predictive-maintenance-fleet

bySoumil Jaiswal

Create a professional, competition-ready PowerPoint submission for **Teliport Season 4 — Round 1: Idea Submission**, based on the attached problem statement PDF and the uploaded official PowerPoint template. ## 1. MANDATORY TEMPLATE REQUIREMENT **Use the uploaded PowerPoint template as the ONLY template for this presentation.** - Edit the existing PowerPoint file directly. - Preserve the original template's branding, logos, visual identity, colors, fonts, layout style, and overall design language. - Do not replace the template with a new theme. - Do not import a different template or use a generic AI-generated presentation design. - Preserve the existing cover slide and its original structure. - Fill the blank submission slide with the actual idea submission. - Add slides only if explicitly required by the template or event instructions. The event requires a **1-slide idea submission**, so use one substantive submission slide. - Retain the original PowerPoint's editable elements wherever possible. - Do not distort, remove, cover, or reposition official logos and branding. ## 2. PROJECT DETAILS **Project title:** Self-Healing Machines: Predictive Maintenance for a Connected Fleet **Round:** Round 1 — Idea Submission **Objective:** Propose an end-to-end system that detects early signs of machine degradation, predicts Remaining Useful Life (RUL), and automatically initiates appropriate maintenance workflows, with human intervention for exceptions. Use the attached case study as the primary source. Do not invent requirements, results, experiments, performance metrics, or completed functionality. ## 3. REQUIRED CONTENT FOR THE SINGLE SUBMISSION SLIDE Organize the content into a clear, visually coherent architecture diagram with concise supporting sections. ### A. Problem and proposed solution Briefly explain that unexpected machine failures cause downtime and higher maintenance costs. Propose a predictive maintenance system that identifies degradation early and initiates maintenance before a failure occurs. ### B. End-to-end system architecture Create a clear left-to-right workflow: Sensor Data / NASA C-MAPSS Dataset → Data Validation and Preprocessing → Rolling-Window Feature Engineering → Anomaly Detection + RUL Prediction → Rule-Based Triage Agent → Maintenance Work Order → Monitoring and Human Escalation for Exceptions Show how the components connect. Clearly distinguish the data-processing pipeline, ML components, decision-making agent, and maintenance action. ### C. Data engineering approach Briefly cover: - Validation of sensor readings and data quality. - Handling missing values and invalid readings. - Basic noise treatment. - Rolling-window features, such as averages, variability, and trends. - Reproducible preprocessing. ### D. Machine learning approach Propose suitable baseline methods and explain their purpose: - **Anomaly detection:** Isolation Forest to identify unusual sensor patterns. - **RUL prediction:** Random Forest Regressor as a baseline for estimating remaining useful life. Explain that the anomaly detector identifies unusual behavior while the regression model estimates remaining useful life. Mention appropriate evaluation using RUL MAE/RMSE and suitable anomaly metrics where labels permit. These are proposed choices, not verified best-performing models. Do not claim any achieved accuracy or performance. ### E. Rule-based Triage Agent Show a concise decision flow: - **Normal:** No sustained risk detected. - **Warning:** Low predicted RUL or persistent anomalous behavior. - **Critical:** Very low predicted RUL or a validated high-risk condition. - **Exception:** Poor data quality, conflicting signals, or uncertain decisions are escalated for human review. The agent should create a maintenance work order with machine identifier, severity, reason, and recommended action. Include duplicate prevention and work-order tracking. Any thresholds must be identified as illustrative and subject to validation, not established facts. ### F. MLOps and monitoring Briefly show: - Experiment and model-version tracking with MLflow. - Monitoring of data quality, model performance, and prediction drift. - A controlled retraining workflow when new data becomes available. - Validation of candidate models before promotion. - A simple dashboard for machine health, alerts, work orders, and model status. ### G. Dataset strategy Use the NASA C-MAPSS Turbofan Engine Degradation dataset specified in the case study. - Start with FD001 as the baseline subset. - Design the pipeline to extend to FD002, FD003, and FD004. - Acknowledge that the subsets have increasing complexity. ### H. Round 2 implementation roadmap Conclude with a concise planned progression: FD001 data pipeline → trained RUL model → anomaly detection → automated work-order workflow → monitoring dashboard → retraining demonstration. Clearly label these as planned implementation steps. ## 4. DESIGN AND VISUAL QUALITY - Make the slide look like a polished engineering proposal, not a text-heavy report. - Prioritize the architecture diagram and use compact supporting sections. - Use clear typography, consistent spacing, readable labels, and logical visual hierarchy. - Use simple arrows, restrained icons, and clean diagram elements that match the official template. - Avoid excessive text, decorative graphics, unnecessary gradients, oversized headings, and crowded sections. - Ensure the slide is readable in presentation mode and when exported to PDF. - Keep all text within the slide boundaries. - Use precise technical language and maintain consistency across the architecture, model choices, agent logic, and roadmap. ## 5. CONTENT INTEGRITY - Do not claim that the system has already been built, deployed, tested, or validated. - Do not fabricate results, benchmarks, datasets, citations, team details, or credentials. - Do not add technologies that conflict with the proposed architecture. - Do not change the problem statement or introduce unrelated features. - Keep the submission understandable enough for the team to explain and defend every design decision. ## 6. FINAL OUTPUT Deliver the completed presentation as an editable **.pptx PowerPoint file**, using the uploaded official template as the base file. Before finalizing, verify: 1. The original template and branding are preserved. 2. There is only one substantive idea-submission slide. 3. All Round 1 deliverables from the case study are addressed. 4. The architecture is clear and internally consistent. 5. No text overlaps, gets clipped, or becomes unreadable. 6. No invented results or unsupported claims appear. 7. The file opens correctly in Microsoft PowerPoint and can be exported to PDF. **Priority:** Strict template compliance, technical clarity, architecture quality, and completeness of the Round 1 idea submission.