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For industries that rely on physical assets, smart connectivity enables vastly superior performance by unlocking value from digital transformation. At the heart of Industry 4.0 and the Industrial Internet of Things (IIoT) are Smart Connected Assets and the evolution of APM 4.0.

Digital transformation empowers organisations, such as Tata Power, to shift from reactive maintenance to predictive maintenance (PdM) strategies. The largest power generation company in India uses predictive asset analytics software to continuously monitor the health and performance of critical assets. This enables front line personnel to act before costly failures occur and maximise value throughout the entire asset lifecycle

Tata Power has an installed generation capacity of 8,750MW in India, with another 9,100MW under development. The organisation has created a plan to implement a fleet-wide monitoring and diagnostics programme to continuously monitor the health and performance of critical power plant assets. Tata Power was seeking a solution that would transform their data into real-time insights to be used for proactive maintenance and more efficient operations.

After evaluating several options, Tata Power selected AVEVA’s PRiSM Predictive Asset Analytics software as the key tool for their remote monitoring and diagnostics centre.

Solution

The AVEVA’s PRiSM Predictive Asset Analytics is based on an algorithm called OPTiCS, which uses Advanced Pattern Recognition (APR) and machine learning technology to learn an asset’s unique operating profile during all loading, ambient and operational process conditions. Tata Power uses the software 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. Existing machinery sensor data is input into the software’s advanced modelling process and compared to real-time operating data to determine and alert upon subtle deviations from expected equipment behaviour. Once an issue has been identified, it 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 800MW 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 pulverises, fans, boiler feed pumps, generators and transformers.

The power generation company uses the PRiSM Web application to manage alerts, quickly retrain models and analyse and trend model results. PRiSM Web organises 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 centre (ADoRE – Advanced centre for Diagnostics & Reliability Enhancement) where they are using PRiSM Predictive Asset Analytics software. The ADoRE team can alert and dispatch plant personnel almost immediately after an emerging problem is identified by PRiSM. 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.

Fault Catching

Tata Power has more than 300 models deployed in Phase 1 and they provide regular monitoring and diagnostics for mechanical failures, performance deviations and transients. Because PRiSM Predictive Asset Analytics provides early warning of equipment degradations, one measurement for success is based on the amount and significance of avoided equipment failures and degradations, which they call a “catch”.

During the Phase 1 deployment a few significant catches were identified, which provided management with confidence in the programme and the tool. In one catch, operations and maintenance personnel determined that one of the bypass valves of a low pressure heater was partially open when it should have been completely closed. This was causing condensate flow to bypass through the heater and resulting in a higher extraction temperature, meaning the plant was operating inefficiently.

In another find, top thrust and guide bearing temperatures of circulation water pumps were rising well above expected levels. Each unit is provided with two pumps for handling a full load of 800MW. Circulation water pump bearings are supplied by the external sealing and cooling arrangement of clarified water. During a low demand period, the pump was taken for a brief outage to inspect and clear the suspected clogging in the bearing cooling water line. After clearing the block, the bearing temperature normalised and generation potential was normalised.

Identifying and investigating these issues before they caused serious equipment damage resulted in substantial savings, as well as performance and reliability improvements. Estimated cost savings on this catch are US$270,000.

After validating the usefulness of PRiSM Predictive Asset Analytics software in Phase 1, Tata Power is continuing with fleet-wide implementation. The organisation has already experienced a number of initial operational and maintenance improvements and has been able to manage risk, mitigate damage and identify and correct asset performance problems continuously and in real time.

The predictive analytics software has enabled personnel to spend less time manually collecting and analysing data, allowing engineers and specialists to perform higher value tasks and creating more time to be proactive. The solution has been configured so that alerts and relevant reports are sent to the right people at the right time, enabling information sharing between various stakeholders before a decision is made.

AVEVA’s PRiSM Predictive Asset Analytics has also equipped Tata Power with knowledge capture capabilities, which can be a challenge for utilities faced with transitional workforces. This helps in timely detection of operations and maintenance problems and standardisation of detection and resolution procedures.

Developing an APM strategy enables companies to balance asset utilisation, cost control, regulatory compliance, and shows how people, processes and technology can drive optimal performance.

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