We use AI to maximize asset performance through neural network learning
Training AI models with historical data
Existing SCADA data (x1,x2,x3) is used to construct the initial deep learning model, and baseline prediction accuracy.
Iterative approach to making predictions
Through systemized learning, an ideal coefficient set is generated to maximize model accuracy and to predict future outcomes (k1,k2).
Providing enhanced datasets to improve prediction models
New levels of insight are dervied from high Resolution / highly Structured data streams
Incorporating h2RS™ insights into AI models - significantly improves prediction accuracy
Knowing Your Prediction Accuracy
Using asset data to predict future performance is key to avoiding failure
A Message from the CEO
President & CEO
This is an exciting time in the world as we are all beginning to see new levels of performance in artificial intelligence and the value it can provide to society.
We, at Fischer Block, Inc., are on the leading edge of applying AI and deep learning techniques to energy and power system assets.
Traditional asset performance data (typically residing in SCADA historians) can only get so far in being able to optimize performance and predict failure. This is because the health condition of internal components (within critical power system assets) rarely shows up in SCADA data streams.
However, we are finding that by enhancing input data streams into prediction models, to include high-fidelity information (down to the sub-component level), then accuracy of detecting/predicting asset failure, and optimizing performance, can be significantly improved.
Our team is committed to providing our customers with these new insights to help optimize asset performance and make energy more affordable, reliable, and sustainable.
Join us at the frontier of advanced AI and deep learning
We aim to build the best AI company in the energy industry and are hiring ambitious, gritty innovators with the determination to build the future.