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Fault Diagnosis in Transportation and Industry: Sensors, Methods, and Experimental Applications (2nd Edition)

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Fault Diagnosis & Sensors".

Deadline for manuscript submissions: 20 October 2026 | Viewed by 1297

Special Issue Editor

Prof. Steven Chatterton

E-Mail Website
Guest Editor
Department of Mechanical Engineering, Politecnico di Milano, Via G. La Masa 1, 20156 Milano, Italy
Interests: rotor dynamics; fault diagnostics; rolling element bearings; oil-film bearings
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The current operation of both transportation and industry must be efficient and safe—in particular, safety is the prerequisite of high-efficiency operation. The development of sensor technology and signal processing makes it possible to detect the real-time health status of mechanical equipment in the above two fields. Most existing methods for fault diagnosis work well only under light noise and in stationary conditions. However, strong noise and non-stationary conditions (including load variation, speed variation and temperature variation) are very common in these two fields, making it challenging to detect and monitor the severity of machine defects.

Bandpass filtering, wavelet transform, singular value decomposition, and empirical mode decomposition, among others, are often applied to extract fault components from original signals with strong noise. Envelope demodulation, moving average, and other methods have been applied to solve the problem of load variation. In addition, many order tracking methods have been proposed to address the velocity variation problem. However, considering application potential, the diagnosis of machine failure under strong noise and in non-stationary conditions can still be improved.

The Special Issue “Fault Diagnosis in Transportation and Industry: Sensors, Methods, and Experimental Applications (2nd Edition)” welcomes original and review articles on fault diagnosis in transportation and industry, particularly with high noise and in non-stationary conditions, with a strong emphasis on real-world applications.

Prof. Steven Chatterton
Guest Editor

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 250 words) can be sent to the Editorial Office for assessment.

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

  • machine fault diagnosis
  • gear fault diagnosis
  • rolling element bearing diagnosis
  • heavy noise
  • nonstationary condition
  • experimental implementations

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Related Special Issue

Published Papers (2 papers)

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Research

28 pages, 4429 KB  
Article
Reliability Assessment of Harmonic Reducers Based on the Two-Phase Hybrid Stochastic Degradation Process
by Lai Wei, Peng Liu, Hailong Tian, Haoyuan Li and Yunshenghao Qiu
Sensors 2026, 26(8), 2437; https://doi.org/10.3390/s26082437 - 15 Apr 2026
Viewed by 400
Abstract
Harmonic reducers exhibit non-stationary and phase-dependent degradation behavior during long-term service, challenging the ability of classical stochastic degradation models to accurately assess reliability. To address phase-dependent differences in degradation behavior, this paper proposes a reliability assessment model based on a two-phase hybrid stochastic [...] Read more.
Harmonic reducers exhibit non-stationary and phase-dependent degradation behavior during long-term service, challenging the ability of classical stochastic degradation models to accurately assess reliability. To address phase-dependent differences in degradation behavior, this paper proposes a reliability assessment model based on a two-phase hybrid stochastic degradation process. In the proposed framework, the Wiener process is employed to characterize early-phase gradual degradation dominated by stochastic fluctuations, while the Inverse Gaussian process is used to describe later-phase monotonically accelerated degradation driven by cumulative damage. The framework allows for sample-level variability in transition times to more realistically capture individual degradation behavior. The Schwarz Information Criterion is also adopted to detect change points. Maximum likelihood estimation is performed for model parameter inference, and analytical expressions for the reliability function, cumulative distribution function, and probability density function are derived. Numerical results indicate that a change point exists for each tested product and that the proposed model achieves the best goodness of fit among the considered candidates, demonstrating its superiority in capturing phase-dependent characteristics of harmonic reducer degradation. In terms of reliability assessment bias, the proposed model (0.06%) significantly outperforms the Wiener degradation model (32%) and the IG degradation model (9.9%). These results further confirm that, under an identical failure threshold, the proposed approach yields more accurate and realistic reliability assessment outcomes. Full article
(This article belongs to the Special Issue Fault Diagnosis in Transportation and Industry: Sensors, Methods, and Experimental Applications (2nd Edition))
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29 pages, 10535 KB  
Article
Novel Fault Diagnosis Technology Based on Integrated Spectral Kurtosis for Gearboxes
by Len Gelman, Rami Kerrouche and Abdulmumeen Onimisi Abdullahi
Sensors 2026, 26(7), 2185; https://doi.org/10.3390/s26072185 - 1 Apr 2026
Viewed by 475
Abstract
This paper proposes a novel integrated spectral kurtosis (ISK) technology, which is a new conceptualization for fault diagnosis, and compares it with conventional spectral kurtosis technology. The vibration signals from a gearbox are processed by time synchronous averaging (TSA) and analysed using the [...] Read more.
This paper proposes a novel integrated spectral kurtosis (ISK) technology, which is a new conceptualization for fault diagnosis, and compares it with conventional spectral kurtosis technology. The vibration signals from a gearbox are processed by time synchronous averaging (TSA) and analysed using the spectral kurtosis (SK). The ISK feature is estimated across the entire frequency domain, while the envelope is obtained through SK-based filtering and a Hilbert demodulation. The ISK technology demonstrates the ability to distinguish between healthy and defected gearbox cases, achieving a total probability of correct diagnosis (TPCD) of 91.5% for pinions and 96.1% for gears, whereas the SK-based squared envelope technology provides a limited diagnosis effectiveness, with a maximum TPCD of 80%. The motor current signals are also analysed through harmonic amplitude tracking within the current spectrum. A comparison of the ISK and motor current technologies is also made, showing that the motor current technology reaches a maximum of 90% TPCD for gears, which remains lower than the TPCD for the ISK technology. Full article
(This article belongs to the Special Issue Fault Diagnosis in Transportation and Industry: Sensors, Methods, and Experimental Applications (2nd Edition))
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