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Bearing Diagnostics [clear filter]
Thursday, May 16
 

8:30am

Defect Severity Measurement in Rolling Element Bearings
Although vibration analysis has been used to monitor the health of rolling element bearings for more than half a century, a reliable method to accurately determine the severity of defects from vibration data remains an elusive goal for both academics and industry.

Rolling element bearings are critical to the safe and economic operation of industrial machines in all sectors. Determining whether a defect is present or not only answers part of the problem. Without a knowledge of the severity of a defect, engineers and plant operators have only limited information upon which to base planning and maintenance decisions. Accurate information on the size and location of the defect as well as the current operating conditions are all essential to properly characterize defect severity and to allow effective corrective actions to be determined.

The complexity of the defect impacts in rolling element bearings, the influence of the vibration response and the transmission path between the defect and the sensor, changes in operating conditions and other sources of vibration all conspire to challenge the accurate measurement of defect severity even in the simplest of machines. Historical trending of vibration data from multiple machines and/or multiple failure events remains the only option and even this can have limited success.

In the early 1990s, ground-breaking research by the author into the origins of vibrations generated by discrete faults in rolling element bearings revealed previously hidden features within the impact events caused by defects. These features offered the possibility of a new approach to determine defect severity whereby defect size could be accurately and directly determined from vibration data without the need for historical trending.

In recent years this original research has been further progressed by the author and the original approach has been significantly enhanced to provide more information to characterize defect severity. Additionally, the enhanced method has proven to be more effective in industrial environments where the features of interest can be masked by noise and other source of impact.

This presentation will background the challenge of determining defect severity in rolling element bearings and summarize the findings of original research. The enhancements that have been made to the method will be detailed and the additional condition information provided by the enhanced method will be described. The simplicity of the method and its potential to be integrated as an on-sensor IIOT device will be demonstrated and results from testing of recent prototype devices will be shared.

Speakers
avatar for Iain Epps

Iain Epps

Managing Director, Mobolo Technology Ltd
Iain’s professional career has been focused on innovation and growth by successfully combining new ideas and talented people to bring new technologies to life in a wide range of industries across the globe. On completing his engineering studies, Iain travelled to Europe where he... Read More →


Thursday May 16, 2019 8:30am - 9:00am
Freedom Ballroom I

10:30am

Damage Evolution in a Rolling Element Bearing: Spall Propagation
Failure prognosis of the rolling element bearings (REBs) is crucial in the rotating machinery. The damage evolution in the REBs consists of two main phases: damage initiation and propagation. The conventional REB life models address the lifetime of the bearing to the damage initiation, i.e. first defect formation. However, after the first defect formation, the bearing might be fully operational for millions of cycles. There has been a growing awareness of the need to understand the damage mechanism during the propagation phase. Over the past two decades, studies attempting to understand the damage mechanism and to develop damage propagation models have been published. Nevertheless, the damage mechanism is only partially understood, the existing models are inefficient and the physical phenomena are not well represented. Once the damage mechanism is understood a physics-based prognostic tool can be developed. In order to understand the spall propagation phase, a physics-based model has been developed. The model aims to study the material behavior at the trailing edge of the spall during the rolling element (RE) impact. Based on the model results a qualitative damage analysis for crack evolution within the spall edge was conducted. Moreover, a metallurgical analysis of the bearing from endurance tests was carried out. The metallurgical analysis added insights regarding the damage mechanism and was used for model validation. The results achieved from the damage analysis are in good agreement with the experimental observations. To our best knowledge, this is the first study attempting to simulate damage evolution within the spall edge based on physical insight.

Speakers
JB

Jacob Bortman

Prof, BGU


Thursday May 16, 2019 10:30am - 11:00am
Freedom Ballroom I

11:00am

Bearing Faults Detection and Identification Using Relational Data Clustering with Composite Differential Evolution Optimization
Bearing faults in machinery are among the most critical faults that require attention by maintenance personnel at early stages of fault initiation. In many cases it is difficult to directly and accurately identify the fault type and its extent under varying operating conditions. This work demonstrates a novel procedure for bearing fault detection and identification in an experimental set-up. Three seeded faults in the rotating machinery supported by the test roller bearing include inner race fault, outer race fault and one roller fault. The rotor is run at different speeds and with different levels of rotating mass unbalance. Accelerometer based vibration signals are analyzed for the different bearing faults’ signatures using statistical moments, frequency spectra and wavelet coefficients. These are then used as features to create a data-partition using relational data clustering. The composite differential evolution technique is proposed for optimizing the clustering parameters. The objective is to correlate bearing faults to the extracted vibration features. The results of this analysis will be extended for applications in real time bearing condition monitoring system.

Speakers
IA

Issam Abu-Mahfouz

Professor of Mechanical Engineering, Penn State University Harrisburg


Thursday May 16, 2019 11:00am - 11:30am
Freedom Ballroom I

11:30am

Bearing Fault Diagnosis with Improved Variational Mode Decomposition and Optimized Frequency Band Entropy
Rolling element bearings are essential components in rotating machinery, it is of great importance to detect the bearing fault as earlier as possible during the operation of machinery. In recent years, variational mode decomposition (VMD) has been widely used in signal decomposition and feature extraction from non-stationary signals. However, the determination of the decomposition layers and quadratic penalty parameter of VMD is still puzzling. In this paper, an improved VMD and optimized frequency band entropy (OFBE) method is proposed. Firstly, the energy entropy maximum principle is utilized to select the decomposition layers and quadratic penalty parameter. After that, a detection method of OFBE based on the principle of maximum kurtosis is presented to determine the bandwidth parameters. Finally, envelope analysis is employed for the detection of fault-related frequency components based on the obtained sub-band signal. The performance of the proposed approach is validated by faults in different parts of the bearing. Results show that the new methodology yields a good accuracy in bearing fault diagnosis.
Keywords: Fault diagnosis; bearing; VMD; entropy; envelope analysis

Speakers
DJ

Dabin Jie

Chingqing University
XD

Xiaoxi Ding

student, Chingqing University


Thursday May 16, 2019 11:30am - 12:00pm
Freedom Ballroom I