Modal analysis with piezoelectric sensors: key tool for condition-based maintenance

Introduction

Condition-based maintenance (CBM) has transformed the way modern industry manages its assets. Unlike corrective or preventive approaches, CBM allows equipment to be intervened only when there are real signs of deterioration, maximizing operational availability and reducing unnecessary costs.

In this context, structural modal analysis has gained prominence as an advanced diagnostic technique, especially when combined with highly sensitive piezoelectric sensors. This synergy offers a robust and accurate solution for monitoring the dynamic behavior of structures and equipment, facilitating early failure detection, efficient maintenance planning and strategic decision-making based on reliable data.

What is modal analysis, and why is it important?

Modal analysis is a technique used to characterize the dynamic properties of a structure by identifying its natural frequencies, modes of vibration and damping coefficients. These characteristics reflect the dynamic behavior of the system and allow the evaluation of its structural integrity in terms of changes that may arise due to wear, corrosion, cracking or other forms of mechanical degradation.

The importance of modal analysis lies in its ability to detect subtle alterations in the mass, stiffness or boundary conditions of a structure. Such alterations, which are often not visually evident, can affect the stability, safety and performance of equipment. For this reason, modal analysis is considered an essential tool within structural health monitoring (SHM), with critical applications in sectors where structural integrity is vital, such as power generation, aeronautics, petrochemical industry and rail transportation.

What are piezoelectric sensors, and how do they work?

Piezoelectric sensors are devices that exploit the piezoelectric effect to transform mechanical stimuli, such as pressure, acceleration or vibration, into proportional electrical signals. This phenomenon occurs in specific materials, such as natural quartz or synthetic ceramics such as PZT (lead zirconate titanate), which generate an electrical charge when mechanically deformed.

This property makes piezoelectric sensors extremely useful for real-time dynamic data acquisition, especially in industrial environments where high-frequency measurements, high sensitivity and long-term operational stability are required.

Their compact, robust design and their ability to operate in adverse conditions make them a preferred choice for integration into modal analysis systems. Since they allow recording structural vibrations with high accuracy, facilitating the identification of anomalous patterns that may be related to incipient structural defects.

The following table shows a comparative summary between conventional and piezoelectric sensor-assisted modal analysis:

Table 1. Traditional modal analysis and sensor-assisted modal analysis.

Aspect	Traditional modal analysis	Modal analysis with piezoelectric sensors
Type of sensors used	Conventional accelerometers, strain gauges, mechanical transducers	High-sensitivity piezoelectric sensors
Monitoring frequency	Occasional or scheduled campaigns	Continuous or automated intervals
Sensitivity to dynamic changes	Medium	High (detects subtle variations in stiffness, mass or damping)
Hostile environment capability	Limited by size, weight or temperature	High resistance to vibration, temperature and humidity
Sensor size and weight	Bulky, require fixed installation	Compact, lightweight and easy to install
Data acquisition rate	Moderate	High (capture of transient events in real time)
Maintenance requirements	High (frequent calibration and complex wiring)	Low (long service life, self-calibration on some models)
Integration with digital systems	Limited or manual	High (IIoT, AI and cloud platforms supported)
Initial cost	Moderate	Higher initial investment, but with better long-term returns
CBM application	Complementary or limited	Fully compatible, ideal for structural predictive maintenance

Integration of modal analysis and piezoelectric sensors in CBM

The combination of piezoelectric sensors with modal analysis offers a powerful approach to implement predictive maintenance strategies. In this integration, sensors are strategically installed at critical points of the structure or equipment to capture dynamic responses to mechanical excitations.

Subsequently, the collected data are processed by modal analysis algorithms, which allow extracting the dynamic characteristics of the system and comparing them with the reference conditions or “healthy state”. In this way, any significant deviation can be interpreted as an early sign of deterioration.

This type of integration has proven to be especially useful in rotating equipment, hydraulic turbines, metallic structures subject to cyclic fatigue, heat exchangers and support systems subjected to variable loading. In addition, the possibility of continuous or scheduled interval monitoring allows the strategy to be tailored to the operational needs of each plant, maximizing maintenance efficiency and improving asset reliability.

Advantages of this methodology in predictive maintenance

The implementation of modal analysis with piezoelectric sensors within a CBM program offers multiple benefits to the industry. First, it provides a highly accurate diagnosis of the structural condition by allowing the detection of subtle dynamic changes before they translate into functional failures. This anticipatory capability reduces the risk of unscheduled shutdowns and prevents the occurrence of catastrophic damage, improving operational safety.

Secondly, it contributes significantly to maintenance cost optimization, since it allows intervention only when it is really necessary, eliminating costly routine inspections and extending component life.

It also favors efficient planning of resources and personnel, since interventions can be scheduled in advance based on actual data. Finally, this methodology improves traceability and documentation of asset status, which is crucial in regulated environments or in industries where high technical traceability is required, such as nuclear or pharmaceutical.

Use cases and industry sectors

Several industrial sectors have successfully adopted modal analysis methodology with piezoelectric sensors as part of their predictive maintenance programs. In the aerospace sector, for example, this technique is used to monitor aircraft wings, fuselages and structural components, allowing the detection of delaminations or fatigue cracks before they compromise flight safety.

In the wind energy field, it has been applied in the analysis of rotor blades, where vibrations can reveal impact damage or wind-induced fatigue. In the oil and gas industry, the technology has been used to evaluate the dynamic behavior of high-pressure pipelines, manifolds and structural supports on offshore platforms.

In the wind energy field, it has been applied in rotor blade analysis, where vibrations can reveal impact damage or wind-induced fatigue. In the oil and gas industry, the technology has been used to evaluate the dynamic behavior of high-pressure pipelines, manifolds and structural supports on offshore platforms.

Modal analysis future with smart sensors

Technological evolution is taking modal analysis with piezoelectric sensors into a new dimension, driven by the development of artificial intelligence, the industrial internet of things (IIoT) and cloud computing.

Modern sensors increasingly incorporate local processing, energy self-powering and wireless communication functionalities, allowing the implementation of distributed and intelligent structural monitoring networks. The application of machine learning algorithms makes it possible to recognize anomalous patterns and establish predictive models that more accurately anticipate the evolution of failures.

In addition, cloud platforms facilitate multisource data integration, remote visualization and comparative analysis between similar assets. These advances are revolutionizing predictive maintenance, enabling continuous, automated and more efficient monitoring of structural integrity in all types of industrial facilities.

Conclusions

Piezoelectric sensor-assisted modal analysis represents an advanced and highly effective technology within condition-based maintenance. Its ability to detect dynamic variations with millimeter accuracy makes it a strategic ally in anticipating failures, extending asset life and improving operational safety.

In an increasingly digitized and competitive industrial environment, its implementation not only offers technical advantages, but also provides a tangible economic return by reducing downtime, avoiding incidents and optimizing resources. With the progressive incorporation of artificial intelligence and IoT solutions, this methodology is poised to establish itself as a fundamental pillar of predictive maintenance in the 21st century.

References

1. Brincker, R., and Ventura, C. (2015). Introduction to operational modal analysis. Wiley.
2. Rainieri, C., and Fabbrocino, G. (2014). Operational modal analysis of civil engineering structures. Springer.
3. Weijtjens, W., De Sitter, G., Devriendt, C., and Guillaume, P. (2014). Operational modal parameter estimation of MIMO systems using transmissibility functions. Automatica , 50(2), 559-564.