| 1. |
- Kulkarni, Rohan, 1991-, et al.
(författare)
-
iVRIDA: intelligent Vehicle Running Instability Detection Algorithm for high-speed rail vehicles using Temporal Convolution Network : – A pilot study
- 2022
-
Ingår i: Proceedings of the 7th European Conference of the Prognostics and Health Management Society 2022. - : PHM Society. ; , s. 269-277
-
Konferensbidrag (refereegranskat)abstract
- Intelligent fault identification of rail vehicles from onboard measurements is of utmost importance to reduce the operating and maintenance cost of high-speed vehicles. Early identification of vehicle faults responsible for an unsafe situation, such as the instable running of highspeed vehicles, is very important to ensure the safety of operating rail vehicles. However, this task is challenging because of the nonlinear dynamics associated with multiple subsystems of the rail vehicle. The task becomes more challenging with only accelerations recorded in the carbody where, nevertheless, sensor maintenance is significantly lower compared to axlebox accelerometers. This paper proposes a Temporal Convolution Network (TCN)-based intelligent fault detection algorithm to detect rail vehicle faults. In this investigation, the classifiers are trained and tested with the results of numerical simulations of a high-speed vehicle (200 km/h). The TCN based fault classification algorithm identifies the rail vehicle faults with 98.7% accuracy. The proposed method contributes towards digitalization of rail vehicle maintenance through condition-based and predictive maintenance.
|
|
| 2. |
- Kulkarni, Rohan, 1991-, et al.
(författare)
-
Identification of vehicle response features for onboard diagnosis of vehicle running instability
- 2022
-
Ingår i: 2022 IEEE International Conference on Prognostics and Health Management (ICPHM). - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 52-57
-
Konferensbidrag (refereegranskat)abstract
- Condition Monitoring (CM) of dynamic vehicle track interaction is an important research topic in rail vehicle dynamics. The most cost-effective method for CM is through carbody floor mounted accelerometers because this is most safe and reliable location for onboard accelerometers onboard inservice train. However, the dynamic response of carbody is influenced not only by excitations coming from track but also by various nonlinearities such as wheel-rail interface and vehicle suspension elements. Thus, it is very challenging to accurately monitor track subsystems via carbody floor accelerations. In this article, two feature extraction algorithms are proposed with the objective of obtaining crucial information on the stability of vehicle using carbody floor accelerations. The first algorithm is based on spectral analysis and the latter is on adaptive signal processing technique. The first algorithm calculates transfer function between track irregularities and carbody floor acceleration using Multiple Input Multiple Output (MIMO) system identification method. The later method analyses the carbody floor accelerations with Empirical Mode Decomposition followed by Singular Value Decomposition (EMD+SVD). These algorithms are evaluated on simulated carbody floor accelerations obtained with vehicle dynamic simulations. In this investigation, it is observed that the first method extracts more crucial information from carbody floor acceleration in comparison to EMD+SVD method. These features are planned to be used in future research to develop machine learning based intelligent fault identification algorithm for identification of root cause of vehicle running instability occurrence.
|
|
| 3. |
- Kulkarni, Rohan, 1991-, et al.
(författare)
-
Investigating the effect of the equivalent conicity function's nonlinearity on the dynamic behaviour of a rail vehicle under typical service conditions
- 2022
-
Ingår i: Vehicle System Dynamics. - : Taylor & Francis. - 0042-3114 .- 1744-5159. ; 60:10, s. 3484-3503
-
Tidskriftsartikel (refereegranskat)abstract
- Generally, the equivalent conicity function (ECF) is denoted by equivalent conicity at 3mm (λ3mm) and a Nonlinearity Parameter (NP). NP describes the nonlinearity of the ECF and its influence on a vehicle design is explored thoroughly, however, NP’s role in vehicle and track maintenance is not researched yet. This paper investigates the influence of track maintenance actions on vehicle dynamics with help of NP vs λ3mm scatter plots of ECF database. The ECF database is constructed by combining measured worn wheel and rail profile pairs of the Swedish high-speed vehicle and rail network, respectively. The ECF database revealed an inverse relationship between λ3mm and NP, i.e., NP is negative for larger λ3mm values. The combination of negative NP and high λ3mm causes reduction in the vehicle’s nonlinear critical speed and vehicle often exhibit the unstable running on the Swedish rail network. Thus, the occurrence of ECF with negative NP and high λ3mm is undersirable and the undesirable ECF can be converted into desirable ECF by grinding the rail, which converts ECF’s into positive NP and low λ3mm combinations. Thus, the NP parameter along with the λ3mm must be considered in track maintenance decisions.
|
|
