Methodology for An Innovative and Efficient Diagnostics of Electric Locomotive

This paper presents the development and deployment of a novel, Good Innovative Diagnostics for an Efficient Action (referred to as Good IDEA program) methodology for an electric locomotive rolling stock design and development project. To achieve this, we share a systematic and pragmatic approach within system engineering framework that addresses the outcomes of some of the most critical transversal disciplines mainly, safety, reliability and availability as the pedigree of Good IDEA[1]. We discuss certain techniques of functional analysis that automatically shift the focus to satisfy the objectives of Design for Testability (DfT). These include the ability to detect and report that a function is no longer available or responding as it should; And the ability to localize the faulty component causing the function deemed unavailable. Case studies are presented through which it is shown that diagnostics built around a context of functional failure not only helps in satisfying the objectives of testability, but it also rewards by enabling Condition Based Maintenance (CBM), perform big data analytics to support various use cases and different stakeholder needs. One of the most critical steps involving transmission of onboard data from locomotive to ground on the near real-time basis is highlighted as a typical use case of Machine-to-Machine (M2M) communication supporting remote monitoring.