Case Study – Compressors
- Decades-long issues with compressors failing in spite of $millions on vibration systems & RCM
- Regular, periodic failures requiring $multi-million overhauls and production losses several time larger
- The goal to find root cause very early before major damage
- Autonomous Agents™ cast a “wider net” around equipment to cover process & machine issues
- Root cause detected = Liquid carry-over
- Chief Rotating Engineer: “You can do that? I cannot apply enough people to do that level of monitoring & analysis.”
- Third-stage valve issues detected
- Alert to Cause = 8 weeks: 7 weeks before vibration suggests damage
- Savings: $MM Repairs and Loss of Product
Case Study – Pumps
- Refinery; Long time proponent of RCM, Mtell user since 2011
- Lightly Instrumented Pumps – Only 4 signals; Fluid Pressure in/out, Temperature and Motor Amps
- Complete change in maintenance culture now SWOT team responding to agent alerts 4000+ agents on 400+ asset classes
- ONLY Mtell detects the cause with limited inputs
- Learns behavioural differences during seasons; summer, winter, etc.
- Early warning of motor burn up 2 weeks in advance
- Value: One unit saves;
- Year 1 – $1.5 MM
- Year 2 – $3 MM
Case Study – Pharmaceutical Chillers & Compressors
- At a large-scale pharmaceutical plant several large chillers and compressors are critical equipment infrastructure. Despite all six sigma efforts, failures still caused enormous losses.
- Aging equipment, increasing energy usage, higher maintenance (where 35% is corrective and 65% preventive), and inadequate equipment health status reporting contributed to the problem.
- Mtell’s industrialized machine-learning reversed the situation.
- Autonomous Agents turned corrective and preventive maintenance into prescriptive maintenance.
- Autonomous agents now advise when equipment should be maintained with early warning of impending failures.
- This gives sufficient notice for orderly, rapid problem correction at the lowest cost.
- Additionally, the Mtell View client application increases situational awareness of the current and future health of machines with direct work order creation, asset performance displays and reports, and health/status alerts and notifications.
- Overall production has improved dramatically, valued at $millions/year.