Success story
Tata Power
- Prevent failures in critical assets and reduce unscheduled shutdowns.
- Identify subtle changes in the system and equipment behavior.
- Have early warnings of critical equipment problems.
- Monitor the status and performance of critical equipment in real time.
- Improve maintenance planning.
- Enable knowledge capture to optimize information sharing among plant personnel.
- Monitoring of Tata Power's entire fleet to identify which assets and components to monitor based on their strategic importance to the business.
- Determine which sensors would best correlate with the operating characteristics of the chosen assets.
- Model building process. These were trained with one year of historical operating data and fine-tuned according to the equipment history.
- Establish thresholds for early alerts and alert notifications based on the individual operating profile of each asset.
- Early warnings of problems in equipment, days, weeks, or months before failure.
- Dynamic information and accurate diagnostics of equipment behavior change.
- Improvement in reliability and performance.
- Better maintenance planning and cost control.
- Knowledge gathering of equipment failure modes
Tata Power uses AVEVA PRiSM Predictive Asset Analytics to continuously monitor the health and performance of critical assets while providing early warning notification of equipment that is malfunctioning or is highly likely to fail.
MUMBAI, Maharashtra, India – Founded in 1915, Tata Power is one of the largest energy companies in India with a significant international presence. The company has an installed generation capacity of 8,750 MW in India, with another 9,100 MW under development. Tata Power has a presence in all segments of the electricity sector, including generation (thermal, hydro, solar, and wind), transmission, distribution, and marketing.
In 2014, the organization created a plan to implement a fleet-wide monitoring and diagnostics program to continuously monitor the health and performance of critical assets at each power plant across all of Tata Power’s plants. The company was looking for a solution that would transform its data into real-time information to be used to perform proactive maintenance and have more efficient operations. They wanted to achieve early warning of equipment problems to avoid unscheduled shutdowns and forced outages.
