Teck, a major Canadian mining company, needed to optimize haul truck operations by predicting failures. With access to a vast amount of IoT data from operating machines, Teck partnered with Pythian to leverage AI and Machine Learning and produce unique actionable insights.
Teck Resources, one of Canada’s largest diversified energy and mining companies, needed to optimize the efficiency of its mining haul trucks. These trucks must keep moving day and night because operating them represents 40% of mining site costs, and each minute of productive operation generates revenue.
A modern mining haul truck is a million-pound IoT device on wheels producing 2GB of raw data per day and this presents a major opportunity to use AI for optimizing performance.
The company needed to harness the power of the sensor data to predict costly issues—such as electrical failures and suspension degradation—before they happened. This would involve analyzing terabytes of raw operational sensor and alerts data, coupled with maintenance, scheduling, and other truck lifecycle records to produce actionable insights.
Teck partnered with Pythian because of their proven expertise in data science and deep knowledge and experience implementing innovative solutions on Google Cloud.
What we did
Leveraged raw telemetry data to produce machine learning models
Built a production solution on Google Cloud that is embedded into the company’s existing on-premises systems
Technologies used
Google Cloud Platform
Google Cloud
Storage
Google BigQuery
Google Datalab
Google Dataprep
Google Compute Engine
Google Cloud Endpoints
Google Apps Engine
Scikit Learn
TensorFlow
Apache NiFi
Apache Airflow
Key Outcomes
Teck now has a machine learning application built on Google Cloud with integrated IoT data ingestion and predictive microservices embedded into its on-premises products.
