scholarly journals Optimization of Railroad Bearing Health Monitoring System for Wireless Utilization

2020 ◽  
Author(s):  
Jonas Cuanang ◽  
Constantine Tarawneh ◽  
Martin Amaro ◽  
Jennifer Lima ◽  
Heinrich Foltz
Keyword(s):  
Monitoring System ◽  
Condition Monitoring ◽  
Battery Life ◽  
Ambient Conditions ◽  
Accurate Analysis ◽  
Vibration Measurements ◽  
Vibration Data ◽  
Bearing Condition Monitoring ◽  
The University ◽  
Sample Window

Abstract In the railroad industry, systematic health inspections of freight railcar bearings are required. Bearings are subjected to high loads and run at high speeds, so over time the bearings may develop a defect that can potentially cause a derailment if left in service operation. Current bearing condition monitoring systems include Hot-Box Detectors (HBDs) and Trackside Acoustic Detection Systems (TADS™). The commonly used HBDs use non-contact infrared sensors to detect abnormal temperatures of bearings as they pass over the detector. Bearing temperatures that are about 94°C above ambient conditions will trigger an alarm indicating that the bearing must be removed from field service and inspected for defects. However, HBDs can be inconsistent, where 138 severely defective bearings from 2010 to 2019 were not detected. And from 2001 to 2007, Amsted Rail concluded that about 40% of presumably defective bearings detected by HBDs did not have any significant defects upon teardown and inspection. TADS™ use microphones to detect high-risk bearings by listening to their acoustic sound vibrations. Still, TADS™ are not very reliable since there are less than 30 active systems in the U.S. and Canada, and derailments may occur before bearings encounter any of these systems. Researchers from the University Transportation Center for Railway Safety (UTCRS) have developed an advanced algorithm that can accurately and reliably monitor the condition of the bearings via temperature and vibration measurements. This algorithm uses the vibration measurements collected from accelerometers on the bearing adapters to determine if there is a defect, where the defect is within the bearing, and the approximate size of the defect. Laboratory testing is performed on the single bearing and four bearing test rigs housed at the University of Texas Rio Grande Valley (UTRGV). The algorithm uses a four second sample window of the recorded vibration data and can reliably identify the defective component inside the bearing with up to a 100% confidence level. However, about 20,000 data points are used for this analysis, which requires substantial computational power. This can limit the battery life of the wireless onboard condition monitoring system. So, reducing the vibration sample window to conduct an accurate analysis should result in a more optimal power-efficient algorithm. A wireless onboard condition monitoring module that collects one second of vibration data (5,200 samples) was manufactured and tested to compare its efficacy against a wired setup that uses a four second sample window. This study investigates the root-mean-square values of the bearing vibration and its power spectral density plots to create an optimized and accurate algorithm for wireless utilization.

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

2020 ◽  
Author(s):  
Jennifer Lima ◽  
Constantine Tarawneh ◽  
Jesse Aguilera ◽  
Jonas Cuanang
Keyword(s):  
High Risk ◽  
Monitoring System ◽  
Condition Monitoring ◽  
The United States ◽  
Defect Size ◽  
Residual Service Life ◽  
Detection Systems ◽  
Bearing Condition Monitoring ◽  
Condition Monitoring System ◽  
The University

Abstract There are currently two primary wayside detection systems for monitoring the health of freight railcar bearings in the railroad industry: The Trackside Acoustic Detection System (TADS™) and the wayside Hot-Box Detector (HBD). TADS™ uses wayside microphones to detect and alert the train operator of high-risk defects. However, many defective bearings may never be detected by TADS™ since a high-risk defect is a spall which spans about 90% of a bearing’s raceway, and there are less than 30 systems in operation throughout the United States and Canada. HBDs sit on the side of the rail-tracks and use non-contact infrared sensors to acquire temperatures of bearings as they roll over the detector. These wayside bearing detection systems are reactive in nature and often require emergency stops in order to replace the wheelset containing the identified defective bearing. Train stoppages are inefficient and can be very costly. Unnecessary train stoppages can be avoided if a proper maintenance schedule can be developed at the onset of a defect initiating within the bearing. Using a proactive approach, railcars with defective bearings could be allowed to remain in service operation safely until reaching scheduled maintenance. The University Transportation Center for Railway Safety (UTCRS) research group at the University of Texas Rio Grande Valley (UTRGV) has been working on developing a proactive bearing condition monitoring system which can reliably detect the onset of bearing failure. Unlike wayside detection systems, the onboard condition monitoring system can continuously assess the railcar bearing health and can provide accurate temperature and vibration profiles to alert of defect initiation. 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 onboard condition monitoring system to identify defective bearings as well as the correlations developed for spall growth rates of defective bearing outer rings (cups). The system first uses the root-mean-square (RMS) value of the bearing’s acceleration to assess its health. Then, an analysis of the frequency domain of the acquired vibration signature determines if the bearing has a defective inner ring (cone) and the RMS value is used to estimate the defect size. This estimated size is then used to predict the residual life of the bearing. The methodology proposed in this paper can assist railroads and railcar owners in the development of a proactive and cost-efficient maintenance cycle for their rolling stock.

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.

A Conceptual Framework for the Design and Development of Automated Online Condition Monitoring System for Elevators (AOCMSE) Using IoT

2019 ◽  
pp. 189-207
Author(s):  
M. S. Starvin ◽  
A. Sherly Alphonse
Keyword(s):  
Conceptual Framework ◽  
Monitoring System ◽  
Condition Monitoring ◽  
Principal Component ◽  
Design And Development ◽  
Vibration Data ◽  
Vibration Sensors ◽  
Traction Motors ◽  
Condition Monitoring System ◽  
Learning Machine

The reliability of an elevator system in a smart city is of great importance. This chapter develops a conceptual framework for the design and development of an automated online condition monitoring system for elevators (AOCMSE) using IoT techniques to avoid failures. The elevators are powered by the traction motors. Therefore, by placing vibration sensors at various locations within the traction motor, the vibration data can be acquired and converted to 2D grayscale images. Then, maximum response-based directional texture pattern (MRDTP) can be applied to those images which are an advanced method of feature extraction. The feature vectors can also be reduced in dimension using principal component analysis (PCA) and then given to extreme learning machine (ELM) for the classification of the faults to five categories. Thus, the failure of elevators and the consequences can be prevented by sending this detected fault information to the maintenance team.

scholarly journals Train Type Identification at S&C

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Martina Kratochvílová ◽  
Jan Podroužek ◽  
Jiří Apeltauer ◽  
Ivan Vukušič ◽  
Otto Plášek
Keyword(s):  
Machine Learning ◽  
Monitoring System ◽  
Condition Monitoring ◽  
Dynamical Response ◽  
Vibration Data ◽  
Suitable Tool ◽  
Acceleration Data ◽  
Condition Monitoring System

The presented paper concerns the development of condition monitoring system for railroad switches and crossings that utilizes vibration data. Successful utilization of such system requires a robust and efficient train type identification. Given the complex and unique dynamical response of any vehicle track interaction, the machine learning was chosen as a suitable tool. For design and validation of the system, real on-site acceleration data were used. The resulting theoretical and practical challenges are discussed.

Defect Prognostics Models for Spall Growth in Railroad Bearing Rolling Elements

2018 ◽  
Author(s):  
Nancy De Los Santos ◽  
Constantine M. Tarawneh ◽  
Robert E. Jones ◽  
Arturo Fuentes
Keyword(s):  
Monitoring System ◽  
Health Monitoring ◽  
Safety Concern ◽  
Continuous Growth ◽  
Health Monitoring System ◽  
Bearing Condition Monitoring ◽  
Railroad Bearings ◽  
Railroad Bearing ◽  
The University ◽  
Proactive Maintenance

Prevention of railroad bearing failures, which may lead to catastrophic derailments, is a central safety concern. Early detection of railway component defects, specifically bearing spalls, will improve overall system reliability by allowing proactive maintenance cycles rather than costly reactive replacement of failing components. A bearing health monitoring system will provide timely detection of flaws. However, absent a well verified model for defect propagation, detection can only be used to trigger an immediate component replacement. The development of such a model requires that the spall growth process be mapped out by accumulating associated signals generated by various size spalls. The addition of this information to an integrated health monitoring system will minimize operation disruption and maintain maximum accident prevention standards enabling timely and economical replacements of failing components. An earlier study done by the authors focused on bearing outer ring (cup) raceway defects. The developed model predicts that any cup raceway surface defect (i.e. spall) once reaching a critical size (spall area) will grow according to a linear correlation with mileage. The work presented here investigates spall growth within the inner rings (cones) of railroad bearings as a function of mileage. The data for this study were acquired from defective bearings that were run under various load and speed conditions utilizing specialized railroad bearing dynamic test rigs owned by the University Transportation Center for Railway Safety (UTCRS) at the University of Texas Rio Grande Valley (UTRGV). The experimental process is based on a testing cycle that allows continuous growth of railroad bearing defects until one of two conditions are met; either the defect is allowed to grow to a size that does not jeopardize the safe operation of the test rig, or the change in area of the spall is less than 10% of its previous size prior to the start of testing. The initial spall size is randomly distributed as it depends on the originating defect depth, size, and location on the rolling raceway. Periodic removal and disassembly of the railroad bearings was carried out for inspection and defect size measurement along with detailed documentation. Spalls were measured using optical techniques coupled with digital image analysis, as well as, with a manual coordinate measuring instrument with the resulting field of points manipulated in MatLab™. Castings were made of spalls using low-melting, zero-shrinkage bismuth-based alloys, so that a permanent record of the spall geometry and its growth history can be retained. The main result of this study is a preliminary model for spall growth, which can be coupled with bearing condition monitoring tools that will allow economical and effective scheduling of proactive maintenance cycles that aim to mitigate derailments, and reduce unnecessary train stoppages and associated costly delays on busy railways.

Design a degradation condition monitoring system scheme for rolling bearing using EMD and PCA

2017 ◽  
Vol 117 (4) ◽  
pp. 713-728 ◽  
Author(s):  
Jun Wu ◽  
Chaoyong Wu ◽  
Yaqiong Lv ◽  
Chao Deng ◽  
Xinyu Shao
Keyword(s):  
Monitoring System ◽  
Condition Monitoring ◽  
Rolling Bearing ◽  
Sensor Data ◽  
Health Index ◽  
Rolling Bearings ◽  
Content Type ◽  
Bearing Condition Monitoring ◽  
Condition Monitoring System ◽  
Monotonicity Analysis

Purpose Rolling bearings based on rotating machinery are one of the most widely used in industrial applications because of their low cost, high performance and robustness. The purpose of this paper is to describe how to identify degradation condition of rolling bearing and predict its fault time in big data environment in order to achieve zero downtime performance and preventive maintenance for the rolling bearing. Design/methodology/approach The degradation characteristic parameters of rolling bearings including intrinsic mode energy and failure frequency were, respectively, extracted from the pre-processed original vibration signals using EMD and Hilbert transform. Then, Spearman’s rank correlation coefficient and PCA were used to obtain the health index of the rolling bearing so as to detect the appearance of degradations. Furthermore, the degradation condition of the rolling bearings might be identified through implementing the monotonicity analysis, robustness analysis and degradation analysis of the health index. Findings The effectiveness of the proposed method is verified by a case study. The result shows that the proposed method can be applied to monitor the degradation condition of the rolling bearings in industrial application. Research limitations/implications Further experiment remains to be done so as to validate the effectiveness of the proposed method using Apache Hadoop when massive sensor data are available. Practical implications The paper proposes a methodology for rolling bearing condition monitoring representing the steps that need to be followed. Real-time sensor data are utilized to find the degradation characteristics. Originality/value The result of the work presented in this paper form the basis for the software development and implementation of condition monitoring system for rolling bearings based on Hadoop.

Development of Prognostic Techniques for Surface Defect Growth in Railroad Bearing Rolling Elements

2017 ◽  
Author(s):  
Nancy De Los Santos ◽  
Robert Jones ◽  
Constantine M. Tarawneh ◽  
Arturo Fuentes ◽  
Anthony Villarreal
Keyword(s):  
Growth Rate ◽  
Early Detection ◽  
Condition Monitoring ◽  
Surface Defect ◽  
Full Load ◽  
Defect Growth ◽  
Bearing Condition Monitoring ◽  
Full Load Operation ◽  
Railroad Bearing ◽  
The University

Prevention of bearing failures which may lead to catastrophic derailment is a major safety concern for the railroad industry. Advances in bearing condition monitoring hold the promise of early detection of bearing defects, which will improve system reliability by permitting early replacement of failing components. However, to minimize disruption to operations while providing the maximum level of accident prevention that early detection affords, it will be necessary to understand the defect growth process and try to quantify the growth speed to permit economical, non-disruptive replacement of failing components rather than relying on immediate removal upon detection. The study presented here investigates the correlation between the rate of surface defect (i.e. spall) growth per mile of full-load operation and the size of the defects. The data used for this study was acquired from defective bearings that were run under various load and speed conditions utilizing specialized railroad bearing dynamic test rigs operated by the University Transportation Center for Railway Safety (UTCRS) at the University of Texas Rio Grande Valley (UTRGV). Periodic removal and disassembly of the railroad bearings was carried out for inspection and defect size measurement and documentation. Castings were made of spalls using low-melting, zero shrinkage Bismuth-based alloys so that a permanent record of the full spall geometry could be retained. Spalls were measured using optical techniques coupled with digital image analysis and also with a manual coordinate measuring instrument with the resulting field of points manipulated in MatLab™ and Solidworks™. The spall growth rate in area per mile of full-load operation was determined and, when plotted versus spall area, clear trends emerge. Initial spall size is randomly distributed as it depends on originating defect depth, size, and location on the rolling raceway. The growth of surface spalls is characterized by two growth regimes with an initial slower growth rate which then accelerates when spalls reach a critical size. Scatter is significant but upper and lower bounds for spall growth rates are proposed and the critical dimension for transition to rapid spall growth is estimated. The main result of this study is a preliminary model for spall growth which can be coupled to bearing condition monitoring tools to permit economical scheduling of bearing replacement after the initial detection of spalls.

Research on Compressive and Coding Algorithm of Bearing Vibration Signal in Wireless Transmission

2015 ◽  
Vol 757 ◽  
pp. 195-199
Author(s):  
Nan Wang ◽  
Yuan Zhang ◽  
Wei Ning
Keyword(s):  
Condition Monitoring ◽  
Vibration Signal ◽  
Wireless Transmission ◽  
Huffman Coding ◽  
Memory Space ◽  
Vibration Data ◽  
Bearing Condition Monitoring ◽  
Huffman Algorithm ◽  
Bearing Vibration ◽  
Performance Of Algorithm

The resources of node including the storage capacity, processing capacity, the energy supply in wireless sensor networks (WSNs) are limited, and the energy of node is mainly consumed in process of data transmission. The vibration signal which changes quickly, and has large amount of data is usually used in bearing condition monitoring. So in the process of bearing condition monitoring using WSNs, if the bearing vibration signal is acquired and transmitted, then the node will be fault for running out of node resources earlier. In order to solve the problems above, and prolong the node life, a compressive and coding algorithm of bearing vibration signal is proposed. The algorithm is based on 5/3 integer lifting wavelet, and fuses the embedded zerotree wavelet and Huffman coding algorithm. The bearing vibration data is firstly processed by 5/3 wavelet, then the wavelet coefficients obtained is compressed and coded by zerotree wavelet, and finally the results above are further compressed and coded by Huffman algorithm to improve the compressing ratio. The algorithm is programmed and transplanted in DSP of node, the evaluative criteria of compressive ratio (CR) and root mean square error (RMSE) are adopted to verify the performance of algorithm. The experiment results show that the vibration signal is compressed and coded efficiently, the main frequency feature of vibration signal is retained although the compressing ratio is up to 9.5. The amount of vibration data in wireless transmission decreases greatly, and at the same time, the memory space of host computer is saved.

Property requirements of vibration measurements in wind turbine drivetrain bearing condition monitoring

2021 ◽  
Vol 63 (11) ◽  
pp. 667-674
Author(s):  
D Strömbergsson ◽  
P Marklund ◽  
K Berglund ◽  
P-E Larsson
Keyword(s):  
Wind Turbine ◽  
Condition Monitoring ◽  
Signal Analysis ◽  
Field Data ◽  
Measurement Time ◽  
Frequency Range ◽  
Main Bearing ◽  
Vibration Measurements ◽  
Bearing Failures ◽  
Bearing Condition Monitoring

Wind turbine drivetrain bearing failures continue to lead to high costs resulting from turbine downtime and maintenance. As the standardised tool to best avoid downtime is online vibration condition monitoring, a lot of research into improving the signal analysis tools of the vibration measurements is currently being performed. However, failures in the main bearing and planetary gears are still going undetected in large numbers. The available field data is limited when it comes to the properties of the stored measurements. Generally, the measurement time and the covered frequency range of the stored measurements are limited compared to the data used in real-time monitoring. Therefore, it is not possible to either reproduce the monitoring or to evaluate new tools developed through research for signal analysis and diagnosis using the readily available field data. This study utilises 12 bearing failures from wind turbine condition monitoring systems to evaluate and make recommendations concerning the optimal properties in terms of measurement time and frequency range the stored measurements should have. The results show that the regularly stored vibration measurements that are available today are, throughout most of the drivetrain, not optimal for research-driven postfailure investigations. Therefore, the storage of longer measurements covering a wider frequency range needs to begin, while researchers need to demand this kind of data.

scholarly journals Bearing Condition Monitoring based on the Indicator Generated in Time-frequency Domain

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Teng Wang ◽  
Zheng Liu ◽  
Guoliang Lu
Keyword(s):  
Condition Monitoring ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Data Driven ◽  
Time Frequency ◽  
Vibration Data ◽  
Bearing Condition Monitoring ◽  
Condition Monitoring Systems ◽  
Early Fault Detection ◽  
Condition Indicator

Most condition monitoring systems rely on system-driven generation of indicators or features for early fault detection. However, this strategy requires the prior knowledge on the system kinematics and/or exact structure parameters of monitored system. To address this problem, this paper presents a novel condition monitoring framework where the condition indicator is generated via data-driven method. In this framework, the time-frequency periodogram is extracted from raw vibration signal first. Then, the acquired time-frequency periodogram is mapped by pseudo Perron vector, which is learned from vibration data, to generate the condition indicator. Finally, the bearing can be monitored via analyzing this indicator using gaussian based control chart. Based on experimental results on a publicly-available database, we show the effectiveness of presented framework for early fault detectionin the continuous operation of rolling bearing, indicating its great potentials in real engineering applications.

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