Special Issue "Machine Fault Diagnostics and Prognostics"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Mechanical Engineering".

Deadline for manuscript submissions: 15 March 2020.

Special Issue Editors

Guest Editor
Prof. Dr. Jong-Myon Kim

School of IT Convergence, University of Ulsan, South Korea
Website | E-Mail
Interests: machine fault diagnosis; health prognosis; condition monitoring; deep learning; embedded systems
Guest Editor
Prof. Dr. Cheol Hong Kim

School of Electronics and Computer Engineering, Chonnam National University, 77 Yongbong-ro, Yongbong-dong, Buk-gu, Kwangju, South Korea
E-Mail
Interests: fault diagnostics; health prognosis; mobile system design; machine learning; edge computing; embedded system

Special Issue Information

Dear Colleagues,

We are currently living through the fourth Industrial revolution, which is riding on the wave of cutting-edge technologies in computing, artificial intelligence, and communications. The past decade has witnessed incredible advances in the field of artificial intelligence (AI) and has seen massive proliferation of cloud computing technologies. These technological advances have further fueled the integration of the cyber and the physical worlds, with intelligence and autonomy as its key hallmarks, which would lead to more reliable, productive, and efficient industries and businesses in the future.

Machines and mechanical structures in industries undergo inevitable degradation and loss of performance during operation. The timely diagnosis of symptoms of their degradation and a reliable estimate of their future health condition are essential for Industrial productivity and reliability. Models constructed from historical measurement data using AI techniques have shown great promise in fault diagnosis and prognosis of industrial equipment. AI-based techniques are poised to gain even more significance in the future as huge amounts of measurement data are to be available for decision making due to the deployment of the internet-of-things and cloud-based technologies for condition-based maintenance (CBM).

This Special Issue will focus on the topic of fault diagnosis and prognosis of industrial equipment and mechanical structures. We invite researchers and practicing engineers to contribute original research articles that discuss issues related but not limited to condition-based monitoring, fault diagnosis and prognosis of industrial machines and mechanical structures, diagnostic and prognostic techniques based on AI, such as deep learning, transfer learning, and neuro-fuzzy inference techniques, AI-based solutions that are explainable, solutions utilizing the Internet. of Things, cloud computing, cyber physical systems, and machine-to-machine interfaces and paradigms for fault diagnosis and prognosis in the context of Industry 4.0. We would also welcome review articles that capture the current state-of-the art and outline future areas of research in the fields relevant to this Special Issue.

Prof. Dr. Jong-Myon Kim
Prof. Dr. Cheol Hong Kim
Guest Editors

Manuscript Submission Information

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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. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

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Keywords

  • Condition monitoring
  • Fault diagnosis
  • Health prognosis
  • Remaining useful life
  • Deep learning
  • Artificial intelligence
  • Condition-based maintenance
  • Cyber physical systems

Published Papers (8 papers)

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Research

Open AccessArticle
Improved Hierarchical Adaptive Deep Belief Network for Bearing Fault Diagnosis
Appl. Sci. 2019, 9(16), 3374; https://doi.org/10.3390/app9163374 (registering DOI)
Received: 9 July 2019 / Revised: 6 August 2019 / Accepted: 13 August 2019 / Published: 16 August 2019
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Abstract
Rotating machinery plays a vital role in modern mechanical systems. Effective state monitoring of a rotary machine is important to guarantee its safe operation and prevent accidents. Traditional bearing fault diagnosis techniques rely on manual feature extraction, which in turn relies on complex [...] Read more.
Rotating machinery plays a vital role in modern mechanical systems. Effective state monitoring of a rotary machine is important to guarantee its safe operation and prevent accidents. Traditional bearing fault diagnosis techniques rely on manual feature extraction, which in turn relies on complex signal processing and rich professional experience. The collected bearing signals are invariably complicated and unstable. Deep learning can voluntarily learn representative features without a large amount of prior knowledge, thus becoming a significant breakthrough in mechanical fault diagnosis. A new method for bearing fault diagnosis, called improved hierarchical adaptive deep belief network (DBN), which is optimized by Nesterov momentum (NM), is presented in this research. The frequency spectrum is used as inputs for feature learning. Then, a learning rate adjustment strategy is applied to adaptively select the descending step length during gradient updating, combined with NM. The developed method is validated by bearing vibration signals. In comparison to support vector machine and the conventional DBN, the raised approach exhibits a more satisfactory performance in bearing fault type and degree diagnosis. It can steadily and effectively improve convergence during model training and enhance the generalizability of DBN. Full article
(This article belongs to the Special Issue Machine Fault Diagnostics and Prognostics)
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Open AccessArticle
Fault Parameter Estimation Using Adaptive Fuzzy Fading Kalman Filter
Appl. Sci. 2019, 9(16), 3329; https://doi.org/10.3390/app9163329
Received: 30 May 2019 / Revised: 26 July 2019 / Accepted: 8 August 2019 / Published: 13 August 2019
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Abstract
Early detection and diagnosis of wind turbine faults is critical for applying a possible maintenance and control strategy to avoid catastrophic incidents. This paper presents a novel method to estimate the parameter of faults in a wind turbine. In this work, the estimation [...] Read more.
Early detection and diagnosis of wind turbine faults is critical for applying a possible maintenance and control strategy to avoid catastrophic incidents. This paper presents a novel method to estimate the parameter of faults in a wind turbine. In this work, the estimation of fault parameters is reformulated as the state estimation problem by augmenting the parameters as an additional state. The novelty of the proposed method lies in the use of an adaptive fuzzy fading algorithm for the adaptive Kalman filter so that the convergence property during the estimation of fault parameter can be improved. The performance of the proposed method is evaluated through a set of numerical simulations with both linear and non-linear models. Full article
(This article belongs to the Special Issue Machine Fault Diagnostics and Prognostics)
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Open AccessArticle
Spring Failure Analysis of Mining Vibrating Screens: Numerical and Experimental Studies
Appl. Sci. 2019, 9(16), 3224; https://doi.org/10.3390/app9163224
Received: 25 July 2019 / Revised: 5 August 2019 / Accepted: 6 August 2019 / Published: 7 August 2019
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Abstract
Spring failure is one of the critical causes of the structural damage and low screening efficiency of mining vibrating screens. Therefore, spring failure diagnosis is necessary to prompt maintenance for the safety and reliability of mining vibrating screens. In this paper, a spring [...] Read more.
Spring failure is one of the critical causes of the structural damage and low screening efficiency of mining vibrating screens. Therefore, spring failure diagnosis is necessary to prompt maintenance for the safety and reliability of mining vibrating screens. In this paper, a spring failure diagnosis approach is developed. A finite element model of mining vibrating screens is established. Simulations are carried out and the spring failure influence rules of spring failure on the dynamic characteristics of mining vibrating screens are obtained. These influence rules indicate that the amplitude variation coefficients (AVCs) of the four spring seats in the x, y, and z directions can reveal two kinds of single spring failure and four kinds of double spring failure, which are useful for diagnosing spring failure. Furthermore, experiments are conducted. Comparison analyses of the experimental results and simulation results indicate that the proposed approach is capable of revealing various kinds of spring failure. Therefore, this approach provides useful information for diagnosing spring failure and guiding technical staff to routinely maintain mining vibrating screens. Full article
(This article belongs to the Special Issue Machine Fault Diagnostics and Prognostics)
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Open AccessArticle
Effect of Multiple Factors on Identification and Diagnosis of Skidding Damage in Rolling Bearings under Time-Varying Slip Conditions
Appl. Sci. 2019, 9(15), 3033; https://doi.org/10.3390/app9153033
Received: 28 June 2019 / Revised: 23 July 2019 / Accepted: 25 July 2019 / Published: 27 July 2019
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Abstract
Skidding damage mechanism of rolling bearings is not clear, due to the influence of various coupling factors. To solve this problem, it is important to identify and diagnose skidding damage and study the vibration characteristics in rolling bearings. Based on Fast Fourier Transform [...] Read more.
Skidding damage mechanism of rolling bearings is not clear, due to the influence of various coupling factors. To solve this problem, it is important to identify and diagnose skidding damage and study the vibration characteristics in rolling bearings. Based on Fast Fourier Transform (FFT) and Discrete Wavelet Transform (DWT), vibration signals of rolling bearings are extracted and analyzed, and then the skidding damage of rolling bearings from multiple signals perspectives is identified. The relationship between the variation in the radial load, temperature, slip and the skidding damage of rolling bearings under time-varying slip conditions is analyzed comprehensively, and then the influence of different factors on bearing skidding damage is studied. The integrated analysis of the vibration, load, temperature, slip rate and other multivariate signals information shows the starting time of skidding damage. This research can be conducive to reduce vibration and prolong the life of rolling bearings. Full article
(This article belongs to the Special Issue Machine Fault Diagnostics and Prognostics)
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Open AccessArticle
CNN-Based Fault Localization Method Using Memory-Updated Patterns for Integration Test in an HiL Environment
Appl. Sci. 2019, 9(14), 2799; https://doi.org/10.3390/app9142799
Received: 31 May 2019 / Revised: 2 July 2019 / Accepted: 9 July 2019 / Published: 12 July 2019
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Abstract
Automotive electronic components are tested via hardware-in-the-loop (HiL) testing at the unit and integration test stages, according to ISO 26262. It is difficult to obtain debugging information from the HiL test because the simulator runs a black-box test automatically, depending on the scenario [...] Read more.
Automotive electronic components are tested via hardware-in-the-loop (HiL) testing at the unit and integration test stages, according to ISO 26262. It is difficult to obtain debugging information from the HiL test because the simulator runs a black-box test automatically, depending on the scenario in the test script. At this time, debugging information can be obtained in HiL tests, using memory-updated information, without the source code or the debugging tool. However, this method does not know when the fault occurred, and it is difficult to select the starting point of debugging if the execution flow of the software is not known. In this paper, we propose a fault-localization method using a pattern in which each memory address is updated in the HiL test. Via a sequential pattern-mining algorithm in the memory-updated information of the transferred unit tests, memory-updated patterns are extracted, and the system learns using a convolutional neural network. Applying the learned pattern in the memory-updated information of the integration test can determine the fault point from the normal pattern. The point of departure from the normal pattern is highlighted as a fault-occurrence time, and updated addresses are presented as fault candidates. We applied the proposed method to an HiL test of an OSEK/VDX-based electronic control unit. Through fault-injection testing, we could find the cause of faults by checking the average memory address of 3.28%, and we could present the point of fault occurrence with an average accuracy of 80%. Full article
(This article belongs to the Special Issue Machine Fault Diagnostics and Prognostics)
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Open AccessArticle
Adaptive Fuzzy-Based Fault-Tolerant Control of a Continuum Robotic System for Maxillary Sinus Surgery
Appl. Sci. 2019, 9(12), 2490; https://doi.org/10.3390/app9122490
Received: 7 May 2019 / Revised: 13 June 2019 / Accepted: 14 June 2019 / Published: 19 June 2019
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Abstract
Continuum robots represent a class of highly sensitive, multiple-degrees-of-freedom robots that are biologically inspired. Because of their flexibility and accuracy, these robots can be used in maxillary sinus surgery. The design of an effective procedure with high accuracy, reliability, robust fault diagnosis, and [...] Read more.
Continuum robots represent a class of highly sensitive, multiple-degrees-of-freedom robots that are biologically inspired. Because of their flexibility and accuracy, these robots can be used in maxillary sinus surgery. The design of an effective procedure with high accuracy, reliability, robust fault diagnosis, and fault-tolerant control for a surgical robot for the sinus is necessary to maintain the high performance and safety necessary for surgery on the maxillary sinus. Thus, a robust adaptive hybrid observation method using an adaptive, fuzzy auto regressive with exogenous input (ARX) Laguerre Takagi–Sugeno (T–S) fuzzy robust feedback linearization observer for a surgical robot is presented. To address the issues of system modeling, the fuzzy ARX-Laguerre technique is represented. In addition, a T–S fuzzy robust feedback linearization observer is applied to a fuzzy ARX-Laguerre to improve the accuracy of fault estimation, reliability, and robustness for the surgical robot in the presence of uncertainties. For fault-tolerant control in the presence of uncertainties and unknown conditions, an adaptive fuzzy observation-based feedback linearization technique is presented. The effectiveness of the proposed algorithm is tested with simulations. Experimental results show that the proposed method reduces the average position error from 35 mm to 2.45 mm in the presence of faults. Full article
(This article belongs to the Special Issue Machine Fault Diagnostics and Prognostics)
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Open AccessArticle
Data Driven Leakage Detection and Classification of a Boiler Tube
Appl. Sci. 2019, 9(12), 2450; https://doi.org/10.3390/app9122450
Received: 7 May 2019 / Revised: 5 June 2019 / Accepted: 13 June 2019 / Published: 15 June 2019
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Abstract
Boiler heat exchange in thermal power plants involves tubes to transfer heat from the fuel to the water. Boiler tube leakage can cause outages and huge power generation loss. Therefore, early detection of leaks in boiler tubes is necessary to avoid such accidents. [...] Read more.
Boiler heat exchange in thermal power plants involves tubes to transfer heat from the fuel to the water. Boiler tube leakage can cause outages and huge power generation loss. Therefore, early detection of leaks in boiler tubes is necessary to avoid such accidents. In this study, a boiler tube leak detection and classification mechanism was designed using wavelet packet transform (WPT) analysis of the acoustic emission (AE) signals acquired from the boiler tube and a fully connected deep neural network (FC-DNN). WPT analysis of the AE signals enabled the extraction of features associated with the different conditions of the boiler tube, that is, normal and leak conditions. The deep neural network (DNN) effectively explores the salient information from the wavelet packet features through a deep architecture instead of considering shallow networks, such as k-nearest neighbors (k-NN) and support vector machines (SVM). This enhances the classification performance of the leak identification and classification model developed. The proposed model yielded a 99.2 % average classification accuracy when tested with AE signals from the boiler tube. The experimental results prove the efficacy of the proposed model for boiler tube leak detection and classification. Full article
(This article belongs to the Special Issue Machine Fault Diagnostics and Prognostics)
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Open AccessArticle
A Novel Rolling Bearing Fault Diagnosis and Severity Analysis Method
Appl. Sci. 2019, 9(11), 2356; https://doi.org/10.3390/app9112356
Received: 26 May 2019 / Revised: 29 May 2019 / Accepted: 4 June 2019 / Published: 8 June 2019
PDF Full-text (2048 KB) | HTML Full-text | XML Full-text
Abstract
To improve the fault identification accuracy of rolling bearing and effectively analyze the fault severity, a novel rolling bearing fault diagnosis and severity analysis method based on the fast sample entropy, the wavelet packet energy entropy, and a multiclass relevance vector machine is [...] Read more.
To improve the fault identification accuracy of rolling bearing and effectively analyze the fault severity, a novel rolling bearing fault diagnosis and severity analysis method based on the fast sample entropy, the wavelet packet energy entropy, and a multiclass relevance vector machine is proposed in this paper. A fast sample entropy calculation method based on a kd tree is adopted to improve the real-time performance of fault detection in this paper. In view of the non-linearity and non-stationarity of the vibration signals, the vibration signal of the rolling bearing is decomposed into several sub-signals containing fault information by using a wavelet packet. Then, the energy entropy values of the sub-signals decomposed by the wavelet packet are calculated to generate the feature vectors for describing different fault types and severity levels of rolling bearings. The multiclass relevance vector machine modeled by the feature vectors of different fault types and severity levels is used to realize fault type identification and a fault severity analysis of the bearings. The proposed fault diagnosis and severity analysis method is fully evaluated by experiments. The experimental results demonstrate that the fault detection method based on the sample entropy can effectively detect rolling bearing failure. The fault feature extraction method based on the wavelet packet energy entropy can effectively extract the fault features of vibration signals and a multiclass relevance vector machine can identify the fault type and severity by means of the fault features contained in these signals. Compared with some existing bearing rolling fault diagnosis methods, the proposed method is excellent for fault diagnosis and severity analysis and improves the fault identification rate reaching as high as 99.47%. Full article
(This article belongs to the Special Issue Machine Fault Diagnostics and Prognostics)
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