Operators face increasing pressure to maintain capital spend within guidance. Machine learning systems can interrogate historic spend, scheduling and operations data to forecast expected expenditures. By providing accurate visualizations of expected and actual costs, planning teams have the flexibility to scale operations up or down in response to available capital, thereby avoiding shutting down operations too soon or not investing all available capital.
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.