Technology
Using AI to maximize asset performance through large language models and neural network learning
Training AI models with historical data
Iterative approach to making predictions
High resolution samples along with existing SCADA data (x1,x2,x3) are used to construct the initial deep learning model, and baseline prediction accuracy.
Through systemized learning, an ideal coefficient set is generated to maximize model accuracy and to predict future outcomes (k1,k2).
Applications
Providing enhanced datasets to improve prediction models
Minimizing false positive and false negative predictions
h2RS™ dataset
New levels of insight are dervied from high Resolution / highly Structured data streams
Incorporating h2RS™ insights into AI models - can significantly improve prediction accuracy
Mission
Advancing AI to improve prediction models for a sustainable future
Knowing Your Prediction Accuracy
Using asset data to predict future performance
is key to avoiding failure
is key to avoiding failure
A Message from the CEO
Greg Wolfe
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, not only to provide predictive analytics, but also to help our customers understand the causes of the conditions observed and to recommend proven solutions.
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.
We, at Fischer Block, Inc., are on the leading edge of applying AI and deep learning techniques to energy and power system assets, not only to provide predictive analytics, but also to help our customers understand the causes of the conditions observed and to recommend proven solutions.
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.
Careers
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.
