Using Aveva PI operational data greatly improves outage planning. Plant managers can use machine learning applications to better forecast asset outage spend and duration. Engine degradation and equipment events can be predicted and monitored by using both supervised and unsupervised machine learning techniques.

Forecasting power generation from renewable assets including wind, solar and storage can get complicated. We can help renewable operators optimize their asset dispatch by building deep learning and deep reinforcement learning algorithms that outperform traditional methods.

Particularly useful for generation asset owners who don’t want to hire control engineers, have limited access to them, or cannot justify the cost, based on the asset size. One of our engineers can monitor multiple assets from multiple owners, achieving significant benefits of scale.Services fall into three broad categories:
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- Monitoring for anomaly detection.
- Monitoring for maintenance optimization.
- Monitoring for performance improvement and cost reduction.
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BKO Services will work with any client-side hardware or software, bringing you the benefits that come from monitoring by skilled engineers, analysis using Machine Learning, and reporting from data stored in Modern Real-Time databases, and Cloud technologies.

Our engineers perform anomaly detection using in-house trained predictive models to provide early warning alerts and diagnostic guidance to our customers. In a complex industry setting such as power plants, aviation etc. Anomaly detection is critical to raise alarms beforehand to prevent significant damages and mishaps. Here, we propose a novel approach of using a multimodal neural network-based autoencoder. These detected anomalies generally tend to be more accurate and robust than anomalies detected by a single data source as latent representations capture inter-dependencies between different parts of the system through those multiple data sources.