From Failure Reports to Grounded Diagnosis: A Review of Spacecraft Anomaly Detection and Root-Cause Analysis, and the Process Behind an Evidence-Grounded AI Framework for Lunar & Space Missions

Author

Sangharaj Jadhav, Alihana Memon, Aditya Yadav, Prof. Deepti Vijay Chandran


Abstract

 Five lunar landing attempts between 2019 and 2024 — Beresheet, Hakuto-R Mission 1, Peregrine Mission One, SLIM and IM-1 Odysseus — failed or ended in off-nominal landings, and in each case the cause was established only after weeks or months of expert investigation. This review paper describes how we identified this problem, what the existing literature offers, and how a structured study of its gaps led us to the idea of LUNAR.AI, an evidence-grounded framework for detecting and explaining anomalies in spacecraft telemetry. We first analyse the publicly reported causes of the five failures and observe that each one broke a known physical relationship between sensors. We then review four bodies of work: classical fault detection, isolation and recovery (FDIR) and analytical redundancy; data-driven anomaly detection with deep learning and isolation-based methods; benchmark and evaluation critiques of time-series anomaly detection; and large language models (LLMs) for root-cause analysis with retrieval and agent architectures. From this review we derive five gaps: detectors report when telemetry is abnormal but not why; labelled failure data are scarce; raw descent telemetry is strongly non-stationary; LLM explanations are fluent but often unsupported; and evaluations rarely report uncertainty. We show how these gaps shaped five guiding principles — check physics rather than raw numbers, create the data that cannot be collected, start simple, explain like an investigation board with evidence, and measure honestly — and how the principles combine into the idea of LUNAR.AI. We then describe, at a conceptual level, how the proposed framework is intended to work, illustrate it with an example, and discuss its expected outcomes, open challenges and future scope.



Keywords

Spacecraft telemetry; Anomaly detection; Root-cause analysis; Analytical redundancy; Large language models; Retrieval-augmented generation; Lunar landing



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References


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