Holistic embedding of equivalent conicity in wheelset maintenance

Abstract: With continuing digitization of railways an increasing number of data is recorded but particularly in operation advanced analysis tends to be partially rudimentary. Yet, it is essential to implement sophisticated processing for all records in order to develop more purposeful and predictive vehicle maintenance strategies that adhere to the increasing requirements imposed by the homologation. Typically developing requisites are permissible track forces and lateral accelerations which are directly affected by the vehicle's condition. The present work addresses this issue by executing a case study focused on a Swiss high-speed electric multiple unit with equivalent conicity being the main parameter of interest. This geometry quantity holds high relevance in determining the running stability of track guided vehicles, respectively in assessment of comfortable and safe operation. Currently, it experiences an increasing significance in the homologation as well. Thus, wheelset maintenance is challenged to elaborately embed equivalent conicity to the other influencing factors in the re-profiling strategy. A framework is established on how operational data can be analyzed and findings systematically be evaluated. The required records are provided by a Swiss railway operator and majorly processed by visualization as well as statistic tools while considering vehicle design and operational aspects. The subsequent proposition of strategies is accompanied by holistic balancing of vehicle needs, maintenance resources, and vehicle scheduling needs. As a result, correlations concerning the vehicle's configuration, design, and operational properties are observed. Incorporating these observations, for example by applying advanced warning limits, enables deduction of more predictive and holistic strategies. The potentially emerging benefits are manifold and range from lower demand on engineering staff, increased mileages, to fewer unplanned servicing tasks and subsequently increased operational stability. Further, the findings emphasize the need of in depth understanding about relevant data to derive more advanced and holistic maintenance strategies.