A leading global automobile manufacturer leverages Databricks to efficiently ingest and analyze IoT sensor data, transforming it into actionable intelligence for risk assessment and predictive maintenance.
Industry & Region: Manufacturing, US
Tech Stack:
Cloud Computing Platform: Amazon Web Services (AWS); Data Storage: Amazon S3; Serverless Compute Service: AWS Lambda; Data Platform: Databricks Delta Lake; Real-time Data Streaming: AWS Kinesis; Machine Learning Model: MLOps
Our client is a global automobile manufacturer committed to optimizing product development processes by adopting highly innovative technologies that deliver long-term value and excellence.
The automobile manufacturer relied on a diverse range of machinery, each equipped with IoT sensors transmitting performance data. However, ingesting this sensor data, analyzing it, and extracting actionable intelligence through analytics and AI proved increasingly complex due to variations in data structure and attributes across different equipment types. This hindered the proactive detection of anomalies, such as abnormal machine vibrations, and accurate risk quantification. Without timely intervention, the company faced potential equipment failures, underscoring the need for a streamlined approach to predictive maintenance.
To address this challenge, we designed and implemented a comprehensive Databricks-based data platform with data visualization capabilities. The solution leveraged Databricks’ lakehouse architecture to enable seamless data ingestion, transformation, and AI model building.
A scalable data ingestion pipeline, powered by Apache Spark and real-time streaming with AWS Kinesis, ensured efficient and timely data processing. This allowed for seamless integration of high-frequency sensor data into the platform.
The AI model was trained and tested using ten-second vibration sequences sampled at a rate of 25.6 kHz, ensuring the accuracy and reliability of anomaly detection. The cloud-hosted solution provided automatic predictive recommendations on anomalous behavior, enabling the calculation of the magnitude of the detected anomalies.
To enhance usability, we developed interactive Tableau dashboards, that displayed input data and anomalous patterns in an intuitive format. Additionally, trend charts were implemented to track and analyze anomalous behavior over time.
MLOps processes allowed continuous monitoring and optimization of machine learning models for consistent and reliable performance.
Automation, Cloud, AI-driven Insights – more than “Dreams of the Future” these have become the “Demands of the Present”, to set the stage for a business to be truly digital.
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