Solution

Tata Power uses AVEVA Predictive Analytics to continuously monitor the health and performance of critical assets while providing early warning notification of equipment that is performing poorly or is likely to fail. AVEVA Predictive Analytics is based on an algorithm called OPTiCS, which uses Advanced Pattern Recognition (APR) and machine learning technology to learns an asset’s unique operating profile during all loading, ambient and operational process conditions.

Existing machinery sensor data is input into the software’s advanced modeling process and compared to real-time operating data to determine and alert upon subtle deviations from expected equipment behavior. Once an issue has been identified, AVEVA Predictive Analytics can assist in root cause analysis and provide fault diagnostics to help the user understand the cause and significance of the problem.

Phase 1 of the fleet-wide monitoring project at Tata Power was for two 800 MW power generation plants at the Mundra Power Station. Tata Power identified which assets and components to monitor based on their strategic importance to the business: boilers, steam turbines, CW pumps, coal pulverizes, fans, boiler feed pumps, generators and transformers.

Tata Power worked closely with AVEVA to determine which sensors would best correlate with the chosen asset’s operational characteristics and then collaborated together on the model-building process. The models were trained with one year of historical operational data and fine-tuned according to the equipment history. Thresholds were set for early warning and alert notifications based on each asset’s individual operational profile.

The utility uses the AVEVA Predictive Analytics Web application to manage alerts, quickly retrain models and analyze and trend model results. AVEVA Predictive Analytics Web organizes alert information in a hierarchical structure allowing users to identify systems that are in an abnormal state and then view the individual components of the alert for further analysis.

Tata Power built a remote monitoring & diagnostics center (they named it ADoRE- Advanced center for Diagnostics & Reliability Enhancement) where they are using AVEVA Predictive Analytics software. The ADoRE team can alert and dispatch plant personnel almost immediately after an emerging problem is identified by AVEVA Predictive Analytics. ADoRE also facilitates increased knowledge sharing between the team and enables collaboration for timely problem solving. Additionally, the team encourages consistent procedures across the fleet for operations and maintenance problem identification and resolution while increasing opportunities for knowledge capture and understanding of equipment failure modes.

Goals

• Avoid asset failures and reduce equipment downtime.
• Identify subtle changes in system and equipment behavior.
• Gain advanced warning of emerging equipment issues.
• Monitor the health and performance of critical assets fleet-wide in real time.
• Improve maintenance planning.
• Enable knowledge capture to optimize information sharing between plant personnels.

Challenges

• Reduce unscheduled maintenance.
• Move from reactive to proactive maintenance.
• Quickly analyze large amounts of asset data for accurate equipment condition assessments.
• Information sharing between groups to get the right, information to the right people at the right time.
• Complexities in the process and technology of Tata Power fleets

Solutions 

• AVEVA™ Predictive Analytics

Results

• Early warning identification of equipment problems, days weeks or months before failure.
• Dynamic insights and deep-dive diagnostics for equipment behavior changes.
• Improved equipment reliability and performance.
• Better maintenance planning and cost control.
• Knowledge capture of equipment failure modes.

“We found AVEVA Predictive Analytics to be an effective tool in the predictive diagnostics space for detecting functional deviations and impending failures at an early stage for initiating suitable prioritized maintenance actions for enhanced reliability of critical power plant equipment.”

Praveen Chorghade, Chief - Core Technology and Diagnostics, Tata Power