Architecture overview
The Predictive Maintenance solution follows a multi-stage architecture that processes vehicle telemetry data through parallel prediction pipelines.
High-Level Architecture
The architecture consists of:
-
Data Source Layer: Amazon Redshift cluster containing vehicle telemetry data
-
ETL Processing Layer: AWS Glue jobs that transform and prepare data hourly
-
Prediction Layer: Dual-path processing with ML and filter-based approaches
-
Alert Layer: Consolidation and delivery of maintenance alerts
-
Storage Layer: Amazon S3 for intermediate data and Amazon DynamoDB for alert state
Data Flow
The solution processes data through the following stages:
Stage 1: Data Ingestion
-
Telemetry data is accessed from Amazon Redshift via Datashare or S3 unload
-
Root ETL pipeline runs hourly to extract new data
-
Data is transformed into standardized formats and stored in S3
Stage 2: Parallel Processing
Two independent pipelines process the data:
Machine Learning Pipeline: * ML ETL prepares features from historical data * Training pipeline updates models weekly using Amazon SageMaker * Inference pipeline runs batch predictions on new data * Anomaly scores are generated for each vehicle/tire combination
Filter-Based Pipeline: * Statistical filters analyze pressure trends * Leak rates are calculated using time-series analysis * Alerts are generated when thresholds are exceeded
Stage 3: Alert Consolidation
-
Results from both pipelines are merged
-
Duplicate alerts are deduplicated
-
Severity levels are assigned based on leak rates
-
Alert status is tracked in DynamoDB
Stage 4: Alert Delivery
-
Alerts are formatted for downstream systems
-
Integration APIs provide real-time access to alert data
-
Historical alert data is maintained for analysis
Key AWS Services
The solution leverages the following AWS services:
-
Amazon Redshift: Source data warehouse for vehicle telemetry
-
AWS Glue: Serverless ETL for data transformation
-
Amazon SageMaker: ML model training and batch inference
-
Amazon S3: Data lake storage for intermediate and processed data
-
Amazon DynamoDB: Alert state management and tracking
-
AWS Lambda: Event-driven processing and orchestration
-
Amazon EventBridge: Scheduling and workflow coordination
-
AWS Step Functions: ML pipeline orchestration
