Monitoring bogie performance on straight track Part 1. Wheel set tracking position
Grigory Izbinsky1, Gérard Sirois2, Yan Liu3, Denis D’Aoust1
1Wayside Inspection Devices Inc., Montreal, Canada, 2Quebec Cartier Mining Company, Canada, 3Centre for Surface Transportation Technology, National Research Council, Ottawa, Canada
Abstract
Monitoring of railway traffic on straight track has been successfully used for detection and repair of sub- performing bogies. There are certain advantages of using this method of inspection compared to inspection on curved track. Practical experience is illustrated by the results of monitoring iron ore traffic on Quebec Cartier Mining (QCM) with a laser-based inspection system. Relevance and importance of Tracking Error, one of the bogie performance parameters available with this method, is illustrated by the results of dynamics simulations at Centre for Surface Transportation Technology (CSTT). Wheel wear index is offered as a bogie performance descriptor. This paper is a first instalment of publications planned to discuss other aspects of wayside rolling stock inspection on straight track.
Introduction Shifting to predictive rolling stock management has become one of the vital needs of railroads in North America and around the world. Predictive management requires a different approach to inspection of railroad equipment [1]. Methods and technology of inspection for predictive management must be able to evaluate rolling stock performance, to identify equipment that performs inadequately, to predict and report potential failure of components before reaching an “alarm” situation, to assist railroads in improving utilization of rolling stock and repair facilities. In order to achieve these objectives the following qualities are important when selecting inspection methods and technology: · Methods of inspection for predictive management must provide results that are repeatable at the inspection point and reproducible at other locations. Only these methods can help to set uniform standards across the railway network. · Evaluation of performance requires the selection of performance parameters that form a specific and persistent “signature” of individual piece of equipment, reflect the “health state” of equipment, and follow the equipment condition trend.
Method of monitoring: Curve vs. Tangent Inspection in curves was a conventional method of monitoring bogie performance. While this method can provide some useful information, it is difficult to adapt for the purpose of predictive management. Bogie performance while negotiating a curve is strongly affected by external, to the bogie, factors.
90 90
60 60
30 30 Force, kN Force, kN
0 0
-30 -30 -10 0 10 20 30 40 -10 0 10 20 30 40 AOA, mrad AOA, mrad Figure 1: Two similar revenue trains on curve R=290 m on the same day with changing top-of-rail conditions Figure 1 illustrates results of monitoring two revenue trains on the same day on a curve with R=290 m. The trains belonged to the same owner and equally maintained . Average bogie performance for these trains was expected to be similar, yet the results were actually very different. The range of values of lateral forces and angles of attack varied due to the changing friction coefficient in the wheel/rail interface caused by changing weather conditions and operation of a nearby rail lubricator. Figure 2 shows the effect of changing of friction in the wheel/rail interface along one train on the same curve (only the leading axles shown). Low friction caused by rail surface contamination resulted in “warping” of the great majority of the bogies at the head of the train (large angles of attack) and low lateral forces. Apparently, burning off the surface contamination by the wheels of the first third of the train had restored friction thereby reducing the number of “warped” bogies and generating larger lateral forces close to the values expected for the curvature.
80 60 lateral force angle 60 50
40 40
20 30
0 20 Angle, mrad Lateral Force, kN -20 10
-40 0 13 53 93 133 173 213 253 293 333 373 Axle
Figure 2: Changing top-of-rail conditions along train. Curve R=290 m. Leading axles
Similar effect on bogie behavior as a result of changing train velocity is illustrated in Figure 3 that represents the results of monitoring on a shallow non-lubricated curve (R=1500 m) on a day with stable dry weather conditions.
70 70 60 60 50 50
40 40 30 30
20 20 10 10 Lateral force, kN Lateral force, kN 0 0 -10 -10
-20 -20 -2 0 2 4 6 8 -2 0 2 4 6 8 AOA, mrad AOA, mrad Figure 3: Two similar revenue trains on curve R=1100 m at 70 km/h (balance speed) and 30 km/h
As shown in the above examples, the influence of external factors can be overwhelming and completely mask the performance parameters and specific characteristics of bogies negotiating a curve, regardless of the monitoring technology employed. In contrast, the behavior of bogies on the straight track is much more stable. Figures 4 and 5 illustrate repeatable bogie performance in changing operating conditions. The measured parameters are the angle of attack (AOA) and the tracking position (TP) of each axle (see inset in the next section and [2] for definitions). The range of deviations of the parameters measured from pass to pass is small, which allows recognition of the specific “signature” of each bogie determined by the bogie geometry in dynamics of main line conditions with actual lading and motion.
angle-08-08 angle-08-12 angle-08-14 angle 201km/h angle 67km/h position-08-08 position-08-12 position-08-14 position 201km/h position 67km/h
12 30 10 30 20 20 8 9 10 10 6 0 6 position, mm -10 position, mm 4 0 -20 3 2 -30 -10 -40 0 -20
0 angle, mrad -50
angle, mrad -2 -60 -3 -30 9 13 17 21 25 29 33 9 13 17 21
axle # axle #
Figure 4: Segment of train – 3 passes in one week Figure 5: Segment of train at different speeds
Bogie parameters on tangent track The results of bogie inspection shown in this paper are obtained on tangent track with TBOGI™, a laser- based inspection system manufactured by WID Inc. The TBOGI scans trains AOA passing at track speed to determine by optical means both the angle of attack (AOA) and the tracking position (TP) of each wheel set. The TBOGI detects TP geometric faults in the alignment and tracking of bogie wheel sets on tangent track [3], which characterize a bogie behavior on tangent track and have a strong influence on the bogie steering in curves. Repeatability of results obtained from monitoring on straight track allows detecting the trend in a bogie condition. Figure 6 illustrates the results of monitoring of two bogies during the ~ 5 months interval with the most unstable weather in Eastern part of Canada. Bogie A of car QCM3082 is an example of a bogie in good condition: small AOA and TP for both wheel sets testify that both axles of the bogie are well aligned and are tracking close to the track center. The main feature of Bogie A
Car QCM 3082 Bogie A, Loaded Car QCM 3125 Bogie A Loaded
angle 3 angle 4 position 3 position 4 angle 3 angle 4 position 3 position 4
4 42 4 42
2 35 2 35
0 28 0 28
-2 21 angle, mrad A-end -2 21 B-end B-end angle, mrad B-end A-end B-end leading leading leading leading leading leading -4 14 -4 14
-6 7 -6 7
-8 0 -8 0
-10 -7 -10 -7 position, mm position, mm
-12 -14 -12 -14 2005-08-06 2005-09-05 2005-10-05 2005-11-04 2005-12-04 2006-01-03 2005-08-06 2005-09-05 2005-10-05 2005-11-04 2005-12-04 2006-01-03 date date
Figure 6: Two QCM cars. 3082 – “good actor”, 3125 – large tracking error, wheels replaced Oct 12 car QCM3125 are deviation s of both wheel sets from the track center line in opposite directions. This difference in the tracking positions of the leading and the trailing wheel sets is called the tracking error (TE). It was measured ~ 14 mm at the beginning of the monitoring interval and steadily grew to ~ 20 mm in 60 days (~0.2 mm in one 400 km round run, empty and with 93.4 tonne load). One of the main objectives of inspection for predictive management is prediction of component failure in order to proactively schedule checks and repairs to vehicles before they suffer extra damage and stress track and structures [1]. Inspection on straight track can be successfully used for this purpose. Bogie geometry defects that can be detected on straight track include angular and lateral misalignments of wheel sets, reduced steering ability manifested by improper rotation of a bogie relative to the car body or shifting to one side of the track, and lateral instability. The information contained in accurate bogie geometry measurements made on straight track facilitates finding the root cause of the problem. The volume limitation of this paper does not allow for a detailed discussion of all the features of this method. The following material will focus on one of the bogie performance parameters – the tracking position of each wheel set.
Wheel set tracking position and bogie tracking error
The position of each axle in relation to the track centerline is not only very repeatable at the inspection point located on the straight section of the track but also is closely reproducible at different locations along the route. It is a permanent feature of a bogie; it reflects the bogie mechanical conditions. Typically, shifting of the wheel set position over time is indicative of a destructive wear process that reduces the life span of the wheel set. Table 1 below illustrates the results of wayside monitoring of a bogie in service on a North American railway. While the angle of attack of each wheel set was negligible, the repeated positions assumed by the wheel sets deviated away from the track center line. A sketch of the bogie geometry in motion is shown in Figure 7. The magnitude of the deviations suggested the asymmetric wear of the wheels. The measurements of the corresponding wheel profiles, carried out at the depot with help of MiniProf, are also shown in Figure 7.
Lead dir Pass Date AOA1, AOA2, TP1 TP2, TE, Speed, End millirad millirad mm mm mm km/h A E 2001-01-10 0.2 -0.2 15.9 -7.2 -21.1 47.2 A E 2001-01-27 -0.3 0.1 15.2 -6.7 -21.9 50.3 A E 2001-02-01 0.4 0 15.6 -9.2 -24.8 41.6
Table 1: Results of monitoring of a bogie with a large tracking error
Axle 2
Axle 1
Figure 7: Tracking positions of wheel sets and corresponding wheel profiles A shifted position of a wheel set promotes accelerated asymmetric wheel wear. If this condition is not rectified in time, re-profiling of the wheel sets worn in these patterns would require removal of a large portion of the wheel material, shortening the wheel life span. Left in place, the initial defect propagates, the shifted wheels do not allow adequate steering in curves, leading to large angles of attack, lateral forces, excessive rail wear, and compromising the efficiency and safety of the railway. The effect of a large tracking error detected on straight track on steering of a bogie traveling through a curve was studied with help of the VAMPIRE™ dynamics simulations conducted at CSTT. The mathematical model for a generic freight car equipped with typical three-piece trucks was used for this purpose. The modeled car has a gross rail load of 130,000 kg. The truck shearing (warping) characteristics is quite accurately described by the CSTT’s truck model, which has been developed from more than 20 years of truck testing and modeling experiences. The wheel profiles shown in Figure 7, together with typical rail profiles measured on North American tracks, were used to construct the wheel/rail contact sub-model in the VAMPIRE™ simulation. The simulation has been conducted for both tangent and curved track. First, in the case of tangent track simulation, the tracking position feature shown in Table 1 was correctly reproduced by the model. Then simulations were conducted on a 5 degree curve (R = 350 m) under the balance speed of the vehicle. The results of the modeling were expressed in terms of the bogie wear index (WI), the energy dissipated in wheel/rail contact as the bogie rolls on the rails. WI is a function of the individual wheel/rail contact creepages and traction forces. It is calculated from values of creep (γ1: longitudinal, γ2: lateral and ω3: spin) and creep force (T1: longitudinal, T2: lateral, and M3: spin moment), for all four wheels in the bogie (n = 1 to 4), Equation (1):
n=4 WI = å(T1g1 + T2g 2 + M 3w3 ) (1) n=1
This dissipated energy increases rolling resistance and hence fuel use and wheel and rail wear. Additionally, lateral forces tend to increase as dissipated energy increases. The effect of tracking error on a bogie steering in a left-hand (LH) and a right-hand (RH) curves were compared to a bogie equipped with new wheels by a normalized index (the ratio of indices LH/New and RH/New). Results of the modeling are summarized in Figure 8.
2.0 RH curve LH curve New AAR 1BWF 1.8
1.6
1.4
1.2
1.0
0.8 Normalized wear index 0.6
0.4 0.2 0.3 0.4 0.5 friction in contact zone
Figure 8: Normalized wear index for a bogie with tracking error in curve
While a shift of the wheel sets similar to Figure 7 alleviates “stress” when steering in a curve to which it is predisposed (the left-hand curve for a given example), compared to a pair of new wheel sets, by approximately 20%, it does not compensate for much higher “stress” and losses (50 – 80% for the range of friction 0.3 – 0.5) incurred when steering in a curve of opposite direction. (The difference in “stress” is actually higher if comparison is made to in service symmetrically worn wheels instead of new wheels).
Figure 9 shows percentage of bogie-passes with different degree of asymmetry in wheel sets for a typical major North American railroad (left) that uses the AAR rule for wheel removal and the same for QCM (right) that follows a much stricter policy for wheel wear. While the tracking errors > 10 mm were detected in QCM fleet in only 1% of bogie-passes, this number grows to 15-20% on the other railroad, and so are the associated losses.
100.0 100.0
Easbound ~ 16000 truck-passes ~ 19000 bogie-passes Westbound - 15000 truck-passes in each direction
10.0 10.0
% %
1.0 1.0
0.1 0.1 0
Figure 9: Tracking error and wheel maintenance. AAR rule – left; QCM 2 mm hollow- right
The wheel set life at QCM has approached 800,000 km. There are many measures taken for extension of wheel set service at QCM; use of TBOGI for reporting and fixing defective bogies is one of them.
Conclusions
Experience with monitoring bogie performance has shown that straight track offers better than curve environment from the point of view of rolling stock predictive management. Bogie dynamics and parameters of bogie performance on straight track are not as complex for interpretation as in curve. Repeatability of the results makes defect detection and trending of bogie condition more reliable.
The tracking error, one of the bogie performance parameters used on straight track, has shown to be an extremely repeatable parameter closely linked to asymmetric wear of the wheels. Computer simulation confirmed that the tracking error is a result of a degenerative wear process that increases the energy dissipated in wheel/rail contact, leading to wheel and rail wear and increased fuel use and likely leading to higher lateral forces in curves. If left unchecked, a bogie with the tracking error will degenerate eventually into a condition with serious safety implications.
Bogie geometry measured on a vehicle rolling on straight track is expressed in physical units (milliradians and millimeters) rather than in indices. It is closely related to the bogie mechanical condition of a bogie and manifests itself consistently on straight tracks everywhere along the route. Unified standards based on bogie geometry parameters can be used across the system and can be easily shared with other owners of interchange traffic.
Locating a monitoring system on straight track additionally creates the opportunity to co-locate it with other systems for monitoring multiple aspects of train performance (acoustic bearing detector, wheel impact detector, wheel profile measurement system) at the same time and place, such as system supplied by the Wayside Monitoring Alliance [1].
Work is in progress for publication and discussion of other performance parameters based on bogie geometry.
Acknowledgements
The authors are grateful for advice provided by K. Sawley and J. Preston-Thomas of CSTT. References
[1] K. Bladon et al. “Predictive condition monitoring of railway rolling stock”. Conference on Railway Engineering, Darwin, 20-23 June 2004.
[2] G. Izbinsky, D. D’Aoust. “Wayside rail traffic monitoring with angle-of-attack measurement system”. Computers in Railways V, Volume 2, pp 45-57. Computational Mechanics Publications, 1996.
[3] G. Izbinsky. “Detection and rehabilitation of bad acting trucks with T/BOGI”. Rail/Wheel Interface Seminar, Advanced Rail Management, May 12-13, 1998. Chicago, Il. U.S.A.
[4] R. Lewis et al. “Railway wheel wear predictions with adams/rail”. http://www.mscsoftware.com/support/library/conf/adams/euro/2002/papers/033_EUC_019_Sheffield%20 University.pdf