scholarly journals Detection of Rolling-Element Bearing Faults in Non-stationary Quasi-Parallel Machinery Using Residual Analysis Augmented by Neural Networks

2021 ◽  
Vol 12 (2) ◽  
Author(s):  
Dustin Helm ◽  
Markus Timusk
Keyword(s):  
Fault Detection ◽  
Operating Conditions ◽  
Rolling Element Bearing ◽  
Detection Scheme ◽  
Bearing Faults ◽  
Feature Vectors ◽  
Gear Mesh ◽  
Rolling Element ◽  
Element Bearing ◽  
The Relationship

This work proposes a methodology for the detection of rolling-element bearing faults in quasi-parallel machinery. In the context of this work, parallel machinery is considered to be any group of identical components of a mechanical system that are linked to operate on the same duty cycle.  Quasi-parallel machinery can further be defined as two components not identical mechanically, but their operating conditions are correlated and they operate in the same environmental conditions. Furthermore, a new fault detection architecture is proposed wherein a feed-forward neural network (FFNN) is utilized to identify the relationship between signals. The proposed technique is based on the analysis of a calculated residual between feature vectors from two separate components. This technique is designed to reduce the effects of changes in the machines operating state on the condition monitoring system. When a fault detection system is monitoring multiple components in a larger system that are mechanically linked, signals and information that can be gleaned from the system can be used to reduce influences from factors that are not related to condition. The FFNN is used to identify the relationship between the feature vectors from two quasi-parallel components and eliminate the difference when no fault is present. The proposed method is tested on vibration data from two gearboxes that are connected in series. The gearboxes contain bearings operating at different speeds and gear mesh frequencies. In these conditions, a variety of rolling-element bearing faults are detected. The results indicate that improvement in fault detection accuracy can be achieved by using the additional information available from the quasi-parallel machine. The proposed method is directly compared to a typical AANN novelty detection scheme.

Rolling element bearing multi-fault diagnosis using morphological joint time–frequency adaptive kernel–based semi-smart framework

2021 ◽  
pp. 107754632110228
Author(s):  
Sunil Lonare ◽  
Neville Fernandes ◽  
Aditya Abhyankar
Keyword(s):  
Wavelet Transform ◽  
Fault Detection ◽  
Fault Location ◽  
Operating Conditions ◽  
Experimental Results ◽  
Rolling Element Bearing ◽  
Time Frequency ◽  
Rolling Element ◽  
Adaptive Kernel ◽  
Element Bearing

The wavelet transform is a state of the art time–frequency analysis method for rolling element bearing localized fault detection, using vibration signals. When these localized faults are present at more than one location of bearing, it is called “multi-fault.” Using wavelet transform fault detection with high severity is possible, but this method fails to detect the presence of fault as well as the location of a fault in multi-fault case and when the fault severity is low. The identification of the fault location, in rolling element bearing when more than one location of bearing contains a localized fault, is very useful for further root cause analysis; therefore, multi-fault detection is a challenge today. In the present work, a new morphological joint time–frequency adaptive kernel–based semi-smart framework is developed to address this challenge. In morphological joint time–frequency adaptive kernel, the kernel will adapt itself by analyzing the basic morphology of the bearing under observation and by considering the location of a fault. The simulation and experimental results show that morphological joint time–frequency adaptive kernel–based framework is able to detect low severity single fault as well as the location of the localized fault on rolling element bearing in the multi-fault case. Experimental results also show that the morphological joint time–frequency adaptive kernel framework is independent of bearing dimensions as well as machine operating conditions.

scholarly journals A Novel Deep Learning Model for the Detection and Identification of Rolling Element-Bearing Faults

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5112 ◽  
Author(s):  
Alex Shenfield ◽  
Martin Howarth
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Real Time ◽  
Operating Conditions ◽  
Rolling Element Bearing ◽  
Series Data ◽  
Frequency Noise ◽  
Bearing Faults ◽  
Rolling Element ◽  
Element Bearing

Real-time acquisition of large amounts of machine operating data is now increasingly common due to recent advances in Industry 4.0 technologies. A key benefit to factory operators of this large scale data acquisition is in the ability to perform real-time condition monitoring and early-stage fault detection and diagnosis on industrial machinery—with the potential to reduce machine down-time and thus operating costs. The main contribution of this work is the development of an intelligent fault diagnosis method capable of operating on these real-time data streams to provide early detection of developing problems under variable operating conditions. We propose a novel dual-path recurrent neural network with a wide first kernel and deep convolutional neural network pathway (RNN-WDCNN) capable of operating on raw temporal signals such as vibration data to diagnose rolling element bearing faults in data acquired from electromechanical drive systems. RNN-WDCNN combines elements of recurrent neural networks (RNNs) and convolutional neural networks (CNNs) to capture distant dependencies in time series data and suppress high-frequency noise in the input signals. Experimental results on the benchmark Case Western Reserve University (CWRU) bearing fault dataset show RNN-WDCNN outperforms current state-of-the-art methods in both domain adaptation and noise rejection tasks.

Wavelet Analysis and Envelope Detection For Rolling Element Bearing Fault Diagnosis—Their Effectiveness and Flexibilities

2001 ◽  
Vol 123 (3) ◽  
pp. 303-310 ◽  
Author(s):  
Peter W. Tse ◽  
Y. H. Peng ◽  
Richard Yam
Keyword(s):  
Fault Diagnosis ◽  
Wavelet Analysis ◽  
Rolling Element Bearing ◽  
Frequency Spectra ◽  
Bearing Faults ◽  
Time Frequency ◽  
Envelope Detection ◽  
Rolling Element ◽  
Element Bearing ◽  
Bearing Characteristic

The components which often fail in a rolling element bearing are the outer-race, the inner-race, the rollers, and the cage. Such failures generate a series of impact vibrations in short time intervals, which occur at Bearing Characteristic Frequencies (BCF). Since BCF contain very little energy, and are usually overwhelmed by noise and higher levels of macro-structural vibrations, they are difficult to find in their frequency spectra when using the common technique of Fast Fourier Transforms (FFT). Therefore, Envelope Detection (ED) is always used with FFT to identify faults occurring at the BCF. However, the computation of ED is complicated, and requires expensive equipment and experienced operators to process. This, coupled with the incapacity of FFT to detect nonstationary signals, makes wavelet analysis a popular alternative for machine fault diagnosis. Wavelet analysis provides multi-resolution in time-frequency distribution for easier detection of abnormal vibration signals. From the results of extensive experiments performed in a series of motor-pump driven systems, the methods of wavelet analysis and FFT with ED are proven to be efficient in detecting some types of bearing faults. Since wavelet analysis can detect both periodic and nonperiodic signals, it allows the machine operator to more easily detect the remaining types of bearing faults which are impossible by the method of FFT with ED. Hence, wavelet analysis is a better fault diagnostic tool for the practice in maintenance.

scholarly journals Cyclostationary Analysis for Gearbox and Bearing Fault Diagnosis

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Zhipeng Feng ◽  
Fulei Chu
Keyword(s):  
Rolling Element Bearing ◽  
Bearing Faults ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Cyclic Frequency ◽  
Fm Signals ◽  
Rolling Element ◽  
Element Bearing ◽  
Bearing Vibration ◽  
Cyclic Spectrum

Gearbox and rolling element bearing vibration signals feature modulation, thus being cyclostationary. Therefore, the cyclic correlation and cyclic spectrum are suited to analyze their modulation characteristics and thereby extract gearbox and bearing fault symptoms. In order to thoroughly understand the cyclostationarity of gearbox and bearing vibrations, the explicit expressions of cyclic correlation and cyclic spectrum for amplitude modulation and frequency modulation (AM-FM) signals are derived, and their properties are summarized. The theoretical derivations are illustrated and validated by gearbox and bearing experimental signal analyses. The modulation characteristics caused by gearbox and bearing faults are extracted. In faulty gearbox and bearing cases, more peaks appear in cyclic correlation slice of 0 lag and cyclic spectrum, than in healthy cases. The gear and bearing faults are detected by checking the presence or monitoring the magnitude change of peaks in cyclic correlation and cyclic spectrum and are located according to the peak cyclic frequency locations or sideband frequency spacing.

Rolling element bearing faults diagnosis based on autocorrelation of optimized: wavelet de-noising technique

2009 ◽  
Vol 40 (3-4) ◽  
pp. 393-402 ◽  
Author(s):  
Khalid F. Al-Raheem ◽  
Asok Roy ◽  
K. P. Ramachandran ◽  
D. K. Harrison ◽  
Steven Grainger
Keyword(s):  
Rolling Element Bearing ◽  
Bearing Faults ◽  
Rolling Element ◽  
Element Bearing ◽  
Faults Diagnosis
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