How a Major North American Refinery Secured Operations and Saved ~$1.9M by Preempting Critical Rotating Equipment Failures
In the high-consequence environment of downstream energy production, asset reliability is the linchpin of profitability. This case study examines the successful deployment of the Shoreline AI Predictive Maintenance (PdM) platform at a major North American refinery.
By shifting from time-based maintenance to an AI-driven predictive model, the facility successfully identified and resolved two critical developing faults in their rotating asset fleet:
The platform’s deep learning algorithms detected subtle spectral anomalies—specifically structural misalignment and rotor imbalance—weeks before traditional monitoring methods would have flagged them.
This advance warning allowed for:
Refineries operate complex, continuous processes where the failure of a single critical asset can force unplanned shutdowns of entire units (such as Fluid Catalytic Cracking or Sulfur Recovery units). The customer faced a dual challenge:
Managing the health of legacy blowers and turbines where replacement parts have long lead times (6–12 months).
Traditional route-based vibration analysis often resulted in a "lag," identifying problems only after they had caused secondary damage.
The refinery turned to Shoreline AI to implement continuous, wireless monitoring capable of providing automated root-cause analysis and actionable repair recommendations.
Asset: Critical Process Blower (K-1520B)
Application: Refining Process Unit Air Handling
Fault Detected: Structural Misalignment & Broken Shims
The sustained alarm frequency indicated a structural issue, not a transient upset.
The sustained alarm frequency indicated a structural issue, not a transient upset.
Acting on the specific recommendation to check alignment and couplings, the plant scheduled a targeted intervention.
Asset: Main Turbo-Blower (TK-X-XXX C)
Application: High-Speed Turbine Drive
Fault Detected: Rotor Imbalance, Blade Shroud Damage & Mechanical Looseness
The system provided a 7-day advance notice before failure risk escalated.
This allowed maintenance to shift from emergency trip to a planned shutdown on December 22.
During the planned shutdown on December 22, the maintenance team executed a major overhaul based on the AI’s “Imbalance and Looseness” diagnosis.
The financial value of Shoreline AI is calculated by comparing the Actual Cost (Planned Repair) against the Avoided Cost (Catastrophic Failure & Unplanned Downtime).
| Cost Category | Scenario A: Turbo-Blower (TK-K-6418 C) | Scenario B: Process Blower (K-1520B) | Total Impact |
|---|---|---|---|
| Asset Risk | High: Rotor imbalance and shroud damage often lead to "blade liberation," destroying the rotor and casing. | Medium: Broken shims lead to coupling seizure or shaft bending. | |
| Avoided Replacement Cost | $850,000*(Cost of new custom rotor/casing: $950k - Repair cost: $100k)* | $40,000*(Cost of shaft repair/motor rewind: $50k - Shim repair: $10k)* | $890,000 |
| Avoided Production Loss | $1,000,000*(5 days downtime @ $200k/day)Note: The 7-day advance notice allowed repairs to move to a planned window.* | Minimal*(Assumed redundancy or short repair window) | $1,000,000 |
| Performance Gain | +12% Capacity*(Speed increased from ~4200 to >4700 RPM)* | Reliability Restoration | Operational Efficiency |
| TOTAL ESTIMATED SAVINGS | ~$1,850,000 | ~$40,000 | ~$1,890,000 |
The adoption of Shoreline AI at this North American refinery demonstrates the transition from reactive “fire-fighting” to proactive asset management.
By digitizing asset health, the refinery has secured its operations against the high cost of unplanned downtime, ensuring that critical machinery supports, rather than hinders, production goals.
Shoreline AI’s plug-and-play asset performance management delivers breakthrough simplicity and cost efficiencies. Completely self-installed by non-experts, smart sensors automatically connect to the cloud and are auto-provisioned via a rich library of 30,000+ pre-built asset physics models.
This cloud-native approach requires no new CapEx, on-site experts or data scientists, operationalizing in days and delivering powerful machine-specific analytics. This highly secure, 100% subscription approach creates unprecedented industrial APM economics and scales easily for new applications such as emissions monitoring.
Shoreline AI helps clients in asset-intensive industries maximize the performance and profitability of their operations, create a proactive and predictive approach to asset management, and accelerate sustainability initiatives. The company’s solutions are designed for machinery serving the energy, manufacturing, pharma and data-center cooling industries.
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