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Emerson Enhances NI SystemLink with Connected Scheduling and NI Nigel AI
Emerson has updated its NI SystemLink test operations software, adding resource scheduling and NI Nigel AI integration to streamline product validation and accelerate root-cause analysis.
www.ni.com
Emerson has introduced architectural and functional updates to its NI SystemLink software platform, expanding the system's test management footprint with automated resource scheduling and native NI Nigel AI capabilities. Showcased during the keynote at NI Days Japan, the enhancements target test operations in the semiconductor, automotive, aerospace, defense, and electronics manufacturing sectors. The additions aim to link discrete measurement datasets with the operational context required to diagnose engineering failures and maintain product development schedules.
Operational Context and Resource Optimization
Validation workflows often separate raw measurement results from the underlying test environment, isolating test metrics from equipment states, firmware baselines, software versions, and lab scheduling queues. NI SystemLink addresses these data silos by binding execution data directly to asset histories, test configurations, and active schedules within a centralized repository.
Case studies cited by Emerson demonstrate up to a 95% reduction in weekly analysis and reporting labor, alongside gains of up to 2x in daily automated test bench throughput.
Platform Capabilities and Enterprise Deployment
Functional updates introduced in the latest release comprise four main software functional areas:
- Connected Test Planning and Resource Scheduling: Coordinates physical instrumentation, fixture availability, and test station workloads across distributed labs to alleviate capacity constraints.
- AI-Powered Engineering Insights: Leverages NI Nigel AI to evaluate operational parameters alongside test outputs, flagging anomalies and surfacing failure trends.
- Out-of-the-Box Operational Visibility: Provides preconfigured dashboards for station utilization, test yields, and calibration schedules while retaining custom dashboard configuration.
- Updated SystemLink Architecture: Introduces simplified product tiers, consolidated licensing schemes, and modernized deployment configurations to accelerate installation across enterprise test networks.
First launched in 2018 as a centralized systems and asset management tool, NI SystemLink has expanded into an enterprise test orchestration platform supporting modular customization across multi-site manufacturing facilities and engineering laboratories.
Additional Context
This section details technical specifications not included in the original news release.
Centralized test data orchestration platforms utilize microservice architectures and polyglot persistence to manage disparate engineering data streams. High-volume parametric results, time-series sensor streams, and transient waveform captures are ingested via secure REST APIs, AMQP message queues, or gRPC endpoints before parsing into indexed data lakes using open technical data formats such as ASAM ODS (Open Data Services) and TDMS (Technical Data Management Streaming). Integrating Large Language Models (LLMs) and agentic artificial intelligence directly into test execution frameworks relies on retrieval-augmented generation (RAG) coupled to domain-specific vector databases. These AI engines query structured equipment metadata—including PXI chassis slot assignments, calibration expiration timestamps, instrument firmware hashes, and sequence execution logs generated by execution engines like NI TestStand—to correlate hardware degradation (such as relay contact resistance drift or ADC thermal offset) with test failure signatures without manual engineering data cross-referencing.
Edited by Romila DSilva, Induportals Editor, with AI assistance.
Additional Context
This section details technical specifications not included in the original news release.
Centralized test data orchestration platforms utilize microservice architectures and polyglot persistence to manage disparate engineering data streams. High-volume parametric results, time-series sensor streams, and transient waveform captures are ingested via secure REST APIs, AMQP message queues, or gRPC endpoints before parsing into indexed data lakes using open technical data formats such as ASAM ODS (Open Data Services) and TDMS (Technical Data Management Streaming). Integrating Large Language Models (LLMs) and agentic artificial intelligence directly into test execution frameworks relies on retrieval-augmented generation (RAG) coupled to domain-specific vector databases. These AI engines query structured equipment metadata—including PXI chassis slot assignments, calibration expiration timestamps, instrument firmware hashes, and sequence execution logs generated by execution engines like NI TestStand—to correlate hardware degradation (such as relay contact resistance drift or ADC thermal offset) with test failure signatures without manual engineering data cross-referencing.
Edited by Romila DSilva, Induportals Editor, with AI assistance.
