Advanced analytics – including condition-based and PRiSM Predictive Asset Analytics solutions – provide early warning notification and diagnosis of equipment problems days, weeks, or months before failure. This change improves maintenance planning and reduces downtime by shifting from reactive to predictive or prescriptive maintenance strategies.
Apply the right analytics mix to maximise economic return on asset investments.
Real-time condition management solution that collects and manages data from all assets to drive appropriate action for improving overall asset performance.
Predict asset failures days, weeks, or months before they occur with advanced pattern recognition and machine learning technology to reduce operations and maintenance costs.
Reduce maintenance costs and capital expenditures by leveraging our Monitoring and Diagnostics Services Center for remote monitoring of your industrial assets.
Quickly transform raw data into actionable insights to prevent equipment failure and make smart decisions that improve operations.
Catch asset failures days, weeks, or months before they occur, and schedule maintenance operations around the most economically viable time.
When a potential problem is identified, instead of shutting down equipment immediately, the situation can be assessed for more convenient outcomes to optimise asset utilisation.
Ensure knowledge capture so that maintenance decisions and processes are repeatable even when organisations are faced with transitioning workforces.
Early warning with advanced analytics enables proactive maintenance planning allowing parts to be ordered and shipped without rush and equipment can continue running.
Discover which asset or groups of assets are underperforming to prioritise replacements or optimisation opportunities through fleet-wide monitoring.
Maintenance engineers are provided with increased situational awareness of their asset’s health, allowing them to maximise asset utilisation for the enterprise while reducing maintenance costs due to better planning. Parts can be ordered and shipped without rush, and equipment can continue running.
Operations management can schedule downtime for asset maintenance at the least economically disruptive time to the enterprise. Keep production lines running and product shipping with improved visibility into how asset performance impacts the enterprise value chain.
Reliability engineers use advanced machine learning built into PRiSM Predictive Asset Analytics to empower them with increased visibility into asset health and operations. This enables them to accurately predict and eliminate the root cause of all failures and plan downtime accordingly.