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Diagnostics of Rotating Machinery through Vibration Monitoring: Signal Processing and...
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Diagnostics of Rotating Machinery through Vibration Monitoring: Signal Processing and Pattern Analysis

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

Deadline for manuscript submissions: closed (20 May 2023) | Viewed by 33909

Special Issue Editors

Dr. Alessandro P. Daga

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Guest Editor
Dipartimento di Ingegneria Meccanica e Aerospaziale—DIMEAS, Politecnico di Torino, Corso Duca degli Abruzzi, 24, I-10129 Torino, Italy
Interests: condition monitoring; vibration monitoring; diagnostics
Prof. Dr. Luigi Garibaldi

E-Mail Website
Guest Editor
Dipartimento di Ingegneria Meccanica e Aerospaziale—DIMEAS, Politecnico di Torino, Corso Duca degli Abruzzi, 24, I-10129 Torino, Italy
Interests: modal analysis; nonlinear systems; identification; monitoring and diagnostics

Special Issue Information

Rotating machinery diagnostics is the discipline investigating possible causes of symptoms reflecting the presence of an unusual state in machines with rotating shafts. Through vibration monitoring (VM), particular patterns in the extracted features are related to departures from normal functioning, which can indicate a damaged state.

This kind of minimally invasive nondestructive testing (NDT) has the purpose of increasing the reliability of complex and expensive machines, switching from programmed maintenance to preventive maintenance regimes based on VM, so as to foster both safety and economic.

The problem of diagnosing damage is practically a data mining of the recorded datasets. Three fundamental steps are necessary:

  • Signal processing to highlight the diagnostic information: e.g., enhancement of the signal of interest with respect to the background noise, estimation and compensation of nonstationarities induced by variable speed (instantaneous angular speed (IAS) estimation, computed order tracking, resampling, etc.), demodulation, filtering, etc.;
  • Feature selection: quantities which summarize the dataset and whose behavior is correlated with the damage but, if possible, not with the operational and the environmental variables (confounders such as variable speed, load, temperature, humidity, etc.), which should be otherwise compensated with signal processing techniques;
  • Diagnostics: producing knowledge about the health state of the machine, detecting the presence of incipient damage, tracking the damage evolution over time while understanding its severity, distinguishing between damage location and types and finally, switching to prognostics to predict the remaining useful life.

Works on available benchmark datasets will be preferred. Examples taken from the literature are:

Dr. Alessandro P. Daga
Prof. Dr. Luigi Garibaldi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • vibration monitoring
  • machine diagnostics
  • rotating shafts
  • turbines
  • windmills
  • gearboxes
  • bearings
  • signal processing
  • variable speed
  • nonstationarities
  • confounders
  • IAS
  • order tracking
  • tip-timing
  • vibration features
  • statistical learning
  • pattern recognition
  • damage detection
  • damage identification
  • damage classification
  • prognostics

Published Papers (11 papers)

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Research

Jump to: Review

13 pages, 1065 KiB  
Article
Unbalance Detection in Induction Motors through Vibration Signals Using Texture Features
by Uriel Calderon-Uribe, Rocio A. Lizarraga-Morales and Igor V. Guryev
Appl. Sci. 2023, 13(10), 6137; https://doi.org/10.3390/app13106137 - 17 May 2023
Cited by 1 | Viewed by 955
Abstract
The detection of faults in induction motors has been one of the main challenges to the industry in recent years. An effective fault detection method is fundamental to ensure operational security and productivity. Different models for intelligent fault diagnosis have been recently proposed. [...] Read more.
The detection of faults in induction motors has been one of the main challenges to the industry in recent years. An effective fault detection method is fundamental to ensure operational security and productivity. Different models for intelligent fault diagnosis have been recently proposed. However, not all of them are accessible for some manufacturing processes because of the black-box approach, the complexity of hyperparameter tuning, high-dimensionality feature vectors, and the need for sophisticated computational resources. In this paper, a method for the detection of an unbalance fault in induction motors based on a low-dimensional feature vector and a low-complexity classification approach is proposed. The feature vector presented in this manuscript is based on texture features, which are a basic tool for image processing and image understanding. Nevertheless, texture features have not been explored as a powerful instrument for induction motor fault analysis. In this approach, texture features are used to analyze a set of vibration signals belonging to two different classes: an unbalanced motor and a healthy motor. Training-validation and testing stages are developed to build and evaluate the performance of the classifier, respectively. The results show higher accuracy and lower training time in comparison with different state-of-the-art approaches. Full article
(This article belongs to the Special Issue Diagnostics of Rotating Machinery through Vibration Monitoring: Signal Processing and Pattern Analysis)
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27 pages, 8771 KiB  
Article
Mechanical Incipient Fault Detection and Performance Analysis Using Adaptive Teager-VMD Method
by Huipeng Li, Bo Xu, Fengxing Zhou and Pu Huang
Appl. Sci. 2023, 13(10), 6058; https://doi.org/10.3390/app13106058 - 15 May 2023
Viewed by 711
Abstract
For large rotating machinery with low speed and heavy load, the incipient fault characteristics of rolling bearings are particularly weak, making it difficult to identify them effectively by direct signal processing methods. To resolve this issue, we propose a novel approach to detecting [...] Read more.
For large rotating machinery with low speed and heavy load, the incipient fault characteristics of rolling bearings are particularly weak, making it difficult to identify them effectively by direct signal processing methods. To resolve this issue, we propose a novel approach to detecting incipient fault features that combines signal energy enhancement and signal decomposition. First, the structure of a conventional Teager algorithm is modified to further increase the energy of the micro-impact component and hence the impact amplitude. Then, a kind of composite chaotic mapping is constructed to extend the original fruit fly optimization algorithm (FOA) framework, improving the FOA’s randomness and search power. The effective intrinsic mode functions (IMFs) are determined by searching for the optimal combination values of the key parameters of the variational mode decomposition (VMD) with the improved chaotic FOA (ICFOA). The kurtosis index is then used to select the IMFs that are most relevant to the fault characteristics information. Finally, the sensitive components are analyzed to identify multiple early fault characteristics and determine detailed information about the faults. Moreover, the approach is evaluated by a simulation signal and a measured signal. The comprehensive evaluation indicates that the approach has clear advantages over other excellent methods in extracting the incipient fault feature information of the equipment and has great potential for application in engineering. Full article
(This article belongs to the Special Issue Diagnostics of Rotating Machinery through Vibration Monitoring: Signal Processing and Pattern Analysis)
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20 pages, 7222 KiB  
Article
Identification of Aerodynamic Tonal Noise Sources of a Centrifugal Compressor of a Turbocharger for Large Stationary Engines
by Jiří Vacula and Pavel Novotný
Appl. Sci. 2023, 13(10), 5964; https://doi.org/10.3390/app13105964 - 12 May 2023
Cited by 1 | Viewed by 1212
Abstract
The aerodynamics of centrifugal compressors is a topical issue, as the vibrations and noise reduce the comfort of people who are in proximity to the compressor. The current trend in rotating machinery research is therefore not only concerned with performance parameters but also [...] Read more.
The aerodynamics of centrifugal compressors is a topical issue, as the vibrations and noise reduce the comfort of people who are in proximity to the compressor. The current trend in rotating machinery research is therefore not only concerned with performance parameters but also increasingly with the effect on humans. An analysis of aerodynamic noise based on external acoustic field measurements may be a way to assess the nature of aerodynamic excitation. In this research, the experimental measurements at 20 operating points covered the entire characteristic operating range of the selected centrifugal compressor. The dominant noise arising at blade-passing frequency (BPF) was identified at all the operational points, and the dominant effect of the buzz-saw noise was identified at the maximum rotor speed. The determination of the total sound pressure level LPA showed a trend towards an increasingly higher rotor speed and compressor surge line. In the amplitude-frequency characteristics, the sound pressure was found to be dependent on the rotor speed for BPF. On the other hand, non-monotonicity was detected between the operational points at given speed lines, confirming the complexity of the aerodynamics of rotating machines. The metric chosen to identify prominent tones determined by the tonality of individual tones in the frequency spectrum showed a clear effect of integer multiples of the rotational frequency on the overall noise. Thus, the results presented here confirm the dominant influence of BPF in terms of the psychoacoustic impact on humans. Full article
(This article belongs to the Special Issue Diagnostics of Rotating Machinery through Vibration Monitoring: Signal Processing and Pattern Analysis)
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15 pages, 586 KiB  
Article
A Bearing Fault Diagnosis Method Based on Wavelet Denoising and Machine Learning
by Shaokun Fu, Yize Wu, Rundong Wang and Mingzhi Mao
Appl. Sci. 2023, 13(10), 5936; https://doi.org/10.3390/app13105936 - 11 May 2023
Cited by 6 | Viewed by 1368
Abstract
There are a lot of interference factors in the operating environment of machinery, which makes it ineffective to use traditional detection methods to judge the fault location and type of fault of the machinery, and even misjudgment of the fault location and type [...] Read more.
There are a lot of interference factors in the operating environment of machinery, which makes it ineffective to use traditional detection methods to judge the fault location and type of fault of the machinery, and even misjudgment of the fault location and type may occur. In order to solve these problems, this paper proposes a bearing fault diagnosis method based on wavelet denoising and machine learning. We use sensors to detect the operating conditions of rolling bearings under different working conditions to obtain datasets of different types of bearing failures. On the basis of using the wavelet denoising algorithm to reduce noise, we comprehensively evaluated five machine learning models, including K-means clustering, decision tree, random forest, and support vector machine to classify bearing faults and compare their results. By designing the fault classification evaluation prediction criteria, the following conclusions are drawn. The model proposed in this paper is significantly better than other traditional diagnostic models for bearing faults. In order to solve the problem of weak signal strength and background noise interference, this paper selects a better noise reduction algorithm under different quantitative evaluation indicators for wavelet denoising, which can better restore the true characteristics of the fault signal. Using unsupervised learning and supervised machine learning classification algorithms, the evaluation indicators before and after denoising are compared to make the classification results more accurate and reliable. This article will help researchers to intelligently diagnose the faults of rolling bearing equipment in rotating machinery. Full article
(This article belongs to the Special Issue Diagnostics of Rotating Machinery through Vibration Monitoring: Signal Processing and Pattern Analysis)
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18 pages, 2926 KiB  
Article
A Novel Method for Fault Diagnosis of Bearings with Small and Imbalanced Data Based on Generative Adversarial Networks
by Qingbin Tong, Feiyu Lu, Ziwei Feng, Qingzhu Wan, Guoping An, Junci Cao and Tao Guo
Appl. Sci. 2022, 12(14), 7346; https://doi.org/10.3390/app12147346 - 21 Jul 2022
Cited by 11 | Viewed by 1885
Abstract
The data-driven intelligent fault diagnosis method of rolling bearings has strict requirements regarding the number and balance of fault samples. However, in practical engineering application scenarios, mechanical equipment is usually in a normal state, and small and imbalanced (S & I) fault samples [...] Read more.
The data-driven intelligent fault diagnosis method of rolling bearings has strict requirements regarding the number and balance of fault samples. However, in practical engineering application scenarios, mechanical equipment is usually in a normal state, and small and imbalanced (S & I) fault samples are common, which seriously reduces the accuracy and stability of the fault diagnosis model. To solve this problem, an auxiliary classifier generative adversarial network with spectral normalization (ACGAN-SN) is proposed in this paper. First, a generation module based on a deconvolution layer is built to generate false data from Gaussian noise. Second, to enhance the training stability of the model, the data label information is used to make label constraints on the generated fake data under the basic GAN framework. Spectral normalization constraints are imposed on the output of each layer of the neural network of the discriminator to realize the Lipschitz continuity condition so as to avoid vanishing or exploding gradients. Finally, based on the generated data and the original S & I dataset, seven kinds of bearing fault datasets are made, and the prediction results of the Bi-directional Long Short-Term Memory (BiLSTM) model is verified. The results show that the data generated by ACGAN-SN can significantly promote the performance of the fault diagnosis model under the S & I fault samples. Full article
(This article belongs to the Special Issue Diagnostics of Rotating Machinery through Vibration Monitoring: Signal Processing and Pattern Analysis)
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16 pages, 27093 KiB  
Article
Deep Transfer Learning for Machine Diagnosis: From Sound and Music Recognition to Bearing Fault Detection
by Eugenio Brusa, Cristiana Delprete and Luigi Gianpio Di Maggio
Appl. Sci. 2021, 11(24), 11663; https://doi.org/10.3390/app112411663 - 08 Dec 2021
Cited by 18 | Viewed by 3911
Abstract
Today’s deep learning strategies require ever-increasing computational efforts and demand for very large amounts of labelled data. Providing such expensive resources for machine diagnosis is highly challenging. Transfer learning recently emerged as a valuable approach to address these issues. Thus, the knowledge learned [...] Read more.
Today’s deep learning strategies require ever-increasing computational efforts and demand for very large amounts of labelled data. Providing such expensive resources for machine diagnosis is highly challenging. Transfer learning recently emerged as a valuable approach to address these issues. Thus, the knowledge learned by deep architectures in different scenarios can be reused for the purpose of machine diagnosis, minimizing data collecting efforts. Existing research provides evidence that networks pre-trained for image recognition can classify machine vibrations in the time-frequency domain by means of transfer learning. So far, however, there has been little discussion about the potentials included in networks pre-trained for sound recognition, which are inherently suited for time-frequency tasks. This work argues that deep architectures trained for music recognition and sound detection can perform machine diagnosis. The YAMNet convolutional network was designed to serve extremely efficient mobile applications for sound detection, and it was originally trained on millions of data extracted from YouTube clips. That framework is employed to detect bearing faults for the CWRU dataset. It is shown that transferring knowledge from sound and music recognition to bearing fault detection is successful. The maximum accuracy is achieved using a few hundred data for fine-tuning the fault diagnosis model. Full article
(This article belongs to the Special Issue Diagnostics of Rotating Machinery through Vibration Monitoring: Signal Processing and Pattern Analysis)
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16 pages, 1837 KiB  
Article
Multi-Scale Wind Turbine Bearings Supervision Techniques Using Industrial SCADA and Vibration Data
by Francesco Natili, Alessandro Paolo Daga, Francesco Castellani and Luigi Garibaldi
Appl. Sci. 2021, 11(15), 6785; https://doi.org/10.3390/app11156785 - 23 Jul 2021
Cited by 28 | Viewed by 2500
Abstract
Timely damage diagnosis of wind turbine rolling elements is a keystone for improving availability and eventually diminishing the cost of wind energy: from this point of view, it is a priority to integrate high-level practices into the real-world operation and maintenance of wind [...] Read more.
Timely damage diagnosis of wind turbine rolling elements is a keystone for improving availability and eventually diminishing the cost of wind energy: from this point of view, it is a priority to integrate high-level practices into the real-world operation and maintenance of wind farms. On this basis, the present study is devoted to the formulation of reliable methodologies for the supervision of wind turbine bearings, which possibly can be integrated in the industrial practice. For this reason, this study is a collaboration between a company (ENGIE Italia), the University of Perugia and the Politecnico di Torino. The analysis is based on the exploitation of the data types which are available to wind farm managers from industrial control systems: SCADA (Supervisory Control And Data Acquisition) and TCM (Turbine Condition Monitoring). Due to the intrinsic sampling time difference between SCADA and TCM data (a few minutes the former, up to the millisecond for the latter), the proposed methodology is designed as multi-scale. At first, historical SCADA data are processed and the behavior of the oil filter pressure is analyzed for all the wind turbines in the farm: this provides preliminary advice for identifying presumably healthy wind turbines from those suspected of damage. A second step for the SCADA analysis is then represented by the study of the temperature trends of the bearings through a Support Vector Regression: the incoming damage is individuated from the analysis of the mismatch between measurements and estimates provided by the normal behavior model. Finally, the healthy units are selected as the reference and the faulty as the target for the analysis of TCM vibration data in the time domain: statistical features are computed on independent chunks of the signals and, using a Novelty Index, it was possible to distinguish the damaged wind turbines with respect to the reference ones. In light of the interest in application of the proposed methodology, good practice criteria in selecting and managing the data are discussed as well. Full article
(This article belongs to the Special Issue Diagnostics of Rotating Machinery through Vibration Monitoring: Signal Processing and Pattern Analysis)
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12 pages, 2827 KiB  
Article
Performance of Envelope Demodulation for Bearing Damage Detection on CWRU Accelerometric Data: Kurtogram and Traditional Indicators vs. Targeted a Posteriori Band Indicators
by Daga Alessandro Paolo, Garibaldi Luigi, Fasana Alessandro and Marchesiello Stefano
Appl. Sci. 2021, 11(14), 6262; https://doi.org/10.3390/app11146262 - 06 Jul 2021
Cited by 5 | Viewed by 2246
Abstract
Envelope demodulation of vibration signals is surely one of the most successful methods of analysis for highlighting diagnostic information of rolling element bearings incipient faults. From a mathematical perspective, the selection of a proper demodulation band can be regarded as an optimization problem [...] Read more.
Envelope demodulation of vibration signals is surely one of the most successful methods of analysis for highlighting diagnostic information of rolling element bearings incipient faults. From a mathematical perspective, the selection of a proper demodulation band can be regarded as an optimization problem involving a utility function to assess the demodulation performance in a particular band and a scheme to move within the search space of all the possible frequency bands {f, Δf} (center frequency and band size) towards the optimal one. In most of cases, kurtosis-based indices are used to select the proper demodulation band. Nevertheless, to overcome the lack of robustness to non-Gaussian noise, different utility functions can be found in the literature. One of these is the kurtosis of the unbiased autocorrelation of the squared envelope of the filtered signal found in the autogram. These heuristics are usually sufficient to highlight the defect spectral lines in the demodulated signal spectrum (i.e., usually the squared envelope spectrum (SES)), enabling bearings diagnostics. Nevertheless, it is not always the case. In this work, then, posteriori band indicators based on SES defect spectral lines are proposed to assess the general envelope demodulation performance and the goodness of traditional indicators. The Case Western Reserve University bearing dataset is used as a test case. Full article
(This article belongs to the Special Issue Diagnostics of Rotating Machinery through Vibration Monitoring: Signal Processing and Pattern Analysis)
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15 pages, 54614 KiB  
Article
Bearing Fault Diagnosis Approach under Data Quality Issues
by Ashraf AlShalalfeh and Laith Shalalfeh
Appl. Sci. 2021, 11(7), 3289; https://doi.org/10.3390/app11073289 - 06 Apr 2021
Cited by 7 | Viewed by 3232
Abstract
In rotary machinery, bearings are susceptible to different types of mechanical faults, including ball, inner race, and outer race faults. In condition-based monitoring (CBM), several techniques have been proposed in fault diagnostics based on the vibration measurements. For this paper, we studied the [...] Read more.
In rotary machinery, bearings are susceptible to different types of mechanical faults, including ball, inner race, and outer race faults. In condition-based monitoring (CBM), several techniques have been proposed in fault diagnostics based on the vibration measurements. For this paper, we studied the fractal characteristics of non-stationary vibration signals collected from bearings under different health conditions. Using the detrended fluctuation analysis (DFA), we proposed a novel method to diagnose the bearing faults based on the scaling exponent (α1) of vibration signal at the short-time scale. In vibration data with high sampling rate, our results showed that the proposed measure, scaling exponent, provides an accurate identification of the health state of the bearing. At the end, we evaluated the performance of the proposed method under different data quality issues, data loss and induced noise. Full article
(This article belongs to the Special Issue Diagnostics of Rotating Machinery through Vibration Monitoring: Signal Processing and Pattern Analysis)
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16 pages, 7518 KiB  
Article
Improvement of Intake Structures in a Two-Way Pumping Station with Experimental Analysis
by Yanjun Li, Rong Lu, Huiyan Zhang, Fanjie Deng and Jianping Yuan
Appl. Sci. 2020, 10(19), 6842; https://doi.org/10.3390/app10196842 - 29 Sep 2020
Cited by 9 | Viewed by 2628
Abstract
Pumping stations are important regulation facilities in a water distribution system. Intake structures can generally have a great influence on the operational state of the pumping station. To analyze the effects of the bell mouth height of the two-way intake on the performance [...] Read more.
Pumping stations are important regulation facilities in a water distribution system. Intake structures can generally have a great influence on the operational state of the pumping station. To analyze the effects of the bell mouth height of the two-way intake on the performance characteristics and the pressure pulsations of a two-way pumping station, the laboratory-sized model pump units with three different intakes were experimentally investigated. To facilitate parameterized control, ellipse and straight lines were used to construct the profile of the bell mouth. The frequency domain and time-frequency domain of the pressure pulsations on the wall of intakes were analyzed by the Welch’s power spectral density estimate and the continuous wavelet transform (CWT) methods, respectively. The results showed that the bell mouth height (H) has significant influences on the uniformity of the impeller inflow and the operation stability of the pump unit. When H = 204 mm, the data fluctuated greatly throughout the test process and the performance curves are slightly lower than the other two schemes. As the bell mouth height gradually decreases, the average pressure difference of each measuring point began to decrease, more homogeneous velocity distribution of impeller inflow can be ensured. The amplitude of blade passing frequency is obvious in the spectrum. While when (H) is more than 164 mm, the main frequency of pressure pulsations at three points fluctuates with the rotation of the impeller. When H decreases to 142 mm, pressure pulsations will be independent of the operating conditions and positions which contributes to the long-term stable operation of the pump unit. Full article
(This article belongs to the Special Issue Diagnostics of Rotating Machinery through Vibration Monitoring: Signal Processing and Pattern Analysis)
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Review

Jump to: Research

44 pages, 14009 KiB  
Review
A Review on Vibration-Based Condition Monitoring of Rotating Machinery
by Monica Tiboni, Carlo Remino, Roberto Bussola and Cinzia Amici
Appl. Sci. 2022, 12(3), 972; https://doi.org/10.3390/app12030972 - 18 Jan 2022
Cited by 84 | Viewed by 10975
Abstract
Monitoring vibrations in rotating machinery allows effective diagnostics, as abnormal functioning states are related to specific patterns that can be extracted from vibration signals. Extensively studied issues concern the different methodologies used for carrying out the main phases (signal measurements, pre-processing and processing, [...] Read more.
Monitoring vibrations in rotating machinery allows effective diagnostics, as abnormal functioning states are related to specific patterns that can be extracted from vibration signals. Extensively studied issues concern the different methodologies used for carrying out the main phases (signal measurements, pre-processing and processing, feature selection, and fault diagnosis) of a malfunction automatic diagnosis. In addition, vibration-based condition monitoring has been applied to a number of different mechanical systems or components. In this review, a systematic study of the works related to the topic was carried out. A preliminary phase involved the analysis of the publication distribution, to understand what was the interest in studying the application of the method to the various rotating machineries, to identify the interest in the investigation of the main phases of the diagnostic process, and to identify the techniques mainly used for each single phase of the process. Subsequently, the different techniques of signal processing, feature selection, and diagnosis are analyzed in detail, highlighting their effectiveness as a function of the investigated aspects and of the results obtained in the various studies. The most significant research trends, as well as the main innovations related to the various phases of vibration-based condition monitoring, emerge from the review, and the conclusions provide hints for future ideas. Full article
(This article belongs to the Special Issue Diagnostics of Rotating Machinery through Vibration Monitoring: Signal Processing and Pattern Analysis)
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