scholarly journals Application of Vibration Signals for the Quantitative Analysis of the Optimal Threshold of Bearing Failure

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
YaoChi Tang ◽  
Kuohao Li
Keyword(s):  
Distribution Curve ◽  
Health Management ◽  
Fractal Dimensions ◽  
Optimal Threshold ◽  
Normal Operation ◽  
Bearing Failure ◽  
Vibration Signals ◽  
Time Domain Signal ◽  
Overlap Rate ◽  
Gauss Distribution

This study established the prognostics and health management system for bearing failure. The vibration signals measured during the bearing operation were used for prognostics. First, the time-domain signal of vibration was calculated through generalized fractal dimensions, and the relationship diagram of generalized fractal dimensions and time was obtained. Then, the trend of bearing failure was compared by the GFDal results. However, the results can only be used for qualitative feature extraction. The bearing failure at the beginning cannot be determined by qualitative methods. Therefore, this study further converted the calculation results of GFDs into a Gauss distribution curve based on the statistical method under normal operation of the bearing. The Gauss distribution curve of the bearing under normal operation and at different time was overlapped. The overlap rate of the bearing area under different times was calculated. The minimum value was taken as the diagnostic standard, which was the optimal threshold of bearing failure defined in this study and was used as the quantitative basis for bearing failure. Therefore, the comparison of the area overlap rate under the Gauss distribution curve between the normal bearing and the bearing under test could provide diagnosis to the bearing failure. Moreover, the time point of the initial failure of the bearing could also be estimated based on the optimal failure threshold.

scholarly journals STUDY OF EFFECT OF SOLID CONTAMINANTS IN THE LUBRICANT ON BALL BEARINGS VIBRATION

2012 ◽  
pp. 28-31
Author(s):  
ONKAR L. MAHAJAN ◽  
ABHAY A. UTPAT
Keyword(s):  
Experimental Tests ◽  
Solid Particles ◽  
Particle Sizes ◽  
Ball Bearings ◽  
Bearing Failure ◽  
Mean Square ◽  
Rolling Bearings ◽  
Vibration Signals ◽  
Deep Groove ◽  
Concentration Levels

In deep groove ball bearings contamination of lubricant grease by solid particles is one of the main reason for early bearing failure. To deal with such problem, it is fundamental not only the use of reliable techniques concerning detection of solid contamination but also the investigation of the effects of certain contaminant characteristics on bearing performance. Nowadays the techniques such as vibration measurements are being increasingly used for on-time monitoring of machinery performance. The present work investigates the effect of lubricant contamination by solid particles on the dynamic behavior of rolling bearings, in order to determine the trends in the amounts of vibration affected by contamination in the Grease and by the bearing wear itself. Experimental tests are performed with Deep-groove ball bearings. The Dolomite powder in three concentration levels and different particle sizes was used to contaminate the grease. Vibration signals were analyzed in terms of Root Mean Square (RMS) values and also in terms of defect frequencies.

scholarly journals Phase Synchrony Analysis of Rolling Bearing Vibrations and Its Application to Failure Identification

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2964 ◽  
Author(s):  
Qing Zhang ◽  
Tingting Jiang ◽  
Joseph D. Yan
Keyword(s):  
Phase Synchronization ◽  
Chaotic Behavior ◽  
Rolling Bearing ◽  
Bearing Failure ◽  
Phase Synchrony ◽  
Vibration Signals ◽  
Mode Decomposition ◽  
Initial Stage ◽  
Failure Evolution ◽  
Band Limited

As the failure-induced component (FIC) in the vibration signals of bearings transmits through housings and shafts, potential phase synchronization is excited among multichannel signals. As phase synchrony analysis (PSA) does not involve the chaotic behavior of signals, it is suitable for characterizing the operating state of bearings considering complicated vibration signals. Therefore, a novel PSA method was developed to identify and track the failure evolution of bearings. First, resonance demodulation and variational mode decomposition (VMD) were combined to extract the mono-component or band-limited FIC from signals. Then, the instantaneous phase of the FIC was analytically solved using Hilbert transformation. The generalized phase difference (GPD) was used to quantify the relationship between FICs extracted from different vibration signals. The entropy of the GPD was regarded as the indicator for quantifying failure evolution. The proposed method was applied to the vibration signals obtained from an accelerated failure experiment and a natural failure experiment. Results showed that phase synchronization in bearing failure evolution was detected and evaluated effectively. Despite the chaotic behavior of the signals, the phase synchronization indicator could identify bearing failure during the initial stage in a robust manner.

The Effect of Operational Parameters on Vibration Signals of Wind Turbine Gearboxes

2019 ◽  
Author(s):  
Sofia Koukoura ◽  
Eric Bechhoefer ◽  
James Carroll ◽  
Alasdair McDonald
Keyword(s):  
Wind Turbine ◽  
Operating Conditions ◽  
Operating Costs ◽  
Normal Operation ◽  
Operational Parameters ◽  
Wind Turbine Gearbox ◽  
Vibration Signals ◽  
Gearbox Vibration ◽  
Maintenance Decisions ◽  
Yaw Angle

Abstract Vibration signals are widely used in wind turbine drivetrain condition monitoring with the aim of fault detection, optimization of maintenance actions and therefore reduction of operating costs. Signals are most commonly sampled by accelerometers at high frequency for a few seconds. The behavior of these signals varies significantly, even within the same turbine and depends on different parameters. The aim of this paper is to explore the effect of operational and environmental conditions on the vibration signals of wind turbine gearboxes. Parameters such as speed, power and yaw angle are taken into account and the change in vibration signals is examined. The study includes examples from real wind turbines of both normal operation and operation with known gearbox faults. The effects of varying operating conditions are removed using kalman filtering as a state observer. The findings of this paper will aid in understanding wind turbine gearbox vibration signals, making more informed decisions in the presence of faults and improving maintenance decisions.

Multi-fault Condition Monitoring of Slurry Pump with Principle Component Analysis and Sequential Hypothesis Test

2019 ◽  
Vol 34 (07) ◽  
pp. 2059019 ◽  
Author(s):  
Hanxin Chen ◽  
Wenjian Huang ◽  
Jinmin Huang ◽  
Chenghao Cao ◽  
Liu Yang ◽  
...  
Keyword(s):  
Condition Monitoring ◽  
Hypothesis Test ◽  
Wavelet Packet ◽  
Principal Component ◽  
Vibration Signal ◽  
Component Analysis ◽  
Ratio Test ◽  
Vibration Signals ◽  
Time Domain Signal ◽  
Sequential Probability

A new method about the multi-fault condition monitoring of slurry pump based on principal component analysis (PCA) and sequential probability ratio test (SPRT) is proposed. The method identifies the condition of the slurry pump by analyzing the vibration signal. The experimental model is established using the normal impeller and the faulty impellers where the collected vibration signals were preprocessed using wavelet packet transform (WPT). The characteristic parameters of the vibration signals are extracted by time domain signal analysis and the dimension of data was reduced by PCA. The principal components with the largest contribution rate are chosen as the inputted signal to SPRT to assess the proposed algorithm. The new methodology is reasonable and practical for the multi-fault diagnosis of slurry pump.

scholarly journals Rolling Element Bearing Fault Diagnosis Based on Multiscale General Fractal Features

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Weigang Wen ◽  
Zhaoyan Fan ◽  
Donald Karg ◽  
Weidong Cheng
Keyword(s):  
Fault Diagnosis ◽  
Fractal Dimensions ◽  
Feature Space ◽  
Operating Conditions ◽  
Rolling Element Bearing ◽  
Rolling Element Bearings ◽  
Vibration Signals ◽  
Bearing Fault Diagnosis ◽  
Rolling Element ◽  
Element Bearing

Nonlinear characteristics are ubiquitous in the vibration signals produced by rolling element bearings. Fractal dimensions are effective tools to illustrate nonlinearity. This paper proposes a new approach based on Multiscale General Fractal Dimensions (MGFDs) to realize fault diagnosis of rolling element bearings, which are robust to the effects of variation in operating conditions. The vibration signals of bearing are analyzed to extract the general fractal dimensions in multiscales, which are in turn utilized to construct a feature space to identify fault pattern. Finally, bearing faults are revealed by pattern recognition. Case studies are carried out to evaluate the validity and accuracy of the approach. It is verified that this approach is effective for fault diagnosis of rolling element bearings under various operating conditions via experiment and data analysis.

scholarly journals Fault Diagnosis for Rotating Machinery Based on Convolutional Neural Network and Empirical Mode Decomposition

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Yuan Xie ◽  
Tao Zhang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Empirical Mode Decomposition ◽  
Spatial Information ◽  
Health Management ◽  
Rotating Machinery ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Mode Decomposition

The analysis of vibration signals has been a very important technique for fault diagnosis and health management of rotating machinery. Classic fault diagnosis methods are mainly based on traditional signal features such as mean value, standard derivation, and kurtosis. Signals still contain abundant information which we did not fully take advantage of. In this paper, a new approach is proposed for rotating machinery fault diagnosis with feature extraction algorithm based on empirical mode decomposition (EMD) and convolutional neural network (CNN) techniques. The fundamental purpose of our newly proposed approach is to extract distinguishing features. Frequency spectrum of the signal obtained through fast Fourier transform process is trained in a designed CNN structure to extract compressed features with spatial information. To solve the nonstationary characteristic, we also apply EMD technique to the original vibration signals. EMD energy entropy is calculated using the first few intrinsic mode functions (IMFs) which contain more energy. With features extracted from both methods combined, classification models are trained for diagnosis. We carried out experiments with vibration data of 52 different categories under different machine conditions to test the validity of the approach, and the results indicate it is more accurate and reliable than previous approaches.

scholarly journals Predicting Remaining Useful Life Based on Hilbert–Huang Entropy with Degradation Model

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Yuhuang Zheng
Keyword(s):  
Health Management ◽  
Detection Threshold ◽  
Prediction Method ◽  
Remaining Useful Life ◽  
Health State ◽  
Health Indicator ◽  
Degradation Model ◽  
Vibration Signals ◽  
Current Health ◽  
Useful Life

Prognostics health management (PHM) of rotating machinery has become an important process for increasing reliability and reducing machine malfunctions in industry. Bearings are one of the most important equipment parts and are also one of the most common failure points. To assess the degradation of a machine, this paper presents a bearing remaining useful life (RUL) prediction method. The method relies on a novel health indicator and a linear degradation model to predict bearing RUL. The health indicator is extracted by using Hilbert–Huang entropy to process horizontal vibration signals obtained from bearings. We present a linear degradation model to estimate RUL using this health indicator. In the training phase, the degradation detection threshold and the failure threshold of this model are estimated by the distribution of 600 bootstrapped samples. These bootstrapped samples are taken from the six training sets. In the test phase, the health indicator and the model are used to estimate the bearing’s current health state and predict its RUL. This method is suitable for the degradation of bearings. The experimental results show that this method can effectively monitor bearing degradation and predict its RUL.

The State Recognition for Rotary Machines Based on Fractal Theory and Neural Network

2013 ◽  
Vol 404 ◽  
pp. 485-489 ◽  
Author(s):  
Qiang Huang ◽  
Xiao Zhuo Ouyang ◽  
Qiu Ping Huang
Keyword(s):  
Neural Network ◽  
Rbf Neural Network ◽  
Fractal Theory ◽  
Fractal Dimensions ◽  
The State ◽  
Connecting Rod ◽  
Wavelet Theory ◽  
Vibration Signals ◽  
State Recognition ◽  
Rotary Machines

A new state recognition method for rotary machines based on the fractal theory and neural network is proposed, and it is analyzed with the example of bushing abrasion of the connecting rod in diesel engine. Firstly, the wavelet theory is used to reduce noises in the vibration signals and then pick up the generalized fractal dimensions with different iterative steps. They will be the input parameters of the RBF neural network, and the output ones are the four working states. After being trained, the model of neural network can identify the states by the vibration signals. According to the experiment and simulation, the wavelet noise reduction can reproduce the vibration signals clearly and optimize the state recognition. The method based on the fractal theory and neural network is demonstrated to be efficient and feasible, and it can identify the states correctly. It has preferable engineering applicability and the referenced value to other vibration diagnosis of rotary machines.

Detection of Wear Condition of Micro Milling Cutters Based on Length Fractal Dimension

2014 ◽  
Vol 577 ◽  
pp. 697-700
Author(s):  
Zhi Qiang Wang ◽  
Gui Ying Zhang ◽  
Bin Liu
Keyword(s):  
Fractal Dimension ◽  
Time Domain ◽  
Fractal Dimensions ◽  
Experimental Results ◽  
New Method ◽  
Micro Milling ◽  
Vibration Signals ◽  
Reference Samples ◽  
Wear Condition

In this paper, a new method to realize online wear detection of micro-milling cutters based on length fractal dimension is proposed. On the basis of expression derivation of length fractal dimension, experiments are conducted. First, several cutters with different wear condition are chosen as reference samples. Their multi-section vibration signals in time-domain are collected and the clustering domain δ of each sample are obtained based on length fractal dimensions. Then, the vibration signals of tested cutters are monitored and analysed in time domain, thus their length fractal dimension are abstracted. Comparing the length fractal dimension of tested cutters with the clustering domain δ of reference samples, the wear condition of tested cutters are detected. The experimental results show that the length fractal dimension of each tested cutter falls in the clustering domain corresponding to the actual wear condition.

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

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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