Experimental and numerical investigation of the possibilities for the structural health monitoring of railway axles based on acceleration measurements

Abstract

In this article, numerical and experimental investigations are carried out to assess the possible use of vibration measurements to identify the presence of a fatigue crack in railway axles, detecting components of axle vibration occurring at frequencies that are integer multiples of the axle’s frequency of revolution (N×Rev components). A model of a cracked axle is defined using the Timoshenko beam finite elements incorporating an equivalent beam element having cross-sectional area and moments which are periodically changing with the angular position of the axle turn to reflect the crack breathing mechanics. Two levels of validation are considered for this model: first, verification with numerical results from a solid model of railway cracked axle (solid model was built before using the solid finite element model) and, second, validation against experimental results obtained from full-scale tests performed on cracked axles using a rotating bending fatigue test rig available at the labs of Politecnico di Milano. To consider the effect of disturbances arising from train–track interaction, track irregularity and wheel out-of-roundness, a multi-body model of a complete railway vehicle is defined, incorporating one axle modelled as a flexible cracked axle. This model is used to evaluate the N×Rev components of axle vibration occurring in the presence of axle cracks having different positions along the axle and different depths, in combination with different sources of disturbance. The results show that the 2×Rev and 3×Rev components of the horizontal axle-box acceleration are well correlated to the size of the crack and are almost insensitive to the effect of all disturbances considered in this study. Hence, they can be used for the continuous monitoring of axel integrity. Finally, a criterion for crack detection is defined and applied to the experimental and numerical results, showing the possibility of detecting cracks with sizes in the order of 8% of the total section area.

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“…even earlier than the usual procedures in preventive maintenance. Improvement of health monitoring of railway axles, by adapting the distance between non-destructive test (NDT) inspections to the actual service loads experienced by the wheelset and/or by implementing solutions to identify fatigue cracks in the axle based on measuring the axle’s bending acceleration. 8 Identification of defects in bearings, to reduce in-service failures. The focus here is on early detection of faults because the time interval between correct operation and catastrophic breakdown is very short. Study of the vibration in gearboxes to determine the condition of the wheelset in advance of the train being stopped for the major overhaul. Condition monitoring of vehicle suspensions, as this topic seems to be seldom covered by existing applications, despite the maintenance and replacement of suspension components takes a significant share of maintenance costs, especially for some types of railway rolling stock, e.g.…”

“…Improvement of health monitoring of railway axles, by adapting the distance between non-destructive test (NDT) inspections to the actual service loads experienced by the wheelset and/or by implementing solutions to identify fatigue cracks in the axle based on measuring the axle’s bending acceleration. 8…”

“…This is usually done using wayside equipment, which is less expensive than using on board equipment. 3) Improvement of health monitoring of railway axles, by adapting the distance between non-destructive test (NDT) inspections to the actual service loads experienced by the wheelset and/or by implementing solutions to identify fatigue cracks in the axle based on measuring the axle's bending acceleration [27]. 4) Identification of defects in bearings, to reduce in-service failures.…”

“…To reduce the effect of estimation bias on fault detection, the values of the stiffness and damping parameters obtained from the RLS/DLS estimation are processed according to the outlier detection technique known as Mahalanobis Squared Distance (MSD) [26,27] so that the fault can be detected based on the deviation of the set of estimated parameters from the values corresponding to the 'nominal' behaviour of the suspensions. A population of 'healthy suspension' cases was created considering different running conditions (e.g.…”

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“…Frequently, the key mechanical components studied are faulty bearings within the transmission system 18 and the axle box, 19,20 the existence of wheel flats, [21][22][23] and axle cracks. [24][25][26][27][28] Generally, this kind of researches is carried out in laboratory conditions and combining numerical models with actual vibration measurements.…”