Advances in Condition Monitoring and Fault Diagnosis

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Industrial Electronics".

Deadline for manuscript submissions: 15 June 2025 | Viewed by 254

Special Issue Editors


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Guest Editor
School of Mechanical Engineering, Zhejiang University, Hangzhou 310007, China
Interests: computer vision; prognostic health monitoring for power equipment; reliability analytics of power transmission systems
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Guest Editor
Hainan Institute of Zhejiang University, Zhejiang University, Sanya 572025, China
Interests: smart grid; wind energy generation and conversion; data-driven fault diagnosis; modelling and optimal control of complex industrial processes; fault-tolerant control of real-time systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, significant progress has been made in advanced technologies for rotating machinery in the fields of condition monitoring (CM) and fault detection and diagnosis (FDD). These advances are mainly driven by the growing demand in the industrial sector for improved reliability, efficiency, and safety. In particular, CM for expensive and high-value machinery is crucial for tracking work status and performance, reducing maintenance costs, improving efficiency and reliability, and the early warning of mechanical failures. The CM and FDD for rotating machinery mainly focus on signal processing and diagnostic techniques, including time, frequency, and time–frequency domains, as well as anomaly detection, image processing, data fusion, data mining, time series analysis, and expert systems. In addition, there are some studies focusing on specific fields, such as wind energy, photovoltaic power generation, and drive motors and batteries in electric vehicles. These studies involve various technologies and methods, and expand the exploration of the application of CM and FDD.

This Special Issue welcomes theoretical and practical contributions aimed at further understanding intelligent techniques, including anomaly detection, image processing, data fusion, data mining, time series analysis, and expert systems. Moreover, it seeks reports on innovative machines and power systems with applications of CM and FDD. This Special Issue welcomes both original research articles and review articles. Potential topics include, but are not limited to, the following:

  • Advanced data observation and acquisition techniques to carry out rotating machinery condition monitoring;
  • Advanced digital signal processing methodologies for big data to solve the Prognostic and Health Management (PHM) problem of power equipment;
  • Evolution and detection technology of key mechanical component defects in extreme environments;
  • Data repair, verification methods, and anomaly monitoring methods for incomplete observation samples;
  • Advanced fault-informative feature (e.g., on the time domain and time–frequency domain) representative methods for local defect detection;
  • Spectrum-based capability evaluation on noise disturbance robustness, and weak diagnostic signal enhancement;
  • Denoising methods and real state signal separation techniques under strong background noise;
  • Exploration of hybrid expert model applications based on large language model architectures.

We look forward to receiving your contributions.

Dr. Yunfeng Yan
Dr. Xian-Bo Wang
Guest Editors

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Keywords

  • condition monitoring
  • fault detection and diagnosis
  • reliability analytics of power systems

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Published Papers (1 paper)

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Research

19 pages, 4785 KiB  
Article
A Deep Equilibrium Model for Remaining Useful Life Estimation of Aircraft Engines
by Spyridon Plakias and Yiannis S. Boutalis
Electronics 2025, 14(12), 2355; https://doi.org/10.3390/electronics14122355 - 9 Jun 2025
Abstract
Estimating Remaining Useful Life (RUL) is crucial in modern Prognostic and Health Management (PHM) systems providing valuable information for planning the maintenance strategy of critical components in complex systems such as aircraft engines. Deep Learning (DL) models have shown great performance in the [...] Read more.
Estimating Remaining Useful Life (RUL) is crucial in modern Prognostic and Health Management (PHM) systems providing valuable information for planning the maintenance strategy of critical components in complex systems such as aircraft engines. Deep Learning (DL) models have shown great performance in the accurate prediction of RUL, building hierarchical representations by the stacking of multiple explicit neural layers. In the current research paper, we follow a different approach presenting a Deep Equilibrium Model (DEM) that effectively captures the spatial and temporal information of the sequential sensor. The DEM, which incorporates convolutional layers and a novel dual-input interconnection mechanism to capture sensor information effectively, estimates the degradation representation implicitly as the equilibrium solution of an equation, rather than explicitly computing it through multiple layer passes. The convergence representation of the DEM is estimated by a fixed-point equation solver while the computation of the gradients in the backward pass is made using the Implicit Function Theorem (IFT). The Monte Carlo Dropout (MCD) technique under calibration is the final key component of the framework that enhances regularization and performance providing a confidence interval for each prediction, contributing to a more robust and reliable outcome. Simulation experiments on the widely used NASA Turbofan Jet Engine Data Set show consistent improvements, with the proposed framework offering a competitive alternative for RUL prediction under diverse conditions. Full article
(This article belongs to the Special Issue Advances in Condition Monitoring and Fault Diagnosis)
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