Beyond Thresholds: How behaviour-based AI is transforming vibration analysis in industrial monitoring
Modern industrial operations are larger, more complex, and increasingly interdependent than ever. Plants and production lines are under constant pressure to do more with less, maintain tight margins, and operate streamlined supply chains with virtually no room for unplanned downtime. In this environment, accurate condition monitoring is critical for maintaining safety, protecting equipment, and ensuring consistent production quality.
Vibration analysis – the measurement and interpretation of a machine’s distinctive vibration signals to detect anomalies and diagnose potential faults – is a key method for condition monitoring.
One of the earliest approaches to vibration analysis is threshold-based monitoring, which alerts operators whenever vibration measurements exceed predefined limits, allowing obvious faults such as imbalance or misalignment to be detected. Early implementations of threshold-based monitoring relied on mechanical devices like dial indicators or simple vibration meters. As technology advanced, electronic sensors such as accelerometers and velocity sensors became common, allowing for continuous monitoring by the 1970s and 1980s. This evolution provided operators with timely insights into equipment health, helping prevent unexpected failures and laying the foundation for more advanced condition-monitoring techniques.
While threshold-based monitoring is effective for detecting obvious faults, it can miss subtle or early-stage issues, especially in today’s increasingly complex industrial systems. These limitations have driven the development of trend analysis, spectral analysis, and, more recently, AI-driven behavior-based techniques.
Behaviour-based AI takes a fundamentally different approach. Instead of relying on fixed thresholds, it analyses patterns and trends in vibration signals from individual machines, detecting subtle deviations that may indicate emerging faults. By identifying meaningful anomalies early, behaviour-based AI reduces false alarms, alleviates alert fatigue, and provides actionable insights that allow maintenance teams to intervene proactively – often before traditional thresholds would have been triggered.
In this blog, we explore why traditional threshold-based vibration analysis is insufficient for today’s complex industrial systems, and how behaviour-based AI delivers predictive, actionable insights that help operations teams detect faults early, minimise unplanned downtime, and make data-driven decisions that safeguard both equipment and productivity.
While threshold-based alerts have been a mainstay in machine vibration analysis, real-world operations reveal several practical challenges. Fixed limits can generate frequent false alarms, miss subtle early warning signs, and fail to account for patterns or changing conditions — issues that can compromise both equipment reliability and operational efficiency. The following sections explore these key limitations in more detail.
Vibration measurements naturally fluctuate due to normal operational variations, changes in load, or environmental factors. Fixed thresholds often trigger alarms for these benign deviations, overwhelming operators with false positives. This alert fatigue is common – operators in manufacturing plants and power generation facilities frequently report repeated nuisance alarms from motors, pumps, and gearboxes when relying solely on threshold triggers. These false alarms can desensitise analysts, causing real issues to be overlooked.
Thresholds are typically set to detect faults only once vibration levels reach a certain severity. Subtle shifts in frequency, amplitude, or phase – often the earliest indicators of misalignment, imbalance, or bearing wear – can go unnoticed until they exceed the threshold, potentially leading to unplanned downtime and costly repairs. Early-stage bearing defects or rotor misalignments may progress unnoticed in equipment that generates frequent minor alarms but never crosses the fixed threshold for action.
Thresholds do not account for patterns, trends, or correlations between different vibration measurements. A single reading above a limit may not always indicate a fault, while combinations of smaller deviations could signal a developing issue that behaviour-based AI can flag. Fixed thresholds are therefore rigid and cannot adapt to changes in operating conditions, limiting the effectiveness of early fault detection.
Behaviour-based AI moves vibration analysis beyond fixed thresholds by analysing patterns and trends across multiple measurements to detect early signs of mechanical issues. Rather than treating each vibration reading in isolation, AI systems group measurements from key sensor locations – such as bearing housings, gearboxes, or motor shafts – and identify recurring patterns, also known as modes, that reflect the machine’s normal operating behaviour.
These systems then define “events”, which represent meaningful deviations from expected vibration patterns. By tracking these events over time, AI identifies subtle trends indicating emerging faults, such as early-stage bearing wear, shaft misalignment, or rotor imbalance – changes that might never trigger a traditional threshold alert. Maintenance teams are alerted only when deviations are significant and indicative of real problems, reducing false alarms and helping prioritise interventions.
By transforming raw vibration data into context-rich insights, behaviour-based AI allows operators to make faster, more confident, and proactive maintenance decisions, ultimately protecting equipment, minimising unplanned downtime, and saving costs.
MultiViz, Viking Analytics’ AI-assisted vibration analysis system, is a real-world implementation of this approach. It applies mode identification to process entire waveforms and spectra, grouping similar measurements to capture the full range of machine behaviours.
The platform defines events by detecting deviations from established behavioural norms, such as unusual modulation or rising kurtosis, before they escalate into failures. Alerts are context-rich, providing information like spectrum shifts, pattern deviations, and confidence levels, enabling maintenance teams to prioritise interventions effectively and reduce the cognitive load of false alarms.
By applying MultiViz’s practical implementation of behaviour-based AI, organisations can detect faults early, reduce nuisance alerts, and make smarter, data-driven maintenance decisions – providing weeks of advance warning compared with traditional threshold-based monitoring.
With the shortcomings of threshold-based monitoring clear, industrial teams are increasingly turning to AI-driven, behaviour-based approaches. By analysing patterns and trends across multiple sensors, these systems reduce false alarms, detect emerging faults early, and provide actionable insights for proactive maintenance decisions. The following examples illustrate how AI-based monitoring delivers tangible benefits in real-world operations.
Traditional threshold-based monitoring often generates a flood of alerts, and many false positives, overwhelming maintenance teams and reducing the effectiveness of the system. In contrast, behaviour-based AI, as implemented in Viking Analytics’ MultiViz, analyses patterns and trends across multiple measurements, drastically reducing false alarms and allowing analysts to focus on genuine issues.
At CMPC Celulosa’s pulp mill in Laja, Chile, for instance, maintenance was a reactive process. Engineers relied on manual data checks and periodic inspections, often missing subtle warning signs until a machine failed. Nicolaides Industrial, the facility’s trusted maintenance partner, had deployed wireless sensors for continuous machine monitoring in August 2023 but still faced a major challenge: Analysing the flood of raw sensor data that still had to be manually processed by analysts. This delayed risk detection.
That’s when Nicolaides brought in Viking Analytics’ MultiViz. The platform immediately eliminated the need for manual checks of raw data by continuously analysing vibration patterns, detecting subtle anomalies, and automatically highlighting and prioritising machines based on risk, providing Nicolaides with actionable insights for proactive maintenance.
“We don’t gain anything by installing 5,000 sensors if we can’t interpret the data,” said Nicolás Pérez Briones, monitoring manager at Nicolaides. “MultiViz bridges that gap, turning raw measurements into clear, actionable information.”
Across the globe, in Sweden, Mälarenergi, a Swedish utility providing sustainable energy, water, and broadband services, faced similar challenges. Weekly manual vibration readings offered only snapshots of machine health, allowing faults to develop unnoticed between checks.
The solution came in the form of 350 pureMEMS wireless sensors that started feeding real-time data into Viking Analytics’ MultiViz platform. Analysts now spend far less time sifting through raw data, while potential failures are identified by the platform and addressed before they escalate.
“One of the biggest benefits is that the technology picks up alerts well in advance,” said Tommy Persson, maintenance engineer with almost 30 years of experience in vibration analytics. “The fact that AI is continuously working, analysing, and sorting different events to present patterns and trends saves us a lot of time.”
Robert Hjorth, group manager at Mälarenergi, added: “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.”
Across industries, AI-based, behaviour-driven monitoring transforms raw sensor data into actionable insights. By detecting subtle changes in equipment early, it highlights the machines that need attention, enabling maintenance teams to prioritise interventions, act proactively, and make quicker, more informed decisions. The result is reduced downtime, improved operational efficiency, and greater confidence in every maintenance action.
At CMPC Celulosa’s Laja facility, for instance, early alerts flagged cavitation in a vacuum pump and bearing damage in a gas fan motor. Each prevented unplanned downtime saved roughly $65,000 in combined repair, lost production, and emergency maintenance costs.
“The real value is preventive diagnostics weeks or even months in advance; CMPC can confirm findings in the field and act before failures ever happen,” said monitoring manager Pérez Briones.
A similar approach proved effective at another paper mill, in Sweden. VKT, a distribution partner of CTC, an industrial sensor provider, piloted CTC Connect Wireless Vibration Monitoring to track the input shaft of a gearbox at this facility. The sensors paired with MultiViz detected unusually high vibrations, indicating a shaft alignment issue that was damaging the coupling. VKT advised the facility to adjust the machine load while operations continued until a planned shutdown could be arranged. During the shutdown, the coupling was replaced, and visual inspection confirmed the AI-driven insights.
At Mälarenergi too, MultiViz detected an issue on a machine with a nearly new bearing. Thanks to the AI-powered analysis of time waveform signals, it flagged subtle changes that might have gone unnoticed with manual methods – very likely preventing a costly breakdown.
AI-based monitoring continuously evaluates patterns across multiple machines and sensors, detecting subtle deviations and emerging issues over time. These systems don’t just respond to immediate anomalies – they allow teams to see trends in equipment performance, such as gradual increases in vibration or shifts in load behaviour, which could indicate wear or misalignment developing over weeks or months. This long-term visibility helps maintenance teams prioritise interventions based on risk and operational impact, plan maintenance schedules more effectively, and reduce the likelihood of unplanned downtime. By converting vast amounts of sensor data into actionable, trend-based insights, AI transforms monitoring from a reactive task into a strategic tool for operational planning and risk reduction.
By moving beyond traditional threshold monitoring, organisations can therefore detect anomalies sooner, reduce false alarms, and focus on the machines that truly need attention. The result is smarter maintenance, faster decision-making, and measurable savings – a strategic advantage that bridges data, insight, and action.
Successfully deploying behaviour-based AI for vibration monitoring requires careful planning across hardware, software, and team workflows. Here are a few things to look out for:
Behaviour-based AI relies on high-quality vibration data, so ensuring compatibility with existing sensors is critical. Before implementing any AI monitoring solution, it’s important to verify that any existing sensors can capture the necessary vibration parameters – such as velocity, acceleration, and displacement – at the appropriate sampling rates for your machines. In some cases, minor hardware upgrades or additional sensors may be required to fully leverage AI-driven insights.
MultiViz is sensor-agnostic, supporting over 50 vibration and condition monitoring sensor brands, from handheld analyzers to wireless accelerometers. It offers plug-and-play integration as well as customizable API connections for clients with specific integration needs, enabling cloud-to-cloud, edge gateway, or batch data uploads. There’s no vendor lock-in, so you can continue using your preferred hardware while extracting actionable insights. Click here for more details.
Effective deployment requires seamless integration with your existing monitoring systems, historical data repositories (data historians), and maintenance management software (MMS). Behaviour-based AI platforms typically provide APIs or connectors to ingest sensor data and export insights to existing dashboards, CMMS tools, or enterprise software. Proper integration ensures that alerts, events, and actionable insights flow into the workflows already used by maintenance teams, avoiding duplication of effort or siloed information.
Introducing AI-driven vibration monitoring represents a shift in how maintenance teams interpret data and make decisions. Clear training and change management are crucial to maximise adoption. Teams need to understand not just how to read alerts, but how to contextualise AI-generated events, prioritise actions, and update maintenance schedules based on insights. Involving operators, engineers, and analysts early in the implementation helps ensure smooth adoption and that the system enhances existing workflows rather than disrupting them.
By carefully considering sensor compatibility, integration, and team readiness, organisations can ensure a successful deployment of behaviour-based AI, unlocking the full potential of predictive vibration monitoring and proactive maintenance strategies.
Behaviour-based AI is more than a technological upgrade – it’s a strategic differentiator. By moving beyond simple threshold monitoring, organisations can detect anomalies earlier, reduce false alarms, and make maintenance decisions with greater confidence, helping prevent costly unplanned downtime.
Viking Analytics’ MultiViz platform learns the unique “normal” behaviour of each machine and sensor, enabling early detection of subtle anomalies such as unusual modulation, rising kurtosis, and harmonic shifts. This proactive approach not only improves diagnostic accuracy but also helps maintenance teams optimise asset performance and avoid expensive repairs, ultimately saving significant operational costs.
For facilities seeking professional vibration analysis services, platforms like MultiViz show the power of AI-driven, behaviour-based monitoring. To see behaviour-based AI in action, explore real-world MultiViz case studies or request a personalised demo by clicking the links below.
Case studies: Explore how MultiViz works on the ground
Get in touch: Contact Viking Analytics for a demo
MultiViz - AI powered vibration analysis-conditioning monitoring for smarter predictive maintenance.