For decades, a screwdriver to the ear was the only sensor that technicians listened to detect rough bearings. Today, tri-axial accelerometers have transformed that basic check into high-resolution spectra, identifying mechanical issues weeks before they escalate into costly failures.
This illustrates the evolution from traditional “tricks” to advanced maintenance practices, highlighting the importance of Predictive Maintenance (PdM), involving accurate assessments to detect equipment issues before they escalate into significant faults.
Vibration Analysis in Predictive Maintenance solutions is the dominant Condition Monitoring technique used to detect early signs of wear and defects in machines by analyzing mechanical vibration patterns. As a key element of Predictive Maintenance services, it enables the detection of issues, often long before a functional breakdown occurs, significantly reducing unplanned downtime and optimizing maintenance activities.
By delivering real-time vibration data streams, collected with wireless vibration monitoring sensors, Vibration Analysis complements other condition monitoring techniques, enhancing analytics, strengthening maintenance decision-making, and supporting long-term asset health through optimized work order schedules.
This article serves as a guide to Vibration Analysis, detailing its process, necessary tools, and integration with a Predictive Maintenance platform. Additionally, this article covers the typical faults Vibration Analysis identifies, the specific assets it monitors, and why it is considered a key solution in Predictive Maintenance strategies. It also highlights real-world examples and practical benefits of applying this powerful technique in various industries.
Table of Contents
What Is Vibration Analysis?
Vibration Analysis is a powerful, non-invasive Condition Monitoring technique to detect mechanical anomalies by measuring and analyzing vibration signals, including time waveforms and frequency-domain spectra, emitted by rotating or reciprocating machinery during operation.
This method is widely used in Predictive Maintenance, alongside other Condition Monitoring Techniques: Infrared Thermography, Oil Analysis, Ultrasound Analysis, Motion Magnification, and Motor Circuit Analysis.
Rotating industrial equipment naturally vibrates, but mechanical issues like imbalance, misalignment, bearing defects, or gear wear create distinct alterations in these vibration patterns.
Within Predictive Maintenance, technicians perform vibration monitoring using specialized sensors (portable or mounted) to identify emerging defects long before they impact machine health or result in equipment failure or downtime.
What Does Vibration Analysis Aim to Detect?
Vibration Analysis detects a wide range of mechanical anomalies that threaten equipment health and overall operational performance. These are early indicators of failure, allowing earlier intervention and more effective maintenance. The key benefit lies in identifying these problems through abnormal vibration signatures in frequency spectra or time-domain waveforms, before breakdown occurs.
Each fault mode generates a distinct vibration pattern, often visible as dominant peaks, harmonic series, or sidebands in the spectrum. These patterns act as fingerprints of underlying mechanical issues, enabling fault diagnosis and trending over time.
Behind every vibration signature is a physical phenomenon: imbalance creates centrifugal forces, misalignment introduces cyclic stresses, defects in bearings or gears produce characteristic fault frequencies, and structural resonance amplifies vibration amplitudes.
Specifically, Vibration Analysis can detect:
- Imbalance: A dominant peak at 1x shaft rotational speed, indicating mass distribution issues on rotors, fans, or impellers.
- Misalignment: Vibration at 1x and 2x shaft speed, often with elevated axial components, suggesting angular or parallel shaft misalignment.
- Bearing Defects: High-frequency patterns at characteristic defect frequencies (BPFO, BPFI, BSF, FTF), associated with rolling-element fatigue or spalling.
- Looseness: Non-sinusoidal waveforms and multiple harmonics (3x, 4x, 5x), pointing to mechanical play or inadequate fastening.
- Gear Faults: Sidebands around gear mesh frequencies or amplitude changes, reflecting tooth wear, pitting, or eccentricity.
- Resonance: Amplified vibration at natural frequencies of components or structures, typically dependent on load or speed.
- Rotor Bar Defects (in induction motors): Sidebands at pole pass frequency in the spectrum, signaling broken or cracked rotor bars.
Which Assets Are Typically Monitored with Vibration Analysis?
In practice, Vibration Analysis is applied to a wide range of rotating and reciprocating assets across industrial and manufacturing environments. Its effectiveness is particularly evident when monitoring equipment critical to operation.
Even minor variations in vibration signatures can reveal emerging issues, allowing maintenance teams to intervene before performance, safety, or efficiency are compromised. In reciprocating assets, this analysis requires particular attention to parasite frequencies, secondary vibration components generated by impacts or structural resonances, which can mask or distort genuine fault indicators if not properly filtered or interpreted.
Typical assets monitored with Vibration Analysis include:
- Pumps
- Electric motors
- Gearboxes
- Turbines
- Fans and blowers
- Conveyors
- Agitators
How Does Vibration Analysis Work?
Vibration analysis is a systematic process comprising the following five detailed steps:
- Deployment: route-based or mounted sensors
- Data collection of vibration signals in amplitude and frequency at key points like bearings and housings.
- Data transformation using FFT and advanced analysis
- Baseline comparison of the analysis against known-good signatures under matching operating conditions.
- Fault-signature mapping using frequency libraries and RPM reference
Step 1: Deployment Modes
Vibration Analysis can be deployed through route-based or mounted-sensor monitoring, depending on the criticality of the asset, accessibility, and the desired monitoring frequency.
- Route-Based mode: Technicians use handheld or portable devices to periodically collect vibration data along predefined inspection routes. This mode remains suitable for assets where periodic checks are sufficient and continuous monitoring would not deliver additional value. It remains a cost-effective option for detecting developing faults on a scheduled basis, requires trained personnel and regular route planning.
- Mounted Sensors mode: Mounted-sensor mode involves dedicated wired or wireless IoT sensors permanently installed on assets. These sensors provide continuous, automated, real time vibration monitoring, making this approach ideal for remote or critical or high-speed machinery where early detection is crucial to avoid unplanned downtime. Once connected to a Predictive Maintenance platform, these sensors enable immediate fault detection, automated alerting, and trend analysis.
Thanks to our Predictive Maintenance as a Service model, continuous monitoring is no longer reserved for critical assets only. This subscription-based approach makes permanent vibration monitoring accessible and cost-efficient for semi-critical and even non-critical assets, eliminating the need for manual routes while ensuring early fault detection and faster decision-making.
Step 2: Data Collection
Vibration data collected typically includes amplitude, frequency, and occasionally phase information. The sensors capture data along three axes (X, Y, Z) for comprehensive motion analysis.
Sensors are strategically positioned at critical points on assets, such as bearings, housings, or gearbox casings, to effectively detect potential mechanical issues.
Step 3: Data Transformation
Before data transformation, the raw vibration signal typically undergoes filtering to remove extraneous noise and improve signal clarity. This step ensures the accuracy and reliability of subsequent analyses by reducing interference and highlighting relevant patterns linked to mechanical conditions.
Next, the filtered vibration data (time waveform) is transformed from the time domain into the frequency domain using a Fast Fourier Transform (FFT), one of the key processes that turns raw vibration signals into actionable insights. This transformation reveals the amplitude of vibration at various discrete frequencies, enabling analysts to identify characteristic vibration patterns associated with specific mechanical faults.
In more sophisticated applications, advanced techniques like envelope analysis can be applied. Envelope analysis isolates modulations within vibration signals, making it particularly effective at detecting subtle defects in bearings or gears, which are not detected with traditional analysis methods. Wavelet transforms, on the other hand, offer enhanced detection capabilities for faults that produce transient or time-varying vibration signatures, providing higher sensitivity compared to traditional FFT methods. Together, these approaches represent advanced forms of vibration data analysis, and they complement basic FFT analysis by increasing the accuracy of condition monitoring and diagnostics within a Predictive Maintenance strategy.
Step 4: Baseline Comparison
Current vibration data, whether collected live or recently acquired, is systematically compared against a baseline signature representing the known healthy state of the same asset. Typically, maintenance teams establish these baseline signatures during commissioning or under confirmed optimal operational conditions, serving as reliable reference points for monitoring machine health over time.
The primary focus during comparison is to detect new frequency peaks, shifts in existing frequencies, or increases in amplitude that indicate deviations from the asset’s normal behavior. Identifying these deviations early allows maintenance teams to proactively address developing mechanical issues before they escalate.
For accurate and meaningful comparisons, the operational conditions under which the baseline and current data are collected must match closely. This includes parameters such as rotational speed, applied load, temperature, and operational environment. Any discrepancies in these conditions can significantly compromise the validity of the diagnostic insights.
Advanced vibration monitoring systems further enhance baseline comparison accuracy by incorporating machine learning and statistical modeling. These technologies deeply analyze incoming frequency spectra against historical baseline data, dynamically refining and adjusting baseline signatures over time. By continuously adapting to the evolving operational conditions and normal behavior patterns of equipment, machine learning-driven systems provide increasingly precise fault detection and predictive accuracy.
Step 5: Fault-Signature Mapping
Once the frequency-domain vibration spectrum, also called the frequency spectrum, is generated, specific peaks are closely analyzed to identify potential mechanical faults. Each type of mechanical defect produces distinctive frequency patterns, also known as fault signatures, that result from particular physical interactions within machinery.
In Vibration Analysis, common fault signatures include:
- Ball Pass Frequency Outer race (BPFO) pattern, which means an outer race defect in the bearing.
- Ball Pass Frequency Inner race (BPFI) pattern, which means damage to the inner race.
- Fundamental Train Frequency (FTF) component, which means bearing cage looseness or instability.
- Ball Spin Frequency (BSF) component, which means defects on the rolling elements themselves.
- Sidebands around gear mesh frequencies, which means gear tooth wear, misalignment, or eccentricity.
- Dominant 1x shaft RPM peak, which means imbalance, while 2x or 3x shaft RPM components often mean misalignment or looseness.
It’s important to note that Vibration Analysis itself does not directly measure RPM (rotational speed). Typically, RPM is measured externally using devices such as tachometers, encoders, or laser speed sensors. Nevertheless, RPM is critically important for accurately interpreting vibration spectra because many fault frequencies are expressed as multiples of shaft rotational speed (e.g., 1x, 2x RPM). Knowing the precise RPM allows analysts to translate frequency peaks into actionable diagnostic information. For example, a frequency peak at 60 Hz, equal to 1x shaft speed, strongly indicates imbalance. Thus, RPM functions as a reference value rather than being directly extracted from vibration data.
Trained analysts or automated diagnostic systems systematically compare identified peaks in frequency spectra to known fault frequency libraries, tailored specifically to the geometry and operating conditions of the asset. This systematic mapping process facilitates precise identification and timely remediation of developing faults.
What Tools Are Used in Vibration Analysis?
Vibration Analysis relies on two categories of tools: hardware that captures vibration signals from rotating machinery, and software that processes, transforms, and interprets those signals into actionable diagnostics.
Hardware Tools
- Portable Data Collectors: Rugged handheld devices used during route-based inspections to capture raw vibration data at predefined collection points. They prioritize rapid data acquisition and are typically used by technicians during routine rounds.
- Handheld Vibration Analyzers: Portable instruments with onboard processing, enabling real-time FFT spectra, waveform visualization, and preliminary diagnostics directly at the machine. Ideal for troubleshooting or targeted investigations by trained analysts.
- Mounted Vibration Sensors: Permanently installed wired or wireless sensors that enable continuous or periodic remote monitoring. They capture tri-axial vibration (and often temperature), transmit data to PdM platforms, and support early fault detection on critical, remote, or hard-to-access assets.
Software Tools
- Vibration Analysis Software: Advanced platforms used to process, trend, and interpret vibration data. They provide FFT spectra, time waveforms, envelope analysis, and other diagnostic views, enabling precise fault identification, baseline comparison, and long-term asset health tracking within a Predictive Maintenance program.
How Does Vibration Analysis Integrate with a Predictive Maintenance Platform?
Within a Predictive Maintenance ecosystem, vibration data, collected from wireless sensors, handheld analyzers, or portable data collectors, is centralized in a PdM platform (e.g., I-see software).
The platform processes both offline and online vibration data: route-based measurements are uploaded for post-processing and advanced diagnostics, while online systems continuously stream spectra and waveforms for real-time analysis. This unified environment allows analysts and automated algorithms to work on the same datasets, reviewing trends, refining thresholds, validating alerts, and confirming root causes.
Once uploaded, each data point undergoes normalization and automatic tagging with relevant asset metadata. The platform then performs several automated analysis steps, including:
- Charting machine vibration trends against historical baselines to spot gradual degradation.
- Running anomaly-detection algorithms or AI/ML models that flag sudden deviations or emerging fault patterns.
- Classifying fault types automatically (imbalance, misalignment, bearing wear, looseness), tracking asset health, and estimating Remaining Useful Life (RUL).
- Triggering alerts when user-defined thresholds or baseline-deviation rules are breached.
- Suggesting work orders to be created in the CMMS, EAM, or ERP system for prioritized follow-up and scheduling, improving overall maintenance management efficiency, and ensuring that service tasks are executed at the right time.
What Are the Advantages of Vibration Analysis?
By revealing otherwise undetectable mechanical anomalies early and precisely, Vibration Analysis stands apart with four key advantages.
The first one is its ability to identify a wide range of fault types through distinct vibration signatures. Each issue, such as bearing wear, shaft imbalance, misalignment, gear tooth damage, or looseness, produces a characteristic frequency pattern. By interpreting these patterns, maintenance teams can pinpoint and even predict faults in a machine with high accuracy, often long before they evolve into failures that disrupt production, ensuring higher uptime and improved production quality.
Another key advantage of Vibration Analysis is the richness of the diagnostic data it provides. Unlike many other condition monitoring techniques that rely on single-value indicators, vibration monitoring delivers multi-dimensional datasets, including frequency spectra, time waveforms, and phase-angle information, that together provide a complete picture of a machine’s dynamic behavior. This diagnostic richness allows maintenance teams to identify the root cause of abnormal vibration with exceptional accuracy and apply targeted corrective actions such as dynamic balancing, shaft alignment, or structural adjustments. As a result, vibration analysis not only prevents unexpected failures but also extends asset life, improves machine health, optimizes performance, and delivers measurable cost savings through more informed decision-making and better maintenance planning.
The technique is also especially well-suited for high-speed rotating equipment, including turbines, motors, pumps, and gearboxes. These assets are particularly vulnerable to dynamic imbalances, and even minor inconsistencies can escalate quickly. While other monitoring methods may overlook early warning signs, vibration analysis delivers the sensitivity needed for timely detection, ensuring that hidden anomalies are detected and addressed before escalation.
Finally, Vibration Analysis uniquely detects structural resonance (natural frequency excitation), a phenomenon where the asset or supporting structure amplifies vibration at its natural frequency. These conditions are subtle, hazardous, and rarely visible through other condition monitoring techniques, making vibration analysis indispensable for complex systems operating under variable speeds and loads, while directly contributing to reduced downtime through earlier detection of subtle degradation.
What If You Could Prevent the Failure?
Imbalance, misalignment, bearing wear, or looseness often develop silently until they cause costly downtime and repairs.
With I-care’s Wi-care wireless vibration sensors continuously collecting data and our PdM engineers expertly analysing it, emerging faults are detected early, well before they impact safety, performance, or production continuity.
Turn hidden vibrations into actionable insights.
What Are the Limitations of Vibration Analysis?
While Vibration Analysis is a highly effective technique for monitoring rotating machinery, it has certain limitations that impact diagnostic accuracy:
- Reduced sensitivity at very low rotational speeds, where machine vibration signals tend to be subtle or indistinct. Although advanced high-frequency techniques like I-DNA can detect bearing faults down to 1 RPM, success rates are lower compared to higher-speed applications.
- Complex or overlapping fault signatures can make it difficult to accurately distinguish between multiple simultaneous mechanical issues, such as coexisting imbalance and misalignment. However, with skilled analysis and careful interpretation, root causes can still be reliably identified.
- Sensor placement and mounting quality are critical. Poor installation can degrade signal accuracy and lead to inconsistent or misleading data.
- High implementation costs for complex or distributed equipment can make vibration monitoring less economically feasible for certain assets or smaller facilities, especially when extensive sensor networks and data infrastructure are required. However, this limitation can be overcome through a PdM as a Service solution.
What If Everything Became Easier?
Wi-care as a Service, I-care’s all-inclusive subscription offer, provides sensors, software, and expert analysis for a fixed monthly fee. The system is installed, maintained, and continuously upgraded by specialists, removing the need for heavy upfront investments and making advanced vibration monitoring accessible to every facility.
Real-World Example of Usage
In a major petrochemical refinery, Vibration Analysis was used to monitor centrifugal gas compressors using mounted tri-axial sensors automatically.
Over time, analysts observed a steady increase in vibration amplitude at frequencies corresponding to gear mesh and 1x shaft speed. These patterns matched the signature of shaft misalignment. If left unaddressed, it could have led to bearing or coupling failure.
An early alert enabled maintenance teams to perform the necessary service and correct the alignment during a scheduled shutdown, integrating the task into planned maintenance and avoiding an unplanned failure. The early detection prevented a potential production loss valued in the hundreds of thousands of euros.
Necessary Skills and Training
Vibration Analysis demands a skill set that ranges from basic data collection to advanced diagnostic interpretation, depending on the level of involvement.
Skills Needed
Vibration Analysis requires an intermediate to advanced level of expertise, depending on the depth of application. For basic use, technicians must understand how to handle vibration sensors correctly, collect data safely, and follow standard inspection procedures on a machine.
This level of proficiency is typically achieved through introductory training and is sufficient for routine data collection tasks. However, in-depth analysis and accurate diagnosis demand a stronger technical foundation.
Analysts must be well-versed in vibration theory, signal processing, and be capable of interpreting FFT spectra, waveform patterns, and phase data to pinpoint specific mechanical faults and establish accurate diagnostic conclusions. To operate at this level, formal certification, such as ISO 18436 Category II or III Vibration Analyst, is generally required.
This structured training ensures that professionals can confidently analyze complex signals and make informed maintenance decisions.
Training
Is your team struggling to leverage vibration insights or achieve ISO 18436-2 certification?
Technical Associates of Europe offers a complete range of vibration analysis training, from hands-on introductory workshops and the “Vibration & 4.0 Sensors” seminar to advanced Master Classes and ISO 18436-2 Category I, II, III & IV certification courses. These programs help your teams master vibration analysis as a solution for reliability and performance improvement.
Prefer on-site learning in the United States? Technical Associates of Charlotte provides on-location vibration training along with ISO Category I, II, III & IV certification courses, tailored to your facility and assets.
