Bearing Fault Detection and Diagnosis

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

Deadline for manuscript submissions: closed (30 January 2022) | Viewed by 64361

Special Issue Editor

Department of Electrical Engineering, Universidad de Valladolid, 47011 Valladolid, Spain
Interests: electric machines condition monitoring; power systems reliability and power quality; electric energy efficiency
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Bearings are an essential part of modern machinery, allowing more efficient operation, extending operating life, avoiding mechanical breakdown, and allowing the efficient transmission of power. However, the bearings are not free from failure. In fact, because of the demanding function they perform, they are one of the main headaches for maintenance engineers. For example, in induction motors, more than half of all failures are considered to be due to bearings.

It is logical, therefore, that a research effort is being made to develop procedures that will improve the detectability and diagnostic capacity of the various failures that can occur in the different types of equipment commonly used in a wide range of applications. Traditionally, vibrations have been the signal used in predictive maintenance of bearings, although there are also proposals such as the use of electric current, infrared thermography or axial flow, among others.

This Special Issue focuses on the topic of bearing fault diagnosis and diagnosis. Researchers are invited to contribute original research papers related to fault detection and diagnosis of bearings considering, but not limited to, different applications, signal processing techniques for fault detection, AI applications to diagnosis, condition-based monitoring, bearing lubrication condition, and remaining life prognosis. Solutions in the context of Industry 4.0 are welcome.

Dr. Oscar Duque-Perez
Guest Editor

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Keywords

  • Acoustic monitoring
  • Artificial intelligence-based methods
  • Bearing fault detection
  • Bearing diagnosis
  • Bearing prognosis
  • Big data feature learning
  • Data-based techniques
  • Deep learning
  • Digital signal processing
  • Feature extraction methods
  • Industrial Internet of Things
  • Intelligent sensors
  • Machine current signature analysis
  • Machine learning
  • Model-based techniques
  • Signal-based techniques
  • Statistical diagnosis methods
  • Stray flux monitoring
  • Vibration monitoring

Published Papers (22 papers)

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19 pages, 2374 KiB  
Article
Condition Monitoring Method for the Detection of Fault Graduality in Outer Race Bearing Based on Vibration-Current Fusion, Statistical Features and Neural Network
by Juan-Jose Saucedo-Dorantes, Israel Zamudio-Ramirez, Jonathan Cureno-Osornio, Roque Alfredo Osornio-Rios and Jose Alfonso Antonino-Daviu
Appl. Sci. 2021, 11(17), 8033; https://0-doi-org.brum.beds.ac.uk/10.3390/app11178033 - 30 Aug 2021
Cited by 18 | Viewed by 2355
Abstract
Bearings are the elements that allow the rotatory movement in induction motors, and the fault occurrence in these elements is due to excessive working conditions. In induction motors, electrical erosion remains the most common phenomenon that damages bearings, leading to incipient faults that [...] Read more.
Bearings are the elements that allow the rotatory movement in induction motors, and the fault occurrence in these elements is due to excessive working conditions. In induction motors, electrical erosion remains the most common phenomenon that damages bearings, leading to incipient faults that gradually increase to irreparable damages. Thus, condition monitoring strategies capable of assessing bearing fault severities are mandatory to overcome this critical issue. The contribution of this work lies in the proposal of a condition monitoring strategy that is focused on the analysis and identification of different fault severities of the outer race bearing fault in an induction motor. The proposed approach is supported by fusion information of different physical magnitudes and the use of Machine Learning and Artificial Intelligence. An important aspect of this proposal is the calculation of a hybrid-set of statistical features that are obtained to characterize vibration and stator current signals by its processing through domain analysis, i.e., time-domain and frequency-domain; also, the fusion of information of both signals by means of the Linear Discriminant Analysis is important due to the most discriminative and meaningful information is retained resulting in a high-performance condition characterization. Besides, a Neural Network-based classifier allows validating the effectiveness of fusion information from different physical magnitudes to face the diagnosis of multiple fault severities that appear in the bearing outer race. The method is validated under an experimental data set that includes information related to a healthy condition and five different severities that appear in the outer race of bearings. Full article
(This article belongs to the Special Issue Bearing Fault Detection and Diagnosis)
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28 pages, 9543 KiB  
Article
Convolutional Neural Networks for Automated Rolling Bearing Diagnostics in Induction Motors Based on Electromagnetic Signals
by Marcello Minervini, Maria Evelina Mognaschi, Paolo Di Barba and Lucia Frosini
Appl. Sci. 2021, 11(17), 7878; https://0-doi-org.brum.beds.ac.uk/10.3390/app11177878 - 26 Aug 2021
Cited by 15 | Viewed by 2062
Abstract
Bearing faults account for over 40% of induction motor faults, and for this reason, for several decades, much attention has been paid to their condition monitoring, through vibration measurements and, more recently, through electromagnetic signal analysis. Furthermore, in the last few years, research [...] Read more.
Bearing faults account for over 40% of induction motor faults, and for this reason, for several decades, much attention has been paid to their condition monitoring, through vibration measurements and, more recently, through electromagnetic signal analysis. Furthermore, in the last few years, research has been focused on evaluating deep learning algorithms for the automatic diagnosis of these faults. Therefore, the purpose of this study is to propose a novel procedure to automatically diagnose different types of bearing faults and load anomalies by means of the stator current and the external stray flux measured on the induction motor in which the bearings are installed. All the data were collected by performing experimental tests in the laboratory. Then, these data were processed to obtain images (scalograms and spectrograms), which were elaborated by a pre-trained Deep Convolutional Neural Network, modified through the transfer learning technique. The results demonstrated the ability of the electromagnetic signals, and in particular of the stray flux, to detect bearing faults and mechanical anomalies, in agreement with the recent literature. Moreover, the Convolutional Neural Network has been proven to be able to automatically discriminate bearing defects and with respect to the healthy condition. Full article
(This article belongs to the Special Issue Bearing Fault Detection and Diagnosis)
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18 pages, 4530 KiB  
Article
Fusion Method and Application of Several Source Vibration Fault Signal Spatio-Temporal Multi-Correlation
by Longhuan Cheng, Jiantao Lu, Shunming Li, Rui Ding, Kun Xu and Xianglian Li
Appl. Sci. 2021, 11(10), 4318; https://0-doi-org.brum.beds.ac.uk/10.3390/app11104318 - 11 May 2021
Cited by 13 | Viewed by 1777
Abstract
Combined with other signal processing methods, related algorithms are widely used in the diagnosis and identification of rotor faults. In order to solve the problem that the vibration signal of a single sensor is too single, a new multi-source vibration signal fusion method [...] Read more.
Combined with other signal processing methods, related algorithms are widely used in the diagnosis and identification of rotor faults. In order to solve the problem that the vibration signal of a single sensor is too single, a new multi-source vibration signal fusion method is proposed. This method explores the correlation between vibration sensors at different locations by using multiple cross-correlations of spatial locations. First, wavelet noise reduction and linear normalization are used to process the original data. Then, the signal energy correlation function between the sensors is established, and the adaptive weight is obtained. Finally, the data fusion result is obtained. Taking rotor bearing and gear failures at different speeds as an example, the data of three vibration sensors at different positions are fused using the spatio-temporal multiple correlation fusion method (STMF). Through the intelligent fault diagnosis method stacked auto encoder (SAE), compared with single sensor data, average weighted fusion data and neural network fusion data, STMF method can reach a diagnosis accuracy of more than 94% at 700 rpm, 900 rpm and 1100 rpm. It is concluded that the result of the STMF method is more effective and superior. Full article
(This article belongs to the Special Issue Bearing Fault Detection and Diagnosis)
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18 pages, 5504 KiB  
Article
Short-Time/-Angle Spectral Analysis for Vibration Monitoring of Bearing Failures under Variable Speed
by Edgar F. Sierra-Alonso, Julian Caicedo-Acosta, Álvaro Ángel Orozco Gutiérrez, Héctor F. Quintero and German Castellanos-Dominguez
Appl. Sci. 2021, 11(8), 3369; https://0-doi-org.brum.beds.ac.uk/10.3390/app11083369 - 09 Apr 2021
Cited by 8 | Viewed by 1911
Abstract
Vibration-condition monitoring aims to detect bearing damages of rotating machinery’s incipient failures mainly through time–frequency methods because of their efficient analysis of nonstationary signals. However, by having failures with impulse behavior, short-term events have a tendency to be diluted under variable-speed conditions, while [...] Read more.
Vibration-condition monitoring aims to detect bearing damages of rotating machinery’s incipient failures mainly through time–frequency methods because of their efficient analysis of nonstationary signals. However, by having failures with impulse behavior, short-term events have a tendency to be diluted under variable-speed conditions, while information on frequency changes tends to be lost. Here, we introduce an approach to highlighting bearing impulsive failures by measuring short-term spectral components to deal with variable-speed vibrations. The short-term estimator employs two sliding windows: a small one that measures the instantaneous amplitude level and tracks impulsive components and a large interval that evaluates the average background amplitude. Aiming to characterize cyclo-non-stationary processes with impulsive behavior, the emphasizing high-order-based estimator based on the principle of spectral entropy is introduced. For evaluation, both visual inspection and classifier performance are assessed, contrasting the spectral-entropy estimator with the widely used spectral-kurtosis approach for dealing with impulsive signals. The validation of short-time/-angle spectral analysis performed on three datasets at variable speed showed that the proposed spectral-entropy estimator is a promising indicator for emphasizing bearing failures with impulse behavior. Full article
(This article belongs to the Special Issue Bearing Fault Detection and Diagnosis)
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23 pages, 3014 KiB  
Article
SVM-Based Bearing Anomaly Identification with Self-Tuning Network-Fuzzy Robust Proportional Multi Integral and Smart Autoregressive Model
by Shahnaz TayebiHaghighi and Insoo Koo
Appl. Sci. 2021, 11(6), 2784; https://0-doi-org.brum.beds.ac.uk/10.3390/app11062784 - 20 Mar 2021
Cited by 11 | Viewed by 1511
Abstract
In this paper, the combination of an indirect self-tuning observer, smart signal modeling, and machine learning-based classification is proposed for rolling element bearing (REB) anomaly identification. The proposed scheme has three main stages. In the first stage, the original signal is resampled, and [...] Read more.
In this paper, the combination of an indirect self-tuning observer, smart signal modeling, and machine learning-based classification is proposed for rolling element bearing (REB) anomaly identification. The proposed scheme has three main stages. In the first stage, the original signal is resampled, and the root mean square (RMS) signal is extracted from it. In the second stage, the normal resampled RMS signal is approximated using the AutoRegressive with eXternal Uncertainty (ARXU) technique. Moreover, the nonlinearity of the bearing signal is solved using the combination of the ARXU and the machine learning-based regression, which is called AMRXU. After signal modeling by AMRXU, the RMS resampled signal is estimated using a combination of the proportional multi-integral (PMI) technique, the variable structure (VS) Lyapunov technique, and a self-tuning network-fuzzy system (SNFS). Finally, in the third stage, the difference between the original signal and the estimated one is calculated to generate the residual signal. A machine learning-based classification technique is utilized to classify the residual signal. The Case Western Reserve University (CWRU) dataset is used to evaluate anomaly identification performance of the proposed scheme. Regarding the experimental results, the average accuracy for REB crack identification is 98.65%, 97.7%, 97.35%, and 97.67%, respectively, when the motor torque loads are 0-hp, 1-hp, 2-hp, and 3-hp. Full article
(This article belongs to the Special Issue Bearing Fault Detection and Diagnosis)
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14 pages, 6361 KiB  
Article
Enhanced K-Nearest Neighbor for Intelligent Fault Diagnosis of Rotating Machinery
by Jiantao Lu, Weiwei Qian, Shunming Li and Rongqing Cui
Appl. Sci. 2021, 11(3), 919; https://0-doi-org.brum.beds.ac.uk/10.3390/app11030919 - 20 Jan 2021
Cited by 62 | Viewed by 3089
Abstract
Case-based intelligent fault diagnosis methods of rotating machinery can deal with new faults effectively by adding them into the case library. However, case-based methods scarcely refer to automatic feature extraction, and k-nearest neighbor (KNN) commonly required by case-based methods is unable to determine [...] Read more.
Case-based intelligent fault diagnosis methods of rotating machinery can deal with new faults effectively by adding them into the case library. However, case-based methods scarcely refer to automatic feature extraction, and k-nearest neighbor (KNN) commonly required by case-based methods is unable to determine the nearest neighbors for different testing samples adaptively. To solve these problems, a new intelligent fault diagnosis method of rotating machinery is proposed based on enhanced KNN (EKNN), which can take advantage of both parameter-based and case-based methods. First, EKNN is embedded with a dimension-reduction stage, which extracts the discriminative features of samples via sparse filtering (SF). Second, to locate the nearest neighbors for various testing samples adaptively, a case-based reconstruction algorithm is designed to obtain the correlation vectors between training samples and testing samples. Finally, according to the optimized correlation vector of each testing sample, its nearest neighbors can be adaptively selected to obtain its corresponding health condition label. Extensive experiments on vibration signal datasets of bearings are also conducted to verify the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Bearing Fault Detection and Diagnosis)
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17 pages, 521 KiB  
Article
Mutual Information and Meta-Heuristic Classifiers Applied to Bearing Fault Diagnosis in Three-Phase Induction Motors
by Gustavo Henrique Bazan, Alessandro Goedtel, Marcelo Favoretto Castoldi, Wagner Fontes Godoy, Oscar Duque-Perez and Daniel Morinigo-Sotelo
Appl. Sci. 2021, 11(1), 314; https://0-doi-org.brum.beds.ac.uk/10.3390/app11010314 - 30 Dec 2020
Cited by 6 | Viewed by 2011
Abstract
Three-phase induction motors are extensively used in industrial processes due to their robustness, adaptability to different operating conditions, and low operation and maintenance costs. Induction motor fault diagnosis has received special attention from industry since it can reduce process losses and ensure the [...] Read more.
Three-phase induction motors are extensively used in industrial processes due to their robustness, adaptability to different operating conditions, and low operation and maintenance costs. Induction motor fault diagnosis has received special attention from industry since it can reduce process losses and ensure the reliable operation of industrial systems. Therefore, this paper presents a study on the use of meta-heuristic tools in the diagnosis of bearing failures in induction motors. The extraction of the fault characteristics is performed based on mutual information measurements between the stator current signals in the time domain. Then, the Artificial Bee Colony algorithm is used to select the relevant mutual information values and optimize the pattern classifier input data. To evaluate the classification accuracy under various levels of failure severity, the performance of two different pattern classifiers was compared: The C4.5 decision tree and the multi-layer artificial perceptron neural networks. The experimental results confirm the effectiveness of the proposed approach. Full article
(This article belongs to the Special Issue Bearing Fault Detection and Diagnosis)
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20 pages, 14652 KiB  
Article
Constructing a Reliable Health Indicator for Bearings Using Convolutional Autoencoder and Continuous Wavelet Transform
by Mohammadreza Kaji, Jamshid Parvizian and Hans Wernher van de Venn
Appl. Sci. 2020, 10(24), 8948; https://0-doi-org.brum.beds.ac.uk/10.3390/app10248948 - 15 Dec 2020
Cited by 17 | Viewed by 3379
Abstract
Estimating the remaining useful life (RUL) of components is a crucial task to enhance reliability, safety, productivity, and to reduce maintenance cost. In general, predicting the RUL of a component includes constructing a health indicator (HI) to infer the current [...] Read more.
Estimating the remaining useful life (RUL) of components is a crucial task to enhance reliability, safety, productivity, and to reduce maintenance cost. In general, predicting the RUL of a component includes constructing a health indicator (HI) to infer the current condition of the component, and modelling the degradation process in order to estimate the future behavior. Although many signal processing and data-driven methods have been proposed to construct the HI, most of the existing methods are based on manual feature extraction techniques and require the prior knowledge of experts, or rely on a large amount of failure data. Therefore, in this study, a new data-driven method based on the convolutional autoencoder (CAE) is presented to construct the HI. For this purpose, the continuous wavelet transform (CWT) technique was used to convert the raw acquired vibrational signals into a two-dimensional image; then, the CAE model was trained by the healthy operation dataset. Finally, the Mahalanobis distance (MD) between the healthy and failure stages was measured as the HI. The proposed method was tested on a benchmark bearing dataset and compared with several other traditional HI construction models. Experimental results indicate that the constructed HI exhibited a monotonically increasing degradation trend and had good performance in terms of detecting incipient faults. Full article
(This article belongs to the Special Issue Bearing Fault Detection and Diagnosis)
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16 pages, 5888 KiB  
Article
Early Warning Signals for Bearing Failure Using Detrended Fluctuation Analysis
by Laith Shalalfeh and Ashraf AlShalalfeh
Appl. Sci. 2020, 10(23), 8489; https://0-doi-org.brum.beds.ac.uk/10.3390/app10238489 - 27 Nov 2020
Cited by 6 | Viewed by 2337
Abstract
Prognostic techniques play a critical role in predicting upcoming faults and failures in machinery or a system by monitoring any deviation in the operation. This paper presents a novel method to analyze multidimensional sensory data and use its characteristics in bearing health prognostics. [...] Read more.
Prognostic techniques play a critical role in predicting upcoming faults and failures in machinery or a system by monitoring any deviation in the operation. This paper presents a novel method to analyze multidimensional sensory data and use its characteristics in bearing health prognostics. Firstly, detrended fluctuation analysis (DFA) is exploited to evaluate the long-range correlations in ball bearing vibration data. The results reveal the existence of the crossover phenomenon in vibration data with two scaling exponents at the short-range and long-range scales. Among several data sets, applying the DFA method to vibration signals shows a consistent increase in the short-range scaling exponent toward bearing failure. Finally, Kendall’s tau is used as a ranking coefficient to quantify the trend in the scaling exponent. It was found that the Kendall’s tau coefficient of the vibration scaling exponent could provide an early warning signal (EWS) for bearing failure. Full article
(This article belongs to the Special Issue Bearing Fault Detection and Diagnosis)
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24 pages, 13178 KiB  
Article
Health Indicators Construction for Damage Level Assessment in Bearing Diagnostics: A Proposal of an Energetic Approach Based on Envelope Analysis
by Eugenio Brusa, Fabio Bruzzone, Cristiana Delprete, Luigi Gianpio Di Maggio and Carlo Rosso
Appl. Sci. 2020, 10(22), 8131; https://0-doi-org.brum.beds.ac.uk/10.3390/app10228131 - 17 Nov 2020
Cited by 16 | Viewed by 2606
Abstract
Predictive maintenance strategies are established in the industrial context on account of their benefits in terms of costs abatement and machine failures reduction. Among the available techniques, vibration-based condition monitoring (VBCM) has notably been applied in many bearing fault detection problems. The health [...] Read more.
Predictive maintenance strategies are established in the industrial context on account of their benefits in terms of costs abatement and machine failures reduction. Among the available techniques, vibration-based condition monitoring (VBCM) has notably been applied in many bearing fault detection problems. The health indicators construction is a central issue for VBCM, since these features provide the necessary information to assess the current machine condition. However, the relation between vibration data and its sources intimately related to bearing damage is not effortlessly definable from a diagnostic perspective. This study discloses a diagnostic investigation performed both on the vibration signal and on the contact pressure signal that is supposed to be one of main forcing terms in the dynamic equilibrium of the damaged bearing. Envelope analysis and spectral kurtosis (SK) are applied to extract and compare diagnostic features from both signals, referring to the Case Western Reserve University (CWRU) case-study. Namely, health indicators are constructed by means of physical considerations based on the effect of faults on the signal power contents. These indicators show to be promising not only for damage detection but, also, for damage severity assessment. Moreover, they provide an invaluable reading key of the link occurring between the contact pressure path and the vibration response. Full article
(This article belongs to the Special Issue Bearing Fault Detection and Diagnosis)
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23 pages, 5698 KiB  
Article
Diagnostics 101: A Tutorial for Fault Diagnostics of Rolling Element Bearing Using Envelope Analysis in MATLAB
by Seokgoo Kim, Dawn An and Joo-Ho Choi
Appl. Sci. 2020, 10(20), 7302; https://0-doi-org.brum.beds.ac.uk/10.3390/app10207302 - 19 Oct 2020
Cited by 38 | Viewed by 8123
Abstract
This paper presents a MATLAB-based tutorial to conduct fault diagnosis of a rolling element bearing. While there have been so many new developments in this field, no studies have addressed the tutorial aspects in this field to help the engineers learn the concept [...] Read more.
This paper presents a MATLAB-based tutorial to conduct fault diagnosis of a rolling element bearing. While there have been so many new developments in this field, no studies have addressed the tutorial aspects in this field to help the engineers learn the concept and implement by their own effort. The three most common techniques—the autoregressive model, spectral kurtosis, and envelope analysis—are selected to demonstrate the bearing diagnosis process. Simulation signal is introduced to help understand the characteristics of fault signal and carry out the process toward the fault identification. The techniques are then applied to the two real datasets to demonstrate the practical applications, one made by the authors and the other by the Case Western Reserve University, which is known as a standard reference in testing the diagnostic algorithms. Full article
(This article belongs to the Special Issue Bearing Fault Detection and Diagnosis)
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18 pages, 3838 KiB  
Article
Load Calculation of the Most Loaded Rolling Element for a Rolling Bearing with Internal Radial Clearance
by Radoslav Tomović
Appl. Sci. 2020, 10(19), 6934; https://0-doi-org.brum.beds.ac.uk/10.3390/app10196934 - 03 Oct 2020
Cited by 6 | Viewed by 3887
Abstract
This paper presents a new model for calculation of load for the most loaded rolling element in a rolling bearing with internal radial clearance. The calculation is based on a so-called load factor. By multiplying this factor by the value of the external [...] Read more.
This paper presents a new model for calculation of load for the most loaded rolling element in a rolling bearing with internal radial clearance. The calculation is based on a so-called load factor. By multiplying this factor by the value of the external radial load, the load transferred by the most loaded rolling element of the bearing is obtained. The values of the load factor are shown in the tables and diagrams, which makes the model very suitable for practical use. The load factors are shown for a ball bearing as well as for a roller bearing. The model considers two support positions of the inner ring on an even and odd number of rolling elements. The new model was compared with the most commonly used models up to now. The results showed greater accuracy of the studied model. Full article
(This article belongs to the Special Issue Bearing Fault Detection and Diagnosis)
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17 pages, 4251 KiB  
Article
Bearing Fault Diagnosis Based on Improved Convolutional Deep Belief Network
by Shuangjie Liu, Jiaqi Xie, Changqing Shen, Xiaofeng Shang, Dong Wang and Zhongkui Zhu
Appl. Sci. 2020, 10(18), 6359; https://0-doi-org.brum.beds.ac.uk/10.3390/app10186359 - 12 Sep 2020
Cited by 25 | Viewed by 3021
Abstract
Mechanical equipment fault detection is critical in industrial applications. Based on vibration signal processing and analysis, the traditional fault diagnosis method relies on rich professional knowledge and artificial experience. Achieving accurate feature extraction and fault diagnosis is difficult using such an approach. To [...] Read more.
Mechanical equipment fault detection is critical in industrial applications. Based on vibration signal processing and analysis, the traditional fault diagnosis method relies on rich professional knowledge and artificial experience. Achieving accurate feature extraction and fault diagnosis is difficult using such an approach. To learn the characteristics of features from data automatically, a deep learning method is used. A qualitative and quantitative method for rolling bearing faults diagnosis based on an improved convolutional deep belief network (CDBN) is proposed in this study. First, the original vibration signal is converted to the frequency signal with the fast Fourier transform to improve shallow inputs. Second, the Adam optimizer is introduced to accelerate model training and convergence speed. Finally, the model structure is optimized. A multi-layer feature fusion learning structure is put forward wherein the characterization capabilities of each layer can be fully used to improve the generalization ability of the model. In the experimental verification, a laboratory self-made bearing vibration signal dataset was used. The dataset included healthy bearings, nine single faults of different types and sizes, and three different types of composite fault signals. The results of load 0 kN and 1 kN both indicate that the proposed model has better diagnostic accuracy, with an average of 98.15% and 96.15%, compared with the traditional stacked autoencoder, artificial neural network, deep belief network, and standard CDBN. With improved diagnostic accuracy, the proposed model realizes reliable and effective qualitative and quantitative diagnosis of bearing faults. Full article
(This article belongs to the Special Issue Bearing Fault Detection and Diagnosis)
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21 pages, 12604 KiB  
Article
A Novel Stacked Auto Encoders Sparse Filter Rotating Component Comprehensive Diagnosis Network for Extracting Domain Invariant Features
by Rui Ding, Shunming Li, Jiantao Lu, Kun Xu and Jinrui Wang
Appl. Sci. 2020, 10(17), 6084; https://0-doi-org.brum.beds.ac.uk/10.3390/app10176084 - 02 Sep 2020
Cited by 4 | Viewed by 1538
Abstract
In recent years, the method of deep learning has been widely used in the field of fault diagnosis of mechanical equipment due to its strong feature extraction and other advantages such as high efficiency, portability, and so on. However, at present, most kinds [...] Read more.
In recent years, the method of deep learning has been widely used in the field of fault diagnosis of mechanical equipment due to its strong feature extraction and other advantages such as high efficiency, portability, and so on. However, at present, most kinds of intelligent fault diagnosis algorithms mainly focus on the diagnosis of a single fault component, and few intelligent diagnosis models can simultaneously carry out comprehensive fault diagnosis for a rotating system composed of a shaft, bearing, gear, and so on. In order to solve this problem, a novel stacked auto encoders sparse filter rotating component comprehensive diagnosis network (SAFC) was proposed to extract domain invariant features of various health conditions at different speeds. The model clusters domain invariant features at different speeds through the self-coding network, and then classifies fault types of various parts through sparse filtering. The SAFC model was validated by the vibration data collected, and the results show that this model has higher diagnostic performance than other models. Full article
(This article belongs to the Special Issue Bearing Fault Detection and Diagnosis)
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20 pages, 1302 KiB  
Article
Bearing Fault Identification Using Machine Learning and Adaptive Cascade Fault Observer
by Farzin Piltan and Jong-Myon Kim
Appl. Sci. 2020, 10(17), 5827; https://0-doi-org.brum.beds.ac.uk/10.3390/app10175827 - 23 Aug 2020
Cited by 10 | Viewed by 2737
Abstract
In this work, a hybrid procedure for bearing fault identification using a machine learning and adaptive cascade observer is explained. To design an adaptive cascade observer, the normal signal approximation is the first step. Therefore, the fuzzy orthonormal regressive (FOR) technique was developed [...] Read more.
In this work, a hybrid procedure for bearing fault identification using a machine learning and adaptive cascade observer is explained. To design an adaptive cascade observer, the normal signal approximation is the first step. Therefore, the fuzzy orthonormal regressive (FOR) technique was developed to approximate the acoustic emission (AE) and vibration (non-stationary and nonlinear) bearing signals in normal conditions. After approximating the normal signal of bearing using the FOR technique, the adaptive cascade observer is modeled in four steps. First, the linear observation technique using a FOR proportional-integral (PI) observer (FOR-PIO) is developed. In the second step, to increase the power of uncertaintie rejection (robustness) of the FOR-PIO, the structure procedure is used serially. Next, the fuzzy like observer is selected to increase the accuracy of FOR structure PI observer (FOR-SPIO). Moreover, the adaptive technique is used to develop the reliability of the cascade (fuzzy-structure PI) observer. Additionally to fault identification, the machine-learning algorithm using a support vector machine (SVM) is recommended. The effectiveness of the adaptive cascade observer with the SVM fault identifier was validated by a vibration and AE datasets. Based on the results, the average vibration and AE fault diagnosis using the adaptive cascade observer with the SVM fault identifier are 97.8% and 97.65%, respectively. Full article
(This article belongs to the Special Issue Bearing Fault Detection and Diagnosis)
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14 pages, 1629 KiB  
Article
A Low-Cost, Small-Size, and Bluetooth-Connected Module to Detect Faults in Rolling Bearings
by Erica Raviola and Franco Fiori
Appl. Sci. 2020, 10(16), 5645; https://0-doi-org.brum.beds.ac.uk/10.3390/app10165645 - 14 Aug 2020
Cited by 4 | Viewed by 1888
Abstract
Condition monitoring techniques have been successfully applied to detect damaged bearings. However, the signal acquisition and the subsequent processing are typically outsourced to expensive data acquisition boards and complex software, resulting in expensive solutions. As a side effect, the integration of condition monitoring [...] Read more.
Condition monitoring techniques have been successfully applied to detect damaged bearings. However, the signal acquisition and the subsequent processing are typically outsourced to expensive data acquisition boards and complex software, resulting in expensive solutions. As a side effect, the integration of condition monitoring systems in wireless sensor networks can be tough to achieve. Aiming to overcome such issues, a low-cost and small-size electronic module to be placed in the proximity of the bearing to be monitored was developed. The acoustic signal delivered by the bearing is acquired, and the corresponding frequency spectrum is evaluated on-board. Based on that, the developed module automatically detects the presence of defects and notifies the remote controller via a wireless connection only when a fault is detected, thus avoiding the use of data cables whilst minimizing the amount of transferred data. Experimental tests carried out on the proposed system assessed the accuracy of the evaluated frequency spectrum, resulting in an amplitude error within ±0.6%, as well as the fault detection capability in the presence of environmental acoustic noise. Full article
(This article belongs to the Special Issue Bearing Fault Detection and Diagnosis)
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18 pages, 4477 KiB  
Article
Rolling Bearings Fault Diagnosis Based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise, Nonlinear Entropy, and Ensemble SVM
by Rui Li, Chao Ran, Bin Zhang, Leng Han and Song Feng
Appl. Sci. 2020, 10(16), 5542; https://0-doi-org.brum.beds.ac.uk/10.3390/app10165542 - 11 Aug 2020
Cited by 19 | Viewed by 2367
Abstract
Rolling bearings are fundamental elements that play a crucial role in the functioning of rotating machines; thus, fault diagnosis of rolling bearings is of great significance to reduce catastrophic failures and heavy economic loss. However, the vibration signals of rolling bearings are often [...] Read more.
Rolling bearings are fundamental elements that play a crucial role in the functioning of rotating machines; thus, fault diagnosis of rolling bearings is of great significance to reduce catastrophic failures and heavy economic loss. However, the vibration signals of rolling bearings are often nonlinear and nonstationary, resulting in difficulty for feature extraction and fault recognition. In this paper, a hybrid method for multiple fault diagnosis of rolling bearings is presented. The bearing vibration signals are decomposed with the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) to denoise and extract nonlinear entropy features. The nonlinear entropy features are further processed to select the more discriminative fault features and to reduce feature dimension. Then a multi-class intelligent recognition model based on ensemble support vector machine (ESVM) is constructed to diagnose different bearing fault modes as well as fault severities. The effectiveness of the proposed method is assessed via experimental case studies of rolling bearings under multiple operational conditions (i.e., speeds and loads). The results show that our method gives better diagnosis results as compared to some existing approaches. Full article
(This article belongs to the Special Issue Bearing Fault Detection and Diagnosis)
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17 pages, 73208 KiB  
Article
A Novel Adaptive Mode Decomposition Method Based on Reassignment Vector and Its Application to Fault Diagnosis of Rolling Bearing
by Cancan Yi, Xing Wang, Yajun Zhu and Wei Ke
Appl. Sci. 2020, 10(16), 5479; https://0-doi-org.brum.beds.ac.uk/10.3390/app10165479 - 07 Aug 2020
Cited by 5 | Viewed by 2039
Abstract
To solve the problem that the random distribution of noise in the time-frequency (TF) plane largely affects the readability of TF representations, a novel signal adaptive decomposition algorithm processed in TF domain, which provides adequate information about the time-varying instantaneous frequency, is presented [...] Read more.
To solve the problem that the random distribution of noise in the time-frequency (TF) plane largely affects the readability of TF representations, a novel signal adaptive decomposition algorithm processed in TF domain, which provides adequate information about the time-varying instantaneous frequency, is presented in this paper. The theoretical basis of this algorithm is short-time Fourier transform (STFT). The research into the algorithm comprises two steps: the TF plane denoising takes sparse low-rank matrix estimation as a priority and then achieves signal decomposition based on reassignment vector (RV). A low-rank matrix approximation scheme, which exploits the sparse properties of the TF transformation coefficient and uses non-convex penalty, is put forward to obtain clean STFT. Then, a new approach called RV, which is different from the traditional mode decomposition methods such as Empirical Mode Decomposition (EMD), is used to estimate the characteristic curve corresponding to the TF ridges of the interested modes. Based on the classical reassignment method, RV has a solid theory foundation. Moreover, it can identify different signal components such as stationary signal, modulating signal and impulse characteristic. Combining the advantages of low-rank matrix approximation approach and those of RV defined in TF plane, a novel signal adaptive decomposition method is proposed in this paper to identify fault characteristics. To illustrate the effectiveness of the method, fault signals of rolling bearing under stationary condition and time-varying speed are respectively analyzed. Full article
(This article belongs to the Special Issue Bearing Fault Detection and Diagnosis)
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17 pages, 1176 KiB  
Article
Bearing Health Monitoring Using Relief-F-Based Feature Relevance Analysis and HMM
by José Alberto Hernández-Muriel, Jhon Bryan Bermeo-Ulloa, Mauricio Holguin-Londoño, Andrés Marino Álvarez-Meza and Álvaro Angel Orozco-Gutiérrez
Appl. Sci. 2020, 10(15), 5170; https://0-doi-org.brum.beds.ac.uk/10.3390/app10155170 - 28 Jul 2020
Cited by 13 | Viewed by 2350
Abstract
Nowadays, bearings installed in industrial electric motors are constituted as the primary mode of a failure affecting the global energy consumption. Since industries’ energy demand has a growing tendency, interest for efficient maintenance in electric motors is decisive. Vibration signals from bearings are [...] Read more.
Nowadays, bearings installed in industrial electric motors are constituted as the primary mode of a failure affecting the global energy consumption. Since industries’ energy demand has a growing tendency, interest for efficient maintenance in electric motors is decisive. Vibration signals from bearings are employed commonly as a non-invasive approach to support fault diagnosis and severity evaluation of rotating machinery. However, vibration-based diagnosis poses a challenge concerning the signal properties, e.g., highly dynamic and non-stationary. Here, we introduce a knowledge-based tool to analyze multiple health conditions in bearings. Our approach includes a stochastic feature selection method, termed Stochastic Feature Selection (SFS), highlighting and interpreting relevant multi-domain attributes (time, frequency, and time–frequency) related to the bearing faults discriminability. In particular, a relief-F-based ranking and a Hidden Markov Model are trained under a windowing scheme to achieve our SFS. Obtained results in a public database demonstrate that our proposal is competitive compared to state-of-the-art algorithms concerning both the number of features selected and the classification accuracy. Full article
(This article belongs to the Special Issue Bearing Fault Detection and Diagnosis)
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15 pages, 4812 KiB  
Article
Low-Speed Bearing Fault Diagnosis Based on Permutation and Spectral Entropy Measures
by Diego Sandoval, Urko Leturiondo, Francesc Pozo and Yolanda Vidal
Appl. Sci. 2020, 10(13), 4666; https://0-doi-org.brum.beds.ac.uk/10.3390/app10134666 - 06 Jul 2020
Cited by 12 | Viewed by 3532
Abstract
Despite its influence on wind energy service life, condition-based maintenance is still challenging to perform. For offshore wind farms, which are placed in harsh and remote environments, damage detection is critically important to schedule maintenance tasks and reduce operation and maintenance costs. One [...] Read more.
Despite its influence on wind energy service life, condition-based maintenance is still challenging to perform. For offshore wind farms, which are placed in harsh and remote environments, damage detection is critically important to schedule maintenance tasks and reduce operation and maintenance costs. One critical component to be monitored on a wind turbine is the pitch bearing, which can operate at low speed and high loads. Classical methods and features for general purpose bearings cannot be applied effectively to wind turbine pitch bearings owing to their specific operating conditions (high loads and non-constant very low speed with changing direction). Thus, damage detection of wind turbine pitch bearings is currently a challenge. In this study, entropy indicators are proposed as an alternative to classical bearing analysis. For this purpose, spectral and permutation entropy are combined to analyze a raw vibration signal from a low-speed bearing in several scenarios. The results indicate that entropy values change according to different types of damage on bearings, and the sensitivity of the entropy types differs among them. The study offers some important insights into the use of entropy indicators for feature extraction and it lays the foundation for future bearing prognosis methods. Full article
(This article belongs to the Special Issue Bearing Fault Detection and Diagnosis)
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17 pages, 1637 KiB  
Article
Hybrid Fault Diagnosis of Bearings: Adaptive Fuzzy Orthonormal-ARX Robust Feedback Observer
by Farzin Piltan and Jong-Myon Kim
Appl. Sci. 2020, 10(10), 3587; https://0-doi-org.brum.beds.ac.uk/10.3390/app10103587 - 22 May 2020
Cited by 10 | Viewed by 2005
Abstract
Rolling-element bearings (REBs) make up a class of non-linear rotating machines that can be applied in several activities. Conceding a bearing has complicated and uncertain behavior that exhibits substantial challenges to fault diagnosis. Thus, the offered anomaly-diagnosis algorithm, based on a fuzzy orthonormal-ARX [...] Read more.
Rolling-element bearings (REBs) make up a class of non-linear rotating machines that can be applied in several activities. Conceding a bearing has complicated and uncertain behavior that exhibits substantial challenges to fault diagnosis. Thus, the offered anomaly-diagnosis algorithm, based on a fuzzy orthonormal-ARX adaptive fuzzy logic-structure feedback observer, is developed. A fuzzy orthonormal-ARX algorithm is presented for non-stationary signal modeling. Next, a robust, stable, reliable, and accurate observer is developed for signal estimation. Therefore, firstly, a classical feedback observer is implemented. To address the robustness drawback found in feedback observers, a multi-structure technique is developed. Furthermore, to generate signal estimation performance and reliability, the fuzzy logic technique is applied to the structure feedback observer. Also, to improve the stability, reliability, and robustness of the fuzzy orthonormal-ARX fuzzy logic-structure feedback observer, an adaptive algorithm is developed. After generating the residual signals, a support vector machine (SVM) is developed for the detection and classification of the bearing fault conditions. The effectiveness of the proposed procedure is validated using two different datasets for single-type fault diagnosis based on the Case Western Reverse University (CWRU) vibration dataset and multi-type fault diagnosis of bearing using the Smart Health Safety Environment (SHSE) Lab acoustic emission dataset. The proposed algorithm increases the classification accuracy from 86% in the SVM-based fuzzy orthonormal-ARX feedback observer to 97.5% in single-type fault and from 80% to 98.3% in the multi-type faults. Full article
(This article belongs to the Special Issue Bearing Fault Detection and Diagnosis)
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19 pages, 3679 KiB  
Tutorial
A Tutorial for Feature Engineering in the Prognostics and Health Management of Gears and Bearings
by Jinwoo Sim, Seokgoo Kim, Hyung Jun Park and Joo-Ho Choi
Appl. Sci. 2020, 10(16), 5639; https://0-doi-org.brum.beds.ac.uk/10.3390/app10165639 - 14 Aug 2020
Cited by 20 | Viewed by 4824
Abstract
Gears and bearings are one of the major components of many machines, which can result in operation downtime or even catastrophic failure of a whole system. This paper addresses a tutorial for the features extraction and selection of the gears and bearings, which [...] Read more.
Gears and bearings are one of the major components of many machines, which can result in operation downtime or even catastrophic failure of a whole system. This paper addresses a tutorial for the features extraction and selection of the gears and bearings, which is known as feature engineering, a prerequisite step for the prognostics and health management (PHM) of these components. While there have been many new developments in this field, no studies have addressed the tutorial aspects of features engineering to aid engineers in solving problems by their own effort, which is of practical importance for successful PHM. The paper aims at helping beginners learn the basic concepts, and implement the algorithms using the public datasets as well as those made by the authors. Matlab codes are provided for them to implement the process by their own hands. Full article
(This article belongs to the Special Issue Bearing Fault Detection and Diagnosis)
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