bearing fault diagnosis based on spectrum images of

Condition Monitoring of Chain Sprocket Drive System

This paper proposes a condition monitoring method for the early defect detection in a chain sprocket drive (CSD) system and classification of fault types before a catastrophic failure occurs In the operation of a CSD system early defect detection is very useful in preventing system failure In this work eight fault types associated with the CSD system components such as the gear tooth

2D Empirical Transforms Wavelets Ridgelets and

2018-9-18(2018) Study on a Motor Bearing Fault Diagnosis Method Using Improved EWT Based on Scale Space Threshold Method International Journal of Emerging Electric Power Systems 19 :4 (2018) Gear fault feature extraction and classification of singular value decomposition based on Hilbert empirical wavelet transform

QIBR Bearing Damage Analysis with Lubrication

2020-7-224 TIMKEN BEARING DAMAGE ANALSIS WITH LUBRICATION REFERENCE GUIDE 2015 The QIBR Company Let's face it – bearings work in a tough world Excessive contamination Poor lubrication High heats Heavy vibrations These merely scratch the

Bearing Fault Diagnosis based on Convolutional

Abstract: Mechanical fault diagnosis and analysis is of paramount importance to ensure reliable and safe operation of various industrial systems As the massive field data becomes more available data-driven fault diagnosis becomes feasible and prevalent But the traditional methods have its limitations in feature extraction and most related research focus on improving the classification

Low

In this article a low-cost computer system for the monitoring and diagnosis of the condition of the induction motor (IM) rolling bearings is demonstrated and tested The system allows the on-line monitoring of the IM bearings and subsequent fault diagnostics based on analysis of the vibration measurement data The evaluation of the bearing condition is made by a suitably trained neural

Bearing fault diagnosis based on spectrum images of

2016-3-1Bearing fault diagnosis has been a challenge in the monitoring activities of rotating machinery and it's receiving more and more attention The conventional fault diagnosis methods usually extract features from the waveforms or spectrums of vibration signals in order to correctly classify faults In this paper a novel feature in the form of images is presented namely analysis of the

Liu J Chen G M Dong Singular spectrum analysis and continuous hidden Markov model for rolling element bearing fault diagnosis Journal of Vibration and Control 2015 21(8): 1506-1521 [21] H F Tang J Chen G M Dong Sparse representation based latent components analysis for machinery weak fault detection Mechanical Systems and Signal Processing 2014 46(2): 373-388

Rolling Element Bearing Diagnosis Based on Signal

A feature extraction scheme for rolling element bearing (REB) fault diagnosis by editing the cepstrum of the original vibration is introduced in this paper In the presented approach the order analysis technology is utilized to convert an even-time-spaced scaled signal to an even-angle-spaced signal by resampling the acquired signal The discrete lines belonging to gears are removed by

Rolling Element Bearing Diagnosis Based on Signal

A feature extraction scheme for rolling element bearing (REB) fault diagnosis by editing the cepstrum of the original vibration is introduced in this paper In the presented approach the order analysis technology is utilized to convert an even-time-spaced scaled signal to an even-angle-spaced signal by resampling the acquired signal The discrete lines belonging to gears are removed by

Automated Bearing Fault Diagnosis Using 2D Analysis

Traditional fault diagnosis methods of bearings detect characteristic defect frequencies in the envelope power spectrum of the vibration signal These defect frequencies depend upon the inherently nonstationary shaft speed Time-frequency and subband signal analysis of vibration signals has been used to deal with random variations in speed whereas design variations require retraining a new

Resonance

2020-4-132 Planetary gearbox fault diagnosis based on RSAVMD Analyze the signal by RSAVMD The number of decomposition K is selected firstly the Q of components in the case of different decomposition Numbers (only the variation value was listed when k = 3–10 due to the space limitation) was obtained as shown in Table 2

、、

[17] T Liu J Chen G M Dong Singular spectrum analysis and continuous hidden Markov model for rolling element bearing fault diagnosis Journal of Vibration and Control 2015 21(8): 1506-1521 [18] H F Tang J Chen G M Dong Sparse representation based latent components analysis for machinery weak fault detection Mechanical Systems and Signal Processing 2014 46( 2 ) : 373-388

Incipient fault diagnosis in bearings under variable

2019-3-19Traditional feature extraction based methods for bearing fault diagnosis extract features from the raw fault signal only i e vibration acceleration or AE signal However as demonstrated by the results in Table 4 it results in poor discriminatory models for different fault

Applied Sciences

Finally fault diagnosis is based on the multiple fault dependency matrix and the ranking of each fault type calculated basing on the prior probability of each fault status An illustrative numerical example and a case study are presented to verify the effectiveness and superiority of the proposed method

Rotational speed invariant fault diagnosis in bearings

2016-4-12Structural vibrations of bearing housings are used for diagnosing fault conditions in bearings primarily by searching for characteristic fault frequencies in the envelope power spectrum of the vibration signal The fault frequencies depend on the non-stationary angular speed of the rotating shaft This paper explores an imaging-based approach to achieve rotational speed independence

FaultDiagnosisofBearingswithAdjustedVibration

2019-7-30Most fault diagnosis methods are based on vibration signals [1–3] and the diagnostic procedure mainly includes two steps: (1) extracting fea- end bearing with inner-race fault size being 0 021 inches undertwospeedconditions[12] eanalyzedsignalsboth spectrum images and a CF specifies the window for cap-

International Journal of Pattern Recognition and

The fault diagnosis intelligent algorithm makes full use of the associative memory and pattern recognition function of the neural network to compare the abnormal value of various parameters of the engine fault with the reference value of the known fault mode which can shorten the fault diagnosis time and improve the diagnosis efficiency

Research Article Automated Bearing Fault Diagnosis

2019-7-30Microtexture analysis of fault images Local binary patterns Global histogram Training k-NN classier Grayscale fault image F : e proposed bearing fault diagnosis scheme based on microtexture analysis of the grayscale vibration acceleration fault images 3 The Proposed Fault Diagnosis Scheme Vibration Image Construction e proposed fault diag-

Fault Diagnosis of Roller Bearing Based on Bispectrum

Performing bispectrum analysis on the actual measured vibration signals of the roller bearing with different failure modes it developed that the spectrum distribution regions are similar among the same failure modes and distinguishable among the different failure modes thus this character can be used to classify fault types The binary images extracted from the bispectra are taken as the

Bearing fault diagnosis based on spectrum image

2019-10-30In this article a novel bearing fault diagnosis method is proposed and it uses the sparse coefficients of spectrum image as the features based on vibration signal First of all the frequency spectrum images of normal and faulty bearings are achieved based on fast Fourier transformation (FFT) of vibration signals where all images are with

Sensors

Bearing fault diagnosis of a rotating machine plays an important role in reliable operation A novel intelligent fault diagnosis method for roller bearings has been developed based on a proposed hybrid classifier ensemble approach and the improved Dempster-Shafer theory The improved Dempster-Shafer theory well considered the combination of unreliable evidence sources the uncertainty

Convolutional Neural Net and Bearing Fault Analysis

There has been immense success on the application of Convolutional Neural Nets (CNN) to image and acoustic data analysis In this paper rather than preprocessing vibration signals to denoise or extract features we investigate the usage of CNNs on raw signals in particular we test the accuracy of CNNs as classifiers on bearing fault data by varying the configurations of the CNN from one

Bearing Fault Diagnosis Based on Statistical Locally

2015-7-6Nowadays various fault diagnosis methods have been proposed for actual roller bearing fault detection based on vibration signals obtained from accelerometer sensors Fault diagnosis of rolling bearings is now a very important research area in machinery engineering The essence of fault diagnosis is pattern recognition and classification

Rolling Element Bearing Diagnosis Based on Signal

A feature extraction scheme for rolling element bearing (REB) fault diagnosis by editing the cepstrum of the original vibration is introduced in this paper In the presented approach the order analysis technology is utilized to convert an even-time-spaced scaled signal to an even-angle-spaced signal by resampling the acquired signal The discrete lines belonging to gears are removed by

Vibration Analysis

Rolling Element Bearing Fault Diagnosis (Predictive Maintenance Toolbox) Perform fault diagnosis of a rolling element bearing based on acceleration signals Apply envelope spectrum analysis and spectral kurtosis to fault diagnosis on bearings Modal Analysis of Identified Models

A new rolling bearing fault diagnosis method based

2016-12-154 Rolling bearing fault diagnosis based on GFT impulse component extraction The response of rolling bearings with a fault is often characterized by the presence of periodic impulses When a rolling bearing develops faults a high-frequency shock is generated and the amplitude of the vibration signal is modulated by the impulse force

Convolutional Neural Net and Bearing Fault Analysis

There has been immense success on the application of Convolutional Neural Nets (CNN) to image and acoustic data analysis In this paper rather than preprocessing vibration signals to denoise or extract features we investigate the usage of CNNs on raw signals in particular we test the accuracy of CNNs as classifiers on bearing fault data by varying the configurations of the CNN from one

Bearing Fault Diagnosis based on Convolutional

Abstract: Mechanical fault diagnosis and analysis is of paramount importance to ensure reliable and safe operation of various industrial systems As the massive field data becomes more available data-driven fault diagnosis becomes feasible and prevalent But the traditional methods have its limitations in feature extraction and most related research focus on improving the classification

Fault Diagnosis of Roller Bearing Based on Bispectrum

Performing bispectrum analysis on the actual measured vibration signals of the roller bearing with different failure modes it developed that the spectrum distribution regions are similar among the same failure modes and distinguishable among the different failure modes thus this character can be used to classify fault types The binary images extracted from the bispectra are taken as the