On-Line Tracking and Monitoring of Rolling Element Bearing Faults

2014 ◽  
Author(s):  
S. Chatterton ◽  
P. Borghesani ◽  
P. Pennacchi ◽  
A. Vania
Keyword(s):  
Real Time ◽  
Damage Index ◽  
Vibration Signal ◽  
Operating Conditions ◽  
Rolling Element Bearing ◽  
Simple Task ◽  
Alarm Signaling ◽  
Rolling Element ◽  
On Line ◽  
Element Bearing

Diagnostics of rolling element bearings is usually performed by the analysis of vibration signal using suitable signal analysis tools, such as the most used and simplest method, Envelope Analysis. This method is based on the identification of bearing damage frequency components in the so-called Square Envelope Spectrum. If the assessment of the bearing health is quite a simple task, the on-line monitoring and the real-time evaluation of the trend of a suitable damage index is a complex task to be performed in an automatic way. The damage index must be robust against variations of system operating conditions and external vibration sources to avoid misleading results. The damage index should be also simple to be evaluated in the case of real-time applications. In the paper, the case of a rolling element bearing in which the defect develops until a permanent failure is described as well as the algorithm implemented for alarm signaling.

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.

A Novel Threshold for the Diagnostics of Rolling Element Bearing

2014 ◽  
Author(s):  
P. Borghesani ◽  
S. Chatterton ◽  
P. Pennacchi ◽  
A. Vania
Keyword(s):  
Signal Analysis ◽  
Damage Index ◽  
Vibration Signal ◽  
Rolling Element Bearing ◽  
Rolling Element ◽  
Element Bearing ◽  
Vibration Signal Analysis ◽  
Definition Of ◽  
Envelope Spectrum ◽  
Damage Type

The identification of the damage type in rolling element bearings is usually performed by means of suitable vibration signal analysis tools such as the most used and simplest method, Envelope Analysis through the corresponding Square Envelope Spectrum. The diagnostics and the monitoring of the bearing health are often performed by means of other approaches based on the evaluation of a damage index as the root mean square, the Kurtosis of the filtered signal, or more efficient indexes as the so-called Ratio of Cyclic Content. At any rate, in the case of real-time diagnostics, the definition of a threshold for the assessment of the bearing health is mandatory due to the presence in the vibration signal of additional sources and noises. In the paper, a threshold for the band-Kurtosis index that depends only on the sampling frequency and the bandwidth of the filter used for the demodulation of the vibration signal has been introduced. The effectiveness of the threshold has been proven by the experimental data of a damaged bearing.

Cyclic Spectrum Analysis on Rolling-Element Bearing with Inner-Race Point Defect

2011 ◽  
Vol 291-294 ◽  
pp. 1469-1473
Author(s):  
Wei Ke ◽  
Yong Xiang Zhang ◽  
Lin Li
Keyword(s):  
Point Defect ◽  
Signal To Noise Ratio ◽  
Vibration Signal ◽  
Rolling Element Bearing ◽  
Bearing Fault ◽  
Rolling Element ◽  
Element Bearing ◽  
Simulation Signal ◽  
Inner Race ◽  
Cyclic Spectrum

Vibration signal of rolling-element bearing is random cyclostationarity when a fault develops, the proper analysis of which can be used for condition monitor. Cyclic spectrum is a common cyclostationary analysis method and has a great many algorithms which have distinct efficiency in different application circumstance, two common algorithms (SSCA and FAM) are compared in the paper. The FAM is recommended to be used in diagnosing rolling-element bearing fault via calculation of simulation signal in different signal to noise ratio. The cyclic spectrum of practice signal of rolling-element bearing with inner-race point defect is analyzed and a new characteristic extraction method is put forward. The preferable result is acquired verify the correctness of the analysis and indicate that the cyclic spectrum is a robust method in diagnosing rolling-element bearing fault.

Mahalanobis Taguchi System (MTS) as a Prognostics Tool for Rolling Element Bearing Failures

2010 ◽  
Vol 132 (5) ◽  
Author(s):  
Ahmet Soylemezoglu ◽  
S. Jagannathan ◽  
Can Saygin
Keyword(s):  
Fault Detection ◽  
Real Time ◽  
Operating Conditions ◽  
Fault Detection And Isolation ◽  
Numerical Control ◽  
Rolling Element Bearing ◽  
Time To Failure ◽  
Cnc Machine ◽  
Bearing Failures ◽  
Rolling Element

In this paper, a novel Mahalanobis–Taguchi system (MTS)-based fault detection, isolation, and prognostics scheme is presented. The proposed data-driven scheme utilizes the Mahalanobis distance (MD)-based fault clustering and the progression of MD values over time. MD thresholds derived from the clustering analysis are used for fault detection and isolation. When a fault is detected, the prognostics scheme, which monitors the progression of the MD values, is initiated. Then, using a linear approximation, time to failure is estimated. The performance of the scheme has been validated via experiments performed on rolling element bearings inside the spindle headstock of a microcomputer numerical control (CNC) machine testbed. The bearings have been instrumented with vibration and temperature sensors and experiments involving healthy and various types of faulty operating conditions have been performed. The experiments show that the proposed approach renders satisfactory results for bearing fault detection, isolation, and prognostics. Overall, the proposed solution provides a reliable multivariate analysis and real-time decision making tool that (1) presents a single tool for fault detection, isolation, and prognosis, eliminating the need to develop each separately and (2) offers a systematic way to determine the key features, thus reducing analysis overhead. In addition, the MTS-based scheme is process independent and can easily be implemented on wireless motes and deployed for real-time monitoring, diagnostics, and prognostics in a wide variety of industrial environments.

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 Rolling Element Bearing Fault Detection using Statistical Features and Ensemble Classifiers

2020 ◽  
Vol 9 (3) ◽  
pp. 350-358
Keyword(s):  
Machine Learning ◽  
Fault Diagnosis ◽  
Vibration Signal ◽  
Rolling Element Bearing ◽  
Machine Learning Techniques ◽  
Ensemble Classifiers ◽  
Statistical Features ◽  
Bearing Fault ◽  
Rolling Element ◽  
Element Bearing

Rolling element bearing health condition is monitored by analysing its vibration signature. Raw vibration signal picked up through suitably placed accelerometers is difficult to analyse hence many signal processing techniques have been proposed and developed by researchers to process the data for suitably extracting an effective signal feature set. Various machine learning techniques have been used for interpretation and accurate fault diagnosis using this extracted feature set. In this study “Empirical mode decomposition” is used for pre-processing the raw vibration data. Six “Statistical features” are extracted from the best Intrinsic mode function obtained through EMD and “Ensemble machine learning classifiers” are used for bearing fault diagnosis. A stacked ensemble of five classifiers is proposed for accurate fault diagnosis and results are compared with conventional ensemble classifiers to prove its effectiveness

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