Mengdi Shi

Rolling element bearings are fundamental to the reliability of rotating machinery, yet detecting incipient faults under heavy background noise remains a significant challenge. To address this, this paper proposes a novel diagnostic method termed modulation bispectral slice-based kurtosis (MBSK). The MBSK method begins by applying time–frequency modulation bispectrum to demodulate the vibration signal, transforming it into a two-dimensional time–frequency representation. This process highlights the coupling between resonant bands and modulation frequencies, thereby enhancing key fault signatures while suppressing noise. Subsequently, the harmonic spectral kurtosis (HSK) indicator is introduced to quantify the periodic impulse energy within each modulation frequency slice. Robust identification of the fault characteristic frequency and its harmonics is achieved by analyzing the trend of HSK versus modulation frequency. Simulation and experimental results demonstrate that MBSK effectively extracts fault features from both the outer and inner rings of rolling bearings, maintaining high diagnostic accuracy and stability under strong noise interference. Compared to conventional modulation signal bispectrum methods, MBSK exhibits superior performance in both feature extraction capability and noise immunity.

This paper introduces the application of equipment monitoring and fault diagnosis system of industrial Internet. This system is mainly based on signals from rotating mechanical equipment (such as bearings and gears) to complete the design work of the MoniVIEW equipment operation status fault diagnosis system. This system not only includes algorithms such as statistical index analysis, time-domain analysis, frequency-domain analysis, and short-time Fourier transform in time-frequency analysis, but also includes relatively new adaptive time-frequency analysis methods such as EMD, ITD, and LCD. In mechanical rotating equipment, non-stationary and complex vibration signals are often encountered. Conventional fault diagnosis and analysis algorithms sometimes cannot determine the cause of faults. Introducing adaptive time-frequency analysis methods not only facilitates user fault identification and analysis, but also directly achieves data analysis and processing of vibration signals.

Adaptive Time-Frequency analysis method is of great significance for extracting the fault characteristic frequency of actual complex non-stationary signals. Based on this method, this paper innovatively proposes a multi-channel data acquisition system based on LabVIEW to collect the vibration signal of the bearing test-bed; Through the analysis and comparison of EMD, ITD and LCD algorithms, the characteristics of their algorithms are found. The experimental results show that according to the data collected by LabVIEW multi-channel data acquisition system, the adaptive time-frequency method can be effectively selected to identify the faults of rolling bearings, improve the fault detection rate and practicability of the equipment, and ensure the safe and reliable operation of the equipment.

With the wide application of computer and microelectronics in the field of mechanical fault diagnosis, the combination of data acquisition system, sensor and analysis and processing system becomes more and more close. Based on the working principle and parameter selection of NI-DAQ data acquisition board, the paper integrates hardware acquisition equipment and application software through computer to establish a complete set of practical instruments and equipment and dynamic data acquisition system, so as to realize the solution of obtaining information with high speed, high precision and multi-channel. The data acquisition can realize data acquisition, integration and storage, and provide important data basis for subsequent fault diagnosis and analysis. The veracity of data collected by the system is verified by vibration test.