Abstract

Corrective diagnosis of legacy robotic assets is routinely performed in operating contexts where manufacturer documentation is incomplete, telemetry is unavailable, and the failure mode is non-progressive. These operations fall outside the typical scope of both data-driven and knowledge-based PHM techniques. This paper presents a structured framework for diagnosing legacy AMR startup failures under limited information conditions, together with a comparison of three diagnostic prioritisation strategies applied to the same practical case: (i) a qualitative power-tracing logic that combines Fault Tree Analysis (FTA), Failure Mode and Effects Analysis (FMEA), and a decision flowchart, which was the method applied to the asset; (ii) an analytical FMEA with Risk Priority Number (RPN) scoring; and (iii) an analytical decision cost ordering that ranks investigation steps by expected total cost. Methods (ii) and (iii) are then applied retrospectively to the same case using the operator’s experience gained during the practical investigation to assign scores and parameters. The case is a no-start fault on a legacy MiR100 autonomous mobile robot in a university laboratory. The practical investigation, following method (i), traced the fault to a blown fuse in the robot-side input path; replacement and a brief charging recovery restored normal operation, confirmed via the built-in hardware-health diagnostic. Applied retrospectively, method (ii) would have ordered the investigation by descending RPN, reaching the fuse on the seventh step, method (iii) would have ordered by the lowest per-step decision cost, reaching the fuse within minutes. We compare the three methods on time-to-confirmation and total cost, and found that methods (i) and (ii) reach the fault with comparable total time and cost despite different orderings, while method (iii) reaches it approximately an order of magnitude faster and cheaper in this case.