Special Issue "Algorithms for Fault Detection and Diagnosis"

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Analysis of Algorithms and Complexity Theory".

Deadline for manuscript submissions: 31 December 2020.

Special Issue Editors

Dr. Francesco Ferracuti
Website
Guest Editor
Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy
Interests: brain computer interface; human-robot interaction and cooperation; assistive technologies; signal processing
Special Issues and Collections in MDPI journals
Dr. Alessandro Freddi
Website
Guest Editor
Department of Information Engineering, Università Politecnica delle Marche, 60131 Ancona, Italy
Interests: fault detection and diagnosis; fault-tolerant control; mobile robotics; assistive robotics and home and building automation
Special Issues and Collections in MDPI journals
Dr. Andrea Monteriù
Website
Guest Editor
Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy
Interests: brain computer Interface (BCI); BCI control applications; machine interface; human-machine interaction; assistive technologies; assistive robotics
Special Issues and Collections in MDPI journals

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 (6 papers)

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Research

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Open AccessArticle
Misalignment Fault Prediction of Wind Turbines Based on Combined Forecasting Model
Algorithms 2020, 13(3), 56; https://doi.org/10.3390/a13030056 - 01 Mar 2020
Cited by 3
Abstract
Due to the harsh working environment of wind turbines, various types of faults are prone to occur during long-term operation. Misalignment faults between the gearbox and the generator are one of the latent common faults for doubly-fed wind turbines. Compared with other faults [...] Read more.
Due to the harsh working environment of wind turbines, various types of faults are prone to occur during long-term operation. Misalignment faults between the gearbox and the generator are one of the latent common faults for doubly-fed wind turbines. Compared with other faults like gears and bearings, the prediction research of misalignment faults for wind turbines is relatively few. How to accurately predict its developing trend has always been a difficulty. In this paper, a combined forecasting model is proposed for misalignment fault prediction of wind turbines based on vibration and current signals. In the modelling, the improved Multivariate Grey Model (IMGM) is used to predict the deterministic trend and the Least Squares Support Vector Machine (LSSVM) optimized by quantum genetic algorithm (QGA) is adopted to predict the stochastic trend of the fault index separately, and another LSSVM optimized by QGA is used as a non-linear combiner. Multiple information of time-domain, frequency-domain and time-frequency domain of the wind turbine’s vibration or current signals are extracted as the input vectors of the combined forecasting model and the kurtosis index is regarded as the output. The simulation results show that the proposed combined model has higher prediction accuracy than the single forecasting models. Full article
(This article belongs to the Special Issue Algorithms for Fault Detection and Diagnosis)
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Open AccessArticle
GA-Adaptive Template Matching for Offline Shape Motion Tracking Based on Edge Detection: IAS Estimation from the SURVISHNO 2019 Challenge Video for Machine Diagnostics Purposes
Algorithms 2020, 13(2), 33; https://doi.org/10.3390/a13020033 - 29 Jan 2020
Cited by 2
Abstract
The estimation of the Instantaneous Angular Speed (IAS) has in recent years attracted a growing interest in the diagnostics of rotating machines. Measurement of the IAS can be used as a source of information of the machine condition per se, or for performing [...] Read more.
The estimation of the Instantaneous Angular Speed (IAS) has in recent years attracted a growing interest in the diagnostics of rotating machines. Measurement of the IAS can be used as a source of information of the machine condition per se, or for performing angular resampling through Computed Order Tracking, a practice which is essential to highlight the machine spectral signature in case of non-stationary operational conditions. In these regards, the SURVISHNO 2019 international conference held at INSA Lyon on 8–10 July 2019 proposed a challenge about the estimation of the instantaneous non-stationary speed of a fan from a video taken by a smartphone, a pocket, low-cost device which can nowadays be found in everyone’s pocket. This work originated by the author to produce an offline motion-tracking of the fan (actually, of the head of its locking-screw) and obtaining then a reliable estimate of the IAS. The here proposed algorithm is an update of the established Template Matching (TM) technique (i.e., in the Signal Processing community, a two-dimensional matched filter), which is here integrated into a Genetic Algorithm (GA) search. Using a template reconstructed from a simplified parametric mathematical model of the features of interest (i.e., the known geometry of the edges of the screw head), the GA can be used to adapt the template to match the search image, leading to a hybridization of template-based and feature-based approaches which allows to overcome the well-known issues of the traditional TM related to scaling and rotations of the search image with respect to the template. Furthermore, it is able to resolve the position of the center of the screw head at a resolution that goes beyond the limit of the pixel grid. By repeating the analysis frame after frame and focusing on the angular position of the screw head over time, the proposed algorithm can be used as an effective offline video-tachometer able to estimate the IAS from the video, avoiding the need for expensive high-resolution encoders or tachometers. Full article
(This article belongs to the Special Issue Algorithms for Fault Detection and Diagnosis)
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Open AccessArticle
Blended Filter-Based Detection for Thruster Valve Failure and Control Recovery Evaluation for RLV
Algorithms 2019, 12(11), 228; https://doi.org/10.3390/a12110228 - 01 Nov 2019
Abstract
Security enhancement and cost reduction have become crucial goals for second-generation reusable launch vehicles (RLV). The thruster is an important actuator for an RLV, and its control normally requires a valve capable of high-frequency operation, which may lead to excessive wear or failure [...] Read more.
Security enhancement and cost reduction have become crucial goals for second-generation reusable launch vehicles (RLV). The thruster is an important actuator for an RLV, and its control normally requires a valve capable of high-frequency operation, which may lead to excessive wear or failure of the thruster valve. This paper aims at developing a thruster fault detection method that can deal with the thruster fault caused by the failure of the thruster valve and play an emergency role in the cases of hardware sensor failure. Firstly, the failure mechanism of the thruster was analyzed and modeled. Then, thruster fault detection was employed by introducing an angular velocity signal, using a blended filter, and determining an isolation threshold. In addition, to support the redundancy management of the thruster, an evaluation method of the nonlinear model-based numerical control prediction was proposed to evaluate whether the remaining fault-free thruster can track the attitude control response performance under the failure of the thruster valve. The simulation results showed that the method is stable and allowed for the effective detection of thruster faults and timely evaluation of recovery performance. Full article
(This article belongs to the Special Issue Algorithms for Fault Detection and Diagnosis)
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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
Cited by 2
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
Cited by 1
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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Review

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Open AccessReview
A Review of Lithium-Ion Battery Fault Diagnostic Algorithms: Current Progress and Future Challenges
Algorithms 2020, 13(3), 62; https://doi.org/10.3390/a13030062 - 08 Mar 2020
Cited by 6
Abstract
The usage of Lithium-ion (Li-ion) batteries has increased significantly in recent years due to their long lifespan, high energy density, high power density, and environmental benefits. However, various internal and external faults can occur during the battery operation, leading to performance issues and [...] Read more.
The usage of Lithium-ion (Li-ion) batteries has increased significantly in recent years due to their long lifespan, high energy density, high power density, and environmental benefits. However, various internal and external faults can occur during the battery operation, leading to performance issues and potentially serious consequences, such as thermal runaway, fires, or explosion. Fault diagnosis, hence, is an important function in the battery management system (BMS) and is responsible for detecting faults early and providing control actions to minimize fault effects, to ensure the safe and reliable operation of the battery system. This paper provides a comprehensive review of various fault diagnostic algorithms, including model-based and non-model-based methods. The advantages and disadvantages of the reviewed algorithms, as well as some future challenges for Li-ion battery fault diagnosis, are also discussed in this paper. Full article
(This article belongs to the Special Issue Algorithms for Fault Detection and Diagnosis)
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