Q Fabric is the foundational technology on which the Q Enterprise suite of products is built. Q Fabric consists of a high-speed ingestion engine that takes disparate structured, unstructured, batch and streaming data from your seemingly unrelated data sources such as crew data, ERP systems, SCADA systems and equipment sensors. Data from these systems are normalized and stored in Arundo's Universal Data Store in a human readable form, organized around infrastructure hierarchy models built with industry expert knowledge and a continuous learning data science model. Q Fabric includes the following high-level functions:
Q Insight helps operators and analysts gain a deep understanding of performance and causalities using historical data from their asset infrastructure. Q Insight provides access to a robust search engine and asset browser to access historical plant and equipment data. With Q Insight, operators get access to state-of-the-art diagnostics and descriptive analytics such as reliability analysis, failure pattern analysis, event linkage analysis, root-cause failure analysis, equipment lifetime analysis and sensor event causality analysis. Users annotate data so that domain knowledge can be easily captured and leveraged for e.g. predicting events. KPIs such as cost and uptime and those related to health, safety, and environment are generated and presented in customizable dashboards ready for sharing and collaboration across internal and external teams. Q Insight includes the following high-level functions:
Q Foresight helps operators and analysts gain a real-time understanding of the performance drivers and KPIs of their asset infrastructure using live streaming performance data. Q Foresight extends the powerful analytics and performance monitoring already available with Q Insight by incorporating streaming data. As sufficient amounts of operational data become available, machine learning models specific to the industry use-cases are deployed on the existing Q Enterprise infrastructure. For instance, to detect anomalies, failure prediction models can be trained and deployed on the asset infrastructure. This multi-layered analytical framework enables users to get up to speed as fast a possible even with greenfield assets.
In situations that demand real-time failure prediction and detection to prevent or reduce non-productive downtime (NPT), real-time risk assessments, real-time performance optimization scenarios such as bunker optimization and fleet routing optimization, Q Foresight is an invaluable solution for operators. Following are Q Foresight's features at a glance:
Arundo's Q Composer gives data science developers the power to tap into Q Enterprise. Q Composer enables rapid prototyping, testing, and deployment of analytical modules to deliver business impact. Using Q Composer data science developers can access the Arundo SDK, create and train data sets, deploy machine learning models directly into their Q Enterprise deployments or share their apps on Q Market with internal and external teams. Following are summary highlights of Q Composer's features: