As a core component of mechanical transmission systems, the operating status of an industrial gearbox directly impacts equipment safety and production efficiency. Under complex operating conditions, critical components such as gears and bearings are prone to failure due to wear, fatigue, or assembly deviations. Vibration signals, as sensitive parameters reflecting the health status of equipment, can provide early fault warnings through changes in their time and frequency domain characteristics. The core of vibration signal analysis lies in extracting fault features from non-stationary and nonlinear signals, combining modern signal processing techniques and machine learning algorithms to construct a comprehensive diagnostic system covering data acquisition, feature extraction, pattern recognition, and early warning decision-making.
Vibration signal acquisition is fundamental to fault diagnosis and requires high-precision sensors. Sensors are typically installed in critical locations such as the gearbox bearing housings and gearbox body, prioritizing areas close to the power source to capture raw vibration information. The sampling frequency must be set based on the gearbox's highest characteristic frequency. For example, the meshing frequency of high-speed gears may reach several kilohertz, requiring a sampling frequency at least 2.5 times the highest frequency to ensure signal integrity. Furthermore, simultaneous acquisition of vibration, temperature, and speed signals through multiple channels provides more comprehensive status data, offering multi-dimensional support for subsequent analysis.
Raw vibration signals often contain background noise, requiring denoising to improve the signal-to-noise ratio (SNR). Wavelet transform, due to its excellent time-frequency localization characteristics, has become a commonly used denoising method in engineering. This method decomposes the signal into different frequency bands, uses thresholding to retain effective fault features, and suppresses high-frequency noise. For example, after wavelet denoising, the SNR of a power plant gearbox vibration signal was significantly improved, providing a clear data foundation for subsequent feature extraction. Furthermore, adaptive filtering, empirical mode decomposition, and other techniques can be flexibly applied according to signal characteristics to further optimize signal quality.
Time-domain feature analysis is a direct means of early fault warning. Dimensionless indices such as kurtosis and peak power are sensitive to impact-related faults. For example, in the early stages of gear pitting, the kurtosis value rises from the normal 3.5 to over 6.8, significantly exceeding the threshold. The root mean square (RMS) value reflects the energy change of the signal; during bearing wear, the RMS value continuously increases with the severity of the fault. By monitoring the trends of these indices in real time, potential faults can be detected early. For example, a car transmission, by continuously monitoring its kurtosis coefficient, successfully completed repairs before assembly errors caused abnormal noises, avoiding unplanned downtime.
Frequency domain analysis reveals the frequency components of a signal through Fourier transform or time-frequency analysis, pinpointing the type of fault. The characteristic frequencies of gear faults are typically the meshing frequency and its sidebands. For example, in the case of a high-speed shaft bearing outer ring fault, sidebands will appear in the spectrum at integer multiples of the shaft rotation frequency. The characteristic frequencies of bearing faults are related to the rotation frequencies of the rolling elements and the inner and outer rings; spectral analysis can accurately identify the fault location. Cepstral analysis, as an extension of the frequency domain, can effectively separate periodic components in complex spectra. For instance, in a tunnel boring machine gearbox, cepstral analysis revealed the instantaneous impact caused by a planetary gear crack, providing crucial evidence for fault tracing.
Time-frequency analysis is suitable for non-stationary signals and can capture fault characteristics under variable speed conditions. Short-time Fourier transform achieves localized analysis through windowing, but its time and frequency resolution is limited by the window function. The Hilbert-Huang transform adaptively decomposes signals through empirical mode decomposition, making it more suitable for processing vibration data under variable speed conditions. For example, during pitch control in a wind turbine gearbox, the Hilbert-Huang transform successfully captured the instantaneous impact component caused by a planetary gear crack, compensating for the resolution limitations of traditional methods.
The introduction of machine learning algorithms has significantly improved the intelligence level of fault diagnosis. Algorithms such as support vector machines and random forests learn fault features from historical data to automatically classify fault types. Deep learning algorithms, such as convolutional neural networks, can automatically extract deep-level features of signals. For example, they can reduce the dimensionality of vibration signals through convolutional and pooling layers, and then complete fault identification through fully connected layers. A university laboratory used a transfer learning strategy to transfer fault data from an industrial robot gearbox to a wind turbine gearbox diagnostic model, achieving high accuracy in identification with a small number of samples, demonstrating the algorithm's generalization ability.
Vibration signal analysis of industrial gearboxes integrates multi-dimensional feature extraction with intelligent algorithms, constructing a complete technology chain from signal acquisition to early warning. The combination of time-domain, frequency-domain, and time-frequency-domain analysis, along with machine learning's ability to recognize complex patterns, enables the system to capture subtle features in the early stages of a fault, achieving early warning. In the future, with the deepening application of technologies such as digital twins and edge computing, vibration signal analysis will further develop towards real-time and precision, providing stronger guarantees for the reliable operation of industrial gearboxes.
