Vibration Analysis 101: Beginner’s Guide to Predictive Maintenance

Whether you're maintaining a single motor or managing thousands of assets across an industrial site, equipment failure is always costly – and often avoidable. One of the most effective ways to stay ahead of such failures is through vibration analysis, a machine monitoring technique that helps detect and prevent mechanical issues before they lead to unexpected outages.

In this guide, we break down the fundamentals of vibration analytics: what it is, how it works, and why it's such a critical part of modern maintenance strategies. From traditional handheld tools to AI-powered monitoring systems like MultiViz, this overview will help you understand the evolving role of vibration analysis in predictive maintenance.

What is Vibration Analytics?

Every rotating and reciprocating mechanical system generates a distinct vibration signature during operation. Vibration analytics is the process of collecting and interpreting this data to assess the health and performance of these machines. Changes such as bearing degradation or shaft misalignment register as shifts in vibration frequency and intensity. By establishing a baseline and analysing changes in vibration frequency, amplitude, and phase over time, engineers can detect signs of misalignment, imbalance, bearing wear, and other emerging defects.

This proactive approach – a key component of modern condition monitoring strategies – shifts maintenance from reactive to predictive, allowing teams to track equipment performance in real time or at regular intervals and address potential failures before they cause costly breakdowns and disruptions in production.

Traditionally, vibration analytics was conducted manually by technicians using portable analysers and preinstalled sensors. Today, however, many facilities are shifting to modern software for vibration analysis. These systems automate the collection, processing, and interpretation of vibration signals, and use AI, and machine learning to detect anomalies and predict failures. By examining long-term trends, they uncover subtle signs of deterioration that may not trigger immediate alarms but still point to deeper issues that need attention.

Vibration analysis, explained simply, is therefore about transforming mechanical movement into raw sensor data and then into actionable insights – enabling smarter, faster, and more efficient maintenance decisions that extend equipment life and reduce unplanned downtime.

Why is vibration analysis important?

Vibration analysis is one of the most reliable early warning systems for mechanical failure in rotating and reciprocating equipment. It provides objective, quantifiable data about internal machine conditions often before any symptoms appear unlike maintenance techniques that depend on visual inspection or noticeable performance decline. 

By identifying anomalies early, maintenance teams can diagnose the root cause and take corrective action during planned downtime. This reduces the risk of catastrophic failure, extends equipment lifespan, improves plant reliability, and lowers maintenance costs by minimising operational disruptions.

How vibration analysis fits into condition monitoring

A reliable vibration analysis program is just one of several techniques used to monitor machine health over time and plan maintenance proactively. Other common methods include ultrasonic detection (for identifying air or steam leaks and electrical discharges), thermography (to spot abnormal heat patterns from wear or overload), oil analysis (to detect contamination or wear particles), and motor current analysis (to uncover electrical or mechanical faults in motors).

All of these techniques fall under the umbrella of condition monitoring, each with its own strengths and limitations. The choice of method depends on the type of machinery, its criticality, and your operational goals. That said, machine vibration analytics is indeed one of the most efficient and effective techniques for detecting early faults in rotating equipment.

Where is vibration analytics used?

Vibration analytics is widely applied across several industries. Common applications include:

1. ‍Pumps and compressors: Critical in oil and gas, chemical processing, and water treatment industries. Common issues include cavitation, bearing wear, and imbalance.‍
2. Electric motors: Found in nearly every industrial setting, motors are prone to rotor imbalance, misalignment, and electrical faults.‍
3. Fans and blowers: Used in HVAC and manufacturing systems, these machines often develop imbalance, looseness, or blade damage.‍
4. Gearboxes and drive systems: Susceptible to gear mesh issues, wear patterns, and lubrication problems that can go unnoticed without monitoring.‍
5. Turbines and generators: In the power and energy sectors, even minor anomalies in these high-value assets can lead to costly failures if undetected.
6. ‍CNC machines and industrial tools: In precision manufacturing, vibration data helps identify tool wear, misalignment, and structural inconsistencies that affect product quality.

How does vibration analysis work?

Rotating machines naturally produce vibrations during operation, often accompanied by audible noise, which can reveal valuable insights into their health. A sensor, typically mounted on a stable part of the machine such as the bearing housing, detects these vibrations. Tools like vibration meters or analysers then measure and convert this data into interpretable signals that help identify issues such as imbalance, misalignment, looseness (mechanical or structural), bearing or gear damage, motor faults, or loose foundation bolts.

To isolate and interpret the vibration signals, analysts use a range of techniques including time domain analysis, frequency domain analysis using Fast Fourier Transform (FFT), and more specialised methods such as envelope analysis. Each method offers unique insights:

• Time domain analysis observes the raw signal's behaviour over time.
• FFT converts that signal into the frequency domain, helping identify specific fault signatures.
• Envelope analysis extracts high-frequency patterns to detect early-stage bearing defects.

Together, these techniques support early fault detection and enable more informed, proactive maintenance decisions.

Vibration analysis and your maintenance strategy

The ability to detect issues early through vibration analysis ties directly into plant-wide maintenance strategies. To understand where it fits in, it’s helpful to look at the three main approaches to equipment maintenance. Each has its place depending on the criticality of the equipment and the cost of downtime.

1. ‍Maintenance after failure (Run-to-failure): This approach involves using the machine until it breaks down, and only then performing repairs. While it may seem like the cheapest option upfront, it often proves costly in the long run. It carries the highest risk of unexpected breakdowns, leading to unplanned downtime and expensive emergency repairs.‍
2. Preventative maintenance: This approach involves servicing equipment at regular intervals – inspecting and replacing parts regardless of whether they show signs of trouble. Done frequently, it reduces the risk of unexpected failures and unplanned downtime. However, it can be costly due to high scheduled shutdowns and the potential replacement of parts that may still have useful life left. On the other hand, if inspections are too infrequent, you edge closer to a run-to-failure scenario.‍
3. Predictive maintenance: In this approach, maintenance is carried out just before a failure is likely to occur – based on data, not guesswork. The machine continues running while sensors (such as vibration sensors) monitor its condition in real time. These measurements are compared against standard baselines, and predefined thresholds are set to trigger alerts when values exceed applicable limits. When vibration patterns indicate a developing fault, analysts are alerted and maintenance is scheduled during planned downtime, avoiding sudden breakdowns. This method is more cost-effective and reliable than both run-to-failure and preventive maintenance, as it minimises unnecessary shutdowns and maximises equipment lifespan. A well-executed vibration analysis machine setup helps identify issues long before they result in equipment breakdown. But learning the fundamentals of vibration analysis is essential for building a predictive maintenance strategy.

How do you conduct a vibration analysis?

If you're wondering how to do vibration analysis, here are the broad steps to follow:

Step 1: Determine which machines are critical to the running of your factory or plant

Start by identifying equipment whose failure would result in significant downtime, safety risks, or high repair costs. These are your priority assets for vibration monitoring.

Step 2: Decide how often you will monitor them

The frequency of monitoring depends on how critical the machine is and how fast it wears out. Ideally, experts recommend that you should monitor them weekly, but more realistically monthly. Any later than that and experts say you might as well adopt a run-to-failure maintenance strategy!

Step 3: Choose your monitoring method – manual or automated

Manual monitoring involves technicians using portable vibration meters or analysers during scheduled maintenance rounds. An automated vibration analysis system combines permanently installed sensors that continuously transmit data to a central system for real-time analysis and alerts.

Step 4: Set up your measurement points

Install or identify sensor locations – typically on bearing housings, gearboxes, or other key structural points where vibration patterns are most telling. Be consistent in your measurement locations to allow for accurate trend comparisons over time.

Step 5: Collect and analyse the vibration data

How you collect and analyse vibration data depends on whether you’re using a manual or automated setup

1. Manual setup

In a manual setup, a trained technician uses a portable vibration analyser paired with a handheld accelerometer. Most machines have predefined sensor mounting points (such as metal pads or magnetic bases) at critical locations. The technician attaches the sensor at these points to measure parameters like velocity, acceleration, or displacement.

The collected data is uploaded into analysis software and the frequency spectrum isolated and interpreted. A strong grasp of vibration analysis basics helps technicians understand how to collect accurate data and interpret the results. Faults such as imbalance (low frequencies), misalignment, bearing defects, and gear faults (higher frequencies) are identified based on vibration signatures.

The reliability of the analysis and diagnosis depends on using the same measurement points under consistent operating conditions to track trends over time. Anomaly detection and decision-making rely heavily on human expertise, with technicians comparing current readings to historical baselines and predefined thresholds to flag deviations and recommend action.

2. Automated setup

In an automated setup, permanently installed sensors – wired or wireless – continuously monitor vibrations and send data to a central system. Wireless sensors, often battery-powered with lifespans of 1 to 5 years, are ideal for remote or hard-to-access equipment. They support early fault detection for critical assets, helping avoid operational disruptions.

These automated systems automatically perform frequency analysis and map patterns to known fault signatures. They also handle trend tracking and anomaly detection using rule-based algorithms or machine learning. Alerts are triggered when vibration levels cross set thresholds or deviate from historical norms, reducing dependence on manual analysis.

Step 6: Make informed maintenance decisions

Based on the analysis, decide whether immediate repair is needed, a part must be replaced, or if it’s safe to continue running the machine. This step turns vibration data into actionable insights.

Step 7: Track trends and refine your maintenance strategy

Over time, build a database of vibration signatures for each machine. Use this to fine-tune your predictive maintenance program, reduce unplanned downtime, and improve equipment reliability.

Challenges and Limitations

While vibration analysis is a powerful tool for early fault detection, it comes with challenges. Accurate results depend on proper sensor placement and consistent data collection – even small variations can affect readings and lead to misdiagnosis. In manual setups, this requires skilled technicians trained to collect and interpret complex signals.

Interpreting raw vibration data can be difficult, especially in machines with multiple moving parts. Without the right expertise or tools, subtle fault signatures can be missed or mistaken for normal operational noise, leading to false alarms. And while vibration analysis excels at detecting many mechanical faults, it may not identify structural cracks, electrical anomalies, or issues that don’t produce significant vibrations early on.

Cost and complexity are additional concerns. Manual setups can be time-consuming and labour-intensive, especially in facilities with many assets. Automated systems, while reducing human effort, require significant upfront investment in sensors, infrastructure, and analytics software – along with training to interpret results or integrate insights into maintenance workflows.

Conclusion

That said, many of these limitations are being addressed through advanced software, artificial intelligence, and machine learning. These technologies make it easier to collect, analyse, and interpret vibration data at scale — automatically flagging anomalies, learning from historical patterns, and improving fault detection accuracy over time.

For organisations without in-house expertise, vibration analysis services offer outsourced condition monitoring and diagnostics.

If you're looking to scale condition monitoring without scaling manpower, Viking Analytics’ solution MultiViz – AI-assisted vibration analysis – is designed to do exactly that. MultiViz enables teams to analyse and interpret vibration data more efficiently by automating diagnostics and highlighting only the most relevant alerts. This allows a small team of analysts to monitor a large number of assets, turning complex vibration signals into actionable insights with minimal manual effort.