Experimental Studies to Assess Surface Wear Using Grease Degradation, Bearing Temperature and Statistical Parameter of Vibration Signals in a Roller Bearing

Abstract An on-line monitoring and diagnostic system is needed to detect faulty bearings. In this work, by applying the feature selection technique to the data obtained from vibration signals, six indices were selected. Artificial neural networks were used for nonlinear pattern recognition. An attempt was made to distinguish between normal and defective bearings. Counterpropagation neural networks with various network sizes were trained for these tasks. The counterpropagation neural networks were able to recognize a normal from a defective bearing with the success rate between 88.3% to 100%. The best results were obtained when all the six indices were used for the on-line classification of roller bearings.

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Fault Detection and Discrimination of Rotating Machinery Using Frequency Symptom Parameter and Bayesian Network

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.470.683 ◽

2013 ◽

Vol 470 ◽

pp. 683-688

Author(s):

Hai Yang Jiang ◽

Hua Qing Wang ◽

Peng Chen

Keyword(s):

Fault Diagnosis ◽

Fault Detection ◽

Bayesian Network ◽

Frequency Domain ◽

Evaluation Method ◽

Roller Bearing ◽

Rotating Machinery ◽

Discrimination Index ◽

Vibration Signals ◽

Diagnosis Method

This paper proposes a novel fault diagnosis method for rotating machinery based on symptom parameters and Bayesian Network. Non-dimensional symptom parameters in frequency domain calculated from vibration signals are defined for reflecting the features of vibration signals. In addition, sensitive evaluation method for selecting good non-dimensional symptom parameters using the method of discrimination index is also proposed for detecting and distinguishing faults in rotating machinery. Finally, the application example of diagnosis for a roller bearing by Bayesian Network is given. Diagnosis results show the methods proposed in this paper are effective.

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Application of entropy in identification of breathing cracks in a beam structure: Simulations and experimental studies

Structural Health Monitoring ◽

10.1177/1475921717704626 ◽

2017 ◽

Vol 17 (3) ◽

pp. 549-564 ◽

Cited By ~ 8

Author(s):

Buddhi Wimarshana ◽

Nan Wu ◽

Christine Wu

Keyword(s):

Cantilever Beam ◽

Time Domain ◽

Fatigue Cracks ◽

Unit Length ◽

Crack Depth ◽

Experimental Studies ◽

Sample Entropy ◽

Breathing Crack ◽

Engineering Structures ◽

Vibration Signals

A cantilever beam with a breathing crack is studied to detect the crack and evaluate the crack depth using entropy measures. During the vibration in engineering structures, fatigue cracks undergo the status from close-to-open (and open-to-close) repetitively leading to a crack breathing phenomenon. Entropy is a measure, which can quantify the complexity or irregularity in system dynamics, and hence employed to quantify the bi-linearity/irregularity of the vibration response, which is induced by the breathing phenomenon of a crack. A mathematical model of harmonically excited unit length steel cantilever beam with a breathing crack located near the fixed end is established, and an iterative numerical method is applied to generate accurate time domain vibration responses. The steady-state time domain vibration signals are pre-processed with wavelet transformation, and the bi-linearity/irregularity of the vibration signals due to breathing effect is then successfully quantified using both sample entropy and quantized approximation of sample entropy to detect and estimate the crack depth. It is observed that the method is capable of identifying crack depths even at very early stages of 3% of the beam thickness with significant increment in the entropy values (more than 200%) compared to the healthy beam. In addition, experimental studies are conducted, and the simulation results are in good agreement with the experimental results. The proposed technique can also be applied to damage identification in other types of structures, such as plates and shells.

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Digital Analysis of the Vibration Signals Amplitude Spectrum Based on Fourier Processing of the Binary-Sign Analog-Stochastic Quantization Result

Mekhatronika Avtomatizatsiya Upravlenie ◽

10.17587/mau.20.723-731 ◽

2019 ◽

Vol 20 (12) ◽

pp. 723-731

Author(s):

V. N. Yakimov ◽

V. I. Batyschev ◽

A. V. Mashkov

Keyword(s):

Harmonic Analysis ◽

Computational Efficiency ◽

Amplitude Spectrum ◽

Experimental Studies ◽

Analytical Calculation ◽

Frequency Resolution ◽

Stochastic Quantization ◽

Complex Signals ◽

Workload Analysis ◽

Vibration Signals

The article is devoted to the problem of developing a digital algorithm for operational harmonic analysis of complex vibration signals. The basis for solving this problem was the generalized equation of statistical measurements, which defines the measurement procedure as the sequential execution of interrelated measurement and computational transformations. During the development of the algorithm, special attention is paid to analog-to-digital conversion because it directly affects the computational efficiency of digital procedures for obtaining the final result. As such a conversion, the use of binarysign analog-stochastic quantization is justified, which allows performing two-level quantization without systematic error regardless of the statistical properties of the analyzed signals. The discrete-event model of the binary-sign analog-stochastic quantization result allowed for the analytical calculation of integration operations in the transition to estimating the amplitude spectrum in digital form. As a result, the developed algorithm of harmonic analysis does not require performing digital multiplication operations typical for classical algorithms, which are based on the calculation of the direct discrete Fourier transform. The execution of the algorithm is reduced to the implementation of the addition and subtraction arithmetic operations of the cosine-function values in the time moments determined by the result of the binary-sign analogue-stochastic quantization. The exclusion of digital multiplication operations provided an increase in the computational efficiency of amplitude spectrum estimation. Laboratory studies of the developed algorithm were carried out using simulation modeling. The simulation results showed that the algorithm allows calculating estimates of the amplitude spectrum of complex signals with high accuracy and frequency resolution in the presence of additive noise. In real conditions, the testing of the developed algorithm was carried out during bench studies of the operational status of the MAZ-206067 bus, designed for the transportation of passengers on urban and suburban routes of average workload. Analysis of the results of experimental studies confirmed the possibility of using the algorithm as part of the diagnosability provision for operational monitoring of vibration signals in a complex noise environment.

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Wind turbine bearing fault diagnosis based on adaptive local iterative filtering and approximate entropy

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

10.1177/0954406216642478 ◽

2016 ◽

Vol 231 (17) ◽

pp. 3228-3237 ◽

Cited By ~ 9

Author(s):

Xueli An ◽

Luoping Pan

Keyword(s):

Fault Diagnosis ◽

Wind Turbine ◽

Nearest Neighbor ◽

Roller Bearing ◽

Approximate Entropy ◽

Vibration Signals ◽

Outer Race ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Iterative Filtering

For the unsteady characteristics of a fault vibration signal from a wind turbine rolling bearing, a bearing fault diagnosis method based on adaptive local iterative filtering and approximate entropy is proposed. The adaptive local iterative filtering method is used to decompose original vibration signals into a finite number of stationary components. The components which comprise major fault information are selected for further analysis. The approximate entropy of the selected components is calculated as a fault feature value and input to a fault classifier. The classifier is based on the nearest neighbor algorithm. The vibration signals from a spherical roller bearing on a wind turbine in its normal state, with an outer race fault, an inner race fault and a roller fault are analyzed. The results show that the proposed method can accurately and efficiently identify the fault modes present in the rolling bearings of a wind turbine.

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Vibration measurement of a cantilever beam using root embedded piezoceramic sensor

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

10.1243/09544062jmes621 ◽

2008 ◽

Vol 222 (2) ◽

pp. 147-161 ◽

Cited By ~ 8

Author(s):

M Sunar ◽

B O Al-Bedoor

Keyword(s):

Cantilever Beam ◽

Experimental Studies ◽

Finite Element Approximation ◽

Vibration Measurement ◽

Element Approximation ◽

Structural Vibrations ◽

Vibration Signals ◽

Rotating Blade ◽

Set Up ◽

Transient And Steady State

Numerical and experimental studies are carried out to investigate the usability of a piezoceramic (PZT) sensor placed in the root of a stationary cantilever beam for measuring structural vibrations. The ability of the sensor for picking up the vibration signals during both the transient and steady-state phases is investigated. The piezoelectric equations obtained using the Hamilton's principle together with the finite-element approximation are utilized to extract the voltage outputs of the PZT sensor. An experimental set-up, to validate the theoretical results, is designed and manufactured. The experimentally measured sensor voltages are compared with the numerical ones. The results showed the excellent performance of the sensors in reading vibration signals of the beam. The root embedded PZT approach is an important step towards the application of measuring rotating blade vibrations.

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Intelligent fault diagnosis using image representation of multi-domain features

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-202730 ◽

2021 ◽

pp. 1-13

Author(s):

Yulong Zhang ◽

Chaofei Zhang ◽

Jian Tan ◽

Frank Lim ◽

Menglan Duan

Keyword(s):

Fault Diagnosis ◽

Image Representation ◽

Experimental Studies ◽

Extraction Methods ◽

Single Domain ◽

Image Transformation ◽

Rolling Bearings ◽

Vibration Signals ◽

Multiple Domains ◽

Domain Information

Deep learning (DL) algorithms, especially the convolutional neural network (CNN), have been proven as a newly developed tool in machinery intelligent diagnosis. However, the current CNN-based fault diagnosis studies usually consider features or images extracted from a single domain as model input. This single domain information may not reflect fault patterns comprehensively, leading to low modeling accuracy and inaccurate diagnostic results. To overcome this limitation, this paper proposes a new CNN-based fault diagnosis approach using image representation considering multi-domain features of vibration signals. First, multi-domain features of vibration signals are extracted. These extracted features are then used to construct a n × n matrix, and subsequently to form images by RGB color transformations. This image transformation technique allows for capturing complementary and rich diagnostic information from multiple domains. At last, these images associated with different mechanical defects are fed into a CNN model that is improved based on the classic LeNet-5 CNN architecture for fault diagnosis and identification. Comparative experiments with the traditional feature extraction methods as well as state-of-the-art CNN-based methods are also investigated. Experimental studies on rolling bearings validate the effectiveness and superiorities of the proposed approach.

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Connected Components-based Colour Image Representations of Vibrations for a Two-stage Fault Diagnosis of Roller Bearings Using Convolutional Neural Networks

Chinese Journal of Mechanical Engineering ◽

10.1186/s10033-021-00553-8 ◽

2021 ◽

Vol 34 (1) ◽

Author(s):

Hosameldin O. A. Ahmed ◽

Asoke K Nandi

Keyword(s):

Feature Extraction ◽

Fault Diagnosis ◽

Roller Bearing ◽

Fault Classification ◽

Health Conditions ◽

Connected Components ◽

Rotating Machines ◽

Two Stage ◽

Vibration Signals ◽

Roller Bearings

AbstractRoller bearing failure is one of the most common faults in rotating machines. Various techniques for bearing fault diagnosis based on faults feature extraction have been proposed. But feature extraction from fault signals requires expert prior information and human labour. Recently, deep learning algorithms have been applied extensively in the condition monitoring of rotating machines to learn features automatically from the input data. Given its robust performance in image recognition, the convolutional neural network (CNN) architecture has been widely used to learn automatically discriminative features from vibration images and classify health conditions. This paper proposes and evaluates a two-stage method RGBVI-CNN for roller bearings fault diagnosis. The first stage in the proposed method is to generate the RGB vibration images (RGBVIs) from the input vibration signals. To begin this process, first, the 1-D vibration signals were converted to 2-D grayscale vibration Images. Once the conversion was completed, the regions of interest (ROI) were found in the converted 2-D grayscale vibration images. Finally, to produce vibration images with more discriminative characteristics, an algorithm was applied to the 2-D grayscale vibration images to produce connected components-based RGB vibration images (RGBVIs) with sets of colours and texture features. In the second stage, with these RGBVIs a CNN-based architecture was employed to learn automatically features from the RGBVIs and to classify bearing health conditions. Two cases of fault classification of rolling element bearings are used to validate the proposed method. Experimental results of this investigation demonstrate that RGBVI-CNN can generate advantageous health condition features from bearing vibration signals and classify the health conditions under different working loads with high accuracy. Moreover, several classification models trained using RGBVI-CNN offered high performance in the testing results of the overall classification accuracy, precision, recall, and F-score.

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Deep wavelet sequence-based gated recurrent units for the prognosis of rotating machinery

Structural Health Monitoring ◽

10.1177/1475921720933155 ◽

2020 ◽

pp. 147592172093315

Author(s):

Meng Ma ◽

Zhu Mao

Keyword(s):

Prognostic Model ◽

Health Management ◽

Experimental Studies ◽

Remaining Useful Life ◽

Vibration Signals ◽

Useful Life ◽

Multi Level ◽

Gated Recurrent Units ◽

Gated Recurrent Unit ◽

Domain Information

Prognostics and health management (PHM) is an emerging technique which aims to improve the reliability and safety of machinery systems. Remaining useful life (RUL) prediction is the key part of PHM which provides operators how long the machine keeps working without breakdowns. In this study, a novel prognostic model is proposed for RUL prediction using deep wavelet sequence-based gated recurrent units (GRU). This proposed wavelet sequence-based gated recurrent unit (WSGRU) specifically adopts a wavelet layer and generates wavelet sequences at different scales. Since vibration signals exhibit non-stationary characteristics, wavelet analysis is thereby needed to capture both the time and frequency domain information to fully identify the degradation of the rotating components. In the proposed WSGRU, the vibration signals are decomposed into different frequency sub-bands via wavelet transformation, and then a deep GRU architecture is designed to predict the RUL taking advantage of the temporal dependencies that naturally exist in the waveforms. Experimental studies have been performed for RUL prediction of bearings with collection of vibration signals during the run-to-failure tests. The prediction results show that deep WSGRU outperforms traditional models due to the multi-level feature extraction on the transformed multiscale wavelet sequences.

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Bearing Fault Parameter Identification Under Varying Operating Conditions Using Vibration Signals and Evolutionary Algorithms

Volume 4B: Dynamics, Vibration, and Control ◽

10.1115/imece2014-39124 ◽

2014 ◽

Author(s):

Issam Abu-Mahfouz ◽

Amit Banerjee

Keyword(s):

Evolutionary Algorithms ◽

Differential Evolution ◽

Parameter Identification ◽

Roller Bearing ◽

Optimization Techniques ◽

Operating Conditions ◽

System Response ◽

Vibration Signals ◽

Bearing Fault ◽

Fault Parameter

This paper presents an effective bearing fault parameter identification scheme based on evolutionary optimization techniques. Three seeded faults in the rotating machinery supported by the test roller bearing include inner race fault, outer race fault and a single ball defect. The fault related features are extracted experimentally by processing the acquired vibration signals in both the time and frequency domain. Techniques based on the power spectral density (PSD) and wavelet transform (WT) are utilized for feature extraction. The sensitivity of the proposed method is investigated under varying operating speeds and radial bearing load. In this study, the inverse problem of parameter identification is investigated. The problem of parameter identification is recast as an optimization problem and two well known evolutionary algorithms, differential evolution (DE) and particle swarm optimization (PSO), are used to identify system parameters given a system response. For online parameter identification, differential evolution outperforms particle both in terms of adaptability and tighter convergence properties. The distinction between the two methods is not distinctively obvious on the offline parameter identification problem.

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