filtering process. This paper applies various model-based filters in combination with the IMM strategy. One such estimator employs the recently introduced extended sliding innovation filter (ESIF) known as the IMM-ESIF. The ESIF is an extension of the sliding innovation filter for nonlinear systems based on the sliding mode concept. In the presence of modeling uncertainties, the ESIF has been proven to be more robust compared to methods such as the extended Kalman filter (EKF). The novel IMM-ESIF strategy is also compared with the IMM strategy, which incorporates the unscented Kalman filter (UKF), referred to herein as IMM-UKF. While EKF uses Taylor series approximation to linearize the system model, the UKF uses sigma point to calculate the system’s mean and covariance. The methods were applied to an experimental magnetorheological (MR) damper setup, which was designed for testing control and estimation theory. Magnetorheological dampers exhibit a diverse array of applications in the automotive and aerospace sectors, with particular relevance to attenuating vibrations through adaptive suspension systems. Applied to a magnetorheological (MR) damper with distinct operating modes determined by the damper’s current, the results showcase the effectiveness of IMM-ESIF. In mixed operational conditions, IMM-ESIF demonstrates a notable 80% to 90% reduction in estimation error compared to its counterparts. Furthermore, it exhibits a 4% to 5% enhancement in correctly classifying operational modes, establishing IMM-ESIF as a promising and efficient alternative for adaptive estimation in electromechanical systems. The improved accuracy in estimating the system’s behaviour, even amidst uncertainties and mixed operational scenarios, signifies the potential of IMM-ESIF to significantly enhance the overall robustness and efficiency of estimations. Full article

(This article belongs to the Special Issue Fault Diagnosis Platform Based on the Internet of Things and Intelligent Computing)

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23 pages, 5926 KiB

Open AccessArticle

Fault Knowledge Graph Construction and Platform Development for Aircraft PHM

by Xiangzhen Meng, Bo Jing, Shenglong Wang, Jinxin Pan, Yifeng Huang and Xiaoxuan Jiao

Sensors 2024, 24(1), 231; https://0-doi-org.brum.beds.ac.uk/10.3390/s24010231 - 30 Dec 2023

Viewed by 1030

Abstract

To tackle the problems of over-reliance on traditional experience, poor troubleshooting robustness, and slow response by maintenance personnel to changes in faults in the current aircraft health management field, this paper proposes the use of a knowledge graph. The knowledge graph represents troubleshooting in a new way. The aim of the knowledge graph is to improve the correlation between fault data by representing experience. The data source for this study consists of the flight control system manual and typical fault cases of a specific aircraft type. A knowledge graph construction approach is proposed to construct a fault knowledge graph for aircraft health management. Firstly, the data are classified using the ERNIE model-based method. Then, a joint entity relationship extraction model based on ERNIE-BiLSTM-CRF-TreeBiLSTM is introduced to improve entity relationship extraction accuracy and reduce the semantic complexity of the text from a linguistic perspective. Additionally, a knowledge graph platform for aircraft health management is developed. The platform includes modules for text classification, knowledge extraction, knowledge auditing, a Q&A system, and graph visualization. These modules improve the management of aircraft health data and provide a foundation for rapid knowledge graph construction and knowledge graph-based fault diagnosis. Full article

(This article belongs to the Special Issue Fault Diagnosis Platform Based on the Internet of Things and Intelligent Computing)

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