scholarly journals Confirmation of Thermal Images and Vibration Signals for Intelligent Machine Fault Diagnostics

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Achmad Widodo ◽  
Djoeli Satrijo ◽  
Toni Prahasto ◽  
Gang-Min Lim ◽  
Byeong-Keun Choi
Keyword(s):  
Vibration Signal ◽  
Rolling Element Bearing ◽  
Self Organizing Map ◽  
Fault Diagnostics ◽  
Vibration Signals ◽  
Thermal Images ◽  
Outer Race ◽  
Intelligent Machine ◽  
Machine Fault ◽  
The Time Domain

This paper deals with the maintenance technique for industrial machinery using the artificial neural network so-called self-organizing map (SOM). The aim of this work is to develop intelligent maintenance system for machinery based on an alternative way, namely, thermal images instead of vibration signals. SOM is selected due to its simplicity and is categorized as an unsupervised algorithm. Following the SOM training, machine fault diagnostics is performed by using the pattern recognition technique of machine conditions. The data used in this work are thermal images and vibration signals, which were acquired from machine fault simulator (MFS). It is a reliable tool and is able to simulate several conditions of faulty machine such as unbalance, misalignment, looseness, and rolling element bearing faults (outer race, inner race, ball, and cage defects). Data acquisition were conducted simultaneously by infrared thermography camera and vibration sensors installed in the MFS. The experimental data are presented as thermal image and vibration signal in the time domain. Feature extraction was carried out to obtain salient features sensitive to machine conditions from thermal images and vibration signals. These features are then used to train the SOM for intelligent machine diagnostics process. The results show that SOM can perform intelligent fault diagnostics with plausible accuracies.

A fractal-dimension-based envelope demodulation for rolling element bearing fault feature extraction from vibration signals

2016 ◽  
Vol 230 (18) ◽  
pp. 3194-3211 ◽  
Author(s):  
Juanjuan Shi ◽  
Ming Liang
Keyword(s):  
Feature Extraction ◽  
Fractal Dimension ◽  
Search Algorithm ◽  
Vibration Signal ◽  
Rolling Element Bearing ◽  
Vibration Signals ◽  
Bearing Fault ◽  
Bearing Condition Monitoring ◽  
Interference Frequency ◽  
Fault Feature Extraction

Vibration analysis has been extensively used as an effective tool for bearing condition monitoring. The vibration signal collected from a defective bearing is, however, a mixture of several signal components including the fault feature (i.e. fault-induced impulses), periodic interferences from other mechanical/electrical components, and background noise. The incipient impulses which excite as well as modulate the resonance frequency of the system are easily masked by compounded effects of periodic interferences and noise, making it challenging to do a reliable fault diagnosis. As such, this paper proposes an envelope demodulation method termed short time fractal dimension (STFD) transform for fault feature extraction from such vibration signal mixture. STFD transform calculation related issues are first addressed. Then, by STFD, the original signal can be quickly transformed into a STFD representation, where the envelope of fault-induced impulses becomes more pronounced whereas interferences are partly weakened due to their morphological appearance differences. It has been found that the lower the interference frequency, the less effect the interference has on STFD representations. When interference frequency keeps increasing, more effects on STFD representations will be resulted. Such effects can be reduced by the proposed kurtosis-based peak search algorithm (KPSA). Therefore, bearing fault signature is kept and interferences are further weakened in the STFD-KPSA representation. The proposed method has been favourably compared with two widely used enveloping methods, i.e. multi-morphological analysis and energy operator, in terms of extracting impulse envelopes from vibration signals obscured by multiple interferences. Its performance has also been examined using both simulated and experimental data.

scholarly journals A Signal Based Triangular Structuring Element for Mathematical Morphological Analysis and Its Application in Rolling Element Bearing Fault Diagnosis

2014 ◽  
Vol 2014 ◽  
pp. 1-16 ◽  
Author(s):  
Zhaowen Chen ◽  
Ning Gao ◽  
Wei Sun ◽  
Qiong Chen ◽  
Fengying Yan ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Correlation Analysis ◽  
Vibration Signal ◽  
Classification Performance ◽  
Rolling Element Bearing ◽  
Outer Race ◽  
Structuring Element ◽  
Signal Features ◽  
Cause Of Failure ◽  
Nonlinear Signal

Mathematical morphology (MM) is an efficient nonlinear signal processing tool. It can be adopted to extract fault information from bearing signal according to a structuring element (SE). Since the bearing signal features differ for every unique cause of failure, the SEs should be well tailored to extract the fault feature from a particular signal. In the following, a signal based triangular SE according to the statistics of the magnitude of a vibration signal is proposed, together with associated methodology, which processes the bearing signal by MM analysis based on proposed SE to get the morphology spectrum of a signal. A correlation analysis on morphology spectrum is then employed to obtain the final classification of bearing faults. The classification performance of the proposed method is evaluated by a set of bearing vibration signals with inner race, ball, and outer race faults, respectively. Results show that all faults can be detected clearly and correctly. Compared with a commonly used flat SE, the correlation analysis on morphology spectrum with proposed SE gives better performance at fault diagnosis of bearing, especially the identification of the location of outer race fault and the level of fault severity.

scholarly journals Bearing Health Assessment Based on Chaotic Characteristics

2013 ◽  
Vol 20 (3) ◽  
pp. 519-530 ◽  
Author(s):  
Chen Lu ◽  
Qian Sun ◽  
Laifa Tao ◽  
Hongmei Liu ◽  
Chuan Lu
Keyword(s):  
Health Assessment ◽  
Vibration Signal ◽  
Assessment Method ◽  
Largest Lyapunov Exponent ◽  
Health State ◽  
Self Organizing Map ◽  
Linear Complex ◽  
Vibration Signals ◽  
Confidence Value

Vibration signals extracted from rotating parts of machinery carry a lot of useful information about the condition of operating machine. Due to the strong non-linear, complex and non-stationary characteristics of vibration signals from working bearings, an accurate and reliable health assessment method for bearing is necessary. This paper proposes to utilize the selected chaotic characteristics of vibration signal for health assessment of a bearing by using self-organizing map (SOM). Both Grassberger-Procaccia algorithm and Takens' theory are employed to calculate the characteristic vector which includes three chaotic characteristics, such as correlation dimension, largest Lyapunov exponent and Kolmogorov entropy. After that, SOM is used to map the three corresponding characteristics into a confidence value (CV) which represents the health state of the bearing. Finally, a case study based on vibration datasets of a group of testing bearings was conducted to demonstrate that the proposed method can reliably assess the health state of bearing.

ANN Based Fault Classification of High Speed Ball Bearings

2009 ◽  
Author(s):  
Aashish Bhatnagar ◽  
P. K. Kankar ◽  
Satish C. Sharma ◽  
S. P. Harsha
Keyword(s):  
High Speed ◽  
Fault Analysis ◽  
Fault Classification ◽  
Wavelet Filter ◽  
Vibration Signals ◽  
Training Time ◽  
Outer Race ◽  
Key Issues ◽  
The Time Domain ◽  
Vibration Signatures

In the rotating machines, maintenance of the high speed operated bearings is the major problem and is one of the key issues due to excessive vibrations. Hence, the vibration signatures can be used as a feature for the fault diagnosis. This paper presents the Artificial Neural Networks (ANN) based fault analysis, which is used to classify various known faults using the features extracted from the vibration signals. The vibration signals from the piezoelectric accelerometers are being measured for the following conditions — No defect (NOD), Outer race defect (ORD), Inner race defect (IRD), Ball fault (BF) and Combination of above (COMB). The features are extracted from the time domain using the statistical method. These features are filtered using wavelet filter & kernel filter and compiled as the input vectors. The multilayer neural network is trained by these input vectors. The training and testing results show that wavelet and kernel filter can be effective tool in the diagnosis of ball bearing faults using ANN. Results obtained from the ANN predict that the wavelet filter provides good accuracy with reduction in the training time.

Gearbox Fault Diagnosis Using Vibration Signal with Wavelet De-Noising

2011 ◽  
Vol 86 ◽  
pp. 735-738
Author(s):  
Zhi Feng Dong ◽  
Hui Cheng ◽  
Hui Jia Yang ◽  
Wei Fu ◽  
Ji Wei Chen ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Frequency Domain Analysis ◽  
Sensor Data ◽  
Spectrum Estimation ◽  
Vibration Signals ◽  
Fault Features ◽  
Vibration Signal Analysis ◽  
Simulation Signal ◽  
The Time Domain

This paper dealt with the gearbox fault diagnosis with vibration signal analysis. The vibration signals from experiment contained a lot of noises which result from motor, gears, bears and box, and were collected through accelerate sensor, data collector and computer. The wavelet de-noising stratification was used to de-noise the vibration signals before the frequency-domain analysis was done. The effects of the simulation signal de-noising was contrasted, and the noise cancellation the power spectrum estimation was carried out. The experimental and analytical results show that the different features are indicated with vibration signal of the normal gearbox and the signal with bolts loosened of the gearbox. The gearbox fault with bolts loosened can be diagnosed by extracting the time-domain fault features of vibration signals.

scholarly journals Experimental Investigation and Fault Diagnosis for Buckled Wet Clutch Based on Multi-Speed Hilbert Spectrum Entropy

Entropy ◽  
2021 ◽  
Vol 23 (12) ◽  
pp. 1704
Author(s):  
Jiaqi Xue ◽  
Biao Ma ◽  
Man Chen ◽  
Qianqian Zhang ◽  
Liangjie Zheng
Keyword(s):  
Fault Diagnosis ◽  
Time Domain ◽  
Vibration Signal ◽  
Bench Test ◽  
Vehicle Performance ◽  
Vibration Signals ◽  
Hilbert Spectrum ◽  
Wet Clutch ◽  
Diagnosis Method ◽  
The Time Domain

The multi-disc wet clutch is widely used in transmission systems as it transfers the torque and power between the gearbox and the driving engine. During service, the buckling of the friction components in the wet clutch is inevitable, which can shorten the lifetime of the wet clutch and decrease the vehicle performance. Therefore, fault diagnosis and online monitoring are required to identify the buckling state of the friction components. However, unlike in other rotating machinery, the time-domain features of the vibration signal lack efficiency in fault diagnosis for the wet clutch. This paper aims to present a new fault diagnosis method based on multi-speed Hilbert spectrum entropy to classify the buckling state of the wet clutch. Firstly, the wet clutch is classified depending on the buckling degree of the disks, and then a bench test is conducted to obtain vibration signals of each class at varying speeds. By comparing the accuracy of different classifiers with and without entropy, Hilbert spectrum entropy shows higher efficiency than time-domain features for the wet clutch diagnosis. Thus, the classification results based on multi-speed entropy achieve even better accuracy.

scholarly journals Adaptive online terrain classification method for mobile robot based on vibration signals

2021 ◽  
Vol 18 (6) ◽  
pp. 172988142110620
Author(s):  
Mingming Wang ◽  
Liming Ye ◽  
Xiaoyun Sun
Keyword(s):  
Mobile Robot ◽  
Vibration Signal ◽  
Classification Model ◽  
Classification Method ◽  
Terrain Classification ◽  
Vibration Signals ◽  
Time Frequency ◽  
Combined Features ◽  
Different Dimensions ◽  
The Time Domain

To improve the accuracy of terrain classification during mobile robot operation, an adaptive online terrain classification method based on vibration signals is proposed. First, the time domain and the combined features of the time, frequency, and time–frequency domains in the original vibration signal are extracted. These are adopted as the input of the random forest algorithm to generate classification models with different dimensions. Then, by judging the relationship between the current speed of the mobile robot and its critical speed, the classification model of different dimensions is adaptively selected for online classification. Offline and online experiments are conducted for four different terrains. The experimental results show that the proposed method can effectively avoid the self-vibration interference caused by an increase in the robot’s moving speed and achieve higher terrain classification accuracy.

Window Functions for the Calculation of the Time Domain Averages of the Vibration of the Individual Planet Gears and Sun Gear in an Epicyclic Gearbox

1994 ◽  
Vol 116 (2) ◽  
pp. 179-187 ◽  
Author(s):  
P. D. McFadden
Keyword(s):  
Detailed Analysis ◽  
Time Domain ◽  
Vibration Signal ◽  
Window Function ◽  
Vibration Signals ◽  
Numerical Examples ◽  
Rectangular Window ◽  
Window Functions ◽  
The Time Domain ◽  
The Individual

An existing technique which enables the estimation of the time domain averages of the tooth meshing vibration of the individual planet and sun gears in an epicyclic gearbox from measured vibration signals has been revised. A key feature of the existing technique is the sampling of the vibration signal within a rectangular window in the time domain when one of the planet gears is close to the vibration transducer. The revised technique permits the use of other window functions, and a detailed analysis shows that the errors in the estimate of the time domain average can be expressed in terms of the window function. Several suitable window functions which enable a reduction in the level of the errors are demonstrated by numerical examples and by the analysis of data from a test on a helicopter gearbox with deliberate damage to one of the planet gears.

Application of the Hilbert – Huang Transform for Machine Fault Diagnostics

2012 ◽  
Vol 182-183 ◽  
pp. 1484-1488 ◽  
Author(s):  
Zhao Yan Xuan ◽  
Miao Ge
Keyword(s):  
Fourier Transform ◽  
Hilbert Transform ◽  
Fault Diagnostics ◽  
Vibration Signals ◽  
Hilbert Spectrum ◽  
Hilbert Huang Transform ◽  
Failure Characteristic ◽  
Machine Fault ◽  
The Fourier Transform ◽  
Emd Method

The vibration signals of the running machine contain non-stationary components. Usually, these non-stationary components contain abundant information on machine faults. In this paper, the Hilbert–Huang transform (HHT) method for the machine fault diagnosis is proposed. The empirical mode decomposition (EMD) method and Hilbert transform are key parts of the Hilbert–Huang transform method. The EMD will generate a collection of intrinsic mode functions (IMF). By applying EMD method and Hilbert transform to the vibration signal, we can get the Hilbert spectrum from which the faults in a running machine can be diagnosed and fault patterns can be identified. The practical vibration signals measured from roller machine with eccentric and friction faults are analysed by the Hilbert–Huang transform and Fourier transform in this paper. Finally, HHT’s performance in rolling machine fault detection is compared with that of the Fourier transform. The comparison results have shown that the HHT is superior than the Fourier transform in machine fault diagnostics. The different failure characteristic frequencies can be distinguished in the component of different orders of IMF, and the time and frequency of failure characteristic frequency appearance can be clearly reflected in the Hilbert spectrum.

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