Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.1
1.8
Journals
Algorithms
Special Issues
Artificial Intelligence for Fault Detection and Diagnosis
share announcement

Artificial Intelligence for Fault Detection and Diagnosis

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Evolutionary Algorithms and Machine Learning".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 33039

Special Issue Editors

Dr. Ying Bi

E-Mail Website
Guest Editor
School of Engineering and Computer Science(SECS), Victoria University of Wellington (VUW), Wellington 6012, New Zealand
Interests: artificial intelligence; machine learning; computer vision
Prof. Dr. Mengjie Zhang

E-Mail Website
Guest Editor
School of Engineering and Computer Science(SECS), Victoria University of Wellington (VUW), Wellington 6012, New Zealand
Interests: artificial intelligence; image processing
Prof. Dr. Bing Xue

E-Mail Website
Guest Editor
School of Engineering and Computer Science, Victoria University of Wellington, Wellington 6140, New Zealand
Interests: evolutionary computation; feature selection; computer vision; image analysis; neuroevolution
Special Issues, Collections and Topics in MDPI journals
Dr. Bo Peng

E-Mail Website
Assistant Guest Editor
College of Mechatronical and Electrical Engineering, Hebei Agriculture University, Baoding 071001, China
Interests: fault detection and diagnosis; evolutionary computation; machine learning

Special Issue Information

Dear Colleagues,

Fault detection and diagnosis (FDD) is very important in manufacturing and mechatronic systems to reduce costs and improve productivity. Traditionally, human beings have manually checked the states of the machines and detected their faults, which is time-consuming and expensive. Therefore, it is desirable to develop intelligent systems to achieve automatic FDD. Typically, an intelligent FDD system includes many processes, such as data collection, data processing, feature extraction, feature selection, feature construction, and classification, where different algorithms can be used.

Artificial intelligence (AI) covers a wide range of algorithms that mimic the human mind, thinking and acting like humans to solve important tasks in different fields. AI methods include deep learning, machine learning, rule-based methods, evolutionary computation, and more. AI has achieved great success in many important areas including computer vision and natural language processing.

Many AI algorithms have been applied to FDD, including data processing, data mining, feature analysis, and classification. In recent years, deep neural networks have shown potential in FDD. Other promising methods include evolutionary computation techniques and fuzzy systems. However, the potential of AI has not been comprehensively investigated in FDD. It remains a challenging task due to many factors, such as changeable equipment working state, incomplete information, lack of sufficient training data, complex relationships between faults and symptoms, and the requirement of domain knowledge.

This Special Issue aims to investigate the use of different AI algorithms involving machine learning, deep learning, and computational intelligence techniques in applications to FDD of different machines. We would like to invite researchers to submit papers on the topic, from all viewpoints, including theoretical issues, algorithms, systems, and industrial applications.

 

Possible research themes include:

  • AI-based fault detection and diagnosis methods using vibration signals, electric signals, acoustic signals, thermal images, etc.
  • AI-based fault detection and diagnosis methods based on improved data using spectral analysis, wavelet transform (WT), empirical mode decomposition (EMD), variational mode decomposition (VMD), maximum correlated kurtosis deconvolution (MCKD), fast kurtogram (FSK), e
  • AI-based fault detection and diagnosis methods using various feature analysis techniques, including feature scaling, feature normalization, feature selection, feature extraction, feature construction, and feature learning.
  • Machine learning for fault detection and diagnosis, such as support vector machines (SVMs), k-nearest neighbor (KNN), Bayesian classifier, ensemble methods, etc.
  • Evolutionary computation (EC) techniques for fault detection and diagnosis, such as genetic programming (GP), particle swarm optimization (PSO), differential evolution (DE), genetic algorithms (GAs), etc.
  • Deep learning for fault detection and diagnosis, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), belief networks, reinforcement learning, etc.
  • Fault detection and diagnosis of various machines including rotating machines, electricity-driven machines, and different types of engines.

Dr. Ying Bi
Prof. Dr. Mengjie Zhang
Prof. Dr. Bing Xue
Dr. Bo Peng
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. 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 1600 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.

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (9 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

21 pages, 1388 KiB  
Article
BENK: The Beran Estimator with Neural Kernels for Estimating the Heterogeneous Treatment Effect
by Stanislav Kirpichenko, Lev Utkin, Andrei Konstantinov and Vladimir Muliukha
Algorithms 2024, 17(1), 40; https://doi.org/10.3390/a17010040 - 18 Jan 2024
Viewed by 1706
Abstract
A method for estimating the conditional average treatment effect under the condition of censored time-to-event data, called BENK (the Beran Estimator with Neural Kernels), is proposed. The main idea behind the method is to apply the Beran estimator for estimating the survival functions [...] Read more.
A method for estimating the conditional average treatment effect under the condition of censored time-to-event data, called BENK (the Beran Estimator with Neural Kernels), is proposed. The main idea behind the method is to apply the Beran estimator for estimating the survival functions of controls and treatments. Instead of typical kernel functions in the Beran estimator, it is proposed to implement kernels in the form of neural networks of a specific form, called neural kernels. The conditional average treatment effect is estimated by using the survival functions as outcomes of the control and treatment neural networks, which consist of a set of neural kernels with shared parameters. The neural kernels are more flexible and can accurately model a complex location structure of feature vectors. BENK does not require a large dataset for training due to its special way for training networks by means of pairs of examples from the control and treatment groups. The proposed method extends a set of models that estimate the conditional average treatment effect. Various numerical simulation experiments illustrate BENK and compare it with the well-known T-learner, S-learner and X-learner for several types of control and treatment outcome functions based on the Cox models, the random survival forest and the Beran estimator with Gaussian kernels. The code of the proposed algorithms implementing BENK is publicly available. Full article
(This article belongs to the Special Issue Artificial Intelligence for Fault Detection and Diagnosis)
►▼ Show Figures

Figure 1

26 pages, 8044 KiB  
Article
Wind Turbine Predictive Fault Diagnostics Based on a Novel Long Short-Term Memory Model
by Shuo Zhang, Emma Robinson and Malabika Basu
Algorithms 2023, 16(12), 546; https://doi.org/10.3390/a16120546 - 28 Nov 2023
Cited by 2 | Viewed by 1951
Abstract
The operation and maintenance (O&M) issues of offshore wind turbines (WTs) are more challenging because of the harsh operational environment and hard accessibility. As sudden component failures within WTs bring about durable downtimes and significant revenue losses, condition monitoring and predictive fault diagnostic [...] Read more.
The operation and maintenance (O&M) issues of offshore wind turbines (WTs) are more challenging because of the harsh operational environment and hard accessibility. As sudden component failures within WTs bring about durable downtimes and significant revenue losses, condition monitoring and predictive fault diagnostic approaches must be developed to detect faults before they occur, thus preventing durable downtimes and costly unplanned maintenance. Based primarily on supervisory control and data acquisition (SCADA) data, thirty-three weighty features from operational data are extracted, and eight specific faults are categorised for fault predictions from status information. By providing a model-agnostic vector representation for time, Time2Vec (T2V), into Long Short-Term Memory (LSTM), this paper develops a novel deep-learning neural network model, T2V-LSTM, conducting multi-level fault predictions. The classification steps allow fault diagnosis from 10 to 210 min prior to faults. The results show that T2V-LSTM can successfully predict over 84.97% of faults and outperform LSTM and other counterparts in both overall and individual fault predictions due to its topmost recall scores in most multistep-ahead cases performed. Thus, the proposed T2V-LSTM can correctly diagnose more faults and upgrade the predictive performances based on vanilla LSTM in terms of accuracy, recall scores, and F-scores. Full article
(This article belongs to the Special Issue Artificial Intelligence for Fault Detection and Diagnosis)
►▼ Show Figures

Figure 1

17 pages, 1788 KiB  
Article
A Novel Intelligent Method for Fault Diagnosis of Steam Turbines Based on T-SNE and XGBoost
by Zhiguo Liang, Lijun Zhang and Xizhe Wang
Algorithms 2023, 16(2), 98; https://doi.org/10.3390/a16020098 - 9 Feb 2023
Cited by 13 | Viewed by 3113
Abstract
Since failure of steam turbines occurs frequently and can causes huge losses for thermal plants, it is important to identify a fault in advance. A novel clustering fault diagnosis method for steam turbines based on t-distribution stochastic neighborhood embedding (t-SNE) and extreme gradient [...] Read more.
Since failure of steam turbines occurs frequently and can causes huge losses for thermal plants, it is important to identify a fault in advance. A novel clustering fault diagnosis method for steam turbines based on t-distribution stochastic neighborhood embedding (t-SNE) and extreme gradient boosting (XGBoost) is proposed in this paper. First, the t-SNE algorithm was used to map the high-dimensional data to the low-dimensional space; and the data clustering method of K-means was performed in the low-dimensional space to distinguish the fault data from the normal data. Then, the imbalance problem in the data was processed by the synthetic minority over-sampling technique (SMOTE) algorithm to obtain the steam turbine characteristic data set with fault labels. Finally, the XGBoost algorithm was used to solve this multi-classification problem. The data set used in this paper was derived from the time series data of a steam turbine of a thermal power plant. In the processing analysis, the method achieved the best performance with an overall accuracy of 97% and an early warning of at least two hours in advance. The experimental results show that this method can effectively evaluate the condition and provide fault warning for power plant equipment. Full article
(This article belongs to the Special Issue Artificial Intelligence for Fault Detection and Diagnosis)
►▼ Show Figures

Figure 1

17 pages, 1230 KiB  
Article
Tailored Quantum Alternating Operator Ansätzes for Circuit Fault Diagnostics
by Hannes Leipold, Federico M. Spedalieri and Eleanor Rieffel
Algorithms 2022, 15(10), 356; https://doi.org/10.3390/a15100356 - 28 Sep 2022
Cited by 2 | Viewed by 1607
Abstract
The quantum alternating operator ansatz (QAOA) and constrained quantum annealing (CQA) restrict the evolution of a quantum system to remain in a constrained space, often with a dimension much smaller than the whole Hilbert space. A natural question when using quantum annealing or [...] Read more.
The quantum alternating operator ansatz (QAOA) and constrained quantum annealing (CQA) restrict the evolution of a quantum system to remain in a constrained space, often with a dimension much smaller than the whole Hilbert space. A natural question when using quantum annealing or a QAOA protocol to solve an optimization problem is to select an initial state for the wavefunction and what operators to use to evolve it into a solution state. In this work, we construct several ansatzes tailored to solve the combinational circuit fault diagnostic (CCFD) problem in different subspaces related to the structure of the problem, including superpolynomially smaller subspaces than the whole Hilbert space. We introduce a family of dense and highly connected circuits that include small instances but can be scaled to larger sizes as a useful collection of circuits for comparing different quantum algorithms. We compare the different ansätzes on instances randomly generated from this family under different parameter selection methods. The results support that ansätzes more closely tailored to exploiting the structure of the underlying optimization problems can have better performance than more generic ansätzes. Full article
(This article belongs to the Special Issue Artificial Intelligence for Fault Detection and Diagnosis)
►▼ Show Figures

Figure 1

19 pages, 2519 KiB  
Article
A Cost-Efficient MCSA-Based Fault Diagnostic Framework for SCIM at Low-Load Conditions
by Chibuzo Nwabufo Okwuosa, Ugochukwu Ejike Akpudo and Jang-Wook Hur
Algorithms 2022, 15(6), 212; https://doi.org/10.3390/a15060212 - 16 Jun 2022
Cited by 11 | Viewed by 2728
Abstract
In industry, electric motors such as the squirrel cage induction motor (SCIM) generate motive power and are particularly popular due to their low acquisition cost, strength, and robustness. Along with these benefits, they have minimal maintenance costs and can run for extended periods [...] Read more.
In industry, electric motors such as the squirrel cage induction motor (SCIM) generate motive power and are particularly popular due to their low acquisition cost, strength, and robustness. Along with these benefits, they have minimal maintenance costs and can run for extended periods before requiring repair and/or maintenance. Early fault detection in SCIMs, especially at low-load conditions, further helps minimize maintenance costs and mitigate abrupt equipment failure when loading is increased. Recent research on these devices is focused on fault/failure diagnostics with the aim of reducing downtime, minimizing costs, and increasing utility and productivity. Data-driven predictive maintenance offers a reliable avenue for intelligent monitoring whereby signals generated by the equipment are harnessed for fault detection and isolation (FDI). Particularly, motor current signature analysis (MCSA) provides a reliable avenue for extracting and/or exploiting discriminant information from signals for FDI and/or fault diagnosis. This study presents a fault diagnostic framework that exploits underlying spectral characteristics following MCSA and intelligent classification for fault diagnosis based on extracted spectral features. Results show that the extracted features reflect induction motor fault conditions with significant diagnostic performance (minimal false alarm rate) from intelligent models, out of which the random forest (RF) classifier was the most accurate, with an accuracy of 79.25%. Further assessment of the models showed that RF had the highest computational cost of 3.66 s, while NBC had the lowest at 0.003 s. Other significant empirical assessments were conducted, and the results support the validity of the proposed FDI technique. Full article
(This article belongs to the Special Issue Artificial Intelligence for Fault Detection and Diagnosis)
►▼ Show Figures

Figure 1

19 pages, 3587 KiB  
Article
Lessons for Data-Driven Modelling from Harmonics in the Norwegian Grid
by Volker Hoffmann, Bendik Nybakk Torsæter, Gjert Hovland Rosenlund and Christian Andre Andresen
Algorithms 2022, 15(6), 188; https://doi.org/10.3390/a15060188 - 31 May 2022
Cited by 1 | Viewed by 2352
Abstract
With the advancing integration of fluctuating renewables, a more dynamic demand-side, and a grid running closer to its operational limits, future power system operators require new tools to anticipate unwanted events. Advances in machine learning and availability of data suggest great potential in [...] Read more.
With the advancing integration of fluctuating renewables, a more dynamic demand-side, and a grid running closer to its operational limits, future power system operators require new tools to anticipate unwanted events. Advances in machine learning and availability of data suggest great potential in using data-driven approaches, but these will only ever be as good as the data they are based on. To lay the ground-work for future data-driven modelling, we establish a baseline state by analysing the statistical distribution of voltage measurements from three sites in the Norwegian power grid (22, 66, and 300 kV). Measurements span four years, are line and phase voltages, are cycle-by-cycle, and include all (even and odd) harmonics up to the 96 order. They are based on four years of historical data from three Elspec Power Quality Analyzers (corresponding to one trillion samples), which we have extracted, processed, and analyzed. We find that: (i) the distribution of harmonics depends on phase and voltage level; (ii) there is little power beyond the 13 harmonic; (iii) there is temporal clumping of extreme values; and (iv) there is seasonality on different time-scales. For machine learning based modelling these findings suggest that: (i) models should be trained in two steps (first with data from all sites, then adapted to site-level); (ii) including harmonics beyond the 13 is unlikely to increase model performance, and that modelling should include features that (iii) encode the state of the grid, as well as (iv) seasonality. Full article
(This article belongs to the Special Issue Artificial Intelligence for Fault Detection and Diagnosis)
►▼ Show Figures

Figure 1

19 pages, 5853 KiB  
Article
A Data-Driven Fault Tree for a Time Causality Analysis in an Aging System
by Kerelous Waghen and Mohamed-Salah Ouali
Algorithms 2022, 15(6), 178; https://doi.org/10.3390/a15060178 - 24 May 2022
Cited by 4 | Viewed by 2122
Abstract
This paper develops a data-driven fault tree methodology that addresses the problem of the fault prognosis of an aging system based on an interpretable time causality analysis model. The model merges the concepts of knowledge discovery in the dataset and fault tree to [...] Read more.
This paper develops a data-driven fault tree methodology that addresses the problem of the fault prognosis of an aging system based on an interpretable time causality analysis model. The model merges the concepts of knowledge discovery in the dataset and fault tree to interpret the effect of aging on the fault causality structure over time. At periodic intervals, the model captures the cause–effect relations in the form of interpretable logic trees, then represents them in one fault tree model that reflects the changes in the fault causality structure over time due to the system aging. The proposed model provides a prognosis of the probability for fault occurrence using a set of extracted causality rules that combine the discovered root causes over time in a bottom-up manner. The well-known NASA turbofan engine dataset is used as an illustrative example of the proposed methodology. Full article
(This article belongs to the Special Issue Artificial Intelligence for Fault Detection and Diagnosis)
►▼ Show Figures

Figure 1

18 pages, 4363 KiB  
Article
Machine Learning-Based Monitoring of DC-DC Converters in Photovoltaic Applications
by Marco Bindi, Fabio Corti, Igor Aizenberg, Francesco Grasso, Gabriele Maria Lozito, Antonio Luchetta, Maria Cristina Piccirilli and Alberto Reatti
Algorithms 2022, 15(3), 74; https://doi.org/10.3390/a15030074 - 23 Feb 2022
Cited by 29 | Viewed by 5063
Abstract
In this paper, a monitoring method for DC-DC converters in photovoltaic applications is presented. The primary goal is to prevent catastrophic failures by detecting malfunctioning conditions during the operation of the electrical system. The proposed prognostic procedure is based on machine learning techniques [...] Read more.
In this paper, a monitoring method for DC-DC converters in photovoltaic applications is presented. The primary goal is to prevent catastrophic failures by detecting malfunctioning conditions during the operation of the electrical system. The proposed prognostic procedure is based on machine learning techniques and focuses on the variations of passive components with respect to their nominal range. A theoretical study is proposed to choose the best measurements for the prognostic analysis and adapt the monitoring method to a photovoltaic system. In order to facilitate this study, a graphical assessment of testability is presented, and the effects of the variable solar irradiance on the selected measurements are also considered from a graphical point of view. The main technique presented in this paper to identify the malfunction conditions is based on a Multilayer neural network with Multi-Valued Neurons. The performances of this classifier applied on a Zeta converter are compared to those of a Support Vector Machine algorithm. The simulations carried out in the Simulink environment show a classification rate higher than 90%, and this means that the monitoring method allows the identification of problems in the initial phases, thus guaranteeing the possibility to change the work set-up and organize maintenance operations for DC-DC converters. Full article
(This article belongs to the Special Issue Artificial Intelligence for Fault Detection and Diagnosis)
►▼ Show Figures

Figure 1

Review

Jump to: Research

24 pages, 2504 KiB  
Review
A Survey on Fault Diagnosis of Rolling Bearings
by Bo Peng, Ying Bi, Bing Xue, Mengjie Zhang and Shuting Wan
Algorithms 2022, 15(10), 347; https://doi.org/10.3390/a15100347 - 26 Sep 2022
Cited by 43 | Viewed by 6102
Abstract
The failure of a rolling bearing may cause the shutdown of mechanical equipment and even induce catastrophic accidents, resulting in tremendous economic losses and a severely negative impact on society. Fault diagnosis of rolling bearings becomes an important topic with much attention from [...] Read more.
The failure of a rolling bearing may cause the shutdown of mechanical equipment and even induce catastrophic accidents, resulting in tremendous economic losses and a severely negative impact on society. Fault diagnosis of rolling bearings becomes an important topic with much attention from researchers and industrial pioneers. There are an increasing number of publications on this topic. However, there is a lack of a comprehensive survey of existing works from the perspectives of fault detection and fault type recognition in rolling bearings using vibration signals. Therefore, this paper reviews recent fault detection and fault type recognition methods using vibration signals. First, it provides an overview of fault diagnosis of rolling bearings and typical fault types. Then, existing fault diagnosis methods are categorized into fault detection methods and fault type recognition methods, which are separately revised and discussed. Finally, a summary of existing datasets, limitations/challenges of existing methods, and future directions are presented to provide more guidance for researchers who are interested in this field. Overall, this survey paper conducts a review and analysis of the methods used to diagnose rolling bearing faults and provide comprehensive guidance for researchers in this field. Full article
(This article belongs to the Special Issue Artificial Intelligence for Fault Detection and Diagnosis)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-9
Back to TopTop