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Advances in Machine Fault Diagnosis

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

Deadline for manuscript submissions: closed (31 March 2021) | Viewed by 32141

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Dr. Toomas Vaimann

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Guest Editor

Department of Electrical Power Engineering and Mechatronics, Tallinn University of Technology, 19086 Tallinn, Estonia
Interests: electrical machines and diagnostics of electrical machines
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Grover Zurita Villarroel

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Guest Editor

Laboratory of Industrial Technology Innovation and robotics, Universidad Privada Boliviana, 3967 Casilla, Cochabamba, Bolivia
Interests: vibration analysis; digital signal processing; machine diagnosis modal analysis; condition-based maintenance; machine learning
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Anders Brandt

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Guest Editor

Department of Technology and Innovation, University of Southern Denmark, Campusvej 55, 5230 Odense, Denmark
Interests: structural dynamics; structural health monitoring; vibration condition monitoring

Special Issue Information

Dear Colleagues:

Research on machine fault diagnosis (MFD) methods is receving significant attention in academia and industry due to the importance of identifying underlying causes of machine faults. The overall objective of MFD methods is to develop an effective diagnosis procedure. Recent methodological advances permit compressive MFD, providing detailed information essential for the prevention of future machine failures.

Some of the most promising approaches for the continuous advancement of fault detection and diagnosis technologies are: advanced digital signal processing, vibration-based condition monitoring, modal and operational mode analysis, neural network analysis, and machine learning.

Artificial intelligence (AI) has become one of the most transformative technological revolutions since, e.g., the invention of the steam or electric engines. Robustness, precision automated (online) learning, and the capacity to handle complex data are some of AI’s attributes that hold significant potential for MFD. In hand with the Internet of Things (IoT) and cloud computing, the emerging AI-based diagnostic methods are proving themselves to be powerful tools for the future.

The main objective of this Special Issue is to gather state-of-the-art of the research contributing recent advances in machine fault diagnosis and, hopefully, to outline future research directions in the field.

Dr. Toomas Vaimann
Prof. Dr. Grover Zurita Villarroel
Prof. Dr. Anders Brandt
Guest Editors

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Keywords

Measurement and signal processing
Vibration based condition monitoring
Modal and operational modal analysis
Active vibration control
Vibro-acoustics modelling and prediction Prognostics and health management (PHM)
Vibration-acoustic based structural health monitoring
Neural networks for machine diagnosis
Artificial intelligence for machine diagnosis
Deep learning techniques for fault detection and diagnosis
Development of measurement systems for machine diagnosis
Mathematical modelling for machine diagnostics

Published Papers (12 papers)

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Editorial

Jump to: Research, Review

5 pages, 181 KiB

Open AccessEditorial

Advances in Machine Fault Diagnosis

by Toomas Vaimann

Appl. Sci. 2021, 11(16), 7348; https://doi.org/10.3390/app11167348 - 10 Aug 2021

Cited by 2 | Viewed by 1249

Abstract

The growing need for intelligent machines, the outreach for more efficient use of the machines in industry, and the development of Industry 4 [...] Full article

(This article belongs to the Special Issue Advances in Machine Fault Diagnosis)

Research

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16 pages, 3553 KiB

Open AccessArticle

Analysis of the Frequency Interaction of the Turbine Block in the Stand for the Magnitude of the Error in Measuring the Turbine’s Power

by Anton Petrochenkov, Aleksey Sal’nikov, Sergey Bochkarev and Pavel Ilyushin

Appl. Sci. 2021, 11(9), 4149; https://doi.org/10.3390/app11094149 - 01 May 2021

Cited by 2 | Viewed by 1320

Abstract

An algorithm for constructing a dynamic analysis during the formation of a wave field of stand for testing turbines and the effect of the frequency interaction of the stand’s elements on the measurement of its magnitude is described. The research algorithm involves the use of theoretical solutions of nonlinear wave processes using linear oscillations, refined by experiments. The diagnostic model can determine the technical condition of the stand’s elements and also determine the causes of the discrepancies between the calculated and measured turbine power values. To clarify the stiffness coefficients between the stand’s elements, a modal analysis was used to obtain the range of their changes depending on the external dynamic load, which made it possible to assess the impact of changes in the frequency interaction conditions on the turbine power measurement at different test modes. The conditions for amplifying the amplitude of oscillations at their eigenfrequencies are obtained, and the value of the possible deviation of the expected power value at its measurement for specific modes of the turbine is calculated. The algorithm allows to estimate the dynamic state of the stand-in different research modes of turbines and give recommendations for reducing the level of frequency interaction. Full article

(This article belongs to the Special Issue Advances in Machine Fault Diagnosis)

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17 pages, 5982 KiB

Open AccessArticle

Transient Modeling and Recovery of Non-Stationary Fault Signature for Condition Monitoring of Induction Motors

by Bilal Asad, Toomas Vaimann, Anouar Belahcen, Ants Kallaste, Anton Rassõlkin, Payam Shams Ghafarokhi and Karolina Kudelina

Appl. Sci. 2021, 11(6), 2806; https://doi.org/10.3390/app11062806 - 21 Mar 2021

Cited by 11 | Viewed by 2185

Abstract

This paper presents the modeling and the broken rotor bar fault diagnostics by time–frequency analysis of the motor current under an extended startup transient time. The transient current-based nonstationary signal is retrieved and investigated for its time–frequency response to segregate the rotor faults and spatial harmonics. For studying the effect of reduced voltage on various parameters and the theoretical definition of the fault patterns, the winding function analysis (WFA)-based model is presented first. Moreover, an algorithm to improve the spectrum legibility is proposed. It is shown that by efficient utilization of the attenuation filter and consideration of the area containing the maximum power spectral density, the diagnostic algorithm gives promising results. The results are based on the machine’s analytical model and the measurements taken from the laboratory setup. Full article

(This article belongs to the Special Issue Advances in Machine Fault Diagnosis)

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11 pages, 3803 KiB

Open AccessArticle

Transfer Learning-Based Fault Diagnosis under Data Deficiency

by Seong Hee Cho, Seokgoo Kim and Joo-Ho Choi

Appl. Sci. 2020, 10(21), 7768; https://doi.org/10.3390/app10217768 - 03 Nov 2020

Cited by 14 | Viewed by 3064

Abstract

In the fault diagnosis study, data deficiency, meaning that the fault data for the training are scarce, is often encountered, and it may deteriorate the performance of the fault diagnosis greatly. To solve this issue, the transfer learning (TL) approach is employed to exploit the neural network (NN) trained in another (source) domain where enough fault data are available in order to improve the NN performance of the real (target) domain. While there have been similar attempts of TL in the literature to solve the imbalance issue, they were about the sample imbalance between the source and target domain, whereas the present study considers the imbalance between the normal and fault data. To illustrate this, normal and fault datasets are acquired from the linear motion guide, in which the data at high and low speeds represent the real operation (target) and maintenance inspection (source), respectively. The effect of data deficiency is studied by reducing the number of fault data in the target domain, and comparing the performance of TL, which exploits the knowledge of the source domain and the ordinary machine learning (ML) approach without it. By examining the accuracy of the fault diagnosis as a function of imbalance ratio, it is found that the lower bound and interquartile range (IQR) of the accuracy are improved greatly by employing the TL approach. Therefore, it can be concluded that TL is truly more effective than the ordinary ML when there is a large imbalance between the fault and normal data, such as smaller than 0.1. Full article

(This article belongs to the Special Issue Advances in Machine Fault Diagnosis)

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14 pages, 7275 KiB

Open AccessArticle

Feature Extraction for Bearing Fault Detection Using Wavelet Packet Energy and Fast Kurtogram Analysis

by Xiaojun Zhang, Jirui Zhu, Yaqi Wu, Dong Zhen and Minglu Zhang

Appl. Sci. 2020, 10(21), 7715; https://doi.org/10.3390/app10217715 - 31 Oct 2020

Cited by 15 | Viewed by 1954

Abstract

An integrated method for fault detection of bearing using wavelet packet energy (WPE) and fast kurtogram (FK) is proposed. The method consists of three stages. Firstly, several commonly used wavelet functions were compared to select the appropriate wavelet function for the application of WPE. Then the analyzed signal is decomposed using WPE and the energy of each decomposed signal is calculated and selected for signal reconstruction. Secondly, the reconstructed signal is analyzed by FK to select the best central frequency and bandwidth for the band-pass filter. Finally, the filtered signal is processed using the squared envelope frequency spectrum and compared with the theoretical fault characteristic frequency for fault feature extraction. The procedure and performance of the proposed approach are illustrated and estimated by the simulation analysis, proving that the proposed method can effectively extract the weak transients. Moreover, the analysis results of gearbox bearing and rolling bearing cases show that the proposed method can provide more accurate fault features compared with the individual FK method. Full article

(This article belongs to the Special Issue Advances in Machine Fault Diagnosis)

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21 pages, 9616 KiB

Open AccessFeature PaperArticle

Exploiting Generative Adversarial Networks as an Oversampling Method for Fault Diagnosis of an Industrial Robotic Manipulator

by Ziqiang Pu, Diego Cabrera, René-Vinicio Sánchez, Mariela Cerrada, Chuan Li and José Valente de Oliveira

Appl. Sci. 2020, 10(21), 7712; https://doi.org/10.3390/app10217712 - 31 Oct 2020

Cited by 14 | Viewed by 2179

Abstract

Data-driven machine learning techniques play an important role in fault diagnosis, safety, and maintenance of the industrial robotic manipulator. However, these methods require data that, more often that not, are hard to obtain, especially data collected from fault condition states and, without enough and appropriated (balanced) data, no acceptable performance should be expected. Generative adversarial networks (GAN) are receiving a significant interest, especially in the image analysis field due to their outstanding generative capabilities. This paper investigates whether or not GAN can be used as an oversampling tool to compensate for an unbalanced data set in an industrial manipulator fault diagnosis task. A comprehensive empirical analysis is performed taking into account six different scenarios for mitigating the unbalanced data, including classical under and oversampling (SMOTE) methods. In all of these, a wavelet packet transform is used for feature generation while a random forest is used for fault classification. Aspects such as loss functions, learning curves, random input distributions, data shuffling, and initial conditions were also considered. A non-parametric statistical test of hypotheses reveals that all GAN based fault-diagnosis outperforms both under and oversampling classical methods while, within GAN based methods, an average accuracy difference as high as 1.68% can be achieved. Full article

(This article belongs to the Special Issue Advances in Machine Fault Diagnosis)

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15 pages, 2229 KiB

Open AccessFeature PaperArticle

The Cluster Computation-Based Hybrid FEM–Analytical Model of Induction Motor for Fault Diagnostics

by Bilal Asad, Toomas Vaimann, Anouar Belahcen, Ants Kallaste, Anton Rassõlkin and M. Naveed Iqbal

Appl. Sci. 2020, 10(21), 7572; https://doi.org/10.3390/app10217572 - 27 Oct 2020

Cited by 19 | Viewed by 2185

Abstract

This paper presents a hybrid finite element method (FEM)–analytical model of a three-phase squirrel cage induction motor solved using parallel processing for reducing the simulation time. The growing development in artificial intelligence (AI) techniques can lead towards more reliable diagnostic algorithms. The biggest challenge for AI techniques is that they need a big amount of data under various conditions to train them. These data are difficult to obtain from the industries because they contain low numbers of possible faulty cases, as well as from laboratories because a limited number of motors can be broken for testing purposes. The only feasible solution is mathematical models, which in the long run can become part of advanced diagnostic techniques. The benefits of analytical and FEM models for their speed and accuracy respectively can be exploited by making a hybrid model. Moreover, the concept of cloud computing can be utilized to reduce the simulation time of the FEM model. In this paper, a hybrid model being solved on multiple processors in a parallel fashion is presented. The results depict that by dividing the rotor steps among several processors working in parallel, the simulation time reduces considerably. The simulation results under healthy and broken rotor bar cases are compared with those taken from a laboratory setup for validation. Full article

(This article belongs to the Special Issue Advances in Machine Fault Diagnosis)

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17 pages, 2513 KiB

Open AccessArticle

Degradation State Recognition of Piston Pump Based on ICEEMDAN and XGBoost

by Rui Guo, Zhiqian Zhao, Tao Wang, Guangheng Liu, Jingyi Zhao and Dianrong Gao

Appl. Sci. 2020, 10(18), 6593; https://doi.org/10.3390/app10186593 - 21 Sep 2020

Cited by 29 | Viewed by 3127

Abstract

Under different degradation conditions, the complexity of natural oscillation of the piston pump will change. Given the difference of the characteristic values of the vibration signal under different degradation states, this paper presents a degradation state recognition method based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and eXtreme gradient boosting (XGBoost) to improve the accuracy of state recognition. Firstly, ICEEMDAN is proposed to alleviate the mode mixing phenomenon, which decomposes the vibration signal and obtain the intrinsic mode functions (IMFs) with less noise and more physical meaning, and subsequently the optimal IMF is found by using the correlation coefficient method. Then, the time domain, frequency domain, and entropy of the effective IMF are calculated, and the new characteristic values which can represent the degradation state are selected by principal component analysis (PCA) that it realizes dimension reduction. Finally, the above-mentioned characteristic indexes are used as the input of the XGBoost algorithm to achieve the recognition of the degradation state. In this paper, the vibration signals of four different degradation states are generated and analyzed through the piston pump slipper degradation experiment. By comparing the proposed method with different state recognition algorithms, it can be seen that the method based on ICEEMDAN and XGBoost is accurate and efficient, the average accuracy rate can reach more than 99%. Therefore, this method can more accurately describe the degradation state of the piston pump and has a highly practical application value. Full article

(This article belongs to the Special Issue Advances in Machine Fault Diagnosis)

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19 pages, 5310 KiB

Open AccessArticle

Planetary Gearbox Fault Diagnosis Based on ICEEMD-Time-Frequency Information Entropy and VPMCD

by Yihan Wang, Zhonghui Fan, Hongmei Liu and Xin Gao

Appl. Sci. 2020, 10(18), 6376; https://doi.org/10.3390/app10186376 - 13 Sep 2020

Cited by 7 | Viewed by 1877

Abstract

Planetary gearboxes are more and more widely used in large and complex construction machinery such as those used in aviation, aerospace fields, and so on. However, the movement of the gear is a typical complex motion and is often under variable conditions in real environments, which may make vibration signals of planetary gearboxes nonlinear and nonstationary. It is more difficult and complex to achieve fault diagnosis than to fix the axis gearboxes effectively. A fault diagnosis method for planetary gearboxes based on improved complementary ensemble empirical mode decomposition (ICEEMD)-time-frequency information entropy and variable predictive model-based class discriminate (VPMCD) is proposed in this paper. First, the vibration signal of planetary gearboxes is decomposed into several intrinsic mode functions (IMFs) by using the ICEEMD algorithm, which is used to determine the noise component by using the magnitude of the entropy and to remove the noise components. Then, the time-frequency information entropy of intrinsic modal function under the new decomposition is calculated and regarded as the characteristic matrix. Finally, the fault mode is classified by the VPMCD method. The experimental results demonstrate that the method proposed in this paper can not only solve the fault diagnosis of planetary gearboxes under different operation conditions, but can also be used for fault diagnosis under variable operation conditions. Simultaneously, the proposed method is superior to the wavelet entropy method and variational mode decomposition (VMD)-time-frequency information entropy. Full article

(This article belongs to the Special Issue Advances in Machine Fault Diagnosis)

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21 pages, 5821 KiB

Open AccessArticle

Intelligent Fault Diagnosis of Rotating Machinery Using Hierarchical Lempel-Ziv Complexity

by Bing Han, Shun Wang, Qingqi Zhu, Xiaohui Yang and Yongbo Li

Appl. Sci. 2020, 10(12), 4221; https://doi.org/10.3390/app10124221 - 19 Jun 2020

Cited by 13 | Viewed by 2312

Abstract

The health condition monitoring of rotating machinery can avoid the disastrous failure and guarantee the safe operation. The vibration-based fault diagnosis shows the most attractive character for fault diagnosis of rotating machinery (FDRM). Recently, Lempel-Ziv complexity (LZC) has been investigated as an effective tool for FDRM. However, the LZC only performs single-scale analysis, which is not suitable to extract the fault features embedded in vibrational signal over multiple scales. In this paper, a novel complexity analysis algorithm, called hierarchical Lempel-Ziv complexity (HLZC), was developed to extract the fault characteristics of rotating machinery. The proposed HLZC method considers the fault information hidden in both low-frequency and high-frequency components, resulting in a more accurate fault feature extraction. The superiority of the proposed HLZC method in detecting the periodical impulses was validated by using simulated signals. Meanwhile, two experimental signals were utilized to prove the effectiveness of the proposed HLZC method in extracting fault information. Results show that the proposed HLZC method had the best diagnosing performance compared with LZC and multi-scale Lempel-Ziv complexity methods. Full article

(This article belongs to the Special Issue Advances in Machine Fault Diagnosis)

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21 pages, 24920 KiB

Open AccessArticle

Fatigue Life Analysis of Ball Bearings and a Shaft System Considering the Combined Bearing Preload and Angular Misalignment

by Yu Zhang, Mengqi Zhang, Yawen Wang and Liyang Xie

Appl. Sci. 2020, 10(8), 2750; https://doi.org/10.3390/app10082750 - 16 Apr 2020

Cited by 26 | Viewed by 4087

Abstract

Bearing preload significantly affects the running performance of a shaft-bearing system including the fatigue life, wear, and stiffness. Due to the mounting error, the bearing rings are often angularly misaligned. The effects of the combined bearing preload and angular misalignment on the fatigue life of ball bearings and a shaft-bearing system are analyzed in this paper. The contact force distribution of angular contact ball bearings in the shaft-bearing system is investigated based on the system model. The system model includes the bearing model, and the shaft model is verified by comparing with the manufacturer’s manual and the results from other theoretical models, with the difference between the results from the present bearing model and manufacturer manual within 3%. The global optimization method is used to replace the Newton–Raphson algorithm to solve the ball elements’ displacements and friction coefficients, which improves the computation efficiency of the system model. The fatigue life of each bearing is evaluated with the consideration of the two preload methods and two angular misalignment cases. The fatigue life results show that the system life at the optimal angular misalignment is more than 1.5 times that without angular misalignment at the low preload value, and this ratio decreases as the preload value increases. The optimal angular misalignment of both the shaft-bearing system and the misaligned bearing is not always consistent, which depends on the preload value and bearing life. Both the constant-displacement preload and constant-force preload do not cause a significant difference in the highest system life. The different misaligned bearings can lead to different highest system lives as the preload value is low. Full article

(This article belongs to the Special Issue Advances in Machine Fault Diagnosis)

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Review

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19 pages, 2554 KiB

Open AccessReview

Trends and Challenges in Intelligent Condition Monitoring of Electrical Machines Using Machine Learning

by Karolina Kudelina, Toomas Vaimann, Bilal Asad, Anton Rassõlkin, Ants Kallaste and Galina Demidova

Appl. Sci. 2021, 11(6), 2761; https://doi.org/10.3390/app11062761 - 19 Mar 2021

Cited by 55 | Viewed by 4951

Abstract

A review of the fault diagnostic techniques based on machine is presented in this paper. As the world is moving towards industry 4.0 standards, the problems of limited computational power and available memory are decreasing day by day. A significant amount of data with a variety of faulty conditions of electrical machines working under different environments can be handled remotely using cloud computation. Moreover, the mathematical models of electrical machines can be utilized for the training of AI algorithms. This is true because the collection of big data is a challenging task for the industry and laboratory because of related limited resources. In this paper, some promising machine learning-based diagnostic techniques are presented in the perspective of their attributes. Full article

(This article belongs to the Special Issue Advances in Machine Fault Diagnosis)

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