OSIsoft PI System

OSIsoft PI System
Name	OSIsoft PI System
Developer	OSIsoft
Initial release	1987
Latest release	proprietary
Operating system	Microsoft Windows, Linux
License	Proprietary

OSIsoft PI System

The PI System is an industrial data infrastructure platform created to collect, store, analyze, and visualize time-series and event data from operational environments. It has been used by utilities, oil and gas, manufacturing, and chemical companies to enable operational intelligence, process optimization, and regulatory reporting. The platform integrates with industrial automation products and enterprise systems to bridge operational technology and information technology environments.

Overview

The PI System originated as a historian for process data, evolving into a comprehensive operational data platform that supports historians, asset frameworks, event frames, and data science integrations. Major adopters include ExxonMobil, Shell plc, Siemens, General Electric, and BASF SE. The system interoperates with automation vendors such as ABB Ltd., Schneider Electric SE, Rockwell Automation, Inc., and Honeywell International Inc.. It is frequently deployed alongside enterprise products from SAP SE, Oracle Corporation, Microsoft Corporation, and cloud services from Amazon Web Services, Google LLC, and Microsoft Azure.

Architecture and Components

The PI System architecture comprises modular components that provide collection, storage, contextualization, and access layers. Core components often include a PI Data Archive for time-series storage, a PI Asset Framework for semantic models, and PI Interfaces or PI Connectors for data ingestion. Integration points link to supervisory control and data acquisition systems like OSI SCADA alternatives and historians such as GE Proficy Historian and Honeywell PHD. Complementary tools include visualization servers comparable to Tableau Software, analytics platforms related to SAS Institute offerings, and data science integrations with ecosystems like Anaconda, Inc. and Databricks, Inc..

Data Collection and Integration

Data collection uses a mix of agent-based and connector-based approaches to ingest signals from programmable logic controllers made by Siemens AG, Allen-Bradley (Rockwell Automation), and Schneider Electric SE, distributed control systems from Emerson Electric Co. and Yokogawa Electric Corporation, and sensors conforming to ISA-95 and IEC 61850 standards. PI Interfaces historically supported OPC DA/UA endpoints and protocols such as Modbus and DNP3, while newer PI Connectors provide certified integration with systems like SAP ERP, OSIsoft PI AF, and cloud platforms including AWS IoT Core and Azure IoT Hub. Integration is often orchestrated with middleware from vendors like IBM Corporation and Tibco Software Inc. and message brokers such as Apache Kafka.

Data Storage and Management

Time-series data are stored in a proprietary PI Data Archive optimized for high-frequency writes and efficient retrieval. The PI Asset Framework adds contextual metadata using hierarchies, templates, and attributes to model assets similar to systems such as AVEVA System Platform and Autodesk. Event Frames capture episodic occurrences aligned with identifiers used by ISO 55000 asset management practices. Management tasks include data compression, retention policies, and high-availability clustering analogous to enterprise databases from Oracle Corporation and Microsoft SQL Server. Interoperability with big-data lakes and platforms like Hadoop distributions and Snowflake Inc. enables long-term archiving and cross-domain analytics.

Visualization and Analytics

Visualization options include native PI visualization clients and web-based dashboards comparable to offerings from Microsoft Power BI and Grafana Labs. The platform supports real-time process displays, trending, and situational awareness used alongside control-room systems by vendors such as ABB Ltd. and Schneider Electric SE. Analytics capabilities range from built-in calculations and event detection to integration with machine learning frameworks like TensorFlow, PyTorch, and analytics suites from MathWorks (MATLAB). Third-party partners provide predictive maintenance solutions similar to those from Siemens MindSphere and GE Digital Predix.

Applications and Industry Use Cases

The PI System is applied across sectors for use cases including predictive maintenance, energy optimization, batch tracking, emissions monitoring, and supply-chain synchronization. In utilities, it supports grid operations coordinated with organizations like National Grid plc and Edison International. In oil and gas, it aids reservoir and process monitoring for firms such as Chevron Corporation and BP plc. Manufacturing use cases align with Industry 4.0 initiatives promoted by Verein Deutscher Ingenieure (VDI) and multinational manufacturers including Toyota Motor Corporation and Ford Motor Company. Regulatory and environmental reporting uses data aligned with frameworks from EPA (United States Environmental Protection Agency) and European Environment Agency.

Security, Scalability, and Performance

Security practices for deployments incorporate network segmentation, role-based access control integrated with identity providers like Microsoft Active Directory and Okta, Inc., and transport encryption guided by standards such as NIST publications. Scalability strategies include distributed PI Data Archive clustering, data buffering at edge sites using industrial edge gateways from Cisco Systems, Inc. and HPE, and integration with cloud-scale storage from Amazon S3 and Azure Blob Storage. Performance tuning references benchmarking approaches used by database vendors like Oracle Corporation and high-throughput systems designed by Intel Corporation and AMD for CPU and storage optimization. Operational resilience is enhanced through disaster-recovery topologies similar to enterprise continuity plans employed by Siemens AG and large utilities.

Category:Industrial software