Key activities to improve value return from an established AVEVA PI System

Synopsis

An AVEVA PI System performs a crucial role within many industrial organisations in ingesting, storing, contextualising and serving operational time-series data to end-users and business systems.

As the system grows, organisations face challenges in ensuring high value use cases are realised, and maintained:

• Security and reliability of the PI system becomes increasingly important as business dependency grows, and especially when interfacing with critical operational technology (OT) networks
  • For backwards compatibility, the PI system supports a broad range of application security configurations, including authentication protocols which are now considered legacy and less secure.
  • The PI System typically spans corporate networks, border zones/DMZ and OT networks. Legacy integrations may not meet modern security standards.
  • Enforcing Role-based Access Controls (RBAC) is critical for securing data, both to ensure integrity by only allowing authorised writes, but also in restricting read access to sensitive data which is subject to regulatory restrictions.
• Data quality management effort increases as point count grows or assets change – and may be exacerbated by long-existing issues within SCADA/control systems or instrumentation
• Traditional OT architecture patterns struggle to support modern usage at scale
  • The shift towards more distributed assets (such as battery systems or renewable generation), as well as a general increase in the volume and resolution of signals required to support industrial analytics poses a substantially larger bandwidth demand on OT networks
  • The traditional model of utilising centralised local interfaces against SCADA historians or RTUs is not horizontally scalable, or practical to support with these distributed assets,
• Industrial Analytics often require time series data to be used in combination with other business data sets
  • Relational sources such as asset management records, metering, MES or LIMS systems are often analysed alongside corresponding time-series measurements.
  • It is common for these systems to adopt unique identification standards and data structures – which can be difficult to link together.
  • Engineers and Data Analysts expect to use familiar tools (such as Python or R based notebooks) to interrogate, merge and analyse data from these various sources in a straightforward and efficient way.
  • Enterprise Technology teams expect operational data to be made compatible for use in enterprise information and reporting systems, without requiring proprietary technologies or protocols.

This paper outlines four key exercises which we endorse as best practices for maximising the value of the PI system, minimising waste and mitigating typical pain points: 

1. Getting the house in order – a secure, stable and streamlined system
2. Formalising the role of the PI System within the broader Operational and Information Technology architecture
3. Utilising Asset Framework to translate operational signals to an enterprise compatible data structure
4. Augmenting native calculation technologies

Getting the house in order – a secure, stable and streamlined system

Performing a technical review of the PI System can both identify areas for immediate improvement, and serve as a foundation before conducting a broader, future state architecture review.

Depending on the findings of the review, a system upgrade migration may be warranted – or incremental improvements may be applied in-place.

Typical areas of focus include: