# Anomaly Detection in Rover Telemetry Data ## Executive Summary Rovers operating in extraterrestrial environments generate vast streams of telemetry: temperatures, voltages, motor currents, wheel slip, sensor health, radiation counts, communication metrics, and more. Small, early-stage anomalies in these telemetry streams can prefigure catastrophic failures, mission delays, or lost science time. Presear Softwares PVT LTD offers an end-to-end anomaly detection solution tailored for rover telemetry that reduces risk, extends operational life, and boosts mission success probability by identifying subtle deviations in real-time and providing actionable diagnostics to engineering teams. This use case describes the technical approach, system design, data strategy, evaluation metrics, typical deployment scenarios, and concrete benefits for mission operators. --- ## The challenge: why rover telemetry is special 1. **Harsh, unpredictable environment.** Temperature swings, dust storms, micrometeorites, and radiation can trigger sensor drift or sudden component degradation. Distinguishing environment-driven variations from equipment faults is difficult. 2. **Data sparsity & latency.** Communication windows are limited (especially for deep-space missions). Data arrives in bursts, sometimes with missing packets and variable sampling rates. 3. **Heterogeneous telemetry.** A rover’s telemetry mixes continuous signals (currents, temperatures), discrete events (actuator state changes), images, and periodic health checks. 4. **Unknown failure modes.** Many anomalies are previously unseen; supervised models trained on past failures are insufficient. 5. **High cost of false negatives.** Missing an early-warning anomaly can lead to lost instruments or mission failure; false positives waste precious operator time. Presear’s approach accounts for these constraints with robust, uncertainty-aware detection and prioritization. --- ## Presear's solution overview Presear delivers a layered system consisting of: * **Edge-capable lightweight detectors:** Small models that run on the rover’s onboard computer to raise immediate, high-confidence alerts during a communication blackout. * **Ground-based analytic pipeline:** More compute-heavy models on Earth that perform deeper temporal analysis, root-cause inference, and trend detection over longer windows. * **Hybrid model architecture:** Combines statistical, unsupervised, and self-supervised learning methods to detect both sudden faults and slow degradations. * **Confidence & prioritization engine:** Assigns risk scores and suggests operator actions (safe mode, selective subsystem shutdown, reconfiguration) with explainability traces. * **Visualization & collaboration UI:** A mission operations dashboard that highlights anomalies, shows correlated telemetry, and supports annotation and automated ticketing. --- ## Data pipeline & architecture 1. **Ingestion:** Telemetry (time-series) and episodic logs are buffered on-board and batch-transmitted during comm windows. Images and large payloads use downlink compression and are sampled for anomaly cues. 2. **Preprocessing:** Time alignment, outlier removal, gap interpolation, resampling to mission-standard cadences, and instrument-specific normalization. 3. **Feature engineering:** Domain-aware features such as current-to-torque ratio, thermal gradients across components, wheel-slip indexes, power budget residuals, and cross-sensor correlation matrices. 4. **Detection layers:** * *Baseline statistical monitors* (range checks, derivative thresholds). * *Unsupervised models* (autoencoders, isolation forests, seasonal hybrid ESD) for unknown anomalies. * *Self-supervised sequence models* (transformer/temporal CNN) that learn normal operational dynamics and flag deviations. 5. **Post-processing & fusion:** Results from multiple detectors are fused using Bayesian model averaging and temporal smoothing to reduce spurious alerts. 6. **Explainability:** For each alert, generate a compact explanation: contributing signals, time window, magnitude of deviation, and suggested high-level causes. 7. **Operator interface:** Prioritized alert list, annotated plots, root-cause suggestions, and suggested mitigation steps with risk/benefit notes. --- ## Models & techniques — why hybrid works best * **Statistical monitors** are computationally cheap and capture hard-limit breaches. * **Autoencoders (and variational autoencoders)** compress multivariate telemetry into a latent space and identify samples with large reconstruction error — useful for novel anomalies. * **Isolation Forests / One-Class SVMs** detect sparse anomalous points in high-dimensional feature spaces. * **Temporal deep models** (LSTM, Temporal Convolutional Networks, and lightweight Transformers) model normal sequences and predict next-step behavior; large prediction errors are anomalies. * **Change-point detection** algorithms identify regime shifts (e.g., a slowly worsening motor bearing). * **Uncertainty quantification** (ensemble models, MC dropout) provides calibrated confidence so operators can triage alerts. Combining approaches increases resilience: statistical rules catch immediate safety issues, unsupervised models detect subtle deviations, and temporal models provide context and trajectory. --- ## A sample mission workflow 1. **Onboard monitoring:** A wheel motor current rises 8% above baseline while ambient temperature dips. The onboard detector flags a medium-confidence anomaly and records a high-frequency telemetry buffer. 2. **Immediate action:** To protect the motor, a soft-throttle limit is applied automatically. The rover continues limited operations. 3. **Downlink & ground analysis:** Buffered telemetry and diagnostic logs arrive during the next comm window. Ground-based models detect a slowly increasing harmonic in motor current and correlate it with wheel slip spikes. 4. **Operator decision support:** The Presear dashboard surfaces the most-likely root cause — incipient bearing wear — with an estimated time-to-failure range and recommended actions: schedule a low-speed traverse, prioritize science tasks that avoid heavy-drive operations, and plan a diagnostic command sequence. 5. **Outcome:** The early intervention avoids motor seizure and preserves the mission’s mobility for several extra sols of exploration. --- ## KPIs & success metrics * **Detection lead time:** Average time between the anomaly's onset and the alert (goal: maximize lead time without increasing false positives). * **True positive rate (TPR) & false positive rate (FPR):** Balanced thresholds to minimize operator burden while maintaining safety. * **Reduction in unscheduled downtime:** Measure hours/days of operations saved. * **Mean time to triage:** How quickly an operator can act on an alert using Presear’s diagnostic suggestions. * **Cost avoidance:** Estimated mission cost saved by preventing component failure or lost science. --- ## Implementation roadmap for mission teams 1. **Discovery & data audit:** Presear works with mission engineers to catalog telemetry sources, historical logs, and defined failure modes. 2. **Prototype & offline testing:** Build and validate detectors on archived telemetry, simulated faults, and hardware-in-loop tests. 3. **Edge integration:** Optimize lightweight detectors for the rover’s computing platform and integrate with onboard telemetry managers. 4. **Ground pipeline deployment:** Deploy the full analytic stack in the mission operations center with secure data links and role-based access. 5. **Operational tuning:** Gradually tune sensitivity using real mission data and feedback loops from operators. 6. **Training & handover:** Deliver operator training, runbooks, and maintenance plans to ensure long-term reliability. --- ## Challenges & mitigation strategies * **Label scarcity:** Failures are rare; Presear relies on simulation, synthetic anomalies, and self-supervised learning to build robust detectors. * **Domain shift:** Environmental changes can alter 'normal' signals. Continuous learning pipelines and periodic model retraining mitigate drift. * **Comms constraints:** Onboard buffering, prioritized downlinks, and compact summary telemetry packets ensure critical data reaches ground stations. * **Explainability needs:** Operators demand interpretable alerts. Presear emphasizes compact, actionable explanations tied to physics-based heuristics. --- ## Business value for Presear's clients * **Extended mission lifetime:** Early detection and adaptive responses prolong component life. * **Lower operational cost:** Fewer emergency interventions and targeted diagnostics reduce expensive uplink/downlink cycles. * **Improved science yield:** Less time lost to recovery translates into more productive scientific observations. * **Risk reduction:** Quantifiable reduction in catastrophic failure probability improves stakeholder confidence. --- ## Closing — why Presear Softwares PVT LTD Presear blends aerospace domain expertise, robust data engineering, and cutting-edge machine learning to deliver an anomaly detection solution built for the realities of planetary exploration: intermittent comms, heterogeneous telemetry, and unknown failure modes. Our pragmatic, hybrid approach — from onboard lightweight detectors to deep-ground analytics — gives mission teams real-time situational awareness, prioritized diagnostics, and confidence to act early. If your mission team is planning a rover deployment or wants to retrofit advanced monitoring into an existing operations pipeline, Presear can deliver a tailored proof-of-concept within mission constraints and scale to operational deployment with full integration and support. --- *Prepared by Presear Softwares PVT LTD — technical use case and implementation guidance for anomaly detection in rover telemetry.*