Viking Analytics' MultiViz 2.0 cuts through false alarms and notification fatigue by using AI to surface what really needs attention - so your team can prioritize maintenance without the noise.
Reduce unplanned downtime without switching hardware or changing workflows.
We integrate with your existing and future sensor setup and help you scale your predictive maintenance operations across entire sites and factories, so you can focus on what matters, mitigating unplanned downtime.
With vibration data streaming in every second, it's easy for early warning signs to slip through. MultiViz acts like a second set of eyes, using AI to flag symptoms like unusual modulation, rising kurtosis, and subtle harmonic shifts. By learning what “normal” looks like for each individual sensor, MultiViz helps you detect issues early, before thresholds are breached or costly failures occur.
Trusted by industry leaders worldwide
With MultiViz, we can quickly spot which assets need attention, no more digging through endless charts. It’s made our work much more focused and effective.
When we show the possibilities of linking vibration, lubrication, and speed monitoring in one AI-powered solution, many see the benefits immediately.
My vibration analysts have always been great at identifying issues, but with Viking’s AI, we can now catch them even earlier. AI technology can also add up trends in a completely different way than what's possible manually.
We don’t gain anything by installing 5,000 sensors if we can’t interpret the data – MultiViz bridges that gap, turning raw measurements into clear, actionable information.
We are proud to be early adopters of cutting-edge technology. In testing CTC’s new wireless sensor with Viking Analytics’ MultiViz platform, we identified a coupling issue within two weeks. The customer confirmed and resolved it during planned maintenance.
MultiViz 2.0 marks a significant evolution from its previous version. It introduces notable enhancements in management, features, visualization, and tools—delivering more capabilities within the same space. It’s a more robust, efficient, and visually intuitive solution that empowers our operations with greater access to information and resources.
Built for teams who own the machines.
Built for teams that maintain machines.
Across factories and machines, MultiViz delivers measurable, repeatable outcomes. Here’s what teams achieve when machine learning adapts to their machines, not someone else’s.
From hundreds of meaningless alerts to a focused list of what actually matters.
Let your team analyze smarter, not longer.
Transform early signals into decisive action.
No vendor lock-in. Any sensor, any setup, any scale.
MultiViz isn’t just another vibration dashboard. It’s an AI-powered environment that learns from your machines, prioritizes what matters, and makes complex analysis fast, visual, and actionable.
AI
Instantly see which machines need attention, and why. No more scrolling through hundreds of sensors or guessing what matters.
Deep analysis
Analyze full-spectrum waveforms and frequency trends, side by side. Everything you need for root cause analysis, right where you expect it.
Sensor
MultiViz continuously learns the operational behavior of each machine and sensor, no threshold setup, no fault library required.
Analyst-first
Tag patterns, confirm anomalies, and capture expert insights. MultiViz doesn’t replace your team, it amplifies it.
MultiViz is built for the real people making reliability work every day, whether you’re inside the plant, enabling others, or building the tools.
Grow revenue with AI-enhanced services—under your brand.
Cut false alarms. Confirm faster. Focus on real problems.
Plan interventions with real context and early warnings.
Maximize uptime and OEE, across every asset.
Streamline diagnostics. Scale predictive maintenance.
Enhance your offering with embedded analytics.
Bundle advanced diagnostics. Build smarter machines.
Viking’s AI prioritizes what's important without hiding the why.
No pre-trained templates. No tedious threshold management. It adapts to real operating conditions, detecting subtle shifts, unexpected change in machine state, and early failure signatures.
95% fewer false positives. Your team sees only what matters.
Learns how each asset behaves, without needing failure history.
Every alert includes context: spectrum shifts, pattern deviations, confidence levels.
MultiViz doesn’t ask you to start over. It connects to the systems and sensors you’ve already invested in, and makes them smarter.
We don’t lock you in. And we don’t force you to rip and replace. Your data is secure, always accessible, and never used to train systems outside your organization.
Instead, MultiViz seamlessly connects to 50+ vibration sensors and gateways using MQTT and fits into your existing maintenance workflows, whether you’re running SCADA, a CMMS, a data historian, or all three.
Be it wired or wireless sensors, sensors mapped to a gateway or simple IoT devices, MultiViz is simple to deploy and easy to scale.
Adding a bearing database is currently in our feature roadmap. It will be available soon.
Currently, the hierarchy depends on what it is taken from the customer hierarchy. However, we are currently working on enabling the user to manually edit the hierarchy to their liking. This function will be available soon.
Recent symptoms are highlighted in color, while older symptoms part of a priority that has been cleared appear in grey.
After clearing the sensor priority, the machine status automatically changes to what was defined during the clearing. After this, if the status was incorrect, the user can manually change to the desired status.
The sensor priority is reset once a sensor priority clearing is done. The priority clearing is done by clicking on the checkmark symbol next to the sensor directions. Once clicking here, the user defines what it considers to be the status of the machine and clears the priority. After this, the monitoring continues and if further changes continue or if existing changes increase in strength, a new priority is set.
The sensor priority is a result of the number of identified changes and symptoms and their strength. Each detected change in the data and identified symptoms is associated with a strength value, the combination of all these values results in the priority. This priority is calculated per each direction and the resulting sensor priority is the highest across all directions.
We use unsupervised machine learning algorithms to build explainable models per sensor point. This enables us to identify atypical or abnormal behaviors that had not been seen in that particular spot. Then, we apply a series of detectors on top of that model to identify the symptoms, which provide the explanations of the behavior of the data.
Yes. Multiviz won’t turn a wrench for you. Think of it as your always‑on scout; you’ll still validate findings on site when needed.
Yes—use the Factory filter to isolate sites, or give each plant its own workspace while still rolling data up for corporate KPIs.
Modes are behavioral fingerprints learned from historical data. They spot subtle shifts that static triggers miss, cutting false alarms while catching problems earlier.
Integration possibilities with existing systems or workflows can be discussed during the onboarding process. Our team can assist in integrating MultiViz to complement your existing monitoring setup, ensuring smooth operations.
MultiViz is versatile and can monitor various machine types across industries. It accommodates diverse sensor data and is adaptable to monitor different machinery types, including manufacturing equipment, turbines, pumps, motors, and more.
Multiviz is sensor-agnostic and therefore supports all sensors, however here’s a list of sensors we currently work with.
Security of your data is our priority. For more info on how we keep it safe, read our Security and Privacy Protocols.
You’re the expert. Mark the Alert as “Normal” and add a comment explaining why. Multiviz keeps the note for future reference and model tuning.
MultiViz - AI powered vibration analysis-conditioning monitoring for smarter predictive maintenance.
