Challenges
A global manufacturer is a leading supplier of cement and mining equipment such as pumps, crushers, mills, high-pressure grinding rolls (HPGR), and other industrial equipment, with customers in over 150 countries. A top priority for the company is ensuring its customers maximize value from installed assets by improving their operational efficiency and avoiding equipment downtime.
The company’s monitoring teams and subject matter experts provide advisory and monitoring services, delivering operational advice and machine condition reports to customers. However, the company’s existing advising and condition report solutions had several limitations:
- Historical sensor and KPI dashboards had little predictive capability
- Data science and engineering teams required 4 days to onboard and deploy ML models for new assets
- Data science teams had limited ability to add value through additional machine learning experiments, given the time required for asset onboarding and the solution’s lack of predictive power
- The monitoring support team lacked tools to deploy and manage live production models at scale or to provide access to end customers who want to use the tools to monitor their machines
Approach
Over 24 weeks, C3 AI partnered with the manufacturer to configure C3 AI Reliability and enable predictive maintenance across customer operations. The team began by ingesting, cleansing, and unifying two years of historical time series data across four asset classes and 2,600+ sensors. The company uses a Snowflake data lake for sensor data, so the team configured C3 AI Reliability to integrate directly with Snowflake for model training and inferences, as well as plotting and querying data.
After integrating data from 62 machines, the joint team performed exploratory data analysis and identified high variability in sensor data. Since the manufacturer does not directly operate the machines it produces, it did not have access to downtime and maintenance data. To address these challenges, the C3 AI team designed a modeling approach based entirely on sensor data. The team applied unsupervised anomaly detection algorithms and validated results with SMEs during development using the application’s model feedback user workflows.
The application enabled the manufacturer to provide early warning of critical failure modes, such as seal failure, improper lubrication, cavitation, and high vibration. Furthermore, C3 AI Reliability provided prescriptive corrective actions and detailed data-driven evidence packages to accelerate engineering troubleshooting and risk management. ML models on pumps and other asset classes demonstrated precision of 87%, defined as the proportion of useful alerts to total alerts.
Solution Architecture
Benefits
By using the C3 AI Reliability application, the technology company is able to:
Generate
$25 million in annual economic benefit with enhanced monitoring and advisory services
Onboard
assets 75% faster, in less than 1 day rather than 4 days
Ensure
87% of alerts are useful so operators can efficiently prioritize maintenance activities
Detect
usage anomalies in advance and proactively advise customers on optimal usage
Drive
additional asset and spare parts sales by offering forward looking predictions to pinpoint root cause of failure, including specific component issues
Leverage
AI-based risk scores to guide troubleshooting, reducing operator triage time from days to hours
Monitor
system health and performance in near real time across 8 asset types for customers in 150+ countries
Design
develop, and deploy new AI applications rapidly, including inventory optimization and generative AI to improve troubleshooting
Proven results in weeks, not years
Get insights into C3 AI’s capabilities, enterprise AI best practices, and highest-value use cases.
Gain insights into the C3 AI Platform's capabilities, its model-driven architecture, and test it against your company's sample data set.
Identify a high-impact business problem and collaborate with the C3 AI team to rapidly build an AI application that solves it.
Scale and deploy a tested C3 AI application into production. Incorporate user feedback and optimize algorithms to drive maximum economic value.
Download the PDF to read the full story — Download