Vibration Analysis

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Vibration Analysis

🛠️ Extend the life of your equipment with vibration analysis

In a context where the reliability of mechanical systems is essential to ensure the comfort, safety, and efficiency of buildings, predictive maintenance is a strategic lever. At the heart of this approach is vibration analysis, a technology capable of detecting the slightest mechanical anomalies... before a problem even arises.

Thanks to smart sensors and sophisticated interpretation algorithms, it is now possible to monitor the health of electromechanical equipment (pumps, motors, fans, etc.) in real time, anticipate breakdowns, and plan interventions proactively.

In this article, we explain:

what vibration analysis is,
how it works in practice,
what tangible benefits it offers for the maintenance of your facilities,
and best practices for getting the most out of it.

A must-read for any property manager looking to optimize operations and extend the life of their technical assets.

📌 1. Definition and strategic role

Vibration analysis involves measuring and interpreting the vibrations emitted by rotating machines (motors, pumps, fans, compressors). Sensors such as accelerometers are installed on the equipment: they detect vibrations, then software (often aided by AI) compares this data to a “normal profile.” The goal? To identify anomalies (misalignments, imbalances, bearing wear, etc.) before a critical failure occurs. This method is widely recognized as a pillar of predictive maintenance

🚀 2. Detailed operation: from sensor to predictive alert

Vibration analysis begins with the installation of sensors, often piezoelectric or MEMS accelerometers, placed at critical points on the machine (bearings, housings, shafts). These sensors record dynamic vibrations by generating electrical signals proportional to the detected displacements.

The raw data captured is transmitted to a monitoring platform. The software then applies a fast Fourier transform (FFT) to convert the time signal into a frequency spectrum. This allows peaks at specific frequencies (e.g., harmonics of a failing bearing) to be identified.

Once the spectrum has been obtained, artificial intelligence algorithms or threshold-based rules compare this data with healthy operating profiles. If an abnormal peak is detected (e.g., vibration at twice the rotation frequency), the system generates a predictive alert, allowing intervention before a failure occurs.

⚙️ 3. Concrete benefits

Prevent breakdowns: thanks to early detection, teams can plan interventions before any exceptional failures occur, thereby reducing unplanned downtime.
Reduce costs: costs associated with urgent repairs, unexpected labor, and premature replacement of parts are significantly reduced.
Increase equipment lifespan: early problem correction extends component service life.
Enhance safety: by avoiding sudden breakdowns, risks associated with serious failures are eliminated.

🔧 4. Key technologies used on a daily basis

4.1 Sensors: piezoelectric accelerometers

These sensors use piezoelectric crystals (such as lead zirconate titanate) that generate an electrical signal when deformed by vibration. They are robust, offer a wide bandwidth (typically up to 20 kHz or more), and do not require an external power supply for primary detection. They are most commonly used in industrial predictive maintenance to detect rapid faults such as bearings or imbalances.

4.2 MEMS (Micro-Electro-Mechanical) sensors

Newer and more compact, MEMS sensors operate using capacitive microstructures. They are particularly suitable for IoT applications and large-scale assemblies because they are:

Compact and inexpensive
Autonomous (low energy consumption)
Suitable for low-frequency measurements (up to a few kHz) However, their bandwidth is generally more limited than that of piezo sensors.

4.3 Comparison between piezo and MEMS

Characteristics	Piezoelectric	MEMS (capacitive)
Bandwidth	Very wide (up to 20 kHz+)	More limited (~3–6 kHz)
Power Supply	Passive signal, minimal external power supply	Integrated LED and ADC, sometimes autonomous
Size / Cost	Larger, more expensive	Small, economical, ideal for IoT
Typical Applications	Fast, high-frequency detection	Continuous monitoring, low throughput

4.4 Data processing and AI

Vibration data is first transformed using FFT (Fast Fourier Transform) to identify problematic frequencies. It is then analyzed by algorithms, sometimes based on artificial intelligence (neural networks, supervised learning) or predefined business rules. These tools automatically detect anomalies and generate early alerts, improving diagnostic reliability.

4.5 Edge/IoT monitoring

Modern sensors often have embedded analytics capabilities (edge computing): they can detect abnormal behavior and send only an alert, saving significantly on bandwidth. This configuration is very effective for remote or industrial applications where connectivity is limited.

4.6 Industrial ecosystem

Many companies market complete solutions that integrate:

Sensors (piezo or MEMS)
IoT connectivity (4G, LPWAN)
Online portals or mobile applications

For example, TRACTIAN combines IoT sensors and advanced analytics for proactive maintenance.

🔍 5. Implementation & concrete examples

5.1 Paper industry: monitoring a press roll

In a recent study published on ScienceDirect, vibration sensors were installed on a press roll in a paper mill. Data was collected from November 2020 to August 2021, revealing abnormal vibration trends before they caused major mechanical failures. This approach extended the equipment's service life and avoided costly unplanned downtime.

5.2 TRACTIAN: real-time on-site fault detection

TRACTIAN's “Smart Trac” solution uses wireless sensors that can be installed in less than three minutes, combining vibration and temperature measurements. It automatically collects data, generates diagnostics via AI, and sends alerts to technicians. For example, at Ahlstrom Munksjö, 100 sensors detected faulty bearings caused by poor tightening in time, thus avoiding a production stoppage. Everything is accessible via a mobile app, simplifying remote maintenance.

5.3 Case study on industrial maintenance

Reliability Plant presents a case where vibration analysis identified bearing defects, imbalances, and natural frequencies on electric motors. Combined with oil analysis and thermography data, this method enabled problems to be diagnosed before they caused failures, resulting in less downtime and significant savings.

⚠️ 6. Best practices & limitations of vibration analysis

📍 Calibration and initial reference

First and foremost, it is crucial to define a “vibration reference” based on normal operating conditions. Without this reference, it becomes impossible to distinguish between acceptable vibrations and early signs of failure.
Initial calibration must be performed under stable conditions (speed, load, temperature) and updated regularly.

📋 Sensor location and quality

A poorly positioned sensor can provide erroneous or unrepresentative data. It must be fixed close to bearings or critical parts and on stable surfaces.
It is essential to use industrial sensors suitable for the environment: resistant to dust, humidity, and external vibrations.

🔇 Interference & noise management

Environmental anomalies, such as vibrations from other machines, can mask or generate false signals. Advanced filtering techniques (e.g., frequency filtering, spectral envelope) are essential to isolate relevant signals.
Directional sensors and processing algorithms can also be used to minimize these interferences.

🎓 Skills and interpretation

Vibration analysis requires solid technical expertise: recognition of harmonics, understanding of the physical laws related to bearings, and analytical skills.
Without AI tools or specialists, there is a risk of misinterpretation: false positives (unjustified alerts) or false negatives (undetected anomalies).

🔄 Adaptation to changing conditions

Vibration signatures vary with speed, temperature, or load; a normal change can be mistaken for a fault without an adaptive model.
Advanced technologies (such as LSTM or adaptive models) are needed to distinguish background wear from abnormal behavior.

👥 Sensor maintenance and monitoring

The sensors themselves require periodic verification (calibration, mechanical inspection) to ensure their long-term accuracy.
Without this maintenance, their measurements can drift and generate false reassurances or false alarms.

🧰 Summary

Technical limitations	Best practices
Reference not defined	Initial and regular calibration
Poor sensor positioning	Installation on a stable surface and close to bearings
High environmental noise	Use of advanced filters and directional sensors
Misinterpretation	Technical expertise or AI tools to analyze data
Operational variability	Adaptive models based on machine conditions
Poorly maintained sensor	Periodic inspection and recalibration

✅ Conclusion

Vibration analysis now offers a powerful predictive maintenance method for electromechanical equipment. By combining sensors, scientific analysis, and real-time data, it makes it possible to: