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23 pages, 11804 KiB

Open AccessArticle

Evaluation of a Condition Monitoring Algorithm for Early Bearing Fault Detection

by Hannes Gruber, Anna Fuchs and Michael Bader

Sensors 2024, 24(7), 2138; https://doi.org/10.3390/s24072138 - 27 Mar 2024

Viewed by 440

Abstract

Roller bearings are critical components in various mechanical systems, and the timely detection of potential failures is essential for preventing costly downtimes and avoiding substantial machinery breakdown. This research focuses on finding and verifying a robust method that can detect failures early, without [...] Read more.

Roller bearings are critical components in various mechanical systems, and the timely detection of potential failures is essential for preventing costly downtimes and avoiding substantial machinery breakdown. This research focuses on finding and verifying a robust method that can detect failures early, without creating false positive failure states. Therefore, this paper introduces a novel algorithm for the early detection of roller bearing failures, particularly tailored to high-precision bearings and automotive test bed systems. The featured method (AFI—Advanced Failure Indicator) utilizes the Fast Fourier Transform (FFT) of wideband accelerometers to calculate the spectral content of vibration signals emitted by roller bearings. By calculating the frequency bands and tracking the movement of these bands within the spectra, the method provides an indicator of the machinery’s health, mainly focusing on the early stages of bearing failure. The calculated channel can be used as a trend indicator, enabling the method to identify subtle deviations associated with impending failures. The AFI algorithm incorporates a non-static limit through moving average calculations and volatility analysis methods to determine critical changes in the signal. This thresholding mechanism ensures the algorithm’s responsiveness to variations in operating conditions and environmental factors, contributing to its robustness in diverse industrial settings. Further refinement was achieved through an outlier detection filter, which reduces false positives and enhances the algorithm’s accuracy in identifying genuine deviations from the normal operational state. To benchmark the developed algorithm, it was compared with three industry-standard algorithms: VRMS calculations per ISO 10813-3, Mean Absolute Value of Extremums (MAVE), and Envelope Frequency Band (EFB). This comparative analysis aimed to evaluate the efficacy of the novel algorithm against the established methods in the field, providing valuable insights into its potential advantages and limitations. In summary, this paper presents an innovative algorithm for the early detection of roller bearing failures, leveraging FFT-based spectral analysis, trend monitoring, adaptive thresholding, and outlier detection. Its ability to confirm the first failure state underscores the algorithm’s effectiveness. Full article

(This article belongs to the Special Issue Sensors for Real-Time Condition Monitoring and Fault Diagnosis)

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22 pages, 7160 KiB

Open AccessArticle

Real-Time Speed Estimation for an Induction Motor: An Automated Tuning of an Extended Kalman Filter Using Voltage–Current Sensors

by Ines Miloud, Sebastien Cauet, Erik Etien, Jack P. Salameh and Alexandre Ungerer

Sensors 2024, 24(6), 1744; https://doi.org/10.3390/s24061744 - 07 Mar 2024

Viewed by 481

Abstract

This paper aims at achieving real-time optimal speed estimation for an induction motor using the Extended Kalman filter (EKF). Speed estimation is essential for fault diagnosis in Motor Current Signature Analysis (MCSA). The estimation accuracy is obtained by exploring the noise covariance matrices [...] Read more.

This paper aims at achieving real-time optimal speed estimation for an induction motor using the Extended Kalman filter (EKF). Speed estimation is essential for fault diagnosis in Motor Current Signature Analysis (MCSA). The estimation accuracy is obtained by exploring the noise covariance matrices estimation of the EKF algorithm. The noise covariance matrices are determined using a modified subspace model identification approach. In order to reach this goal, this method compares an estimated model of a deterministic system, derived from available input–output datasets (using voltage–current sensors), with the discrete-time state-space representation used in the Kalman filter equations. This comparison leads to the determination of model uncertainties, which are subsequently represented as noise covariance matrices. Based on the fifth-order nonlinear model of the induction motor, the rotor speed is estimated with the optimized EKF algorithm, and the algorithm is tested experimentally. Full article

(This article belongs to the Special Issue Sensors for Real-Time Condition Monitoring and Fault Diagnosis)

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24 pages, 12550 KiB

Open AccessArticle

Intelligent Fault Diagnosis of Rolling Bearings Based on a Complete Frequency Range Feature Extraction and Combined Feature Selection Methodology

by Zhengkun Xue, Yukun Huang, Wanyang Zhang, Jinchuan Shi and Huageng Luo

Sensors 2023, 23(21), 8767; https://doi.org/10.3390/s23218767 - 27 Oct 2023

Viewed by 956

Abstract

The utilization of multiscale entropy methods to characterize vibration signals has proven to be promising in intelligent diagnosis of mechanical equipment. However, in the current multiscale entropy methods, only the information in the low-frequency range is utilized and the information in the high-frequency [...] Read more.

The utilization of multiscale entropy methods to characterize vibration signals has proven to be promising in intelligent diagnosis of mechanical equipment. However, in the current multiscale entropy methods, only the information in the low-frequency range is utilized and the information in the high-frequency range is discarded. In order to take full advantage of the information, in this paper, a fault feature extraction method utilizing the bidirectional composite coarse-graining process with fuzzy dispersion entropy is proposed. To avoid the redundancy of the full frequency range feature information, the Random Forest algorithm combined with the Maximum Relevance Minimum Redundancy algorithm is applied to feature selection. Together with the K-nearest neighbor classifier, a rolling bearing intelligent diagnosis framework is constructed. The effectiveness of the proposed framework is evaluated by a numerical simulation and two experimental examples. The validation results demonstrate that the extracted features by the proposed method are highly sensitive to the bearing health conditions compared with hierarchical fuzzy dispersion entropy, composite multiscale fuzzy dispersion entropy, multiscale fuzzy dispersion entropy, multiscale dispersion entropy, multiscale permutation entropy, and multiscale sample entropy. In addition, the proposed method is able to identify the fault categories and health states of rolling bearings simultaneously. The proposed damage detection methodology provides a new and better framework for intelligent fault diagnosis of rolling bearings in rotating machinery. Full article

(This article belongs to the Special Issue Sensors for Real-Time Condition Monitoring and Fault Diagnosis)

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17 pages, 11200 KiB

Open AccessArticle

Experimental Study of Wheel-to-Rail Interaction Using Acceleration Sensors for Continuous Rail Transport Comfort Evaluation

by Ioana Mihăilescu, Gabriel Popa, Emil Tudor, Ionuț Vasile and Marius Alin Gheți

Sensors 2023, 23(19), 8064; https://doi.org/10.3390/s23198064 - 25 Sep 2023

Viewed by 922

Abstract

Rail transport comfort is ensured by predictive maintenance and continuous supervision of rail quality. Besides the specialized equipment, the authors are proposing a simple system that can be implemented on operational wagons while in service, aiming to detect irregularities in the rail and [...] Read more.

Rail transport comfort is ensured by predictive maintenance and continuous supervision of rail quality. Besides the specialized equipment, the authors are proposing a simple system that can be implemented on operational wagons while in service, aiming to detect irregularities in the rail and report them using the train’s online communication lines. The sensor itself is an acceleration sensor connected to an electronic microcontroller able to filter the inrush acceleration and send it to the diagnosis system of the wagon. This paper presents a study of real data recorded of the transversal and vertical vibrations of a standard tank wagon, measured on 2 axles and the car body, followed by the interpretation of the recorded data. Full article

(This article belongs to the Special Issue Sensors for Real-Time Condition Monitoring and Fault Diagnosis)

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22 pages, 6604 KiB

Open AccessArticle

Using Feature Engineering and Principal Component Analysis for Monitoring Spindle Speed Change Based on Kullback–Leibler Divergence with a Gaussian Mixture Model

by Yi-Cheng Huang and Ching-Chen Hou

Sensors 2023, 23(13), 6174; https://doi.org/10.3390/s23136174 - 05 Jul 2023

Viewed by 988

Abstract

Machining is a crucial constituent of the manufacturing industry, which has begun to transition from precision machinery to smart machinery. Particularly, the introduction of artificial intelligence into computer numerically controlled (CNC) machine tools will enable machine tools to self-diagnose during operation, improving the [...] Read more.

Machining is a crucial constituent of the manufacturing industry, which has begun to transition from precision machinery to smart machinery. Particularly, the introduction of artificial intelligence into computer numerically controlled (CNC) machine tools will enable machine tools to self-diagnose during operation, improving the quality of finished products. In this study, feature engineering and principal component analysis were combined with the online and real-time Gaussian mixture model (GMM) based on the Kullback–Leibler divergence’s measure to achieve the real-time monitoring of changes in manufacturing parameters. Based on the attached accelerometer device’s vibration signals and current sensing of the spindle, the developed GMM unsupervised learning was successfully used to diagnose the spindle speed changes of a CNC machine tool during milling. The F1-scores with improved experimental results for X, Y, and Z axes were 0.95, 0.88, and 0.93, respectively. The established FE-PCA-GMM/KLD method can be applied to issue warnings when it predicts a change in the manufacturing process parameter. A smart sensing device for diagnosing the machining status can be fabricated for implementation. The effectiveness of the developed method for determining the manufacturing parameter changes was successfully verified by experiments. Full article

(This article belongs to the Special Issue Sensors for Real-Time Condition Monitoring and Fault Diagnosis)

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16 pages, 4010 KiB

Open AccessArticle

Intelligent Diagnostics of Radial Internal Clearance in Ball Bearings with Machine Learning Methods

by Bartłomiej Ambrożkiewicz, Arkadiusz Syta, Anthimos Georgiadis, Alexander Gassner, Grzegorz Litak and Nicolas Meier

Sensors 2023, 23(13), 5875; https://doi.org/10.3390/s23135875 - 25 Jun 2023

Cited by 8 | Viewed by 1239

Abstract

This article classifies the dynamic response of rolling bearings in terms of radial internal clearance values. The value of the radial internal clearance in rolling-element bearings cannot be described in a deterministic manner, which shows the challenge of its detection through the analysis [...] Read more.

This article classifies the dynamic response of rolling bearings in terms of radial internal clearance values. The value of the radial internal clearance in rolling-element bearings cannot be described in a deterministic manner, which shows the challenge of its detection through the analysis of the bearing’s dynamics. In this article, we show the original approach to its intelligent detection through the analysis of short-time intervals and the calculation of chosen indicators, which can be assigned to the specific clearance class. The tests were carried out on a set of 10 brand new bearings of the same type (double row self-aligning ball bearing NTN 2309SK) with different radial internal clearances corresponding to individual classes of the ISO-1132 standard. The classification was carried out based on the time series of vibrations recorded by the accelerometer and then digitally processed. Window statistical indicators widely used in the diagnosis of rolling bearings, which served as features for the machine learning models, were calculated. The accuracy of the classification turned out to be unsatisfactory; therefore, it was decided to use a more advanced method of time series processing, which allows for the extraction of subsequent dominant frequencies into experimental modes (Variational Mode Decomposition (VMD)). Applying the same statistical indicators to the modes allowed for an increase in classification accuracy to over 90%. Full article

(This article belongs to the Special Issue Sensors for Real-Time Condition Monitoring and Fault Diagnosis)

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27 pages, 6326 KiB

Open AccessArticle

Dynamic Evaluation of the Degradation Process of Vibration Performance for Machine Tool Spindle Bearings

by Liang Ye, Wenhu Zhang, Yongcun Cui and Sier Deng

Sensors 2023, 23(11), 5325; https://doi.org/10.3390/s23115325 - 04 Jun 2023

Cited by 2 | Viewed by 917

Abstract

Real-time condition monitoring and fault diagnosis of spindle bearings are critical to the normal operation of the matching machine tool. In this work, considering the interference of random factors, the uncertainty of the vibration performance maintaining reliability (VPMR) is introduced for machine tool [...] Read more.

Real-time condition monitoring and fault diagnosis of spindle bearings are critical to the normal operation of the matching machine tool. In this work, considering the interference of random factors, the uncertainty of the vibration performance maintaining reliability (VPMR) is introduced for machine tool spindle bearings (MTSB). The maximum entropy method and Poisson counting principle are combined to solve the variation probability, so as to accurately characterize the degradation process of the optimal vibration performance state (OVPS) for MTSB. The dynamic mean uncertainty calculated using the least-squares method by polynomial fitting, fused into the grey bootstrap maximum entropy method, is utilized to evaluate the random fluctuation state of OVPS. Then, the VPMR is calculated, which is used to dynamically evaluate the failure degree of accuracy for MTSB. The results show that the maximum relative errors between the estimated true value and the actual value of the VPMR are 6.55% and 9.91%, and appropriate remedial measures should be taken before 6773 min and 5134 min for the MTSB in Case 1 and Case 2, respectively, so as to avoid serious safety accidents that are caused by the failure of OVPS. Full article

(This article belongs to the Special Issue Sensors for Real-Time Condition Monitoring and Fault Diagnosis)

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28 pages, 11677 KiB

Open AccessArticle

Fault Root Cause Tracking of the Mechanical Components of CNC Lathes Based on Information Transmission

by Yingzhi Zhang, Guiming Guo and Jialin Liu

Sensors 2023, 23(9), 4418; https://doi.org/10.3390/s23094418 - 30 Apr 2023

Viewed by 1268

Abstract

This study proposes a new method for the immediate fault warning and fault root tracing of CNC lathes. Here, the information acquisition scheme was formulated based on the analysis of the coupling relationship between the mechanical parts of CNC lathes. Once the collected [...] Read more.

This study proposes a new method for the immediate fault warning and fault root tracing of CNC lathes. Here, the information acquisition scheme was formulated based on the analysis of the coupling relationship between the mechanical parts of CNC lathes. Once the collected status signals were de-noised and coarse-grained, transfer entropy theory was introduced to calculate the net entropy of information transfer between the mechanical parts, after which the information transfer model was constructed. The sliding window method was used to determine the probability threshold interval of the net information transfer entropy between the lathe mechanical parts under different processing modes. Therefore, the transition critical point was determined according to the information entropy, and the fault development process was clarified. By analyzing the information transfer changes between the parts, fault early warning and fault root tracking on the CNC lathe were realized. The proposed method realizes the digitalization and intelligentization of fault diagnosis and has the advantages of timely and efficient diagnosis. Finally, the effectiveness of the proposed method is verified by a numerical control lathe tool processing experiment. Full article

(This article belongs to the Special Issue Sensors for Real-Time Condition Monitoring and Fault Diagnosis)

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16 pages, 3190 KiB

Open AccessArticle

An Online Monitoring Method for Low-Frequency Dielectric Loss Angle of Mining Cables

by Yanwen Wang, Peng Chen, Chen Feng and Jiyuan Cao

Sensors 2023, 23(9), 4273; https://doi.org/10.3390/s23094273 - 25 Apr 2023

Viewed by 1015

Abstract

The dielectric loss angle can better reflect the overall insulation level of mining cables, so it is necessary to implement reliable and effective online monitoring of the dielectric loss angle of mining cables. In order to improve the monitoring accuracy of the dielectric [...] Read more.

The dielectric loss angle can better reflect the overall insulation level of mining cables, so it is necessary to implement reliable and effective online monitoring of the dielectric loss angle of mining cables. In order to improve the monitoring accuracy of the dielectric loss angle tan δ of mining cables, a low-frequency dielectric loss angle online monitoring method combining signal injection method and double-end synchronous measurement method is proposed in this paper. Firstly, the superiority of the low-frequency signal in improving the detection accuracy of dielectric loss angle is explained, and the feasibility of the low-frequency signal injection method is analyzed. Secondly, the cable leakage is calculated using the double-terminal synchronous measurement method to measure the core current at the first and last ends of the cable, and the phase sum of the voltage at the first and last ends is selected as the reference phase quantity to realize the effective calculation of the dielectric loss angle tan δ of the cable. Then, the simulation model for online monitoring of dielectric loss angle of mining cable is built, and the feasibility of the online monitoring method proposed in this paper is verified by combining the simulation results. Finally, the theoretical and simulation analysis of the monitoring error of dielectric loss angle of mining cable is carried out. Full article

(This article belongs to the Special Issue Sensors for Real-Time Condition Monitoring and Fault Diagnosis)

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12 pages, 1816 KiB

Open AccessArticle

A Real-Time Deep Machine Learning Approach for Sudden Tool Failure Prediction and Prevention in Machining Processes

by Mahmoud Hassan, Ahmad Sadek and Helmi Attia

Sensors 2023, 23(8), 3894; https://doi.org/10.3390/s23083894 - 11 Apr 2023

Cited by 1 | Viewed by 1971

Abstract

Tool Condition Monitoring systems are essential to achieve the desired industrial competitive advantage in terms of reducing costs, increasing productivity, improving quality, and preventing machined part damage. A sudden tool failure is analytically unpredictable due to the high dynamics of the machining process [...] Read more.

Tool Condition Monitoring systems are essential to achieve the desired industrial competitive advantage in terms of reducing costs, increasing productivity, improving quality, and preventing machined part damage. A sudden tool failure is analytically unpredictable due to the high dynamics of the machining process in the industrial environment. Therefore, a system for detecting and preventing sudden tool failures was developed for real-time implementation. A discrete wavelet transform lifting scheme (DWT) was developed to extract a time-frequency representation of the AE_rms signals. A long short-term memory (LSTM) autoencoder was developed to compress and reconstruct the DWT features. The variations between the reconstructed and the original DWT representations due to the induced acoustic emissions (AE) waves during unstable crack propagation were used as a prefailure indicator. Based on the statistics of the LSTM autoencoder training process, a threshold was defined to detect tool prefailure regardless of the cutting conditions. Experimental validation results demonstrated the ability of the developed approach to accurately predict sudden tool failures before they occur and allow enough time to take corrective action to protect the machined part. The developed approach overcomes the limitations of the prefailure detection approach available in the literature in terms of defining a threshold function and sensitivity to chip adhesion-separation phenomenon during the machining of hard-to-cut materials. Full article

(This article belongs to the Special Issue Sensors for Real-Time Condition Monitoring and Fault Diagnosis)

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17 pages, 3942 KiB

Open AccessArticle

Hot-Pressing Furnace Current Monitoring and Predictive Maintenance System in Aerospace Applications

by Hong-Ming Chen, Jia-Hao Zhang, Yu-Chieh Wang, Hsiang-Ching Chang, Jen-Kai King and Chao-Tung Yang

Sensors 2023, 23(4), 2230; https://doi.org/10.3390/s23042230 - 16 Feb 2023

Viewed by 1722

Abstract

This research combines the application of artificial intelligence in the production equipment fault monitoring of aerospace components. It detects three-phase current abnormalities in large hot-pressing furnaces through smart meters and provides early preventive maintenance. Different anomalies are classified, and a suitable monitoring process [...] Read more.

This research combines the application of artificial intelligence in the production equipment fault monitoring of aerospace components. It detects three-phase current abnormalities in large hot-pressing furnaces through smart meters and provides early preventive maintenance. Different anomalies are classified, and a suitable monitoring process algorithm is proposed to improve the overall monitoring quality, accuracy, and stability by applying AI. We also designed a system to present the heater’s power consumption and the hot-pressing furnace’s fan and visualize the process. Combining artificial intelligence with the experience and technology of professional technicians and researchers to detect and proactively grasp the health of the hot-pressing furnace equipment improves the shortcomings of previous expert systems, achieves long-term stability, and reduces costs. The complete algorithm introduces a model corresponding to the actual production environment, with the best model result being XGBoost with an accuracy of 0.97. Full article

(This article belongs to the Special Issue Sensors for Real-Time Condition Monitoring and Fault Diagnosis)

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19 pages, 6799 KiB

Open AccessArticle

Implementation of Parameter Observer for Capacitors

by Corneliu Bărbulescu, Dadiana-Valeria Căiman, Sorin Nanu and Toma-Leonida Dragomir

Sensors 2023, 23(2), 948; https://doi.org/10.3390/s23020948 - 13 Jan 2023

Cited by 3 | Viewed by 1079

Abstract

This paper describes the implementation of a parameter observer (PO) intended to estimate the capacitance and equivalent serial resistance of a capacitor (ESR). The implemented observer consists of a dynamic second-order discrete-time system. The input signal of the observer is the [...] Read more.

This paper describes the implementation of a parameter observer (PO) intended to estimate the capacitance and equivalent serial resistance of a capacitor (ESR). The implemented observer consists of a dynamic second-order discrete-time system. The input signal of the observer is the voltage at the terminals of the capacitor measured during its discharge across a variable resistance in two steps. The implemented observer can be used in quasi-online or offline mode. The theoretical and experimental supporting materials provide a comprehensive picture of the implementation and conditions of use of the PO. The experimental verification was carried out with a microcontroller with Cortex^®-M7 core architecture. The sampling time of the PO was 20 μs, and the estimation of the parameters was obtained before the end of the discharge of the capacitor. In the cases described in the paper, this means approximately 25 ms. Due to the PO’s capabilities (estimation speed, reduced computational complexity and precision)—proved by the experiments carried out on three electrolytic capacitors of 100 μF, 220 μF and 440 μF—the implementation is of interest for several applications, primarily in the field of power electronic applications. Full article

(This article belongs to the Special Issue Sensors for Real-Time Condition Monitoring and Fault Diagnosis)

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21 pages, 4688 KiB

Open AccessArticle

Real-Time Remaining Useful Life Prediction of Cutting Tools Using Sparse Augmented Lagrangian Analysis and Gaussian Process Regression

by Xiao Qin, Weizhi Huang, Xuefei Wang, Zezhi Tang and Zepeng Liu

Sensors 2023, 23(1), 413; https://doi.org/10.3390/s23010413 - 30 Dec 2022

Cited by 4 | Viewed by 2250

Abstract

Remaining useful life (RUL) of cutting tools is concerned with cutting tool operational status prediction and damage prognosis. Most RUL prediction methods utilized different features collected from different sensors to predict the life of the tool. To increase the prediction accuracy, it is [...] Read more.

Remaining useful life (RUL) of cutting tools is concerned with cutting tool operational status prediction and damage prognosis. Most RUL prediction methods utilized different features collected from different sensors to predict the life of the tool. To increase the prediction accuracy, it is often necessary to mount a great deal of sensors on the machine in order to collect more types of signals, which can heavily increase the cost in industrial applications. To deal with this issue, this study, for the first time, proposed a new feature network dictionary, which can enlarge the number of candidate features under limited sensor conditions, and the developed dictionary can potentially contain as much useful information as possible. This process can replace the installation of more sensors and incorporate more information. Then, the sparse augmented Lagrangian (SAL) feature selection method is proposed to reduce the number of candidate features and select the most significant features. Finally, the selected features are input to the Gaussian Process Regression (GPR) model for the RUL estimation. Extensive experiments demonstrate that our proposed RUL estimation framework output performs traditional methods, especially for the cost savings for on-line RUL estimation. Full article

(This article belongs to the Special Issue Sensors for Real-Time Condition Monitoring and Fault Diagnosis)

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34 pages, 11498 KiB

Open AccessArticle

Predictive Diagnosis Based on Predictor Symptoms for Isolated Photovoltaic Systems Using MPPT Charge Regulators

by Emilio García, Eduardo Quiles, Antonio Correcher and Francisco Morant

Sensors 2022, 22(20), 7819; https://doi.org/10.3390/s22207819 - 14 Oct 2022

Cited by 5 | Viewed by 1331

Abstract

In this work, new results are presented on the implementation of predictive diagnosis techniques on isolated photovoltaic (PV) systems and installations. The novelties introduced in this research focus on the additional advantages obtained from the point of view of predictive diagnosis of faults [...] Read more.

In this work, new results are presented on the implementation of predictive diagnosis techniques on isolated photovoltaic (PV) systems and installations. The novelties introduced in this research focus on the additional advantages obtained from the point of view of predictive diagnosis of faults caused by partial shading in isolated PV installations using maximum power point tracking (MPPT) regulators. MPPT regulators are comparatively more appropriate than pulse width modulation (PWM) solar regulators in order to implement fault diagnosis systems. MPPT regulators have a physical separation between the electrical parameters belonging to the part of the solar panel with respect to the batteries part. Therefore, these electrical parameters can be used to obtain early predictive symptoms of the effects of partial shading with a greater level of observation and sensitivity. Additionally, modifications are proposed in the PV system assembly to obtain greater homogeneity of all the panels regarding the solar irradiance reception angle. Full article

(This article belongs to the Special Issue Sensors for Real-Time Condition Monitoring and Fault Diagnosis)

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17 pages, 8693 KiB

Open AccessTutorial

Signal Processing for the Condition-Based Maintenance of Rotating Machines via Vibration Analysis: A Tutorial

by Omri Matania, Lior Bachar, Eric Bechhoefer and Jacob Bortman

Sensors 2024, 24(2), 454; https://doi.org/10.3390/s24020454 - 11 Jan 2024

Viewed by 1229

Abstract

One of the common methods for implementing the condition-based maintenance of rotating machinery is vibration analysis. This tutorial describes some of the important signal processing methods existing in the field, which are based on a profound understanding of the component’s physical behavior. Furthermore, [...] Read more.

One of the common methods for implementing the condition-based maintenance of rotating machinery is vibration analysis. This tutorial describes some of the important signal processing methods existing in the field, which are based on a profound understanding of the component’s physical behavior. Furthermore, this tutorial provides Python and MATLAB code examples to demonstrate these methods alongside explanatory videos. The goal of this article is to serve as a practical tutorial, enabling interested individuals with a background in signal processing to quickly learn the important principles of condition-based maintenance of rotating machinery using vibration analysis. Full article

(This article belongs to the Special Issue Sensors for Real-Time Condition Monitoring and Fault Diagnosis)

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