Vibroacoustic machine diagnostics is one of the key tools for assessing the technical condition of industrial equipment. It enables early detection of mechanical faults, evaluation of operational stability, and identification of irregularities without interfering with the production process. Vibration and acoustic signal analysis provides insights that are not available through conventional process monitoring systems.
Vibration and acoustic signal analysis
The foundation of vibroacoustic diagnostics is the acquisition of vibration and acoustic signals using properly selected sensors and measurement chains. These signals reflect the dynamic behavior of machines, as well as the condition of bearings, gearboxes, shafts, and rotating components. Correct measurement configuration is critical — frequency range, sampling rate, and sensor mounting conditions directly affect the quality of subsequent analysis.
Time-domain signal analysis allows evaluation of vibration levels, impulsiveness, and operational instability. At this stage, anomalies such as impacts, looseness, or irregular mechanical behavior can already be detected.
Spectral analysis and FFT
One of the primary diagnostic tools is frequency-domain analysis based on the Fast Fourier Transform (FFT). Transforming data from the time domain to the frequency domain enables identification of characteristic spectral components associated with specific machine elements. FFT analysis makes it possible to detect imbalance, misalignment, rolling bearing damage, and gearbox faults.
In practical applications, FFT is often complemented by envelope analysis, order tracking, and time–frequency methods, which enhance diagnostic sensitivity under variable rotational speeds and load conditions.
The role of machine learning in diagnostics
The increasing number of measurement points and continuous data acquisition make traditional expert-based analysis insufficient. Machine learning algorithms play a critical role in this context. ML models enable automatic extraction of diagnostic features from vibration signals, classification of technical states, and real-time anomaly detection.
Machine learning allows the integration of information from multiple signal domains — time, frequency, and time–frequency — creating models robust to changing operating conditions. When combined with historical data, this approach enables not only fault detection but also prediction of fault progression and maintenance planning.
Modern vibroacoustic machine diagnostics increasingly operates as part of a broader data analytics ecosystem. The integration of measurement systems, advanced signal processing methods, and ML algorithms enables the transition from reactive diagnostics to predictive maintenance. As a result, measurement data becomes a tangible support tool for technical decision-making and a driver of improved reliability and operational efficiency in industrial processes.