Bearing Defect Detection Using On-Board Accelerometer Measurements

Joint Rail ◽  
2002 ◽  
Author(s):  
John Donelson ◽  
Ronald L. Dicus
Keyword(s):  
Monitoring System ◽  
Condition Monitoring ◽  
Roller Bearing ◽  
Spectral Lines ◽  
Roller Bearings ◽  
Bearing Defects ◽  
Bearing Defect ◽  
Freight Car ◽  
Condition Monitoring System ◽  
Vibration Signatures

Vibration signatures of defective roller bearings on railroad freight cars were analyzed in an effort to develop an algorithm for detecting bearing defects. The effort is part of a project to develop an on-board condition monitoring system for freight trains. The Office of Research and Development of the Federal Railroad Administration (FRA) is sponsoring the project. The measurements were made at the Transportation Technology Center (TTC) in Pueblo, CO on July 26 – 29, 1999 during the Phase III Field Test of the Improved Wayside Freight Car Roller Bearing Inspection Research Program sponsored by FRA and the Association of American Railroads (AAR). Wheel sets with specific roller bearing defects were installed on a test train consisting of 8 freight cars designed to simulate revenue service. The consist also contained non-defective roller bearings. Accelerometers were installed on the inboard side of the bearing adapters to measure the vibration signatures during the test. Signatures of both defective and non-defective bearings were recorded. The data were recorded on Sony Digital Audio Tape (DAT) Recorders sampling at a rate of 48 K samples per second. We used both ordinary and envelope spectral analysis to analyze the data in an effort to detect features that could be related to known defects. The spectra of non-defective bearings show no remarkable features at bearing defect frequencies. In general, the ordinary spectra of defective bearings do not exhibit remarkable features at the bearing defect frequencies. In contrast, the envelope spectra of defective bearings contain a number of highly resolved spectral lines at these frequencies. In several cases the spectral lines could be related to specific bearing defects. Based on the analysis performed to date, the envelope spectrum technique provides a promising method for detecting defects in freight car roller bearings using an on-board condition monitoring system.

scholarly journals Vibration-Based Defect Detection for Freight Railcar Tapered-Roller Bearings

2018 ◽  
Author(s):  
Joseph Montalvo ◽  
Constantine Tarawneh ◽  
Arturo A. Fuentes
Keyword(s):  
High Risk ◽  
Monitoring System ◽  
Condition Monitoring ◽  
Detection System ◽  
Field Studies ◽  
The United States ◽  
Bearing Condition Monitoring ◽  
Condition Monitoring System ◽  
The University ◽  
Vibration Signatures

The railroad industry currently utilizes two wayside detection systems to monitor the health of freight railcar bearings in service: The Trackside Acoustic Detection System (TADS™) and the wayside Hot-Box Detector (HBD). TADS™ uses wayside microphones to detect and alert the conductor of high risk defects. Many defective bearings may never be detected by TADS™ due to the fact that a high risk defect is considered a spall which spans more than 90% of a bearing’s raceway, and there are less than 20 systems in operation throughout the United States and Canada. Much like the TADS™, the HBD is a device that sits on the side of the rail tracks and uses a non-contact infrared sensor to determine the temperature of the train bearings as they roll over the detector. The accuracy and reliability of the temperature readings from this wayside detection system have been concluded to be inconsistent when comparing several laboratory and field studies. The measured temperatures can be significantly different from the actual operating temperature of the bearings due to several factors such as the class of railroad bearing and its position on the axle relative to the position of the wayside detector. Over the last two decades, a number of severely defective bearings were not identified by several wayside detectors, some of which led to costly catastrophic derailments. In response, certain railroads have attempted to optimize the use of the temperature data acquired by the HBDs. However, this latter action has led to a significant increase in the number of non-verified bearings removed from service. In fact, about 40% of the bearings removed from service in the period from 2001 to 2007 were found to have no discernible defects. The removal of non-verified (defect-free) bearings has resulted in costly delays and inefficiencies. Driven by the need for more dependable and efficient condition monitoring systems, the University Transportation Center for Railway Safety (UTCRS) research team at the University of Texas Rio Grande Valley (UTRGV) has been developing an advanced onboard condition monitoring system that can accurately and reliably detect the onset of bearing failure. The developed system currently utilizes temperature and vibration signatures to monitor the true condition of a bearing. This system has been validated through rigorous laboratory testing at UTRGV and field testing at the Transportation Technology Center, Inc. (TTCI) in Pueblo, CO. The work presented here provides concrete evidence that the use of vibration signatures of a bearing is a more effective method to assess the bearing condition than monitoring temperature alone. The prototype bearing condition monitoring system is capable of identifying a defective bearing with a defect size of less than 6.45 cm2 (1 in2) using the vibration signature, whereas, the temperature profile of that same bearing will indicate a healthy bearing that is operating normally.

scholarly journals Estimating the Outer Ring Defect Size and Remaining Service Life of Freight Railcar Bearings Using Vibration Signatures

2019 ◽  
Author(s):  
Joseph Montalvo ◽  
Constantine Tarawneh ◽  
Jennifer Lima ◽  
Jonas Cuanang ◽  
Nancy De Los Santos
Keyword(s):  
High Risk ◽  
Service Life ◽  
Monitoring System ◽  
Condition Monitoring ◽  
Defect Size ◽  
Outer Ring ◽  
Remaining Service Life ◽  
Condition Monitoring System ◽  
The University ◽  
Vibration Signatures

The railroad industry currently utilizes two wayside detection systems to monitor the health of freight railcar bearings in service: The Trackside Acoustic Detection System (TADS™) and the wayside Hot-Box Detector (HBD). TADS™ uses wayside microphones to detect and alert the conductor of high-risk defects. Many defective bearings may never be detected by TADS™ since a high-risk defect is a spall which spans more than 90% of a bearing’s raceway, and there are less than 20 systems in operation throughout the United States and Canada. Much like the TADS™, the HBD is a device that sits on the side of the rail-tracks and uses a non-contact infrared sensor to determine the temperature of the train bearings as they roll over the detector. These wayside detectors are reactive in the detection of a defective bearing and require emergency stops in order to replace the wheelset containing the defective bearing. These costly and inefficient train stoppages can be prevented if a proper maintenance schedule can be developed at the onset of a defect initiating within the bearing. This proactive approach would allow for railcars with defective bearings to remain in service operation safely until reaching scheduled maintenance. Driven by the need for a proactive bearing condition monitoring system in the rail industry, the University Transportation Center for Railway Safety (UTCRS) research group at the University of Texas Rio Grande Valley (UTRGV) has been developing an advanced onboard condition monitoring system that can accurately and reliably detect the onset of bearing failure using temperature and vibration signatures of a bearing. This system has been validated through rigorous laboratory testing at UTRGV and field testing at the Transportation Technology Center, Inc. (TTCI) in Pueblo, CO. The work presented here builds on previously published work that demonstrates the use of the advanced onboard condition monitoring system to identify defective bearings as well as the correlations developed for spall growth rates of defective bearing outer rings (cups). Hence, the system uses the root-mean-square (RMS) value of the bearing’s acceleration to assess its health. Once the bearing is determined to have a defective outer ring, the RMS value is then used to estimate the defect size. This estimated size is then used to predict the remaining service life of the bearing. The methodology proposed in this paper can prove to be a useful tool in the development of a proactive and cost-efficient maintenance cycle for railcar owners.

GSM based Transformer Condition Monitoring System

2017 ◽  
Author(s):  
Ms.Swati R. Wandhare ◽  
Ms.Bhagyashree Shikkewal
Keyword(s):  
Monitoring System ◽  
Condition Monitoring ◽  
Condition Monitoring System

Online Real-Time Monitoring System Through Using Adaptive Angular-Velocity VKF Order Tracking

2012 ◽  
Author(s):  
Ting-Chi Yeh ◽  
Min-Chun Pan
Keyword(s):  
Angular Velocity ◽  
Real Time ◽  
Monitoring System ◽  
Condition Monitoring ◽  
Theoretical Derivation ◽  
Periodic Loading ◽  
Order Tracking ◽  
Computation Scheme ◽  
Condition Monitoring System ◽  
Operation Status

When rotary machines are running, acousto-mechanical signals acquired from the machines are able to reveal their operation status and machine conditions. Mechanical systems under periodic loading due to rotary operation usually respond in measurements with a superposition of sinusoids whose frequencies are integer (or fractional integer) multiples of the reference shaft speed. In this study we built an online real-time machine condition monitoring system based on the adaptive angular-velocity Vold-Kalman filtering order tracking (AV2KF_OT) algorithm, which was implemented through a DSP chip module and a user interface coded by the LabVIEW®. This paper briefly introduces the theoretical derivation and numerical implementation of computation scheme. Experimental works justify the effectiveness of applying the developed online real-time condition monitoring system. They are the detection of startup on the fluid-induced instability, whirl, performed by using a journal-bearing rotor test rig.

scholarly journals Intelligent Starting Current-Based Fault Identification of an Induction Motor Operating under Various Power Quality Issues

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 304
Author(s):  
Sakthivel Ganesan ◽  
Prince Winston David ◽  
Praveen Kumar Balachandran ◽  
Devakirubakaran Samithas
Keyword(s):  
Standard Deviation ◽  
Power Quality ◽  
Monitoring System ◽  
Condition Monitoring ◽  
Classification Accuracy ◽  
Induction Motors ◽  
Quality Data ◽  
Starting Current ◽  
Quality Issues ◽  
Condition Monitoring System

Since most of our industries use induction motors, it is essential to develop condition monitoring systems. Nowadays, industries have power quality issues such as sag, swell, harmonics, and transients. Thus, a condition monitoring system should have the ability to detect various faults, even in the presence of power quality issues. Most of the fault diagnosis and condition monitoring methods proposed earlier misidentified the faults and caused the condition monitoring system to fail because of misclassification due to power quality. The proposed method uses power quality data along with starting current data to identify the broken rotor bar and bearing fault in induction motors. The discrete wavelet transform (DWT) is used to decompose the current waveform, and then different features such as mean, standard deviation, entropy, and norm are calculated. The neural network (NN) classifier is used for classifying the faults and for analyzing the classification accuracy for various cases. The classification accuracy is 96.7% while considering power quality issues, whereas in a typical case, it is 93.3%. The proposed methodology is suitable for hardware implementation, which merges mean, standard deviation, entropy, and norm with the consideration of power quality issues, and the trained NN proves stable in the detection of the rotor and bearing faults.

Sign in / Sign up

Export Citation Format

Share Document