About
Viking Analytics delivers industrial AI through its flagship product, MultiViz 2.0 — a sensor-agnostic condition monitoring and vibration analysis platform built for modern reliability teams. By integrating with existing wireless and handheld sensor setups, MultiViz eliminates the need for costly hardware replacements while extending predictive maintenance capabilities across entire sites and factories. The platform uses machine learning to establish a baseline of 'normal' behavior for each individual sensor, then continuously monitors for subtle anomalies such as unusual modulation, rising kurtosis, and harmonic shifts — flagging potential issues long before thresholds are breached or failures occur. This dramatically reduces false alarms and notification fatigue, allowing maintenance teams to prioritize interventions based on real machine insights rather than data noise. MultiViz is designed for multiple stakeholders: vibration analysts gain faster, more accurate fault identification; maintenance planners receive early warnings to schedule smarter interventions; condition monitoring engineers can scale diagnostics with less manual effort; and service providers can deliver higher-value, data-driven maintenance services to clients. OEMs and sensor manufacturers can also embed MultiViz analytics into their offerings to stay competitive in smart maintenance markets. With a globally distributed client base and proven results in reducing unplanned downtime and improving OEE, Viking Analytics is trusted by reliability leaders across manufacturing, energy, and industrial sectors.
Key Features
- AI-Powered Anomaly Detection: Learns what 'normal' looks like for each individual sensor and flags subtle anomalies — like unusual modulation, rising kurtosis, and harmonic shifts — before costly failures occur.
- Sensor-Agnostic Integration: Connects seamlessly to a wide variety of existing wireless and handheld sensors, eliminating the need to replace hardware or disrupt current workflows.
- False Alarm Reduction: Filters out data noise and notification fatigue by surfacing only the alerts that truly require attention, enabling maintenance teams to prioritize effectively.
- Scalable Site-Wide Monitoring: Designed to scale predictive maintenance operations across entire plants and factories, covering every asset with consistent, automated oversight.
- Actionable Maintenance Insights: Converts raw vibration measurements into clear, prioritized recommendations so planners and engineers can act on real machine intelligence rather than raw data.
Use Cases
- Manufacturing plants using MultiViz to monitor rotating equipment across entire factory floors and prevent unplanned downtime.
- Maintenance and service providers embedding AI-powered vibration analytics into their service offerings to improve client outcomes and win long-term contracts.
- OEMs bundling MultiViz diagnostics with their equipment to deliver added value and differentiate in the smart maintenance market.
- Vibration analysts leveraging AI-assisted fault detection to cut through data noise and identify issues faster with fewer false alarms.
- Condition monitoring engineers scaling predictive maintenance programs across multiple sites without proportionally growing their analyst teams.
Pros
- Works with Existing Hardware: Sensor-agnostic design means teams can deploy AI-powered analytics without replacing current sensor infrastructure, lowering adoption costs.
- Early Fault Detection: Catches equipment issues in their earliest stages — well before threshold breaches — giving teams time to plan proactive interventions.
- Reduces Alert Fatigue: Smart filtering surfaces only meaningful alerts, helping maintenance teams stay focused and avoid chasing false positives.
- Scalable for Enterprise Operations: Purpose-built to monitor thousands of assets across multiple sites, making it suitable for large industrial operations and service providers.
Cons
- Enterprise and B2B Focus: The platform is tailored for industrial businesses and service providers; it is not suited for individual users or small-scale, non-industrial applications.
- Primarily Vibration-Centric: Core capabilities are built around vibration and condition monitoring data, which may limit applicability for teams relying on other sensor modalities.
- Integration Setup Required: Getting full value from the platform requires initial configuration of sensor connections and workflow integrations, which may involve technical resources.
Frequently Asked Questions
What is MultiViz 2.0?
MultiViz 2.0 is Viking Analytics' flagship AI-powered platform for vibration analysis and condition monitoring. It integrates with existing sensor setups, learns normal machine behavior, and flags early signs of equipment faults to help reliability teams prevent unplanned downtime.
Does Viking Analytics work with my existing sensors?
Yes. MultiViz 2.0 is sensor-agnostic, meaning it integrates with a wide variety of wireless and handheld sensors you already use, without requiring any hardware replacement.
Who is Viking Analytics designed for?
Viking Analytics serves maintenance and service providers, manufacturing plants, OEMs, sensor manufacturers, vibration analysts, maintenance planners, and condition monitoring engineers — essentially any team responsible for industrial asset reliability.
How does the AI detect faults early?
MultiViz 2.0 establishes a per-sensor baseline of normal behavior and then continuously monitors for subtle deviations such as unusual modulation, rising kurtosis, and harmonic shifts. This enables it to flag potential issues long before conventional threshold-based alerts would trigger.
Can Viking Analytics integrate with existing maintenance workflows and tools?
Yes. MultiViz supports data export to common work tools and is designed to complement existing workflows, allowing teams to adopt AI-driven insights without overhauling their processes.
