A major oil and gas refinery plant, generating annual revenue of $10 billion, faced frequent reliability issues with its compressor pumps, which adversely affected the plant’s output to downstream refineries.
3 Key Challenges:
- Although equipment anomalies were flagged with their in-house legacy software (without self-learning AI), it lacked the ability to take domain knowledge into account. This resulted in a heavy reliance on subject-matter expert (SME) consultants and their corresponding RCA (Root Cause Analysis) delays, leading to increased downtime.
- Analyzing correlations between different operating conditions in the compressors revealed that the sensor data deviated from the expected behaviour across multiple process units.
- The Predictive Maintenance (PdM) solution implemented at the plant also triggered multiple false alarms and reduced the plant operations’ efficiency.
According to Deloitte, Fortune Global 500 manufacturing firms lose 3.3 million production hours annually due to machine failures—a staggering $864B loss despite investments in Predictive Maintenance [PdM] solutions. These challenges directly impact asset utilization, making it difficult for refiners to achieve annual Operational Excellence goals.
Learn how UptimeAI Reasoning Agents empowered this Petroleum Giant to enhance asset utilisation and deliver $9M in annual savings.
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