Special Issue "Algorithms for Fault Detection and Diagnosis"

A special issue of Algorithms (ISSN 1999-4893).

Deadline for manuscript submissions: 31 December 2019.

Special Issue Editors

Guest Editor
Dr. Francesco Ferracuti Website E-Mail
Department of Information Engineering, Università Politecnica delle Marche, 60131 Ancona, Italy
Interests: fault detection and diagnosis; modelling and system identification; data analysis; signal processing; machine learning
Guest Editor
Dr. Alessandro Freddi Website E-Mail
Department of Information Engineering, Università Politecnica delle Marche, 60131 Ancona, Italy
Phone: +39-071-220-4314
Interests: fault detection and diagnosis; fault-tolerant control; mobile robotics; assistive robotics and home and building automation
Guest Editor
Dr. Andrea Monteriù Website E-Mail
Department of Information Engineering, Università Politecnica delle Marche, 60131 Ancona, Italy
Interests: fault diagnosis; fault tolerant control; nonlinear dynamics and control; periodic and stochastic system control; robotics; assistive technologies

Special Issue Information

Dear Colleagues,

Due to the increasing security and reliability demand, early detection and diagnosis of faults for manufacturing and production processes and mechatronic systems are key points to reduce economic losses caused by unscheduled maintenance and downtimes, to prevent the endangerment of human beings involved in the process operations and to improve reliability and availability of autonomous systems.

The development of algorithms for health monitoring, faults and anomalies detection, able to early detect and isolate technical component malfunctioning or even predict them, becomes more and more crucial in this context.

This Special Issue is devoted to new research efforts, developments and results concerning recent advances and challenges in the application of “Algorithms for Fault Detection and Diagnosis”, articulated over a wide range of sectors. The aim is to provide a comprehensive collection of some of the current state-of-the-art algorithms within this context, together with new advanced theoretical solutions. Papers submitted to this Special Issue are expected to provide an original contribution, proposing new solutions, improvements to existing solutions, and new application-oriented research results in the area of the Fault Detection and Diagnosis that are worthy of archival publication in Algorithms.

Dr. Andrea Monteriù
Dr. Alessandro Freddi
Dr. Francesco Ferracuti
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 papers will be 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. Algorithms is an international peer-reviewed open access monthly 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 1000 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
  • Fault detection
  • Fault-tolerant control
  • Monitoring systems
  • Statistical methods for fault diagnosis
  • Data-driven diagnosis methods
  • Model-based diagnosis methods
  • Cyber-physical systems
  • Industrial processes
  • Signal processing
  • Condition based monitoring and maintenance engineering
  • Artificial Intelligence methods for fault diagnosis
  • Autonomous and Semi-Autonomous Systems.

Published Papers (2 papers)

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Research

Open AccessArticle
Fault Diagnosis of Rolling Bearing Using Multiscale Amplitude-Aware Permutation Entropy and Random Forest
Algorithms 2019, 12(9), 184; https://doi.org/10.3390/a12090184 - 04 Sep 2019
Abstract
A rolling bearing is an important connecting part between rotating machines. It is susceptible to mechanical stress and wear, which affect the running state of bearings. In order to effectively identify the fault types and analyze the fault severity of rolling bearings, a [...] Read more.
A rolling bearing is an important connecting part between rotating machines. It is susceptible to mechanical stress and wear, which affect the running state of bearings. In order to effectively identify the fault types and analyze the fault severity of rolling bearings, a rolling bearing fault diagnosis method based on multiscale amplitude-aware permutation entropy (MAAPE) and random forest is proposed in this paper. The vibration signals of rolling bearings to be analyzed are decomposed into different coarse-grained time series by using the coarse-graining procedure in multiscale entropy, highlighting the fault dynamic characteristics of vibration signals at different scales. The fault features contained in the coarse-grained time series at different time scales are extracted by using amplitude-aware permutation entropy’s sensitive characteristics to signal amplitude and frequency changes to form fault feature vectors. The fault feature vector set is used to establish the random forest multi-classifier, and the fault type identification and fault severity analysis of rolling bearings is realized through random forest. In order to demonstrate the feasibility and effectiveness of the proposed method, experiments were fully conducted in this paper. The experimental results show that multiscale amplitude-aware permutation entropy can effectively extract fault features of rolling bearings from vibration signals, and the extracted feature vectors have high separability. Compared with other rolling bearing fault diagnosis methods, the proposed method not only has higher fault type identification accuracy, but also can analyze the fault severity of rolling bearings to some extent. The identification accuracy of four fault types is up to 96.0% and the fault recognition accuracy under different fault severity reached 92.8%. Full article
(This article belongs to the Special Issue Algorithms for Fault Detection and Diagnosis)
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Open AccessArticle
An Intelligent Warning Method for Diagnosing Underwater Structural Damage
Algorithms 2019, 12(9), 183; https://doi.org/10.3390/a12090183 - 30 Aug 2019
Abstract
A number of intelligent warning techniques have been implemented for detecting underwater infrastructure diagnosis to partially replace human-conducted on-site inspections. However, the extensively varying real-world situation (e.g., the adverse environmental conditions, the limited sample space, and the complex defect types) can lead to [...] Read more.
A number of intelligent warning techniques have been implemented for detecting underwater infrastructure diagnosis to partially replace human-conducted on-site inspections. However, the extensively varying real-world situation (e.g., the adverse environmental conditions, the limited sample space, and the complex defect types) can lead to challenges to the wide adoption of intelligent warning techniques. To overcome these challenges, this paper proposed an intelligent algorithm combing gray level co-occurrence matrix (GLCM) with self-organization map (SOM) for accurate diagnosis of the underwater structural damage. In order to optimize the generative criterion for GLCM construction, a triangle algorithm was proposed based on orthogonal experiments. The constructed GLCM were utilized to evaluate the texture features of the regions of interest (ROI) of micro-injury images of underwater structures and extracted damage image texture characteristic parameters. The digital feature screening (DFS) method was used to obtain the most relevant features as the input for the SOM network. According to the unique topology information of the SOM network, the classification result, recognition efficiency, parameters, such as the network layer number, hidden layer node, and learning step, were optimized. The robustness and adaptability of the proposed approach were tested on underwater structure images through the DFS method. The results showed that the proposed method revealed quite better performances and can diagnose structure damage in underwater realistic situations. Full article
(This article belongs to the Special Issue Algorithms for Fault Detection and Diagnosis)
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