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Predicting Rare Failures in Hydro Turbines - SparkCognition Industrial IoT Case Study
Predicting Rare Failures in Hydro Turbines
Utility companies that operate hydro turbines have a vested interest in performing regular maintenance to prevent unexpected failures. Most maintenance occurs on a scheduled basis where the asset is taken offline, inspected, and repaired proactively if needed. Hydro turbine units are highly reliable, meaning that few examples of unplanned downtime exist. However, these failures are very costly to their operators.Given the sensitivity operators have to unplanned downtime, many have equipped turbines and generators with sensors and platforms to collect valuable performance information in real-time. But because there are so few historical hydro failures to compare against, rich streaming data and legacy statistics-based analysis are not very accurate at predicting true failure events. In fact, they often create more problems by overloading monitoring teams with benign false positives that result in unnecessary downtime to evaluate. This begs the question: Can artificial intelligence help maintenance teams extract more value out of their data?
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Identifying Vane Failure From Combustion Turbine Data - SparkCognition Industrial IoT Case Study
Identifying Vane Failure From Combustion Turbine Data
In late 2015, a deployed combustion turbine experienced a row two vane failure, which caused massive secondary damage to the compressor, resulting in nearly two months of downtime and up to $30M in repairs costs and lost opportunity. This failure, though rare, is representative of typical catastrophic events that are very difficult to catch. Though the onsite plant operations team had been monitoring the asset, this specific failure mode was previously unknown and very nuanced, and existing alarms did not have enough information for SMEs to properly diagnose it in time.The OEM decided to evaluate SparkCognition’s predictive analytics solution, SparkPredict®, with the following objectives:1. Demonstrate the ability to detect and distinguish operational and anomalous online steady-state conditions based on blind data provided from the turbine.2. Provide additional insights about the key contributing factors to the underlying anomalies.3. Provide a UI that interfaces to live streaming data from the asset.
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